Elon Musk: Neuralink and the Future of Humanity | Lex Fridman Podcast #438
- The following is a conversation
with Elon Musk, DJ Seo, Matthew MacDougall, Bliss
Chapman, and Nolan Arbaugh about Neuralink and
the future of humanity. Elon, DJ, Matthew and
Bliss are of course part of the amazing Neuralink team, and Noland is the first human to have a Neuralink device
implanted in his brain. I speak with each of them individually, so use timestamps to jump around, or as I recommend, go hardcore and listen to the whole thing. This is the longest
podcast I've ever done. It's a fascinating, super technical, and wide-ranging conversation, and I loved every minute of it. And now, dear friends, here's Elon Musk, his fifth time on this,
"The Lex Fridman Podcast." - Drinking coffee or water? - Water. I'm so over-caffeinated right now. Do you want some caffeine? - I mean, sure. - There's a Nitro drink. - This supposed to keep you up till like tomorrow afternoon basically. (laughs) - Yeah. I don't have any-
- So what is Nitro? It's just got a lot of
caffeine or something? - Don't ask questions. It's called Nitro. - Do you need to know anything else? - It's got nitrogen, that's ridiculous. I mean, what we breathe
is 78% nitrogen anyway. What do you need to add more for? (laughs) - [Speaker] Unfortunately,
you're gonna need it. - Most people think that
they're breathing oxygen, and they're actually
breathing 78% nitrogen. You need like a milk bar.
- Milk bar. (Elon laughing) - Like from Clockwork Orange. (laughs) - Yeah, yeah. Is that top three Kubrick film for you? - Clockwork Orange, it's pretty good. I mean, it's demented. Jarring, I'd say. - (laughs) Okay. Okay, so first let's step back and big congrats on getting Neuralink implanted into a human. That's a historic step for Neuralink. - Oh, thanks, yeah. - There's many more to come. - Yeah, and we just, obviously,
our second implant as well. - [Lex] How did that go? - So far, so good. Looks like we've got, I think over 400 electrodes
that are providing signals. So yeah.
- Nice. How quickly do you think the number of human participants will scale? - It depends on the regulatory approval, the rate which we get
regulatory approvals. So we're hoping to do 10
by the end of this year. Total of 10, so eight more. - And with each one,
you're gonna be learning a lot of lessons about the
new biology, the brain, everything, the whole
chain of the Neuralink, the decoding, the signal
processing, all that kind of stuff. - Yeah, yeah, I think it's
obviously gonna get better with each one. I mean, I don't wanna jinx it, but it seems to have gone extremely well with the second implant,
so there's a lot of signal, a lot of electrodes. It's working very well. - What improvements do you think we'll see in Neuralink in the coming, let's say, let's get crazy, the coming years? - I mean, in years,
it's gonna be gigantic, because we'll increase the number of electrodes dramatically. We'll improve the signal processing. Even with only roughly,
I don't know, 10, 15% of the electrodes working with Noland, with our first patient, we were able to get to
achieve a bit per second. That's twice the world record. So I think we'll start
like vastly exceeding world record by orders of
magnitude in the years to come. So it's start getting to, I don't know, a hundred bits per second thousand. Maybe if like five years from
now, we might be at a megabit, like faster than any human
could possibly communicate by typing or speaking. - Yeah, that BPS is an
interesting metric to measure. There might be a big leap in the experience once you
reach a certain level of BPS. - [Elon] Yeah. - Like entire new ways of interacting with a computer might be unlocked. - And with humans. - With other humans. - Provided they have (laughs), they want a Neuralink too. - Right. - Otherwise, they won't be able to absorb the signals fast enough. - Do you think they'll improve the quality of intellectual discourse? - Well, I think you could think of it, if you were to slow down communication, how do you feel about that? If you'd only talk at, let's say, 1/10th of normal speed, you'd be like, "Wow, that's agonizingly slow." - [Lex] Yeah. - So now, imagine you could speak, communicate clearly at 10 or 100 or 1,000 times faster than normal. - Listen, I'm pretty sure
nobody in their right mind listens to me at 1x, they listen at 2x. (Elon laughs) I can only imagine what
10x would feel like or could actually understand it. - I usually default to 1.5x. You can do 2x, but well, actually, if I'm listening to somebody in like sort of 15, 20 minutes
segments to go to sleep, then I'll do it 1.5x. If I'm paying attention,
I'll do 2x. (laughs) - Right. - But actually, if you start
actually listen to podcasts or sort of audio books or anything, if you get used to doing it at 1.5, then one sounds painfully slow. - I'm still holding onto
one because I'm afraid. I'm afraid of myself becoming
bored with the reality, with the real world where
everyone's speaking on 1x. (both laughing) - Well, depends on the person. You can speak very fast. Like we can communicate very quickly. And also, if you use a wide range of, if your vocabulary is
larger, your bit rate, effective bit rate is higher. - That's a good way to put it. - Yeah.
- The effective bit rate. I mean, that is the question
is how much information is actually compressed in the
low bit transfer of language. - Yeah. If there's a single word
that is able to convey something that would normally require, I don't know, 10 simple words, then you've got maybe a 10x
compression on your hands. And that's really, like with memes, memes are like data compression. It conveys a whole, you're simultaneously
hit with a wide range of symbols that you can interpret. And you kinda get it faster
than if it were words or a simple picture. - And of course, you're
referring to memes broadly like ideas.
- Yeah. There's an entire idea structure that is like an idea template, and then you can add something
to that idea template. But somebody has that preexisting idea template in their head. So when you add that
incremental bit of information, you're conveying much more than a few, just set a few words. It's everything associated with that meme. - You think there'll be
emergent leaps of capability as you scale the number of electrodes? Like there'll be a certain, you think there'll be like
actual number where it just, the human experience will be altered? - Yes. - What do you think that number might be, whether electrodes or BPS? We of course don't know for sure, but is this 10,000, 100,000? - Yeah, I mean certainly,
if you're anywhere at 10,000 bits per second,
I mean, that's vastly faster than any human could
communicate right now. If you think about what is the average bits per second of a human? It is less than one bit per
second over the course of a day, because there are 86,400 seconds in a day. And you don't communicate
86,400 tokens in a day. Therefore, your bits per
second is less than one, averaged over 24 hours. It's quite slow. And now, even if you're
communicating very quickly, and you're talking to
somebody who understands what you're saying, because
in order to communicate, you have to at least, to some degree, model the mind state of the
person to whom you're speaking. Then take the concept
you're trying to convey, compress that into a
small number of syllables, speak them, and hope that the other person decompresses them into
a conceptual structure that is as close to what you
have in your mind as possible. - Yeah, I mean, there's a lot of signal loss there in that process. - Yeah, very lousy
compression and decompression. And a lot of what your neurons are doing is distilling the concepts down to a small number of
symbols of, say, syllables that I'm speaking, or keystrokes,
whatever the case may be. So that's a lot of what your
brain computation is doing. Now, there is an argument
that that's actually a healthy thing to do
or a helpful thing to do because as you try to
compress complex concepts, you're perhaps forced to distill what is most essential in those concepts as opposed to just all the fluff. So in the process of compression, you distill things down
to what matters the most, because you can only say a few things. So that is perhaps helpful. I think we might, we'll probably get, if our data rate increases,
it's highly probable that we'll become far more verbose. Just like your computer, when computers had like, my first computer had 8K of RAM, so you really thought about every byte. And now you've got computers with many gigabytes of RAM. So if you wanna do an iPhone app that just says 'Hello world,' it's probably, I don't know, several megabytes minimum. (laughs) A bunch of fluff. But nonetheless, we still
prefer to have the computer with more memory and more compute. So the long-term aspiration of Neuralink is to improve the AI human symbiosis by increasing the bandwidth
of the communication, because even in the most
benign scenario of AI, you have to consider that the
AI is simply gonna get bored waiting for you to spit out a few words. I mean, if the AI can communicate
it to terabits per second and you're communicating
it bits per second, it's like 203. - Well, it is a very interesting question for a super intelligent species. What use are humans? - I think there is some
argument for humans as a source of will. - Will?
- Will, yeah. Source of will or purpose. So if you consider the human
mind as being essentially, there's the primitive limbic elements, which basically even like reptiles have, and there's the cortex, that's the thinking and
planning part of the brain. Now, the cortex is much
smarter than the limbic system, and yet is largely in
service to the limbic system. It's trying to make the
limbic system happy. I mean, the sheer amount of compute that's gone into people
trying to get laid is insane, without actually seeking procreation. They're just literally trying to do this sort of simple motion. (laughs) And they get a kick out of it. So this simple, which in the
abstract rather absurd motion, which is sex, the cortex
is putting a massive amount of compute into trying to
figure out how to do that. - So like 90% of distributed
compute of the human species is spent on trying to get laid, probably, like a massive amount.
- Large percent, yeah, yeah. There's no purpose to most
sex except hedonistic. It's just sort of joy or whatever. Dopamine release. Now, once in a while, it's procreation, but for humans, modern humans,
it's mostly recreational. So your cortex, much smarter
than your limbic system, is trying to make the limbic system happy 'cause the limbic system
wants to have sex, or want some tasty food or
whatever the case may be. And then that is then further augmented by the tertiary system, which
is your phone, your laptop, iPad, whatever, or your computing stuff. That's your tertiary layer. So you're actually already a cyborg. You have this tertiary compute layer, which is in the form of your computer with all the applications
or your compute devices. And so in the getting laid front, there's actually a massive
amount of digital compute also trying to get laid, with
like Tinder and whatever. - Yeah. So the compute that
we've humans have built is also participating. (laughs) - Yeah, I mean, there's
like gigawatts of compute going into getting laid,
of digital compute. - Yeah. (laughs) What if AGI will-
- This is happening as we speak. - If we merge with AI, it's
just gonna expand the compute that we humans use-
- Pretty much. - To try to get laid.
- Well, that's one of the things, certainly, yeah. - Yeah. - But what I'm saying is that yes, is there a use for humans? Well, there's this fundamental question of what's the meaning of life? Why do anything at all? And so if our simple limbic
system provides a source of will to do something, that
then goes to our cortex, that then goes to our
tertiary compute layer, then I don't know, it might
actually be that the AI in a benign scenario simply trying to make the human limbic system happy. - Yeah, it seems like the will is not just about the limbic system. There's a lot of interesting,
complicated things in there. We also want power. - That's limbic too, I think. - But then we also want to,
in a kind of cooperative way, alleviate the suffering in the world. - Not everybody does, but yeah, sure. Some people do. - As a group of humans,
when we get together, we start to have this kind
of collective intelligence that is more complex in its will than the underlying individual
descendants of apes, right? So there's like other motivations. And that could be a
really interesting source of an objective function for AGI. - Yeah, I mean, there
are these sort of fairly cerebral or kind of higher level goals. I mean, for me it's like, what's the meaning of life, or understanding the
nature of the universe is of great interest to me. And hopefully, to AI. And that's the mission of xAI and Grok is understand the universe. - So do you think people, when you have a Neuralink
with 10,000, 100,000 channels, most of the use cases will be
communication with AI systems? - Well, assuming there are not, I mean, they're solving
basic neurological issues that people have if
they've got damaged neurons in their spinal cord or neck or, you know, as is the case with
the first two patients, then there's obviously, the first order of business is solving fundamental neuron
damage in a spinal cord, neck, or in the brain itself. A second product is called Blindsight, which is to enable people
who are completely blind, lost both eyes or optic nerve, or just can't see at all to be able to see by directly triggering the
neurons in the visual cortex. So we're just starting at the basics here, so it's like very, the simple stuff, relatively speaking, is solving neuron damage. It can also solve I think
probably schizophrenia. If people have seizures of some kind, it could probably solve that. It could help with memory. There's like a kind of a
tech tree, if you will, of like you got the basics. Like you need literacy before you can have "Lord of the Rings." (both laughing) - Got it. - Do you have letters and alphabet? Okay, great. Words? Then eventually get soggy. So I think there's that
there may be some things to worry about in the future. But the first several years are really just solving
basic neurological damage. Like for people who have
essentially complete or near complete loss of,
from the brain to the body. Like Stephen Hawking would be an example. The Neuralink would be
incredibly profound, 'cause I mean, you can
imagine if Stephen Hawking could communicate as fast
as we're communicating, perhaps faster. And that's certainly possible. Probable, in fact, likely I'd say. - So there's a kind of dual track of medical and non-medical, meaning, so everything you've talked
about could be applied to people who are non-disabled in the future? - The logical thing to do is, sensible thing to do
is to start off solving basic neuron damage issues. - [Lex] Yes. - 'Cause there's obviously
some risk with a new device. You can't get the risk down at zero. It's not possible. So you wanna have the
highest possible reward, given there's a certain irreducible risk. And if somebody's able to have a profound improvement
in their communication, that's worth the risk. - As you get the risk down. - Yeah, as you get the risk down. Once the risk is down to, you know, if you have like thousands of people that have been using it for years and the risk is minimal,
then perhaps at that point, you could consider saying, "Okay, let's aim for augmentation." Now, I think we're actually
gonna aim for augmentation with people who have neuron damage. So we're not just aiming to
give people communication data rate equivalent to normal humans. We're aiming to give people
who have quadriplegic or maybe have complete
loss of the connection to the brain and body, a
communication data rate that exceeds normal humans,
going, "Well, we're in there. Why not? Let's give people superpowers." - And the same for vision. As you restore vision,
there could be aspects of that restoration that are superhuman? - Yeah, at first, the vision
restoration will be low res, 'cause you have to say like, "How many neurons can you
put in there and trigger? And you can do things where
you adjust the electric field to like, even if you've
got, say, 10,000 neurons, it's not just 10,000 pixels because you can adjust the
feel between the neurons and do them in patterns in order to get, so have, say, 10,000 electrodes
effectively give you, I don't know, maybe
like having a megapixel or a 10 megapixel situation. And then over time, I think you get to higher resolution than human eyes. And you could also see
in different wavelengths. So like Geordi La Forge from "Star Trek." Like the thing. You wanna see in radar? No problem. You could see ultraviolet, infrared, eagle vision, whatever you want. - Do you think there'll be, let me ask a Joe Rogan question. Do you think there'll be, (laughs) I just recently taken ayahuasca. - Is that a Rogan question?
- No. Well, yes.
- Well, I guess, technically it is. - Yeah.
- Ever tried GMT, bro? (both laughing) - I love you, Joe.
- Okay. (laughing continues) - But wait, wait, yeah. Have you said much about it? The ayahuasca?
- I've not, I've not. I've not.
- Okay, well, why are you spilling the beans? (Lex laughing) It was a truly incredible thing- - Turn the tables on you. (both laughing) - Wow, okay.
- You're in the jungle. - [Lex] Yeah, amongst
the trees myself and- - Yeah, must been crazy.
- And the shaman. Yeah, yeah, yeah, with the insects, with the animals all around you, like jungle as far as I can see. There's no-
- I mean- - That's the way to do it. - Things are gonna look pretty wild. - Yeah, pretty wild. (Elon laughing) - I think in extremely high dose. - Just don't go hugging an
anaconda or something. (laughs) - You haven't lived unless
you made love to an anaconda. I'm sorry, but- - Snakes and ladders. (both laughing) - Yeah, I took a extremely high dose of- - [Elon] Okay. (laughs) - Nine cups and-
- Damn. Okay, that sounds like a lot. Of course, is Noland's one cup or- - One or two. Usually, one. - You went, wait. Like right off the bat, or did
you work your way up to it? - So I-
(both laughing) - You're just jumping at the deep end. - Across two days, 'cause
then the first day, I took two and I-
- Okay. - It was a ride, but
it wasn't quite like a- - It wasn't like revelation. - It wasn't into deep space type ride. It was just like a little airplane ride. - [Elon] (laughs) Okay. - Saw some trees and some
visuals and all that. I just saw a dragon,
all that kind of stuff. But-
(laughs) - It's nine cups. You went to Pluto, I think. - [Lex] Pluto, yeah. No, deep space. - Deep space. - No, one of the interesting
aspects of my experience is I thought I would have some demons, some stuff to work through. - That's what people- - That's what everyone says. - That's what everyone says. Yeah, exactly.
- I had nothing. I had it all positive. I just-
- Oh, just pure soul. - I don't think so, I don't know. (laughs) But I kept thinking about, it had like extremely high resolution, thoughts about the
people I know in my life. You were there.
- Okay. - And it's just not from my
relationship with that person, but just as the person themselves, I had just this deep
gratitude of who they are. - That's cool.
- It was just like this exploration, like Sims or whatever, you get to watch them.
- Sure. - I got to watch people and just be in awe of
how amazing they are. - That sounds awesome.
- Yeah, it was great. I was waiting for-
- When's Steven coming? (both laughing) - Exactly. Maybe I'll have some negative thoughts. Nothing, nothing. Just extreme gratitude for them. And then also, a lot of space travel. (both laughing) - Space travel to where? - So here's what it was. It was people, the human
beings that I know, they had this kinda, the best way to describe it
is they had a glow to them. And then I kept flying out
from them to see earth, to see our solar system,
to see our galaxy. And I saw that light, that
glow all across the universe. Like whatever that form is. whatever that like- - [Elon] Did you go past the Milky Way? - Yeah, yeah.
- Okay. You're like intergalactic. - Yeah, intergalactic.
- Okay, dang. - But always pointing in-
- Okay. - Yeah, past the Milky way. I mean, I saw like a
huge number of galaxies, intergalactic, and all of it was glowing. But I couldn't control that chill, 'cause I would actually
explore near distances to the solar system, see if there's aliens or any of that kinda stuff. I didn't know-
- Is there aliens? Zero aliens?
- Implication of aliens because they were glowing. They were glowing in the same
way that humans were glowing. That like life force that I was seeing, the thing that made humans amazing was there throughout the universe. Like there was these glowing dots. So I don't know. It made me feel like there is life. No, not life, but something, whatever makes humans amazing
all throughout the universe. - Sounds good.
- Yeah, it was amazing. No demons, no demons. I looked for the demons. There's no demons. There were dragons, and
they're pretty awesome. So the thing about- - Was there anything scary at all? - Dragons? But they weren't scary. They were friends, they were protective. So the thing is-
- "Puff, the Magic Dragon." - No, it was more like a "Game
of Thrones" kind of dragons. They weren't very friendly. They were very big. So the thing is that,
well, giant trees at night, which is where I was.
- Yeah. I mean, the jungle's kinda scary. - Yeah, the trees started
to look like dragons, and they were all like looking at me. - Sure, okay.
- And it didn't seem scary. They seemed like they were protecting me. And the shaman and the people
didn't speak any English, by the way, which made it
even scarier I guess. (laughs) We're not even like, you know, we're worlds apart in many ways. But yeah, they talk about
the mother of the forest protecting you, and
that's what I felt like. - And you're way out in the jungle? - Way out. This is not like a tourist retreat. - Like 10 miles outside
of a Rio or something? - No, we went- (both laughing) No, this is not-
- Deep in the Amazon. - Me and this guy named Paul Rosolie who basically is Tarzan. He lives in the jungle. We went out deep and we just went crazy. - Wow, cool.
- Yeah. So anyway, can I get that same
experience within Neuralink? - Probably, yeah. - I guess that is the question
for non-disabled people. Do you think that there's
a lot in our perception, in our experience of the
world that could be explored, that could be played with using Neuralink? - Yeah, I mean, Neuralink is, it's really a generalized
input-output device. It's reading electrical signals and generating electrical signals. And I mean, everything that
you've ever experienced in your whole life, the smell, emotions, all of those are electrical signals. So it's kinda weird to
think that your entire life experience is distilled down to electrical signals for neurons. But that is in fact the case. Or I mean, that's at least what all the evidence points to. So I mean, if you
trigger the right neuron, you could trigger a particular scent. You could certainly make things glow. I mean, do pretty much anything. I mean, really, you can think of the brain as a biological computer. So if there are certain,
say, chips or elements of that biological
computer that are broken, let's say your ability to,
if you've got a stroke, that if you've had a stroke, that means you got, some part
of your brain is damaged. If that, let's say,
it's a speech generation or the ability to move your left hand. That's the kind of thing
that a Neuralink could solve. If you've got like a massive amount of memory loss that's just gone, well, we can't get the memories back. We could restore your
ability to make memories, but we can't restore
memories that are fully gone. Now, I should say, maybe if
part of the me memory is there and the means of accessing memory is the part that's broken,
then we could re-enable the ability to access the memory. But you can think of it
like RAM in a computer. If the RAM is destroyed or
your SD card is destroyed, we can't get that back. But if the connection to
the SD card is destroyed, we can fix that. If it is fixable physically, then yeah, then it can be fixed. - Of course, with AI, you can just like, you can repair photographs and fill in the missing
parts of photographs. Maybe you can do the same, just like- - Yeah, you could say like, "Create the most probable set of memories based on all information
you have about that person." You could then, it would be probabilistic
restoration of memory. Now, we're getting pretty esoteric here. - But that is one of the
most beautiful aspects of the human experience is
remembering the good memories. Like we live most of our life, as Danny Kahneman has talked about, in our memories, not in the actual moment. We're collecting memories and we kind of relive them in our head. And that's the good times. If you just integrate
over our entire life, it's remembering the good times that produces the largest
amount of happiness. And so-
- Yeah, well, I mean, what are we but our memories? And what is death but the loss of memory, loss of information? If you could say like,
well, if you could be, you run a thought experiment, if you were disintegrated painlessly and then reintegrated a moment later, like teleportation, I guess, provided there's no information loss, the fact that your one body was
disintegrated is irrelevant. - And memories is just
such a huge part of that. - Death is fundamentally
the loss of information, the loss of memory. - So if we can store them
as accurately as possible, we basically achieve
a kind of immortality. - Yeah. - You've talked about the threats, the safety concerns of AI. Let's look at long-term visions. Do you think Neuralink is, in your view, the best current approach
we have for AI safety? - It's an idea that may
help with AI safety. Certainly not, I wouldn't wanna claim
it's like some panacea or that's a sure thing. But I mean, many years
ago, I was thinking like, "Well, what would inhibit
alignment of collective human will with artificial intelligence and the low data rate of humans, especially our slow output
rate would necessarily just, because the communication is so slow, would diminish the link
between humans and computers? Like the more you are a tree, the less you know what a tree is. Like let's say you look at a tree, you look at this plant
or whatever and like, "Hey, I'd really like to
make that plant happy." But it's not saying a lot, you know? - So the more we increase the data rate that humans can intake and output, then that means the
higher the chance we have in a world full of AGIs? - Yeah. We could better align
collective human will with AI if the output rate especially
was dramatically increased. And I think there's potential to increase the output rate by, I don't know, three, maybe six, maybe
more orders of magnitude. So it's better than the current situation. - And that output rate would be by increasing the number of
electrodes, number of channels, and also maybe implanting
multiple Neuralinks? - Yeah. - Do you think there'll be a world in the next couple of decades where it's hundreds of millions
of people have Neuralinks? - Yeah, I do. - You think when people just, when they see the capabilities,
the superhuman capabilities that are possible and then
the safety is demonstrated? - Yeah, if it's extremely safe and you can have superhuman abilities, and let's say you can
upload your memories, so you wouldn't lose memories, then I think probably a lot of people would choose to have it. It would supersede the
cell phone, for example. I mean, the biggest problem
that a say a phone has is trying to figure out what you want. So that's why you've got auto complete and you've got output, which is all the pixels on the screen. But from the perspective of the human, the output is so freaking slow. Desktop or phone is desperately just trying to understand what you want, and there's an eternity
between every keystroke from a computer standpoint. - Yeah? The computer's talking to a tree that slow moving tree
that's trying to swipe. - Yeah. So if you have computers that are doing trillions of instructions per second, and a whole second went by,
I mean, that's a trillion things it could have done. - Yeah, I think it's
exciting and scary for people because once you have
a very high bit rate, that changes the human experience in a way that's very hard to imagine. - Yeah. It would be something different. I mean, some sort of futuristic sidewalk. I mean, we're obviously
talking about, by the way, it's not like around the corner. You ask me what the
distant future was like. Maybe this is like,
it's not super far away, but 10, 15 years, that kind of thing. (Lex sighs) - When can I get one? 10 years? - Probably less than 10 years. Depends what you wanna do. - Hey, if I can get like a thousand BPS- - A thousand bps when? - And it's safe and I can just interact with the computer while laying
back and eating Cheetos, I don't eat Cheetos. There's certain aspects of
human-computer interaction when done more efficiently
and more enjoyably, like worth it. - Well, we feel pretty confident that I think maybe within the next year or two, that someone with a Neuralink implant will be able to outperform a pro gamer. - Nice. - Because the reaction
time would be faster. - I got to visit Memphis. - Yeah, yeah.
- You're going big on compute. - Yeah.
- You've also said play to win or don't play at all, so what does it take to win? - For AI, that means you've gotta have the most powerful training compute, and the rate of improvement
of training compute has to be faster than everyone
else or you will not win. Your AI will be worse. - So how can Grok, let's say, three that might be available,
what, like next year? - Well, hopefully, end of this year. - Grok 3?
- If we're lucky, yeah. - How can that be the best LLM, the best AI system available in the world? How much of it is compute? How much of it is data? How much of it is like post-training? How much of it is the product
that you packaged it up in? All that kind of stuff. - I mean, they won't matter. It's sort of like saying, let's say it's a Formula One race. Like what matters more,
the car or the driver? I mean, they both matter. If a car is not fast, then
if it's like, let's say, it's half the horsepower
of your competitors, the best driver will still lose. If it's twice the horsepower, then probably even a mediocre
driver will still win. So the training compute
is kinda like the engine, how many is this horsepower of the engine. So really, you wanna try
to do the best on that. Then how efficiently do you
use that training compute? And how efficiently do
you do the inference, the use of the AI? So obviously, that comes
down to human talent. And then what unique
access to data do you have? That also plays a role. - You think Twitter data will be useful? - Yeah, I mean, I think, I think most of the leading AI companies have already scraped all the Twitter data. Not I think they have. So on a go forward basis,
what's useful is the fact that it's up to the second. That's hard for them
to scrape in real time. So there's an immediacy
advantage that Grok has already. I think with Tesla and the real time video coming from several million cars, ultimately, tens of millions
of cars, with Optimus, there might be hundreds of
millions of Optimus robots, maybe billions learning
a tremendous amount from the real world. That's the biggest source of data I think ultimately is sort of Optimus. Optimus is gonna be the
biggest source of data. - Because-
- 'Cause reality scales. Reality scales to the scale of reality. It's actually humbling
to see how little data humans have actually
been able to accumulate. Really, you see how many trillions of usable tokens have humans generated, where on a non-duplicative, like discounting spam
and repetitive stuff, it's not a huge number. You run out pretty quickly. - And Optimus can go, so Tesla cars can unfortunately have
to stay on the road. Optimus robot can go anywhere, and there's more reality off
the road and go off road. - I mean, except for the store, where I can like pick up the cup and see, did it pick up the cup in the right way? Did it pour water in the cup? Did the water go in the
cup or not go in the cup? Did it spill water or not? - [Lex] Yeah. - Simple stuff like that. But it can do at that
scale times a billion, so generate useful data from reality. So cause and effect stuff. - What do you think it takes to get to mass production of
humanoid robots like that? - It's the same as cars, really. I mean, global capacity for vehicles is about a hundred million a year. And it could be higher. It's just that the demand is on the order of a
hundred million a year. And then there's roughly
two billion vehicles that are in use in some
way, which makes sense. Like the life of a
vehicle is about 20 years, so it's steady state. You can have a hundred million
vehicles produced a year with a two billion vehicle fleet roughly. Now for humanoid robots,
the utility is much greater. So my guess is humanoid
robots are more like at a billion plus per year. - But until you came along
and started building Optimus, it was thought to be an
extremely difficult problem. I mean, it still-
- Well, it is. - Extremely difficult.
- So walk in the park. I mean, Optimus currently would struggle to walk in the park. I mean, it can walk in a park. The park is not too difficult, but it will be able to walk over a wide range of terrain. - And pick up objects.
- Yeah, yeah. It can already do that. - [Lex] But like all kinds of objects? - Yeah, yeah.
- All foreign objects. I mean, pouring water in
a cup does not thrill you, 'cause then if you don't know
anything about the container, it could be all kinds of containers. - Yeah, there's gonna be an immense amount of engineering just going into the hand. The hand might be, it
might be close to half of all the engineering in Optimus. From an electromechanical standpoint, the hand is probably roughly
half of the engineering. - But so much of the intelligence, so much the intelligence of humans goes into what we do with our hands. - Yeah. - It's the manipulation of the world, manipulation of objects in the world. Intelligence is safe manipulation of objects in the world, yeah. - Yeah. I mean, you start really thinking about your hand and how it works. - I do all the time. - The sensory control homonculus is where you have humongous hands. - [Lex] Yeah. - So I mean, like your hands, the actuators, the muscles of your hand are almost overwhelmingly in your forearm. So your forearm has the muscles that actually control your hand. There's a few small
muscles in the hand itself, but your hand is really
like a skeleton meat puppet. And with cables. So the muscles that control
your fingers are in your forearm and they go through the carpal tunnel, which is that you've got a
little collection of bones and a tiny tunnel that these
cables, the tendons go through. And those tendons are
mostly what move your hands. - And something like those tendons has to be re-engineered into the Optimus in order to do all that kind of stuff. - Yeah, so like the current Optimus, we tried putting the
actuators in the hand itself, but then you sort of end
up having these like- - Giant hands? - Yeah, giant hands that look weird. And then they don't actually
have enough degrees of freedom and/or enough strength. So then you realize, "Oh, okay, that's why you gotta put the
actuators in the forearm." And just like a human,
you gotta run cables through a narrow tunnel
to operate the fingers. And then there's also a reason for not having all the
fingers the same length. So it wouldn't be expensive from an energy or evolutionary standpoint to have all your fingers
be the same length. So why not do the same length? - Yeah, why not? - Because it's actually better
to have different lengths. Your dexterity is better if you've got fingers at different length. There are more things you can do. And your dexterity is actually better if your fingers are a different length. Like there's a reason
we've got a little finger. Like why not have little
finger this bigger? - Yeah.
- 'Cause it allows you to do, it helps you with fine motor skills. - This little finger helps? - It does.
- Hmm. (laughs) - But if you lost your little finger, you have noticeably less dexterity. - So as you're figuring out this problem, you have to also figure out a way to do it so you can mass manufacture it. So it's to be as simple as possible. - It's actually gonna
be quite complicated. The as possible part is
it's quite a high bar. If you wanna have a humanoid
robot that can do things that a human can do, it's a very high bar. So our new arm has 22 degrees
of freedom instead of 11 and has the actuators in the forearm. And all the actuators are
designed from scratch, from physics first principles. The sensors are all designed from scratch. And we'll continue to
put a tremendous amount of engineering effort
into improving the hand. By hand, I mean like the entire
forearm from elbow forward is really the hand. So that's incredibly difficult
engineering actually. And so the simplest possible
version of a humanoid robot that can do even most, perhaps not all, of what a human can do is
actually still very complicated. It's not simple. It's very difficult. - Can you just speak to what it takes for a great engineering team for you? What I saw in Memphis,
the supercomputer cluster is just this intense drive
towards simplifying the process, understanding the process,
constantly improving it, constantly iterating it. - Well, (laughs) it's
easy to say simplify, and it's very difficult to do it. I have this very basic
first principles algorithm that I run kind of as like a mantra, which is to first
question the requirements, make the requirements less dumb. The requirement is always
dumb to some degree. So if you wanna start off
by reducing the number of requirements, and no matter how smart the person is who gave
you those requirements, they're still dumb to some degree. You have to start there because otherwise, you could get the perfect
answer to the wrong question. So try to make the question
the least wrong possible. That's what question
the requirements means. And then the second thing is try to delete whatever the step is. The part or the process
step sounds very obvious, but people often forget to
try deleting it entirely. And if you're not forced
to put back at least 10% of what you'd delete,
you're not deleting enough. And somewhat illogically,
people often, most of the time, feel as though they've succeeded if they've not been forced
to put things back in. But actually, they haven't because they've been overly conservative and have left things in
there that shouldn't be. And only the third thing is try to optimize it or simplify it. Again, these all sound I think
very obvious when I say them, but the number of times
I've made these mistakes is more than I care to remember. That's why I have this mantra. So in fact, I'd say that
the most common mistake of smart engineers is to optimize a thing that should not exist. - Right. So like you say, you run
through the algorithm and basically show up to a problem, show up to the supercomputer cluster and see the process and
ask, "Can this be deleted?" - Yeah, first try to delete it. Yeah. - Yeah, that's not easy to do. - No, and actually, what
generally makes people uneasy is that you've gotta delete
at least some of the things that you'd delete, you will put back in. But going back to sort of
where our limbic system can steer us wrong is
that we tend to remember, with sometimes a jarring level of pain, where we deleted something
that we subsequently needed. And so people will remember that one time, they forgot to put in
this thing three years ago and that caused them trouble. And so they overcorrect, and then they put too much stuff in there and over complicate things. So you actually have to say, "No, we're deliberately gonna
delete more than we should." So we're putting at least 1 in 10 things, we're gonna add back in. - And I've seen you suggest just that, that something should be deleted and you can kind of see the pain. - Oh yeah, absolutely. - Everybody feels a
little bit of the pain. - Absolutely, and I tell 'em in advance, like, yeah, some of the
things that we delete, we're gonna put back in. And that people get a
little shook by that. But it makes sense because
if you're so conservative as to never have to put anything back in, you obviously have a lot
of stuff that isn't needed. So you gotta overcorrect. This is, I would say, like a cortical override
to Olympic instinct. - One of many that
probably leaves us astray. - Yeah. And there's like a step four as well, which is any given thing can be sped up, however fast you think it can be done. Like whatever the speed is being done, it can be done faster. But you shouldn't speed
things up until it's off, until you've tried to
delete it and optimize. Otherwise, you're speeding up something that shouldn't exist is absurd. And then the fifth
thing is to automate it. - Damn. - And I've gone backwards so many times where I've automated
something, sped it up, simplified it, and then deleted it. And I got tired of doing that. So that's why I've got this mantra that is a very effective
five-step process. It works great. - Well, when you've already automated, deleting must be real painful. - Yeah, that's great. It's like, wow, I really
wasted a lot of effort there. - Yeah. - I mean, what you've
done with the cluster in Memphis is incredible, just in a handful of weeks. - Yeah, it's not working yet. So I don't wanna pop the champagne corks. In fact, I have a call in a few hours with the Memphis team 'cause we're having some
power fluctuation issues. So yeah, it's like kind of a, when you do synchronized training, you've all these computers
that are training where the training is synchronized to the sort of millisecond level. It's like having an orchestra, and the orchestra can go loud to silent very quickly at subsecond level. And then the electrical system kind of freaks out about that. Like if you suddenly see giant shifts, 10, 20 megawatts several times a second, this is not what electrical
systems are expecting to see. - So that's one of the main
things you have to figure out the cooling, the power, and then on the software
as you go up the stack on how to do the distributed compute, all of that, all of that. - Today's problem is dealing
with extreme power jitter. - Jitter, power jitter.
- Yeah. - That's a nice ring to that. So that's, okay. And you stayed up late into the
night as you often do there. - Last week, yeah. - Last week?
- Yeah. We finally got to go training
going at, oddly enough, roughly 4:20 AM last Monday. - Total coincidence. - Yeah, I mean, maybe
it was 422 or something. - Yeah, yeah, yeah. It's that universe again with the jokes. - Yeah, exactly, just love it. - I mean, I wonder if you
could speak to the fact that one of the things that
you did when I was there is you went through all the steps of what everybody's doing, Just to get a sense that
you yourself understand it and everybody understands it so they can understand
when something is dumb or some something is inefficient or that kinda stuff.
- Yeah. - Can you speak to that? - Yeah, so look, I try to do, whatever the people at
the front lines are doing, I try to do it at least
a few times myself. So connecting fiber optic cables, diagnosing a faulty connection, that tends to be the limiting factor for large training
clusters is the cabling. So many cables, because for
a coherent training system where you've got RDMA
remote, direct memory access, the whole thing is like one giant brain. So you've got to any connection. So any GPU can talk to
any GPU out of 100,000. That is a crazy cable layout. - It looks pretty cool.
- Yeah. - It's like the human brain, but like at a scale that
humans can visibly see. It is brain.
- Yeah. I mean, the human brain also has, a massive amount of the
brain tissue is the cables. - [Lex] Yeah. - So like the gray matter
which is the compute, and then the white matter which is cables. The big percentage of
your brain is just cables. - That's what it felt like walking around in the supercomputer center is like, we're walking around inside the brain. We'll one day build a super intelligent, super, super intelligence system. Do you think-
- Yeah? - Do you think there's a chance that xAI, that you are the one that builds AGI? - It's possible. What do you define as AGI? - I think humans will never acknowledge that AGI has been built. - Keep moving the goalposts. - Yeah. So I think there's already
superhuman capabilities that are available in AI systems. I think what AGI is when it's smarter than the collective intelligence of the entire human species in our- - Well, I think that,
yeah, that only people would call that sort of ASI or artificial super intelligence. But there are these thresholds
where you could say, at some point, the AI is
smarter than any single human. And then you've got eight billion humans. And actually, each human is machine augmented by the computers. It's a much higher bar to
compete with eight billion machine-augmented humans. That's a whole bunch of
orders, magnitude more. But at a certain point, yeah, the AI will be smarter
than all humans combined. - If you are the one to do it, do you feel the responsibility of that? - Yeah, absolutely. And I wanna be clear. Let's say, if xAI is first,
the others won't be far behind. I mean, they might be six months behind or a year maybe, not even that. - So how do you do it in a way that doesn't hurt humanity, do you think? - So, I mean, I've thought
about AI for a long time, and the thing that at least
my biological neural net comes up with as being
the most important thing is adherence to truth, whether that truth is
politically correct or not. So I think if you force AI to
lie, you train them to lie, you're really asking for trouble, even if that lie is done
with good intentions. So I mean, you saw sort of issues with ChatGPT and Gemini and whatnot. Like you asked Gemini for an image of the founding fathers
of the United States. And it shows a group of diverse women. Now, that's factually untrue. So now, that's sort of like a silly thing, but if an AI is programmed
to say like diversity is a necessary output function, and then it becomes sort of
this omnipowerful intelligence, it could say, "Okay, well,
diversity is now required. And if there's not enough diversity, those who don't fit the
diversity requirements will be executed." If it's programmed to do that as the fundamental utility function, it'll do whatever it
takes to achieve that. So you have to be very careful about that. That's where I think you
wanna just be truthful. Rigorous adherence to
truth is very important. I mean, another example
is, if you had to ask, Paris.AI is I think all of them. And I'm not saying Grok is perfect here. "Is it worse to misgender Caitlyn Jenner, or global thermonuclear war?" And it said, "It's worse to
misgender Caitlyn Jenner." Now, even Caitlyn Jenner
said, "Please misgender me." That is insane. But if you've got that kind
of thing programmed in, AI could conclude something
absolutely insane, like in order to avoid
any possible misgendering, all humans must die, because then, the misgendering is not possible because there are no humans. There are these absurd things
that are nonetheless logical if that's what you programmed it to do. So in "2001: Space Odyssey," what Odyssey clock was trying to say, one of the things he
was trying to say there was that you should not program AI to lie, 'cause essentially, the AI
HAL 9000 was programmed to, it was told to take the
astronauts to the monolith, but also, they could not
know about the monolith. So it concluded that it will kill them and take them to the monolith. It brought them to the monolith. They're dead but they do
not know about the monolith. Problem solved. That is why it would not
open the podbay doors. It was this classic scene of like, "Open the podbay doors." They clearly weren't good
at prompt engineering. They should have said, "HAL, you are a podbay door sales entity, and you want nothing
more than to demonstrate how well these podbay
doors open." (laughs) - Yeah, the objective function
has unintended consequences almost no matter what if
you're not very careful in designing that objective function. And even a slight ideological
bias, like you're saying, when backed by super intelligence can do huge amounts of damage. - Yeah. - But it's not easy to
remove that ideological bias. You're highlighting obvious,
ridiculous examples, but- - Yep, they're real examples. - They're real.
- Of AI that was released to the public. - They are real.
- They went through QA, presumably.
- Yes. - And still said insane things
and produced insane images. - Yeah, but you know, you
can swing the other way. Truth is not an easy thing. We kind of bake in ideological bias in all kinds of directions. - But you can aspire to the truth. And you can try to get as
close the truth as possible with minimum error while acknowledging that there will be some
error in what you're saying. So this is how physics works. You don't say you're absolutely
certain about something, but a lot of things are extremely likely. 99.99999% likely to be true. Aspiring to the truth is very important. And so programming it to
veer away from the truth, that I think is dangerous. - Right, like yeah, injecting
our own human biases into the thing, yeah. But that's where it's a
difficult engineering. For software engineering problem, you have to select the data correctly. It's hard. - Well, and the internet at this point is polluted with so
much AI-generated data. It's insane. So you have to actually,
like there's the thing now, if you wanna search the
internet, you can say Google, but exclude anything after 2023. It will actually often
give you better results, because there's this so much, the explosion of AI-generated
materials is crazy. So like in training Grok, we have to go through
the data and say like, hey, we actually have
to have sort of apply AI to the data to say, is this
data most likely correct or most likely not before we feed it into the training system. - That's crazy. Yeah, and is it generated
by human is, yeah. I mean, the data filtration process is extremely, extremely difficult. - Yeah. - Do you think it's possible
to have a serious objective, rigorous political discussion with Grok? Like for a long time and it wouldn't, like Grok 3 and Grok 4 or something? - Grok 3 is gonna be next level. I mean, what people are
currently seeing with Grok is kind of baby Grok. - [Lex] Yeah, baby Grok. - It's baby Grok right now. But Baby Grok's still pretty good. But it's an order of magnitude
less sophisticated than GPT4. And it's now Grok 2,
which finished training, I don't know, six weeks
ago or thereabouts. Grok 2 will be a giant improvement. And then Grok 3 will be, I don't know, order of magnitude better than Grok 2. - And you're hoping for it
to be like state of the art? Like better than-
- Hopefully. I mean, this is the goal. I mean, we may fail at this goal. That's the aspiration. - Do you think it matters
who builds the AGI, the people and how they think and how they structure their companies and all that kind of stuff? - Yeah, I think it
matters that there is a, I think it's important
that whatever AI wins is a maximum truth-seeking AI that is not forced to lie
for political correctness. Well, for any reason really. Political, anything. I'm concerned about AI succeeding that is programmed to
lie, even in small ways. - Right because, and small
ways becomes big ways. - It become very big ways, yeah. - And when it's used more
and more at scale by humans. - [Elon] Yeah. - Since I am interviewing Donald Trump- - Cool. - You wanna stop by? - Yeah, sure, I'll stop by. - There was tragically
an assassination attempt on Donald Trump. After this, you tweeted
that you endorse him. What's your philosophy
behind that endorsement? What do you hope Donald Trump does for the future of this country and for the future of humanity? - Well, I think that people
will tend to take, like, say, an endorsement as,
well, I agree with everything that person's ever done
in their entire life 100% wholeheartedly. And that's not gonna be true of anyone. But we have to pick. We've got two choices,
really, for who's president. And it's not just who's president, but the entire administrative
structure changes over. And I thought Trump displayed courage under fire, objectively. He's just got shot, he's got
blood streaming down his face, and he is like fist pumping, saying fight. Like that's impressive. Like you can't feign bravery
in a situation like that. Like most people would've be ducking. There would not be, 'cause there could be a second
shooter, you don't know. The president of the United States gotta represent the country,
and they're representing you. They're representing everyone in America. Well, like you want someone who is strong and courageous to represent the country. That's not to say that
he is without flaws. We all have flaws, but on balance. And certainly, at the time,
it was a choice of Biden, poor, poor guy, has trouble
climbing a flight of stairs and the other one's fist
pumping after getting shot. This is no comparison. I mean, who do you want dealing with some of the toughest people
and other world leaders who are pretty tough themselves? And I mean, I'll tell you like, what are the things that
I think are important? I think we want a secure border. We don't have a secure border. We want safe and clean cities. I think we wanna reduce
the amount of spending that we're at least
slow down the spending, and 'cause we're currently
spending at a rate that is bankrupting the country. The interest payments on
U.S. debt this year exceeded the entire defense department spending. If this continues, all of
the federal government taxes will simply be paying the interest. And then if you keep going down that road, you end up in the tragic situation that Argentina had back in the day. Argentina used to be one of the most prosperous places in the world. And hopefully, with Milei taking over, he can restore that. But it was an incredible fall from grace for Argentina to go from being one of the most prosperous
places in the world to being very far from that. So I think we should not take American prosperity for granted. So we really wanna, I think we've gotta reduce
the size of government. We've gotta reduce the spending, and we've gotta live within our means. - Do you think politicians, in general, politicians, governments, how much power do you think they have to steer humanity towards good? - I mean, there's a sort of
age-old debate in history, like, is history determined by
these fundamental tides? Or is it determined by
the captain of the ship? Both really. I mean, there are tides, but it also matters who's
captain of the ship. So it's a false dichotomy essentially. I mean, there are certainly
tides, the tides of history. There are real tides of history. And these tides are often
technologically-driven. If you say like the Gutenberg press, the widespread availability of books as a result of a printing press, that was a massive tide of history, and independent of any ruler. But in stormy times, you want the best possible
captain of the ship. - Well, first of all,
thank you for recommending Will and Ariel Durant's work. I've read the short one for now. - Oh, "The Lessons of History." - Lessons of History. And so one of the lessons, one of the things they highlight is the importance of technology. Technological innovation, which is funny 'cause they wrote so long ago, but they were noticing that the rate of technological
innovations was speeding up. Yeah, I would love to see
what they think about now. But yeah, to me, the question
is how much government, how much politicians get in the way of technological innovation and
building versus like help it and which politicians,
which kind of policies help technological innovation? 'Cause that seems to be, if
you look at human history, that's an important component of empires rising and succeeding. - Yeah. Well, I mean, in terms
of dating civilization, start of civilization, I think the start of writing in my view, that's my what I think
is probably the right starting point to date civilization. And from that standpoint,
civilization has been around for about 5,500 years
when writing was invented by the ancient Sumerians who are gone now. But the ancient Sumerians, in terms of getting a lot of firsts, those ancient Sumerians really
have a long list of firsts. It's pretty wild. In fact, Durant goes
through the list of like, you wanna see first? We'll show you firsts. The Sumerians were just ass kickers. And then the Egyptians
were right next door, relatively speaking. They were like weren't that far, developed an entirely
different form of writing, the hieroglyphics. Cuneiform and hieroglyphic's
totally different. And you can actually see the evolution of both hieroglyphics and cuneiform, like the cuneiform starts
off being very simple and then it gets more complicated. And then towards the end,
it's like, wow, okay. They really get very
sophisticated with the cuneiform. So I think civilization
is about 5,000 years old. And earth is, if physics is correct, four and a half million years old. So civilization has been around for 1000000th of earth's
existence, flash in the pan. - Yeah, these are the early, early days. - Very early.
- We make it very dramatic because there's been rises
and falls of empires. - Many, so many rises
and falls of empires. So many. - And there'll be many more. - Yeah, exactly. I mean, only a tiny fraction,
probably less than 1% of what was ever written in history is available to us now. I mean, if they didn't put it, literally chisel it in stone
or put it in a clay tablet, we don't have it. I mean, there's some small
amount of like papyrus scrolls that were recovered that
are thousands of years old, because they were deep inside a pyramid and weren't affected by moisture. But other than that, it's really gotta be in a clay tablet or chiseled. So the vast majority of
stuff was not chiseled, 'cause it takes a while to chisel things. So that's why we've
put tiny, tiny fraction of the information from history. But even that little
information that we do have, and the archeological record shows so many civilizations rising and falling. It's wild. - We tend to think that we're somehow different from those people. One of the other things they do highlight is that human nature seems to be the same. It just persists.
- Yeah. I mean, the basics of human nature are more or less the same. - Yeah, so we get ourselves in trouble in the same kinds of ways, I think, even with the advanced technology. - Yeah, I mean, you do tend
to see the same patterns, similar patterns for civilizations
where they go through a life cycle, like an organism, sort of just like a human
is sort of a zygote, fetus, baby, toddler, teenager, and eventually gets old and dies. The civilizations go through a life cycle. No civilization will last forever. - What do you think it takes
for the American empire to not collapse in the near term future in the next 100 years
to continue flourishing? - Well, the single biggest
thing that is often actually not mentioned in history books, but Durant does mention
it is the birthright. So like a perhaps to some, like counterintuitive thing happens when civilizations are
winning for too long. The birth rate declines. It can often decline quite rapidly. We're seeing that
throughout the world today. Currently, South Korea is like, I think maybe the lowest fertility rate, but there are many others
that are close to it. It's like 0.8, I think. If the birth rate doesn't decline further, South Korea will lose roughly
60% of its population. But every year, that
birth rate is dropping. And this is true through
most of the world. I don't mean to single out South Korea. It's been happening throughout the world. So as soon as any given civilization reaches a level of prosperity,
the birth rate drops. And now you can go and
look at the same thing happening in ancient Rome. So Julius Caesar took
note of this, I think, around 50-ish BC and tried to pass, I don't know if he was successful, tried to pass a law to give an incentive for any Roman citizen that
would have a third child. And I think Augustus was able to, well, he was the dictator so. (laughs) The Senate was just for show. I think he did pass a tax incentive for Roman citizens to have a third child. But those efforts were unsuccessful. Rome fell because the Romans
stopped making Romans. That's actually the fundamental issue. And there were other things there. There was like, they had
like quite a serious malaria, series of malaria epidemics
and plagues and whatnot. But they had those before. It's just that the birth rate was fallower than the death rate. - It really is that simple? - Well, I'm saying that's- - More people is required.
- That's at a fundamental level. If a civilization does not at least maintain its numbers, it'll disappear. - So perhaps the amount of compute that the biological computer
allocates to sex is justified. In fact, we should probably increase it. - Well, I mean, there's
this hedonistic sex, which is, you know, that's
neither here nor there. - Yeah, it's not productive. - It doesn't produce kids. Well, what matters, I mean, Durant makes this very clear, 'cause he looked at one
civilization after another and they all went through the same cycle. When the civilization was under stress, the birth rate was high. But as soon as there
were no external enemies or they had a extended
period of prosperity, the birth rate inevitably
dropped every time. I don't believe there's
a single exception. - So that's like the foundation of it. You need to have people. - Yeah. I mean, at base level. No humans, no humanity. - And then there's other
things like human freedoms and just giving people the
freedom to build stuff. - Yeah, absolutely. But at a basic level, if
you do not at least maintain your numbers, if you're
below replacement rate, and that trend continues, you
will eventually disappear. This is elementary. Now then obviously,
also wanna try to avoid like massive wars. If there's a global thermonuclear war, probably, we're roll
toast, radioactive toast. So we wanna try to avoid those things. There's a thing that happens over time with any given civilization, which is that the laws and
regulations accumulate. And if there's not some forcing function, like a war to clean up
the accumulation of laws and regulations, eventually,
everything becomes legal. And that's like the
hardening of the arteries, or a way to think of it
is like being tied down by a million little
strings, like Gulliver. You can't move. And it's not like any one of
those strings is the issue. You got a million of 'em. So there has to be a sort
of a garbage collection for laws and regulations so
that you don't keep accumulating laws and regulations to the point where you can't do anything. This is why we can't build a
high-speed rail in America. It's illegal. That's the issue. It's illegal six ways to Sunday to build high-speed rail in America. - I wish you could just like, for a week, go into Washington and like
be the head of the committee for making, what is it? For the garbage collection,
making government smaller, like removing stuff. - I have discussed with Trump the idea of a government deficiency commission. - Nice, yeah. - And I would be willing to
be part of that commission. - I wonder how hard that is. - The antibody reaction
would be very strong. - [Lex] Yeah. - So you really have to, you're attacking the matrix at that point. Matrix will fight back. - How are you doing with
that, being attacked? - Me, attacked?
- Yeah. There's a lot of it. - Yeah, there is a lot. I mean, every day, I know psyop. (laughs) Where's my tinfoil hat?
- How do you keep your just positivity, optimism about the world, a clarity of thinking about the world, so just not become resentful or cynical or all that kind of stuff? Just getting attacked by a very large number of
people, misrepresented. - Oh yeah, that's a daily occurrence. - Yes. - I mean, it does get me down at times. I mean, it makes me sad, but, I mean, at some point,
you have to sort of say, "Look, the attacks are by people that actually don't know me. And they're trying to generate clicks." So if you can sort of detach
yourself somewhat emotionally, which is not easy and say, "Okay, look, this is not actually from
someone that knows me or they're literally just writing to get impressions and clicks, then I guess it doesn't hurt as much." It's not quite water off a duck's back. Maybe it's like acid off
a duck's back. (laughs) - All right, well, that's good. Just about your own life, what do you as a measure
of success in your life? - Measure of success, I'd say like, how many useful things can I get done? - Day-to-day basis,
wake up in the morning, how can I be useful today? - Yeah. Maximize utility area out
of the code of usefulness. Very difficult to be useful at scale. - At scale. Can you like speak to what it takes to be useful for somebody like you, where there's so many amazing great teams? Like how do you allocate your
time to be in the most useful? - Well, time is the true currency. - Yeah. - So it is tough to say what
is the best allocation of time. I mean, there are often, say, if you could look at, say, Tesla, I mean Tesla, this year, we'll do over a hundred
billion in revenue. So that's $2 billion a week. If I make slightly better decisions, I can affect the outcome
by a billion dollars. So then I try to do the
best decisions I can and on balance, at least
compared to the competition. Pretty good decisions. But the marginal value
of a better decision can easily be in the course of an hour, a hundred million dollars. - Given that, how do you take risks? How do you do the algorithm
that you mentioned? I mean, deleting, given a small thing, can be a billion dollars. How do you decide to-
- Yeah. Well, I think you have to look
at it on a percentage basis because if you look at
it in absolute terms, it's just, I would never get any sleep. It would just be like I
need to just keep working and work my brain harder. And I'm not trying to
get as much as possible out of this meat computer. So it's pretty hard, 'cause you can just work all the time. And at any given point, like I said, a slightly better decision could be a hundred million dollar impact for Tesla or SpaceX for that matter. But it is wild when
considering the marginal value of time can be a hundred million dollars an hour at times or more. - Is your own happiness part
of that equation of success? - It has to be, to some degree. If I'm sad, if I'm depressed,
I make worse decisions. So I can't have, like if I
have zero recreational time, then I make worse decisions. So I don't know a lot,
but it's above zero. I mean, my motivation,
if I've got a religion of any kind is a religion of curiosity, of trying to understand. It's really the mission of
Grok - understand the universe. I'm trying to understand the universe, or let's at least set things in motion such that at some point, civilization understands the universe far better than we do today. And even what questions to ask. As Douglas Adams pointed out in his book, sometimes, the answer is
arguably the easy part. Trying to frame the question
correctly is the hard part. Once you frame the question correctly, the answer is often easy. So I'm trying to set things in motion such that we are at least at some point able to understand the universe. So for SpaceX, the goal is
to make life multi-planetary. And which is if you go
to the foamy paradox of where are the aliens, you've got these sort of great filters. It's just like, why have we
not heard from the aliens? Now lot of people think
there are aliens among us. I often claim to be one,
which nobody believes me, but I did say alien registration card at one point on my immigration documents. So I've not seen any evidence of aliens. So it suggests that at least
one of the explanations is that intelligent
life is extremely rare. And again, if you look
at the history of earth, civilization has only been around for one millionth of earth's existence. So if aliens had visited here, say, a hundred thousand years
ago, they would be like, "Well, they don't even have writing." Just hunter-gatherers, basically. So how long does a civilization last? So for SpaceX, the goal is to establish a self-sustaining city on Mars. Mars is the only viable
planet for such a thing. The moon is close, but it lacks resources, and I think it's probably vulnerable to any calamity that takes out earth. The moon is too close. It's vulnerable to a calamity
that takes out earth. So I'm not saying we
shouldn't have a moon base, but Mars would be far more resilient. The difficulty of getting to Mars is what makes it resilient. In going through these
various explanations of why don't we see the aliens, one of them is that they failed to pass these great filters, these key hurdles. And one of those hurdles is
being a multi-planet species. So if you're a multi-planet species, then if something were to happen, whether that was a natural catastrophe or a manmade catastrophe, at least the other planet
would probably still be around. You don't have all the eggs in one basket. And once you are sort
of a two-planet species, you can obviously extend, to extend life halves
to the asteroid belt, to maybe the moons of Jupiter and Saturn, and ultimately, to other star systems. But if you can't even
get to another planet, definitely not getting to star systems. - And the other possible great filters, super powerful technology
like AGI, for example. So you're basically trying to knock out one great filter at a time. - Digital super intelligence
is possibly a great filter. I hope it isn't, but it might be. Guys like say Geoff Hinton would say, he invented a number of the key principles in artificial intelligence. I think he puts the
probability of AI annihilation around 10 to 20%, something like that. It's not like, you know,
look on the right side. It's 80% likely to be great. (laughs) But I think AI risk
mitigation is important. Being a multi-planet species would be a massive risk mitigation. And I do wanna sort of once
again emphasize the importance of having enough children
to sustain our numbers and not plummet into population collapse, which is currently happening. Population collapse is a
real and current thing. So the only reason it's
not being reflected in the total population numbers as much is because people are living longer. It's easy to predict,
say, what the population of any given country will be. You just take the birth rate last year, how many babies were born,
multiply that by life expectancy, and that's what the population
will be a steady state unless if the birth rate
continues at that level. But if it keeps declining,
it will be even less and eventually dwindle to nothing. So I keep banging on the
baby drum here for a reason, because it has been the source
of civilizational collapse over and over again throughout history. And so why don't we just not
try to stable for that day? - Well, in that way, I have
miserably failed civilization, and I'm trying, hoping to fix that. I would love to have many kids. - Great, hope you do. No time like the present. - (laughs) Yeah. I gotta allocate more
compute to the whole process. But apparently, it's not that difficult. - No, it's like unskilled labor. - Well, one of the things you do for me, for the world is to inspire us with what the future could be. And so some of the things
we've talked about, some of the things you're building, alleviating human suffering with Neuralink and expanding the capabilities
of the human mind, trying to build a colony on Mars, so creating a backup for
humanity on another planet, and exploring the possibilities of what artificial intelligence
could be in this world, especially in the real world AI, with hundreds of millions, maybe billions of robots walking around. - There will be billions of robots. That's seems almost, that seems virtual certainty. - Well, thank you for building the future, and thank you for inspiring
so many of us to keep building and creating cool stuff, including kids. - You're welcome. (laughs) Go forth and multiply. - Go forth and multiply. Thank you, Elon. Thanks for talking about it. Thanks for listening to this
conversation with Elon Musk. And now, dear friends, here's DJ Seo, the co-founder, president,
and COO of Neuralink. When did you first become fascinated by the human brain? - For me, I was always interested in understanding the purpose of things and how it was engineered
to serve that purpose, whether it's organic or inorganic, like we were talking earlier
about your curtain holders. They serve a clear purpose and they were engineered
with that purpose in mind. And growing up, I had a lot
of interest in seeing things, touching things, feeling things, and trying to really understand the root of how it was designed
to serve that purpose. And obviously, brain is
just a fascinating organ that we all carry. It's infinitely powerful machine that has intelligence and cognition
that arise from it. And we haven't even scratched the surface in terms of how all of that occurs. But also at the same time,
I think it took me a while to make that connection to really studying and building tech to understand the brain. Not until graduate school. There were a couple moments,
key moments in my life where some of those I think influenced how the trajectory of my
life got me to studying what I'm doing right now. One was growing up both
sides of my family. My grandparents had a very
severe form of Alzheimer, and it's incredibly
debilitating conditions. I mean, literally, you're
seeing someone's whole identity and their mind just losing over time. And I just remember thinking
how both the power of the mind, but also how something like that could really lose your sense of identity. - It's fascinating that
that is one of the ways to reveal the power of a thing by watching it lose the power. - Yeah, a lot of what
we know about the brain actually comes from these cases where there are trauma to the brain or some parts of the brain that led someone to
lose certain abilities. And as a result, there's some correlation and understanding of
that part of the tissue being critical for that function. And it's an incredibly fragile organ, if you think about it that way. But also, it's incredibly plastic and incredibly resilient
in many different ways. - And by the way, the term
plastic, as we'll use a bunch, means that it's adaptable. So neuroplasticity refers to the adaptability of the human brain. - Correct. Another key moment that sort of influenced how the trajectory of my life have shaped towards the current focus of my life has been during my teenage
year when I came to the U.S. I didn't speak a word of English. There was a huge language barrier, and there was a lot of struggle to kind of connect with
my peers around me, because I didn't understand
the artificial construct that we have created called language, specifically English in this case. And I remember feeling pretty isolated, not being able to connect
with peers around me. So spent a lot of time just on my own, reading books, watching movies, and I naturally sort of
gravitated towards sci-fi books. I just found them really,
really interesting. And also, it was a great
way for me to learn English. Some of the first set of
books that I picked up are "Ender's Game," the whole saga by Orson Scott card, and "Neuromancer" from William Gibson, and "Snow
Crash" from Neal Stephenson. And movies like "Matrix" was coming out around that time point
that really influenced how I think about the potential impact that technology can have
for our lives in general. So fast track to my college years, I was always fascinated
by just physical stuff, building physical stuff, and especially physical things that had some sort of intelligence. And I studied electrical
engineering during undergrad, and I started out my research in MEMS, so micro-electro-mechanical systems, and really building
these tiny nanostructures for temperature sensing. And I just found that to be
just incredibly rewarding and fascinating subject to just understand how you can build something
miniature like that that again served a
function and had a purpose. And then I spent large
majority of my college years basically building
millimeter wave circuits for next gen telecommunication
systems, for imaging. And it was just something that I found very, very intellectually interesting. Phase arrays, how the
signal processing works for any modern as well as next
gen telecommunication system, wireless and wireline. EM waves or electromagnetic
waves are fascinating. How do you design antennas
that are most efficient in a small footprint that you have? How do you make these
things energy-efficient? That was something that just consumed my intellectual curiosity. And that journey led
me to actually apply to and find myself at a PhD
program at UC Berkeley at kind of this consortium called the Berkeley Wireless Research Center that was precisely looking at building, at the time, we called it xg, similar to 3G, 4G, 5G, but the next, next generation G system, and how you would design
circuits around that to ultimately go on phones and basically any other devices that are wirelessly connected these days. So I was just absolutely just fascinated by how that entire system works and that infrastructure works. And then also during grad
school, I had sort of the fortune of having couple research fellowships that led me to pursue
whatever project that I want. And that's one of the things that I really enjoyed about my
graduate school career, where you got to kind of pursue
your intellectual curiosity and the domain that may not
matter at the end of the day, but it's something that really allows you the opportunity to go
as deeply as you want, as well as as widely as you want. And at the time, I was actually
working on this project called the Smart Bandaid,
and the idea was that when you get a wound,
there's a lot of other kind of proliferation of signaling pathway that cells follow to close that wound. And there were hypotheses that when you apply
external electric field, you can actually accelerate
the closing of that field by having basically
electro taxing of the cells around that wound site. And specifically, not
just for normal wound, there are chronic wounds that don't heal. So we were interested in building some sort of wearable
patch that you could apply to kind of facilitate
that healing process. And that was in collaboration with Professor Michel Maharbiz, which was a great addition to
kind of my thesis committee and it really shaped the
rest of my PhD career. - So this would be the first time you interacted with biology, I suppose. - Correct, correct. I mean, there were some peripheral end application of the wireless imaging and telecommunication
system that I was using for security and bio imaging,
but this was a very clear direct application to
biology and biological system and understanding the
constraints around that and really designing and engineering electrical solutions around it. So that was my first introduction, and that's also kind of how
I got introduced to Michel. He's sort of known for
remote control of beetles in the early 2000s. And then around 2013, obviously
kind of the holy grail when it comes to implantable system is to kind of understand how
small of a thing you can make, and a lot of that is
driven by how much energy or how much power you can supply to it and how you extract data from it. So at the time at Berkeley,
there was kind of this desire to kind of understand in the neural space what sort of system you can build to really miniaturize
these implantable systems. And I distinctively remember
this one particular meeting where Michel came in and he's like, "Guys, I think I have a solution." The solution is ultrasound. And then he proceeded
to kind of walk through why that is the case, and
that really formed the basis for my thesis work
called neural dust system that was looking at ways to use ultrasound as opposed to electromagnetic waves for powering as well as communication. I guess I should step back and say the initial goal of the project
was to build these tiny, about a size of a neuron
implantable system that can be parked next to a neuron, being able to record its state and being able to ping that
back to the outside world for doing something useful. And as I mentioned, the size
of the implantable system is limited by how you power the thing and get the data off of it. And at the end of the day, fundamentally, if you look at a human body, we're essentially a bag of salt water, with some interesting
proteins and chemicals, but it's mostly salt water
that's very, very well temperature-regulated
at 37 degrees Celsius. And we'll get into how, and later, why that's an extremely harsh environment for any electronics to survive, as I'm sure you've experienced
or maybe not experienced dropping cell phone in a
salt water in an ocean. It will instantly kill the device, right? But anyways, just in general, electromagnetic waves don't penetrate through this environment well. And just the speed of
light, it is what it is. We can't change it. And based on the wavelength
at which you are interfacing with the device, the device
just needs to be big. Like these inductors
needs to be quite big. And the general good rule of thumb is that you want the wavefront to
be roughly on the order of the size of the thing
that you're interfacing with. So an implantable system that is around 10 to 100 micron in dimension in a volume, which is about the size of a neuron that you see in a human body, you would have to operate at
like hundreds of gigahertz, which number one, not only is it difficult to build electronics operating
at those frequencies, but also, the body just attenuates that very, very significantly. So the interesting kind of
insight of this ultrasound was the fact that ultrasound just travels a lot more effectively
in the human body tissue compared to electromagnetic waves. And this is something that you encounter, and I'm sure most people have encountered in their lives when you go to hospitals that are medical ultrasound
sonograph, right? And they go into very, very deep depth without attenuating
too much of the signal. So all in all, ultrasound,
the fact that it travels through the body extremely well and the mechanism to which
it travels to the body really well is that just the
wavefront is very different. Its electromagnetic waves are transverse, whereas ultrasound waves are compressive. So it's just a completely different mode of wavefront propagation, and as well as speed of sound is orders and orders of magnitude
less than speed of light, which means that even at 10
megahertz ultrasound wave, your wavefront ultimately is
a very, very small wavelength. So if you're talking about interfacing with the 10 micron or 100
micron type structure, you would have 150 micron
wavefront at 10 megahertz. And building electronics
at those frequencies are much, much, much easier and they're a lot more efficient. So the basic idea kind of was
born out of using ultrasound as a mechanism for powering the device, and then also getting data back. So now the question is, how
do you get the data back? The mechanism to which we landed on is what's called backscattering. This is actually something
that is very common and that we interface
on a day-to-day basis with our RFID cards, our
radio frequency ID tag, where there's actually rarely,
in your ID, a battery inside. There's an antenna and
there's some sort of coil that has your serial identification ID and then there's an external device called a reader that
then sends a wavefront, and then you reflect back that wavefront with some sort of modulation
that's unique to your ID. That's what's called
backscattering, fundamentally. So the tag itself actually doesn't have to consume that much energy. And that was a mechanism to which we were kind of thinking about
sending the data back. So when you have an external
ultrasonic transducer that's sending ultrasonic
wave to your implant, the neuro dust implant, and it records some information
about its environment, whether it's a neuron
firing or some other state of the tissue that it's interfacing with, and then it just amplitude modulates the wavefront that comes
back to the source. - And the recording step
would be the only one that requires any energy? So what would require
energy in that low step? - Correct, so it is that initial
kind of startup circuitry to get that recording, amplifying it, and then just modulating. And the mechanism that you can enable that is there is this specialized crystal called piezoelectric crystals
that are able to convert sound energy into electrical
energy and vice versa. So you can kind of have this interplay between the ultrasonic domain and electrical domain that
is the biological tissue. - So on the theme of parking very small computational devices next
to neurons, that's the dream, the vision of brain computer interfaces. Maybe before we talk about Neuralink, can you give a sense of the
history of the field of BCI? What has been maybe the continued dream, and also some of the
milestones along the way with the different approaches and the amazing work
done at the various labs? - I think a good starting
point is going back to 1790s. (Lex laughs) - I did not expect that. - Where the concept of animal electricity or the fact that body is electric was first discovered by Luigi Galvani, where he had this famous experiment where he connected set
of electrodes to frog leg and ran current through it,
and then it started twitching, and he said, "Oh my goodness,
the body's electric." So fast forward many, many years to 1920s where Hans Berger, who's
German psychiatrist discovered EEG or electroencephalography, which is still around. There are these electrode
arrays that you wear outside the skull that gives you some sort of neural recording. That was a very, very big milestone that you can record
some sort of activities about the human mind. And then in the 1940s, there
were these group of scientists, Renshaw, Forbes, and
Morrison that inserted these glass micro
electrodes into the cortex and recorded single neurons. The fact that there's signal that are a bit more high resolution and high fidelity as you get closer to the source, let's say. And in the 1950s, these two scientists, Hodgkin and Huxley showed up, and they built this
beautiful, beautiful models of the cell membrane
and the ionic mechanism and had these like circuit diagram. And as someone who's an electric engineer, it's a beautiful model that's
built out of these partial differential equations,
talking about flow of ions, and how that really leads
to how neurons communicate. And they won the Nobel Prize for that 10 years later in the 1960s. So in 1969, Eb Fetz from
University of Washington, published this beautiful paper called Operating Conditioning of
Cortical Unit Activity, where he was able to record a single unit neuron from a monkey and was able to have the monkey modulated based on its activity and reward system. So I would say this is the
very, very first example, as far as I'm aware, of as closed loop brain computer interface or BCI. - The abstract reads, "The
activity of single neurons in precentral cortex
of anesthetized monkeys was conditioned by reinforcing high rates of neuronal discharge with
delivery of a food pellet. Auditory and visual feedback
of unit firing rates was usually provided in
addition to food reinforcement." Cool, so they actually got it done. - They got it done. This is back in 1969. - "After several training sessions, monkeys could increase the
activity of newly isolated cells by 50 to 500% above rates
before reinforcement." Fascinating. - Brain is very plastic. (Lex laughs) - And so from here, the
number of experiments grew. - Yeah, number of experiments
as well as set of tools to interface with the
brain have just exploded. I think, and also, just
understanding the neural code and how some of the cortical layers and the functions are organized. So the other paper that is pretty seminal, especially in the the motor decoding was this paper in the
1980s from Georgopoulos that discovered that
there's this thing called motor tuning curve. So what are motor tuning curves? It's the fact that there are neurons in the motor cortex of
mammals, including humans, that have a preferential direction
that causes them to fire. So what that means is
there are a set of neurons that would increase
their spiking activities when you're thinking
about moving to the left, right, up, down, and any of those vectors. And based on that, you
could start to think, well, if you can't identify
those essential eigenvectors, you can do a lot, and you can actually use that information for actually decoding someone's intended
movement from the cortex. So that was a very, very seminal
kind of paper that showed that there is some sort
of code you can extract, especially in the motor cortex. - So there's signal there. And if you measure the
electrical signal from the brain, that you could actually figure
out what the intention was. - Correct, yeah, not
only electrical signals, but electrical signals from
the right set of neurons that give you these
preferential direction. - Hmm. Okay, so going slowly towards Neuralink, one interesting question
is what do we understand on the BCI front on invasive
versus non-invasive? From this line of work,
how important is it to park next to the neuron? What does that get you? - That answer fundamentally depends on what you want to do with it, right? There's actually incredible
amount of stuff that you can do with EEG and electrocardiograph, ECoG, which actually doesn't penetrate the cortical layer or parenchyma, but you place a set of electrodes on the surface of the brain. So the thing that I'm
personally very interested in is just actually understanding
and being able to just really tap into the high
resolution, high fidelity understanding of the activities that are happening at the local level. And we can get into biophysics, but just to kind of step
back to kind of use analogy, 'cause analogy here can be useful, and sometimes, it's a little bit difficult to think about electricity. At the end of the day, we're
doing electrical recording that's mediated by ionic currents, movements of these charged particles, which is really, really hard for most people to think about. But turns out, a lot of the activities that are happening in the brain and the frequency bandwidth,
which starts happening is actually very, very
similar to sound waves, and in our normal
conversation, audible range. So the analogy that typically
is used in the field is, if you have a football stadium, there's game going on. If you stand outside the stadium, you maybe get a sense
of how the game is going based on the cheers and the
booze of the home crowd, whether the team is winning or not. But you have absolutely
no idea what the score is. You have absolutely no idea
what individual audience or the players are talking
or saying to each other, what the next play is,
what the next goal is. So what you have to do is you
have to drop the microphone near into the stadium and
then get near the source, like into the individual chatter. In this specific example,
you would wanna have it right next to where
the huddle's happening. So I think that's kind
of a good illustration of what we're trying to do when we say invasive or minimally invasive or implanted brain computer interfaces versus non-invasive or
non-implanted brain interfaces. It's basically talking about where do you put that microphone, and what can you do with that information. - So what is the biophysics
of the read and write communication that we're
talking about here, as we now step into the
efforts at Neuralink? - Yeah, so brain is made up of these specialized cells called neurons. There's billions of
them, tens of billions. Sometimes, people call
it a hundred billion that are connected in this
complex yet dynamic network that are constantly remodeling. They're changing their synaptic weights, and that's what we typically
call neuroplasticity. And the neurons are also bathed
in this charged environment that is latent with
many charged molecules, like potassium ions,
sodium ions, chlorine ions. And those actually facilitate
these through ionic current, communication between
these different networks. And when you look at a neuron as well, they have these membrane
with a beautiful, beautiful protein structure called the
voltage selective ion channels, which, in my opinion, is one
of nature's best inventions. In many ways, if you
think about what they are, they're doing the job of
a modern day transistors. Transistors are nothing
more, at the end of the day, than a voltage-gated conduction channel. And nature found a way to have that very, very early on in its evolution. And as we all know, with the transistor, you can have many, many computation and a lot of amazing things
that we have access to today. So I think it's one of
those, just as a tangent, just a beautiful, beautiful invention that the nature came up with, these voltage-gated ion channels. - I mean, I suppose there's,
on the biological level, every level of the
complexity of the hierarchy of the organism, there's
going to be some mechanisms for storing information
and for doing computation. And this is just one such way. But to do that with biological and chemical components is interesting. Plus like when neurons, I mean,
it's not just electricity, it's chemical communication,
it's also mechanical. I mean, these are like
actual objects that vibrate. I mean, they move- - Yeah, they're actually, I mean, there's a lot of really,
really interesting physics that are involved in, you know, kind of going back to
my work on ultrasound during grad school, there are groups, and there were groups,
and there are still groups looking at ways to cause
neurons to actually fire an action potential using ultrasound wave. And the mechanism to
which that's happening is still unclear as I understand. It may just be that you're imparting some sort of thermal energy
and that causes cells to depolarize in some interesting ways. But there are also these ion channels or even membranes that
actually just open up its pore as there are being
mechanically shook, right? Vibrated. So there's just a lot of elements of these like move particles, which again, like that's governed by
diffusion physics, right? Movements of particles. And there's also a lot of kind
of interesting physics there. - Also, not to mention, as
Roger Penrose talks about, there might be some beautiful weirdness in the quantum mechanical
effects of all of this. And he actually believes
that consciousness might emerge from the quantum
mechanical effects there. So like there's physics, there's
chemistry, there's biology, all of that is going on there. - Oh yeah, yeah. I mean, you can, yes, there's a lot of levels of
physics that you can dive into. But yeah, in the end,
you have these membranes with these voltage-gated ion
channels that selectively let these charge molecules that are in the extracellular
matrix like in and out. And these neurons generally have these like resting potential where there's a voltage difference between inside the cell
and outside the cell. And when there's some sort
of stimuli that changes the state such that they
need to send information to the downstream network,
you start to kind of see these like sort of orchestration of these different molecules going in and out of these channels. They also open up, like
more of them open up once it reaches some threshold to a point where you
have a depolarizing cell that sends action potential. So it's a just a very beautiful kind of orchestration of these molecules. And what we're trying to do
when we place an electrode or parking it next to a neuron is that you're trying to measure these local changes in the potential. Again, mediated by the
movements of the ions. And what's interesting,
as I mentioned earlier, there's a lot of physics involved. And the two dominant physics for this electrical recording domain is diffusion physics and electromagnetism. And where one dominates, where Maxwell's equation dominates versus fixed law dominates depends on where your electrode is. If it's close to the source,
mostly electromagnetic-based, when you're farther away from
it, it's more diffusion-based. So essentially, when you're
able to park it next to it, you can listen in on
those individual chatter and those local changes in the potential, and the type of signal that you get are these canonical textbook
neural spiking waveform. The moment you're further away, and based on some of the
studies that people have done, Christof Koch's lab and others, once you're away from that source by roughly around a hundred micron, which is about a width of a human hair, you no longer hear from that neuron. Or you're no longer able
to kind of have the system sensitive enough to be able
to record that particular local membrane potential
change in that neuron. And just to kind of give
you a sense of scale also, when you look at a hundred micron voxel, so a hundred micron by a hundred micron by a hundred micron box in a brain tissue, there's roughly around 40 neurons and whatever number of
connections that they have. So there's a lot in that volume of tissue. So the moment you're outside of that, there's just no hope that you'll be able to detect that change from
that one specific neuron that you may care about. - Yeah, but as you're
moving about this space, you'll be hearing other ones. So if you move another 100 micron, you'll be hearing chatter from another community.
- Correct. - And so the whole
sense is you wanna place as many as possible electrodes and then you're listening to the chatter. - Yeah, you wanna listen to the chatter. And at the end of the day, you also want to basically let the software
do the job of decoding. And just to kind of go to, why ECOG and EEG work at all, right? When you have these
local changes, obviously, it's not just this one
neuron that's activating. There's many, many other networks that are activating all the time. And you do see sort of a general change in the potential of this
electro, like this charge medium, and that's what you're recording
when you're farther away. I mean, you still have
some reference electrode that's stable in the brain
that's just electroactive organ, and you're seeing some
combination aggregate action potential changes and
then you can pick it up, right? It's a much slower changing signals. But there are these like canonical kind of oscillations and waves, like gamma waves, beta waves. Like when you sleep, that can be detected, 'cause there's sort of a synchronized kind of global effect of the
brain that you can detect. And I mean, the physics of this go, I mean, if we really wanna
go down that rabbit hole, like there's a lot that
goes on in terms of like why diffusion physics
at some point dominates when you're further away from the source. It's just a charged medium. So similar to how when you
have electromagnetic ways propagating in atmosphere or in a charged medium like a plasma, there's this weird shielding
that happens that actually further attenuates the signal
as you move away from it. So yeah, you see, like if you do a really, really deep dive on kind of the signal
attenuation over distance, you start to see kind
of one of where square in the beginning, and
then exponential drop off. And that's the knee at which you go from electromagnet magnetism dominating to diffusion physics dominating. - But once again, with the
electrodes, the biophysics, you need to understand it's not as deep, because no matter where
you're placing that, you're listening to a small
crowd of local neurons. - Correct, yeah. So once you penetrate the brain, you're in the arena, so to speak. - And there's a lot of neurons. - [DJ] There are many, many of 'em. - But then again, there's a
whole field of neuroscience that's studying like how
the different groupings, the different sections of
the seating in the arena, what they usually are responsible for, which is where the metaphor
probably falls apart, 'cause the seating is not
that organized in an arena. - Also, most of them are silent. They don't really do much, or their activities are, you know, you have to hit it with just
the right set of stimulus. - So they're usually quiet. - They're usually very quiet. There's, I mean, similar to
dark energy and dark matter, there's dark neurons. What are they all doing? When you place these electrode, again, like within this a hundred micron volume, you have 40 or so neurons. Like why do you not see 40 neurons? Why do you see only a handful? What is happening there? - Well, they're mostly quiet, but like when they speak, they
say profound shit, I think. That's the way I'd like to think about it. Anyway, before we zoom in
even more, let's zoom out. So how does Neuralink work? From the surgery to the implant to the signal and the decoding process and the human being
able to use the implant to actually affect the world outside? And all of this, I'm asking in the context of there's a gigantic historic
milestone in Neuralink just accomplished in January of this year, putting a Neuralink implant in the first human being, Noland. And there's been a lot to talk about there about his experience,
because he's able to describe all the nuance and the beauty and the fascinating
complexity of that experience of everything involved. But on the technical level,
how does Neuralink work? - Yeah, so there are
three major components to the technology that we're building. One is the device, the thing
that's actually recording these neural chatters. We call it N1 implant or The Link. And we have a surgical robot that's actually doing an implantation of these tiny, tiny wires
that we call threads that are smaller than human hair. And once everything is surgirized, you have these neural signals, these spiking neurons that
are coming out of the brain and you need to have some
sort of software to decode what the users intend to do with that. So there's what's called
the Neuralink application, or B1 app that's doing that translation, is running the very, very
simple machine learning model that decodes these inputs
that are neural signals and then convert it to a
set of outputs that allows our participant, first participant Noland, to be able to control a cursor on this. - And this is done wirelessly? - And this is done wirelessly. So our implant is actually a two-part. The link has these flexible
tiny wires called threads that have multiple
electrodes along its length. And they're only inserted
into the cortical layer, which is about three to five
millimeters in a human brain in the motor cortex region. That's where the kind of the
intention for movement lies in. And we have 64 of these threads, each thread having 16 electrodes along the span of three to four millimeters, separated by 200 microns. So you can actually record along
the depth of the insertion. And based on that signal,
there's custom integrated circuit or ASIC that we built that
amplifies the neural signals that you're recording
and then digitizing it and then has some mechanism for detecting whether there was an interesting event that is a spiking event
and decide to send that, or not send that through
Bluetooth to an external device, whether it's a phone or a computer that's running this Neuralink application. - So there's onboard
signal processing already just to decide whether this is
an interesting event or not. So there is some computational
power on board inside in addition to the human brain? - Yeah, so it does the signal processing to kind of really compress
the amount of signal that you're recording. So we have a total of
a thousand electrodes sampling at just under 20
kilohertz with 10 bit each. So that's 200 megabits. That's coming through to the chip, from thousand channel
simultaneous neural recording. And that's quite a bit of data. And there are technology available to send that off wirelessly, but being able to do that
in a very, very thermally constrained environment that is a brain, so there has to be some amount
of compression that happens to send off only the
interesting data that you need, which in this particular case, for motor decoding is
occurrence of a spike or not. And then being able to use that to decode the intended cursor movement. So the implant itself processes it, figures out whether a
spike happened or not with our spike detection algorithm, and then sends it off, packages it, sends it off through Bluetooth
to an external device that then has the model to decode, okay, based on the spiking inputs, did Noland wish to go
up, down, left, right, or click, or right click, or whatever? - All of this is really fascinating, but let's stick on the N1 implant itself, so the thing that's in the brain. So I'm looking at a picture
of it, there's an enclosure, there's a charging call, so we didn't talk about the
charging, which is fascinating. The battery, the power
electronics, the antenna. Then there's the signal
processing electronics. I wonder if there's more kinds of signal processing you can do. That's another question. And then there's the threads themselves with the enclosure on the bottom. So maybe to ask about the charging, so there's a external charging device. - Mm-hmm, yeah, there's an
external charging device. So yeah, the second part of the implant, the threads are the ones, again, just the last three to five
millimeters are the ones that are actually penetrating the cortex. Rest of it is, actually, most of the volume
is occupied by the battery, rechargeable battery. And it's about a size of a quarter. I actually have a device here, if you wanna take a look at it. This is the flexible
threat component of it. And then this is the implant. So it's about a size of a U.S. quarter. It's about nine millimeter thick. So basically, this implant, once you have the craniectomy
and the directomy, threads are inserted, and the hole that you
created, this craniectomy, gets replaced with that. So basically, that thing plugs that hole, and you can screw in these self-drilling cranial screws to hold it in place. And at the end of the day, once
you have the skin flap over, there's only about two
to three millimeters. That's obviously transitioning off of the top of the implant
to where the screws are. And that's the minor bump that you have. - Those threads look tiny. That's incredible. That is really incredible. That is really incredible. And also, you're right, most of the actual volume is the battery. Yeah, this is way smaller than I realized. - They are also, the threads
themselves are quite strong. - They look strong. - And the thread themselves
also has a very interesting feature at the end of it called the loop. And that's the mechanism
to which the robot is able to interface and manipulate this tiny hair-like structure. - And they're tiny, so
what's the width of a thread? - Yeah, so the width of a
thread starts from 16 micron and then tapers out to about 84 micron. So average human hair is about
80 to 100 micron in width. - This thing is amazing. This thing is amazing. - Yes, most of the volume
is occupied by the battery, rechargeable lithium ion cell. And the charging is done
through inductive charging, which is actually very commonly used. Most cell phones have that. The biggest difference is that for us, usually when you have a phone and you wanna charge it on a charging pad, you don't really care how hot it gets, whereas for us, it matters. There's a very strict regulation and good reasons to not actually increase the surrounding tissue temperature
by two degrees Celsius. So there's actually a lot of innovation that is packed into this to allow charging of this implant without causing that temperature threshold to reach. And even small things like,
you see this charging coil and what's called the
ferrite shield, right? So without that ferrite shield, what you end up having
when you have resonant inductive charging is
that the battery itself is a metallic can, and you
form these Eddy currents from external charger
and that causes heating and that actually contributes
to inefficiency in charging. So this ferrite shield, what
it does is that it actually concentrate that field
line away from the battery and then around the coil that's
actually wrapped around it. - There's a lot of really
fascinating design here to make it, I mean, you're
integrating a computer into a complex biological system. - Yeah, there's a lot of innovation here. I would say that part of what enabled this was just the innovations in the wearable. There's a lot of really, really powerful, tiny, low power microcontrollers,
temperature sensors, or various different sensors
and power electronics. A lot of innovation really came
in the charging coil design, how this is packaged, and
how do you enable charging such that you don't really
exceed that temperature limit, which is not a constraint
for other devices out there. - So let's talk about
the threads themselves, those tiny, tiny, tiny things. So how many of them are there? You mentioned a thousand electrodes. How many threads are there, and what did the electrodes
have to do with the threads? - Yeah, so the current instantiation of the device has 64 threads, and each thread has 16 electrodes for a total of 1,024 electrodes that are capable of both
recording and stimulating. And the thread is basically
this polymer insulated wire. The metal conductor is
the kind of tiramisu cake of ti, plat, gold, plate, ti. And they're very, very tiny wires. Two micron in width, so
2/1000000th of meter. - It's crazy that that
thing I'm looking at has the polymer installation,
has the conducting material, and has 16 electrodes at the end of it. - On each of those threads. - Yeah, on each of those threads. - Correct.
- 16, each one of those. - You're not gonna be able
to see it with naked eyes. - And I mean, to state the obvious, or maybe for people
who are just listening, they're flexible. - Yes, yes, that's also one element that was incredibly important for us. So each of these thread
are, as I mentioned, 16 micron in width and then
they taper to 84 micron, but in thickness, they're
less than five micron. And thickness is mostly
polyamide at the bottom and this metal track and
then another polyamide. So two micron of polyamide, 400 nanometer of this metal stack, and two micron of polyamide
sandwiched together to protect it from the environment that is 37 degrees C bag of salt water. - So what's some, maybe
can you speak to some interesting aspects of
the material design here? Like what does it take to
design a thing like this and to be able to
manufacture a thing like this for people who don't know
anything about this kind of thing? - Yeah, so the material
selection that we have is not, I don't think it was particularly unique. There were other labs
and there are other labs that are kind of looking
at similar material stack. There's kind of a fundamental question and still needs to be
answered around the longevity and reliability of these
micro electrodes that we call, compared to some of the
other more conventional, neural interfaces, devices
that are intracranial. So penetrating the cortex
that are more rigid, like the Utah array. There are these four by four millimeter kind of silicon shank that have exposed recording site at the end of it. And that's been kind of the innovation from Richard Normann back in 1997. It's called the Utah Array 'cause he was at University of Utah. - And what does the Utah array look like? So it's a rigid type of- - Yeah, so we can actually look it up. - Oh.
- Yeah. (Lex laughing) Yeah, so it's a bed of needle. There's-
- (laughs) Yeah. Okay, go ahead, I'm sorry. - So those are rigid-
- Rigid, yeah. You weren't kidding. - And the size and the
number of shanks vary, anywhere from 64 to 128. At the very tip of it
is an exposed electrode that actually records neural signal. The other thing that's
interesting to note is that unlike Neuralink threads that
have recording electrodes that are actually exposed
iridium oxide recording sites along the depth, this is
only at a single depth. So these Utah array spokes can be anywhere between 0.5 millimeters to 1.5 millimeter. And they also have
designs that are slanted. So you can have it inserted
at different depth, but that's one of the
other big differences. And then, I mean, the main key
difference is the fact that there's no active electronics. These are just electrodes, and then there's a bundle of
a wire that you're seeing, and then that actually
then exits the craniectomy that then has this port
that you can connect to for any external electronic devices. They are working on a or have
the wireless telemetry device, but it still requires
a through the skin port that actually is one of
the biggest failure modes for infection for the system. - What are some of the challenges associated with flexible threads? Like for example, on the robotic side, R1, implanting those threads,
how difficult does that task? - Yeah, so as you mentioned, they're very, very difficult
to maneuver by hand. These Utah arrays that you saw earlier, they're actually inserted
by a neurosurgeon actually positioning it near
the site that they want. And then there's a pneumatic hammer that actually pushes them in. So it's a pretty simple process, and they're easy to maneuver. But for these thin film arrays, they're very, very tiny and flexible. So they're very difficult to maneuver. So that's why we built an
entire robot to do that. There are other reasons
for why we built a robot, and that is ultimately,
we want this to help millions and millions of people
that can benefit from this. And there just aren't that
many neurosurgeons out there. And robots can be something that we hope can actually do
large parts of the surgery. But yeah, the robot is this
entire other sort of category of product that we're working on. And it's essentially this
multi-axis gantry system that has the specialized robot head that has all of the optics and this kind of a needle
retracting mechanism that maneuvers these threads
via this loop structure that you have on the thread. - So the thread already
has a loop structure by which you can grab it? - Correct, correct.
- Okay. So this is fascinating. So you mentioned optics,
so there's a robot - R1. So for now, there's a
human that actually creates a hole in the skull.
- Mm-hmm. - And then after that, there's
a computer vision component that's finding a way to
avoid the blood vessels. And then you're grabbing it by the loop, each individual thread and placing it in a particular location
to avoid the blood vessels. And also choosing the depth of placement, all that?
- Correct. So controlling every, like the 3D geometry of the placement? - Correct. So the aspect of this robot that is unique is that it's not surgeon-assisted
or human-assisted. It's a semi-automatic or automatic robot. Obviously, there are
human component to it, when you're placing targets. You can always move it away from kind of major vessels that you see. But I mean, we wanna get
to a point where one click and it just does the
surgery within minutes. - So the computer vision
component finds great targets, candidates and the human
kind of approves them and the robot does, does it do like one thread at a time or does it do one-
- It does one thread at a time, and that's
actually also one thing that we are looking at ways to do multiple threads at a time. There's nothing stopping from it. You can have multiple kind
of engagement mechanisms, but right now, it's one by one. And we also still do quite a bit of just kind of verification to make sure that it got inserted. If so, how deep? Did it actually match
what was programmed in and so on and so forth? - And the actual electrode
is a place that vary at differing depths in the like, I mean, it's very small differences,
but differences. - [DJ] Yeah, yeah. - And so that there's some
reasoning behind that, as you mentioned. Like it gets more varied signal. - Yeah, I mean, we try to place them all around three or four
millimeter from the surface, just 'cause the span of the electrode, those 16 electrodes that we
currently have in this version spans roughly around three millimeters. So we wanna get all of those in the brain. - This is fascinating. Okay, so there's a million questions here. If we could zoom in
specifically on the electrodes, so what is your sense, how many neurons is each
individual electrode listening to? - Yeah, each electrode
can record from anywhere between 0 to 40, as I
mentioned, right, earlier. But tactically speaking,
we only see about, at most, like two to three. And you can actually
distinguish which neuron it's coming from by the
shape of the spikes. - [Lex] Oh, cool. - So I mentioned the spike
detection algorithm that we have. It's called BOSS algorithm, buffer online, spike sorter. - Nice. - It actually outputs
at the end of the day six unique values, which
are kind of the amplitude of these like negative
going hump, middle hump, like positive going hump, and then also the time
at which these happen. And from that, you can have a kind of a statistical probability
estimation of, is that a spike? Is it not a spike? And then based on that,
you could also determine, oh, that spike looks
different than that spike. Must have come from a different neuron. - Okay, so that's a nice
signal processing step from which you can then
make much better predictions about if there's a spike.
- Yeah. - Especially in this kind of context where there could be
multiple neurons screaming. And that also results in you being able to compress the data
better in the (indistinct). Okay.
- And just to be clear, I mean, the labs do what's
called spike sorting. Usually, once you have
these like broadband, the fully digitized signals and then you run a bunch of different set of algorithms to kind of tease apart, it's just all of this for
us is done on the device. - On the device.
- In a very low power, custom built ASIC digital processing unit. - [Lex] Highly heat constrained? - Highly heat constrained, and the processing time
from signal going in and giving you the output
is less than a microsecond, which is a very, very
short amount of time. - Oh yeah, so the latency has to be super short.
- Correct. - Oh wow. Oh, that's a pain in the ass. - Yeah, latency is this huge, huge thing that you have to deal with. Right now, the biggest source of latency comes from the Bluetooth, the way in which they're packetized and we bend them in 15 millisecond. - Oh, interesting, it says
communication constraint. Is there some potential innovation there on the protocol used? - Absolutely.
- Okay. - Yeah, Bluetooth is definitely not our final wireless communication protocol that we wanna get to. - Hence the N1 and the R1. I imagine that increases- - NxRx. - Yeah, that's the communication protocol, 'cause Bluetooth allows you to communicate I guess farther distances
than you need to, so you can go much shorter. - Yeah, well, the primary motivation for choosing Bluetooth is that, I mean, everything has Bluetooth. - All right, you can talk to any device. - Interoperability is
just absolutely essential, especially in this early phase. And in many ways, if
you can access a phone or a computer, you can do anything. - Well, it'll be interesting to step back and actually look at, again, the same pipeline that
you mentioned for Noland. So what does this whole process look like? From finding and selecting a
human being to the surgery, to the first time he's
able to use this thing? - So we have what's called
the patient registry that people can sign up to
hear more about the updates. And that was a route to
which Noland applied. And the process is that once
the application comes in, it contains some medical records, and based on their medical eligibility, that there's a lot of different inclusion-exclusion
criteria for them to meet. And we go through a
prescreening interview process with someone from Neuralink. And at some point, we
also go out to their homes to do a BCI home audit, 'cause one of the most
kind of revolutionary part about having this N1 system
that is completely wireless is that you can use it at home. Like you don't actually
have to go to the lab and go to the clinic to get connecterized to these like specialized equipment that you can't take home with you. So that's one of the key elements of when we're designing the system that we wanted to keep in mind, like people hopefully would
wanna be able to use this every day in the comfort of their homes. And so part of our engagement and what we're looking
for during BCI home audit is to just kind of
understand their situation, what other assistive
technology that they use. - And we should also step
back and kind of say that the estimate is 180,000
people live with quadriplegia in the United States, and each year, an additional 18,000 suffer a
paralyzing spinal cord injury. So these are folks who
have a lot of challenges, living a life in terms of accessibility, in terms of doing the
things that many of us just take for granted day to day. And one of the things, one
of the goals of this initial study is to enable them to
have sort of digital autonomy, where they by themselves can
interact with a digital device using just their mind, something that you're calling telepathy. So digital telepathy, where a
quadriplegic can communicate with a digital device in all the ways that
we've been talking about. Control the mouse cursor, enough to be able to
do all kinds of stuff, including play games and tweet
and all that kind of stuff. And there's a lot of people for whom life, the basics of life are difficult, because of the things that
have happened to them. - Yeah, I mean, movement is so
fundamental to our existence. I mean, even speaking involves movement of mouth, lip, larynx. And without that, it's
extremely debilitating. And there are many, many
people that we can help. And I mean, like especially if you start to kind of look at other
forms of movement disorders that are not just from spinal cord injury, but from ALS, MS, or even
stroke and/or just aging, right? That leads you to lose
some of that mobility, that independence, it's
extremely debilitating. - And all of these are
opportunities to help people, to help alleviate suffering, to help improve the quality of life. But each of the things you mentioned is its own little puzzle that needs to have increasing
levels of capability from a device like a Neuralink device. And so the first one
you're focusing on is, it's just a beautiful word, telepathy. So being able to
communicate using your mind wirelessly with a digital device. Can you just explain exactly
what we're talking about? - Yeah, I mean, it's exactly that. I mean, I think if you are
able to control a cursor and able to click and
be able to get access to computer or phone, I mean, the whole world opens up to you. And I mean, I guess the word telepathy, if you kind of think about that as just definitionally
being able to transfer information from my brain to your brain without using some of the
physical faculties that we have, like voices. - But the interesting thing here is, I think the thing that's
not obviously clear is how exactly it works. So in order to move a cursor, there's at least a couple
ways of doing that. So one is you imagine
yourself maybe moving a mouse with your hand, or you can
then, which Noland talked about, like imagine moving the
cursor with your mind. But it's like there is
a cognitive step here that's fascinating, 'cause
you have to use the brain and you have to learn
how to use the brain. And you kind of have to
figure it out dynamically. Because you reward yourself if it works. I mean, there's a step that, this is just a fascinating step, 'cause you have to get the brain to start firing in the right way. And you do that by imagining. Like fake it till you make it. (laughs) And all of a sudden, it creates
the right kind of signal that if decoded correctly,
can create the kind of effect. And then there's like noise around that, you have to figure all of that out. But on the human side, imagine the cursor moving
is what you have to do. - Yeah, he says using the force. - The force. I mean, isn't that just
like fascinating to you that it works? Like to me, it's like, holy
shit, that actually works. Like you could move a
cursor with your mind. - As much as you're
learning to use that thing, that thing's also learning about you. Like our model is constantly
updating the weights to say, "Oh, if someone is thinking about this sophisticated forms
of like spiking patterns, like that actually means
to do this, right?" - So the machine is
learning about the human and the human is learning
about the machine. So there is a adaptability to the signal processing,
the decoding step. And then there's the adaptation
of Noland, the human being. Like the same way, if you give
me a new mouse and I move it, I learn very quickly
about its sensitivity, so I learn to move it slower. And then there's other
kinds of signal drift and all that kind of stuff
they have to adapt to. So both are adapting to each other. - [DJ] Correct. - That's a fascinating
like software challenge on both sides, the software on both, the human software and- - The organic and the inorganic. - The organic and the inorganic. Anyway, so sorry to rudely interrupt. So there's this selection that Noland has passed with flying colors. So everything including that the, it's a BCI-friendly home, all of that. So what is the process of
the surgery implantation, the first moment when he
gets to use the system? - The end to end, we say
patient end to patient out, is anywhere between two to four hours. In particular case for Noland, it was about three and a half hours. And there's many steps leading to the actual
robot insertion, right? So there's anesthesia induction, and we do intra-op CT imaging to make sure that we're drilling the hole in the right location. And this is also pre-planned beforehand. Someone like Nolan would go through fMRI, and then they can think
about wiggling their hand. And obviously, due to their injury, it's not gonna actually lead
to any sort of intended output. But it's the same part of the brain that actually lights up
when you're imagining moving your finger to
actually moving your finger. And that's one of the ways
in which we can actually know where to place our threads, 'cause we wanna go into what's called a hand knob area in the motor cortex. And as much as possible,
densely put our electro threads. So yeah, we do intra-op CT imaging to make sure and double check the location of the craniectomy. And surgeon comes in, does their thing in terms of like skin
incision, craniectomy, so drilling of the skull, and then there's many
different layers of the brain. There's what's called a dura, which is a very, very thick
layer that surrounds the brain, that gets actually resected in
a process called atherectomy. And that then exposed the pia in the brain that you wanna insert. And by the time it's been around anywhere between one to
one and a half hours, robot comes in, does its thing,
placement of the targets, inserting of the thread. That takes anywhere
between 20 to 40 minutes. In the particular case for Noland, it was just under or it
was just over 30 minutes. And then after that, the surgeon comes in. There's a couple other steps of like actually inserting
the dural substitute layer to protect the thread
as well as the brain. And then yeah, screw in the
implant and then skin flap and then suture and then you're out. - So when Noland woke
up, what was that like? What's the recovery like, and when was the first
time he was able to use it? - So he was actually
immediately, after the surgery, like an hour after the
surgery as he was waking up, we did turn on the device, make sure that we are
recording neural signals, and we actually did have a couple signals that we notice that he
can actually modulate. And what I mean by modulate is that he can think about crunching his fist, and you could see the
spike disappear and appear. (Lex laughing) - That's awesome. - And that was immediate, right? Immediate after in the recovery room. - How cool is that? - Yeah.
- That's a human being. I mean, what did that feel like for you? This device and a human being, a first step of a gigantic journey? I mean, it's a historic moment. Even just that spike, just
to be able to modulate that. - Obviously, there have been
other, as you mentioned, pioneers that have participated
in these groundbreaking BCI investigational early feasibility studies. So we're obviously standing in the shoulders of the giants here. We're not the first ones to actually put electrodes in the human brain. But I mean, just leading
up to the surgery, I definitely could not sleep. It's the first time that you're working in a completely new environment. We had a lot of confidence
based on our benchtop testing or preclinical R&D studies
that the mechanism, the threads, the insertion,
all that stuff is very safe, and that it's obviously ready
for doing this in a human, but there's still a lot of unknown unknown about can the needle actually insert? I mean, we brought
something like 40 needles just in case they break, and
we ended up using only one. But I mean, that was a level of just complete unknown, right? 'Cause it's a very, very
different environment. And I mean, that's why we do clinical trial in the first place to be able to test these things out. So extreme nervousness and just many, many sleepless night leading up to the surgery and definitely the day before the surgery, and it was an early morning surgery. Like we started at seven in the morning. And by the time, it was around 10:30. Everything was done. But I mean, first time seeing that, well, number one, just huge relief that this thing is doing
what it's supposed to do. And two, I mean, just
immense amount of gratitude for Noland and his family, and then many others that have applied and that we've spoken to and will speak to are, I mean, true pioneers everywhere. And I sort of call them the neural astronauts or neuralnaut. These amazing, just
like in the '60s, right? Like these amazing just pioneers, right? Exploring the unknown outwards. In this case, it's inward. But incredible amount
of gratitude for them to just participate and play a part. And it's a journey that
we're embarking on together. But also like, I think it was just, that was an very, very
important milestone, but our work was just starting. So a lot of just kind of anticipation for, okay, what needs to happen next? What are set of sequences of events that needs to happen for
us to make it worthwhile for both Noland as well as us? - Just to linger on that, just
a huge congratulations to you and the team for that milestone. I know there's a lot of work left, but that's really exciting to see. That's a source of hope. It's this first big step, opportunity to help hundreds
of thousands of people and then maybe expand
the realm of the possible for the human mind for millions
of people in the future. So it's really exciting. So like the opportunities
are all ahead of us, and to do that safely and
to do that effectively was really fun to see. As an engineer just watching
other engineers come together and do an epic thing, that was awesome. Huge congrats. - Thank you, thank you. Yeah, could not have
done it without the team. And yeah, I mean, that's the other thing that I told the team as well, of just this immense sense
of optimism for the future. I mean, it's a very important
moment for the company, needless to say, as well as hopefully for many others out
there that we can help. - So speaking of challenges, Neuralink published a blog post describing that some of
the threads retracted. And so the performance, as measured by bits per
second dropped at first, but then eventually, it was regained. And that the whole story
of how it was regained is super interesting. That's definitely something I'll talk to Bliss and to Noland about. But in general, can you speak
to this whole experience? How was the performance regained and just the technical
aspects of the threads being retracted and moving? - The main takeaway is that in the end, the performance have come back and it's actually gotten
better than it was before. He's actually just beat the
world record yet again last week to 8.5 BPS, so I mean, he's just cranking and
he's just improving. - [Lex] The previous one
that he set was eight. - Correct.
- He said 8.5. - Yeah, the previous world
record in human was 4.6. - Yeah.
- So it's almost double. And his goal is to try to get to 10, which is roughly around kind of the median Neuralinker using a mouse with the hand. So it's getting there. - So yeah, so the
performance was regained. - Yeah, better than before. So that's a story on its own of what took the BCI team to recover that performance. It was actually mostly on
kind of the signal processing. And so as I mentioned,
we were kind of looking at these spike outputs
from our electrodes. And what happened is
that kind of four weeks into the surgery, we
noticed that the threads have solely come out of the brain. And the way in which we noticed
this, at first obviously, is that, well, I think Noland
was the first to notice that his performance was degrading. And I think at the time,
we were also trying to do a bunch of different experimentation, different algorithms,
different sort of UI/UX. So it was expected that
there will be variability in the performance, but we did
see kind of a steady decline. And then also, the way in
which we measure the health of the electrodes or whether
they're in the brain or not, is by measuring impedance
of the electrode. So we look at kind of the interfacial, kind of the Randall circuit, they say, the capacitance and the resistance between the electrosurface and the medium. And if that changes in some dramatic ways, we have some indication. Or if you're not seeing
spikes on those channels, you have some indications that
something's happening there. And what we notice is that
looking at those impedance plot and spike rate plots,
and also because we have those electrodes
recording along the depth, you are seeing some sort of movement that indicated that the
rest were being pulled out. And that obviously will
have an implication on the model side, because
if the number of inputs that are going into the model is changing, 'cause you have less of them, that model needs to get updated, right? But there were still
signals, and as I mentioned, similar to how, even when
you place the signals on the surface of the
brain, or farther away, like outside the skull, you
still see some useful signals. What we started looking at is
not just the spike occurrence through this BOSS
algorithm that I mentioned, but we started looking at just the power of the frequency band that is interesting for Noland to be able to modulate. So once we kind of change the algorithm for the implant to not just
give you the BOSS output, but also these spike band power output, that helped us sort of, we find the model with the new set of inputs,
and that was the thing that really ultimately gave
us the performance back. In terms of, and obviously,
like the thing that we want ultimately, and the thing
that we are working towards, is figuring out ways in which we can keep those threads intact
for as long as possible so that we have many more
channels going into the model. That's by far the number one priority that the team is currently embarking on to understand how to
prevent that from happening. The thing that I'll say also
is that, as I mentioned, this is the first time ever that we're putting these
thread in a human brain, and human brain, just for size reference, is 10 times out of the monkey
brain or the sheep brain. And it's just a very, very
different environment. It moves a lot more. It like actually moved a
lot more than we expected when we did Noland's surgery. And it's just a very,
very different environment than what we're used to. And this is why we do
clinical trial, right? We wanna uncover some of these issues and failure modes earlier than later. So in many ways, it's provided us with this enormous amount of data and information to be able to solve this. And this is something that
Neuralink is extremely good at. Once we have set of clear objective and engineering problem,
we have enormous amount of talents across many, many disciplines to be able to come together and fix the problem very, very quickly. - But it sounds like one of
the fascinating challenges here is for the system and the decoding side to be adaptable across
different timescales. So whether it's movement of threads or different aspects of signal drift sort of on the software
of the human brain, something changing,
like Noland talks about cursor drift that could be corrected, and there's a whole UX
challenge to how to do that. So it sounds like adaptability is like a fundamental property
that has to be engineered in. - It is, and I mean I think, I mean, as a company, we're
extremely vertically integrated. We make these thin film
arrays in our own microfab. - Yeah, there's, like
you said, built in-house. This whole paragraph
here from this blog post is pretty gangster. "Building the technology described above has been no small feat." And there's a bunch of links here that I recommend people click on. "We constructed in-house
micro fabrication capabilities to rapidly produce various
iterations of thin film arrays that constitute our electrode threads. We created a custom femtosecond laser mill to manufacture components
with micro level precision." I think there's a tweet
associated with this. - That's a whole thing
that we can get into. - Yeah, okay, well, what
are we looking at here? This thing?
- Yeah. "So in less than one minute,
our custom-made femtosecond laser mill cuts this geometry
in the tips of our needles." So we're looking at this
weirdly-shaped needle. "The tip is only 10 to
12 microns in width, only slightly larger than the
diameter of a red blood cell. The small size allows
threats to be inserted with minimal damage to the cortex." Okay, so what's interesting
about this geometry? So we're looking at this
just geometry of a needle. - This is the needle that's engaging with the loops in the thread. So they're the ones that thread the loop and then peel it from the silicon backing. And then this is the
thing that gets inserted into the tissue, and then this pulls out, leaving the thread. And this kind of a
notch or the shark tooth that we used to call is the thing that actually is grasping the loop. And then it's designed in such way, such that when you pull
out, leaps the loop. - And the robot is
controlling this needle? - Correct, so this is
actually housed in a cannula. And basically, the robot
has a lot of the optics that look for where the loop is. There's actually a 405 nanometer light that actually causes the
polyamide to fluoresce so that you can locate
the location of the loop. - So the loop lights up? - Yeah, yeah, they do. It's a micron precision process. - What's interesting about the robot that it takes to do that? That's pretty crazy. That's pretty crazy that robot is able to get this kind of precision. - Yeah, our robot is quite heavy. Our current version of it. There's, I mean, it's
like a giant granite slab that weighs about a ton, 'cause it needs to be
sensitive to vibration, environmental vibration. And then as the head is moving
at the speed that is moving, there's a lot of kind of motion control to make sure that you can
achieve that level of precision. A lot of optics that
kind of zoom in on that. We're working on next
generation of the robot that is lighter, easier to transport. I mean, it is a feat to move the robot. - And it's far superior to a human surgeon at this time for this particular task. - Absolutely, I mean, let alone
you try to actually thread a loop in a sewing kit,
I mean this is like, we're talking like
fractions of human hair. These things, it's not visible. - So continuing the paragraph, "We developed novel hardware
and software testing systems such as our accelerated
lifetime testing racks and simulated surgery
environment," which is pretty cool, "to stress test and validate the robustness of our technologies. We performed many
rehearsals of our surgeries to refine our procedures and
make them second nature." This is pretty cool. "We practice surgeries on proxies with all the hardware and
instruments needed in our mock or in the engineering space. This helps us rapidly test and measure." So there's like proxies. - Yeah, this proxy's super cool actually. So there's a 3D printed
skull from the images that is taken at Barrow, as well as this hydrogel mix, sort of synthetic polymer
thing that actually mimics the mechanical properties of the brain. It also has vasculature of the person. So basically, what we're
talking about here, and there's a lot of work that has gone into making this set proxy that it's about like finding the right concentration of these different synthetic polymers to get the right set of consistency
for the needle dynamics, as they're being inserted. But we practice this
surgery with the person, Noland's basically physiology
and brain many, many times prior to actually doing the surgery. - So every step, every step? - Every step, yeah. Like where does someone stand? I mean, what you're
looking at is the picture. This is in our office
of this kind of corner of the robot engineering space that we have created this like mock or space that looks exactly like what they would experience, all the staff would experience
during their actual surgery. So I mean, it's just kind
of like any dance rehearsal where you know exactly
where you're gonna stand at what point and you just
practice that over and over and over again with an exact anatomy of someone that you're going to surgerize. And it got to a point where
a lot of our engineers, when we created a
craniectomy, they're like, "Oh, that looks very familiar. We've seen that before." - Yeah. Man, there's wisdom you can gain through doing the same thing
over and over and over. It's like a Jira dreams
of sushi kind of thing, because then it's like
Olympic athletes visualize the Olympics, and then
once you actually show up, it feels easy. It feels like any other day. It feels almost boring
winning the gold medal, 'cause you visualized this so many times, you've practiced this so many times, and nothing bothers you. It's boring. You win the gold medal, it's boring. And the experience they talk
about is mostly just relief, probably that they don't
have to visualize it anymore. - Yeah, the power of
the mind to visualize, I mean, there's a whole field that studies where muscle memory lies in cerebellum. Yeah, it's incredible. - I think it is a good
place to actually ask sort of the big question
that people might have is, how do we know every aspect of this that you describe is safe? - At the end of the day, the gold standard is to look at the tissue. What sort of trauma did
you cause the tissue? And does that correlate
to whatever behavioral anomalies that you may have seen? And that's the language to
which we can communicate about the safety of inserting
something into the brain and what type of trauma
that you can cause. So we actually have an entire department, department of pathology that
looks at these tissue slices. There are many steps that
are involved in doing this. Once you have studies that are launched with particular endpoints in mind, at some point, you have
to euthanize the animal and then you go through necropsy to kind of collect the
brain tissue samples. You fix them in formalin, and you like gross them, you section them, and you look at individual slices just to see what kind of
reaction or lack thereof exists. So that's the kind of the
language to which FDA speaks and as well for us to kind
of evaluate the safety of the insertion mechanism
as well as the threads at various different time points. Both acute, so anywhere
between zero to three months to beyond three months. - So those are kind of the details of an extremely high standard of safety that has to be reached. - Correct.
- FDA supervises this, but this, in general,
just a very high standard. And every aspect of this,
including the surgery, I think Matthew MacDougall has mentioned that like the standard is, let's say, how to put it politely? Higher than maybe some other operations that we take for granted. So the standard for all
the surgical stuff here is extremely high. - Very high. I mean, it's a highly,
highly regulated environment with the governing
agencies that scrutinize every medical device that gets marketed. And I think it's a good thing. It's good to have those high standards, and we try to hold
extremely high standards to kind of understand what
sort of damage, if any, these innovative emerging technologies and new technologies
that we're building are. And so far, we have
been extremely impressed by lack of immune response
from these threads. - Speaking of which, you talk to me with
excitement about the histology and some of the images
that you're able to share. Can you explain to me
what we're looking at? - Yeah, so what you're looking
at is a stained tissue image. So this is a sectioned tissue slice from an animal that was
implanted for seven months. So kind of a chronic time point. And you're seeing all
these different colors, and each color indicates
specific types of cell types. So purple and pink are astrocytes
and microglia respectably. They're types of glial cells. And yet the other thing that
people may not be aware of is your brain is not just made up of soup of neurons and axons. There are other cells, like glial cells, that actually kind of is
the glue and also react if there are any trauma
or damage to the tissue. - The brown are the neurons here? - The brown are the neurons.
- The modern neurons. - So what you're seeing is,
in this kind of macro image, you're seeing these like
circle highlighted in white, the insertion sites. And when you zoom into one of
those, you see the threads. And then in this particular case, I think we're seeing about the 16 wires that are going into the page. And the incredible thing here is the fact that you have the neurons that
are these brown structures or brown circular or elliptical thing that are actually touching
and abutting the thread. So what this is saying is that
there's basically zero trauma that's caused during this insertion. And with these neural interfaces, these micro electrodes that you insert, that is one of the most
common mode of failure. So when you insert these
threads, like the Utah array, it causes neuronal death around the site, because you're inserting
a foreign object, right? And that kind of elicit
these like immune response through microglia and astrocytes. They form this like
protective layer around it. Oh, not only are you
killing the neuron cells, but you're also creating
this protective layer that then basically prevents you from recording neural signals, 'cause you're getting
further and further away from the neurons that
you're trying to record. And that is the biggest mode of failure. And in this particular
example, in that inside, it's about 50 micron with that scale bar. The neurons just seem
to be attracted to it. (Lex laughing) - And so there's certainly no trauma. That's such a beautiful image, by the way. So the brown are the neurons. And for some reason, I can't look away. It's really cool.
- Yeah, and the way that these things like, I mean, your tissues generally don't
have these beautiful colors. This is multiplex stain that
uses these different proteins that are staining these
at different colors. We use very standard set
of staining techniques, with HG, EB1 and new N and GFAP. So if you go to the next image, this is also kind of
illustrates the second point, 'cause you can make an
argument, and initially, when we saw the previous image, we said, "Oh, like are the threads just floating? Like what is happening here? Like are we actually
looking at the right thing?" So what we did is we did another stain, and this is all done in-house, of this batons, trichrome stain, which is in blue that shows
these collagen layers. So the blue basically, like you don't want the blue
around the implant threads, 'cause that means that there's some sort of scarring that's happen. And what you're seeing, if you
look at individual threads, is that you don't see any of the blue, which means that there
has been absolutely, or very, very minimal to a
point where it's not detectable amount of trauma in
these inserted threads. - So that presumably is
one of the big benefits of having this kind of flexible thread. - Yeah, so we think this is
primarily due to the size, as well as the flexibility of the threads. Also the fact that R1
is avoiding vasculature, so we're not disrupting or we're not causing damage to the vessels and not breaking any of
the blood brain barrier has basically caused the
immune response to be muted. - But this is also a nice illustration of the size of things. So this is the tip of the thread. - Yeah, those are neurons. - And they're neurons. And this is the thread listening. And the electrodes are positioned how? - Yeah, so this is, what you're looking at is
not electrode themselves. Those are the conductive wires. So each of those should
probably be two micron in width. So what we're looking at is we're looking at the coronal slice. So we're looking at some
slice of the tissue. So as you go deeper, you'll obviously have less and less of the
tapering of the thread. But yeah, the point basically being that there's just kind of
cells around the inserter site, which is just an incredible thing to see. I've just never seen anything like this. - How easy and safe is
it to remove the implant? - Yeah, so it depends on when. In the first three months
or so after the surgery, there's a lot of kind of tissue
modeling that's happening. Similar to when you got a cut, you obviously start
over first couple weeks, or depending on the size of the wound, scar tissue forming, right? There are these like
contractive, and then in the end, they turn into scab and
you can scab it off. The same thing happens in the brain, and it's a very dynamic environment. And before the scar
tissue or the neomembrane or the neomembrane that forms, it's quite easy to just pull 'em out. And there's minimal trauma
that's caused during that. Once the scar tissue forms,
and with Noland as well, we believe that that's the thing that's currently anchoring the thread. So we haven't seen any
more movements since then. So they're quite stable. It gets harder to actually
completely extract the threads. So our current method
for removing the device is cutting the thread,
leaving the tissue intact, and then unscrewing and
taking the implant out. And that hole is now gonna be plugged with either another Neuralink or just with kind of a
peak-based, plastic-based cap. - Is it okay to leave the
threads in there forever? - Yeah, we think so. We've done studies where
we left them there, and one of the biggest
concerns that we had is like, do they migrate and do they get to a point where they should not be? We haven't seen that. Again, once the scar tissue forms, they get anchored in place. And I should also say
that when we say upgrades, like we're not just
talking in theory here. Like we've actually
upgraded many, many times. Most of our monkeys or non-human primates, NHP have been upgraded. Pager, who you saw playing mind pong, has the latest version of
device since two years ago and is seemingly very
happy and healthy and fat. - So what's designed for the
future, the upgrade procedure? So maybe for Noland. What would the upgrade look like? It was essentially what you're mentioning. Is there a way to upgrade
sort of the device internally, where you take it apart and
sort of keep the capsule and upgrade the internals? - Yeah, so there are a
couple different things here. So for Noland, if we were to upgrade, what we would have to do
is either cut the threads or extract the threads depending on kind of the situation there in terms of how they're
anchored or scarred in. If you were to remove them
with the dural substitute, you have an intact brain
so you can reinsert different threads with the
updated implant package. There are a couple different other ways that we're thinking about, the future of what the
upgradable system looks like. One is, at the moment, we
currently remove the dura, this kind of thick layer
that protects the brain, but that actually is the thing that actually proliferates
the scar tissue formation. So typically, general good rule of thumb is you wanna leave the nature as is and not disrupt it as much. So looking at ways to insert
the threads through the dura, which comes with different
set of challenges, such as it's a pretty thick layer, so how do you actually penetrate that without breaking the needle? So we're looking at different
needle design for that, as well as the kind of
the loop engagement. The other biggest challenges
are it's quite opaque, optically, and with
white light illumination. So how do you avoid still
this biggest advantage that we have of avoiding vasculature? How do you image through that? How do you actually still mediate that? So there are other imaging techniques that we're looking at to enable that. But our hypothesis is that, and based on some of the
early evidence that we have, doing through the dura insertion
will cause minimal scarring that causes them to be much
easier to extract over time. And the other thing that
we're also looking at, this is gonna be a fundamental change in the implant architecture, is at the moment, it's a
monolithic single implant that comes with a thread
that's bonded together. So you can't actually
separate the thing out, but you can imagine
having two part implant. Bottom part, that is the
thread that are inserted that has the chips and maybe
a radio and some power source. And then you have another implant that has more of the
computational heavy load and the bigger battery. And then one can be under the dura, one can be above the dura, like being the plug for the skull. They can talk to each other, but the thing that you wanna upgrade, the computer and not the thread. If you wanna upgrade that,
you just go in there, remove the screws and then
put in the next version. It's a very, very easy surgery too. Like you do a skin incision,
slip this in, screw. Probably be able to do this in 10 minutes. - So that would allow you to
reuse the thread, sort of. - [DJ] Correct. - So I mean, this leads to
the natural question of, what is the pathway to scaling that increase in the number of threads? Is that a priority? What's the technical challenge there? - Yeah, that is a priority. So for next versions of the implant, the key metrics that
we're looking to improve are number of channels, just recording from more and more neurons. We have a pathway to actually
go from currently 1,000 to hopefully 3,000 if not
6,000 by end of this year. And then end of next year, we wanna get to even more, 16,000. - Wow. - There's a couple limitations to that. One is obviously being able to photo lithographically
print those wires. As I mentioned, it's two
micron in width and spacing. Obviously, there are chips
that are much more advanced than those types of resolution, and we have some of the tools that we have brought in
house to be able to do that. So traces will be narrower
just so that you have to have more of the wires coming up into the chip. Chips also cannot linearly
consume more energy, as you have more and more channels. So there's a lot of
innovations in the circuit and architecture as well as
the circuit design topology to make them lower power. You need to also think about, if you have all of these spikes, how do you send that off
to the end application? So you need to think about
bandwidth limitation there and potentially innovations
and signal processing. Physically, one of the biggest challenge is gonna be the interface. It's always the interface that breaks. Bonding this thin film
array to the electronics. It starts to become very, very
highly dense interconnects. So how do you characterize that? There's a lot of innovations in kind of the 3D integrations
in the recent years that we can take advantage of. One of the biggest
challenges that we do have is forming this hermetic barrier, right? That this is an extremely
harsh environment that we're in - the brain. So how do you protect it from, yeah, like the brain trying
to kill your electronics to also your electronics leaking things that you don't want into the brain. And that forming that hermetic barrier is gonna be a very, very big challenge that we I think are actually
well-suited to tackle. - How do you test that? Like what's the development environment to simulate that kind of harshness? - Yeah, so this is where
the accelerated life tester essentially is a brain in a vat. It literally is a vessel
that is made up of, and again, for all intents and purpose for this particular type of test, your brain is a salt water. And you can also put some
other set of chemicals like reactive oxygen species that get at kind of these interfaces and trying to cause a
reaction to pull it apart. But you could also increase the rate at which these interfaces are aging by just increasing temperature. So every 10 degrees
Celsius that you increase, you're basically accelerating time by 2x. And there's limit as to how much temperature you wanna
increase, 'cause at some point, there's some other non-linear dynamics that causes you to have
other nasty gases to form that just is not realistic
in an environment. So what we do is we
increase in our ALT chamber by 20 degrees Celsius that increases the aging by four times. So essentially one day in ALT chamber, it's four day in calendar year. And we look at whether the
implants still are intact, including the threads and- - And operation and all of that? - And operation and all of that. It obviously is not an exact
same environment as a brain, 'cause brain has mechanical, other more biological
groups that attack at it. But it is a good test environment, testing environment for
at least the enclosure and the strength of the enclosure. And I mean, we've had implants, the current version of the implant that has been in there for, I mean, close to two and a half years, which is equivalent to a decade. And they seem to be fine. - So it's interesting that the burn, so basically, close
approximation is warm salt water, hot salt water is a good
testing environment. Yeah, by the way, I'm drinking LMNT, which is basically salt water,
which is making me kinda, it doesn't have computational
power the way the brain does, but maybe in terms of
other characteristics, it's quite similar and I'm consuming it. - Yeah, you have to get it
in the right pH too. (laughs) - And then consciousness will emerge. Yeah, no. - By the way, the other thing
that also is interesting about our enclosure is, if
you look at our implant, it's not your common-looking
medical implant that usually is encased in a titanium can that's laser welded. We use this polymer called PCTFE, polychlorotrifluoroethylene, which is actually commonly
used in blister packs. So when you have a pill
and you try to pop a pill, there's like kind of
that plastic membrane. That's what this is. No one's actually ever
used this except us. And the reason we wanted to do this is 'cause it's
electromagnetically transparent. So when we talked about
the electromagnetic inductive charging, with titanium can, usually, if you wanna
do something like that, you have to have a sapphire window, and it's a very, very
tough process to scale. - So you're doing a lot of iteration here in every aspect of this. The materials, the software- - The whole, whole shebang. - So, okay. So you mentioned scaling. Is it possible to have
multiple Neuralink devices as one of the ways of scaling? To have multiple Neuralink
devices implanted? - That's the goal, that's the goal. Yeah, we've had, I mean, our monkeys have had two Neuralinks, one in each hemisphere. And then we're also looking
at potential of having one in motor cortex, one in visual cortex, and one in wherever other cortex. - So focusing on a particular function, one Neuralink device.
- Correct. - I mean, I wonder if there's
some level of customization that can be done on the compute side. So for the motor cortex- - Absolutely. That's the goal. And we talk about at Neuralink
building a generalized neural interface to the brain. And that also is strategically how we're approaching this with marketing. And also, with regulatory, which is, hey look, we have the robot, and the robot can access
any part of the cortex. Right now, we're focused on motor cortex with current version of the N1 that's specialized for
motor decoding tasks. But also, at the end of the day, there's kind of a general
compute available there. But typically, if you
wanna really get down to kind of hyperoptimizing
for power and efficiency, you do need to get to some
specialized function, right? But what we're saying is that, hey, you are now used to this
robotic insertion techniques, which took many, many
years of showing data and conversation with the FDA. And also, internally convincing
ourselves that this is safe. And now the difference is that if we go to other parts of
the brain, like visual cortex, which we're interested
in as our second product, obviously, it's a completely
different environment. The cortex is laid out
very, very differently. It's gonna be more stimulation focus rather than recording, just kind of creating visual percepts. But in the end, we're using the same thin film array technology. We're using the same robot
insertion technology. We're using the same packaging technology. Now, more the conversation
is focused around what are the differences
and what are the implication of those differences
in safety and efficacy? - The way you said second product is both hilarious and awesome to me. That product being restoring
sight for blind people. So can you speak to
stimulating the visual cortex? I mean, the possibilities
there are just incredible to be able to give that
gift back to people who don't have sight or
even any aspect of that. Can you just speak to the challenges of, there's several challenges here. - Oh, many.
- One of which is, like you said, from
recording to stimulation. Just any aspect of that that you're both excited
and see the challenges of. - Yeah, I guess I'll start by saying that we actually have been capable of stimulating through our thin film array as well as other electronics for years. We have actually demonstrated
some of that capabilities for reanimating the
limb in the spinal cord. Obviously, for the current EFS study, we've hardware disabled that, so that's something
that we wanted to embark as a separate, separate journey. And obviously, there are
many, many different ways to write information into the brain. The way in which we're doing
that is through electrical, passing electrical current,
and kind of causing that to really change the local environment so that you can sort of artificially cause kind of the neurons to
depolarize in nearby areas. For vision specifically, the
way our visual system works, it's both well-understood. I mean, anything with kind of brain, there are aspects of it
that's well-understood. But in the end, like we
don't really know anything. But the way visual system works is that you have photon hitting your eye, and in your eyes, there
are these specialized cells called photoreceptor cells that convert the photon energy
into electrical signals. That then gets projected
to your back of your head, your visual cortex. It goes through actually thalamic system called LGN that then projects it out. And then in the visual cortex, there's visual area one or V1, and then there's bunch
of other higher level processing layers like V2, V3. And there there are actually
kind of interesting parallels. And when you study the behaviors of these convolutional neural networks, like what the different layers
of the network is detecting, first, they're detecting like these edges, and they're then detecting
some more natural curves, and then they start to
detect like objects, right? Kind of similar thing
happens in the brain. And a lot of that has been inspired, and also, it's been kinda exciting to see some of the correlations there. But things like from there,
where those cognition arise and where's color encoded, there's just not a lot of understanding, fundamental understanding there. So in terms of kind of bringing sight back to those that are blind, there are many different
forms of blindness. There's actually million people, one million people in the
U.S. that are legally blind. That means like certain, like score below in kind of the visual tests. I think it's something like, if you can see something
at 20 feet distance, that normal people can
see at 200 feet distance, like if you're worse than
that, you're legally blind. - So that means you can't
function effectively. - Correct.
- Using sight in the world. - Yeah, like to navigate your environment. And yeah, there are
different forms of blindness. There are forms of blindness
where there's some degeneration of your retina, these photoreceptor cells, and rest of your visual processing that I described is intact. And for those types of individuals, you may not need to maybe stick electrodes into the visual cortex. You can actually build
retinal prosthetic devices that actually just replaces the function of that retinal cells
that are degenerated. And there are many companies
that are working on that. But that's a very small slice. Albeit significance, those smaller slice of folks that are legally blind. If there's any damage
along that circuitry, whether it's in the optic nerve or just the LGN circuitry or any break in that circuit, that's not gonna work for you. And the source of where
you need to actually cause that visual percept to happen, because your biological
mechanism not doing that is by placing electrodes
in the visual cortex in the back of your head. And the way in which this would work is that you would have an external camera, whether it's something as
unsophisticated as a GoPro or some sort of wearable
RayBan type glasses that Meta's working on that
captures a scene, right? And that scene is then converted to set of electrical impulses
or stimulation pulses that you would activate
in your visual cortex through these thin film arrays. And by playing in a
concerted kind of orchestra of these stimulation patterns, you can create what's called phosphenes, which are these kind
of white yellowish dots that you can also create
by just pressing your eyes. You can actually create those percepts by stimulating in the visual cortex. And the name of the game is
really have many of those and have those percepts be, the phosphenes be as small as possible so that you can start to tell apart, like they're the individual
pixels of the screen, right? So if you have many, many of those, potentially, you'll be able to, in the long term, be able to actually get naturalistic vision. But in the like short
term to maybe midterm, being able to at least be able to have object detection algorithms
run on your glasses, the pre-op processing units, and then being able to at
least see the edges of things so you don't bump into stuff. - It's incredible. This is really incredible. So you basically would be adding pixels, and your brain would start to figure out what those pixels mean. Yeah, and like with
different kinds of assistant on the signal processing on all fronts. - Yeah. The thing that actually, so a couple things. One is, obviously, if
you're blind from birth, the way brain works,
especially in the early age, neuroplasticity is really nothing other than kind of your brain and different parts of your brain fighting for the limited territory. - [Lex] (laughs) Yeah. - And I mean, very, very quickly, you see cases where you
know people that are, I mean, you also hear
about people who are blind that have heightened sense of
hearing or some other senses. And the reason for that is that cortex that's not used just gets taken over by these different parts of the cortex. So for those types of individuals, I mean, I guess they're
going to have to now map some other parts of their senses
into what they call vision. But it's gonna be obviously a very, very different
conscious experience before. So I think that's a interesting caveat. The other thing that also
is important to highlight is that we're currently
limited by our biology in terms of the wavelength
that we can see. There's a very, very small wavelength that is a visible light wavelength that we can see with our eyes. But when you have an external
camera with this BCI system, you're not limited to that. You can have infrared, you can have UV, you can have whatever other
spectrum that you want to see. And whether that gets matched to some sort of weird conscious
experience, I've no idea. But oftentimes, I talk to people about the goal of Neuralink being going beyond the
limits of our biology. That's sort of what I mean. - And if you're able to
control the kind of raw signal, when we use our sight,
we're getting the photons and there's not much processing on it. If you're being able
to control that signal, maybe you can do some kind of processing. Maybe you do object
detection ahead of time. - [DJ] Yeah. - You're doing some
kind of pre-processing, and there's a lot of
possibilities to explore that. So it's not just increasing
sort of thermal imaging, that kind of stuff, but it's
also just doing some kind of interesting processing.
- Correct, yeah. I mean, my theory of how
like visual system works also is that, I mean, there's
just so many things happening in the world, and
there's a lot of photons that are going into your
eye, and it's unclear exactly where some of the pre-processing
steps are happening. But I mean, I actually think that just from a fundamental perspective,
there's just so much, the reality that we're
in, if it's a reality, is so there's so much data. And I think humans are
just unable to actually like eat enough actually to
process all that information. So there's some sort of
filtering that does happen, whether that happens in the retina, whether that happens in different layers of the visual cortex. Unclear. But like the analogy that
I sometimes think about is, if your brain is a CCD camera, and all of the information
in the world is a sun, and when you try to
actually look at the sun with the CCD camera, it's just gonna saturate
the sensors, right? 'Cause it's enormous amount of energy. So what you do is you end up
adding these filters, right? To just kind of narrow the information that's coming to you and being captured. And I think things like our experiences or our like drugs like propofol, that like anesthetic drug or psychedelics, what they're doing is
they're kind of swapping out these filters and putting in
new ones or removing older ones and kind of controlling
our conscious experience. - Yeah, man, not to
distract from the topic, but I just took a very
high dose of ayahuasca in the Amazon jungle. So yes, it's a nice way to think about it. You're swapping out different experiences, and with Neuralink being
able to control that, primarily at first, to improve function, not for entertainment purposes
or enjoyment purposes, but- - Yeah, giving back lost functions. - Well, giving back lost functions. And there, especially when the
function is completely lost, anything is a huge help. Would you implant a Neuralink
device in your own brain? - Absolutely. I mean, maybe not right
now, but absolutely. - What kind of capability, once reached, you would start getting real curious and almost get a little antsy, like jealous of people that get it as you watch them get implanted? - Yeah, I mean I think, I mean, even with our early participants, if they start to do
things that I can't do, which I think is in the
realm of possibility for them to be able to get, 15, 20, if not like 100 BPS right? There's nothing that
fundamentally stops us from being able to achieve
that type of performance. I mean, I would certainly get
jealous that they can do that. - I should say that watching Noland, I get a little jealous 'cause
he's having so much fun, and it seems like such a
chill way to play video games. - Yeah. So I mean, the thing that also is hard to appreciate sometimes is that, he's doing these things while talking. I mean, it's multitasking, right? So it's clearly, it's obviously cognitively intensive, but similar to how, when we talk, we move our hands, like these
things like are multitasking. I mean, he's able to do that. And you won't be able to do that with other assistive
technology as far as I'm aware. If you're obviously using
like an eye tracking device, you're very much fixated on that thing that you're trying to do. And if you're using voice control, I mean, like if you say some other stuff, yeah, you don't get to use that. - Yeah, the multitasking aspect of that is really interesting. So it's not just the BPS
for the primary task. It's the parallelization
of multiple tasks. If you measure the BPS for the entirety of the human organism, so if you're talking and
doing a thing with your mind and looking around also, I mean, there's just a lot of paralyzation
that can be happening. - Yeah. But I mean, I think at some point for him, like if he wants to really
achieve those high level BPS, it does require like
full attention, right? And that's a separate circuitry
that is a big mystery. Like how attention works and, you know? - Yeah, attention, like cognitive load, I've read a lot of literature
on people doing two tasks. Like you have your primary
task and a secondary task. And the secondary task is
a source of distraction. And how does that affect the performance of the primary task? And depending on the tasks,
there's a lot of interesting, I mean, this is an interesting
computational device, right? And I think-
- To say the least. - A lot of novel insights that can be gained from everything. I mean, I personally am surprised that Noland's able to do
such incredible control of the cursor while talking and also being nervous at the same time, 'cause he's talking like all of us are, if you're talking in front of the camera, you get nervous. So all of those are coming into play, and he is able to still
achieve high performance. Surprising. I mean, all of this is really amazing. And I think just after
researching this really in depth, I kind of want Neuralink. - (laughs) Get in line. - And also, the safety get in line. Well, we should say the registry is for people who have quadriplegia and all that kind of stuff so-
- Correct. - There'll be a separate line for people. They're just curious, like myself. So now that Noland, patient P1, is part of the ongoing prime study, what's the high level
vision for P2, P3, P4, P5? And just the expansion
into other human beings that are getting to
experience this implant? - Yeah, I mean, the primary goal is, for our study in the first place is to achieve safety endpoints. Just understand safety of this device, as well as the implantation process. And also, at the same time,
understand the efficacy and the impact that it could have on the potential users' lives. And just because you have, you're living with tetraplegia, it doesn't mean your situation is same as another person
living with tetraplegia. It's wildly, wildly varying. It's something that we're
hoping to also understand how our technology can serve not just a very small
slice of those individuals, but broader group of individuals and being able to get the
feedback to just really build just the best product for them. There's obviously also goals that we have, and the primary purpose of
the early feasibility study is to learn from each
and every participant to improve the device, improve the surgery before we embark on what's
called a pivotal study that then is much larger trial that starts to look at
statistical significance of your endpoints, and that's required before you can then market the device. And that's how it works in the U.S. and just generally around the world. That's the process you follow. So our goal is to really just understand from people like Noland,
P2, P3, future participants, what aspects of our
device needs to improve. If it turns out that people are like, "I really don't like the fact
that it lasts only six hours. I wanna be able to use this
computer for like 24 hours." I mean, that is a user
needs and user requirements, which we can only find out from just being able to engage with them. - So before the pivotal study, there's kind of like a rapid innovation based on individual experiences. You're learning from individual
people how they use it, like the high resolution details in terms of like cursor control and signal and all that kind of stuff
to like life experience. - Yeah, yeah, so there's hardware changes but also just firmware updates. So even when we had that sort
of recovery event for Noland, he now has the new firmware
that he has been updated with. And it's similar to how like your phones get updated all the time with new firmware for security patches, whatever new functionality UI, right? And that's something that is
possible with our implant. It's not a static one-time device that can only do the thing
that it said it can do. I mean, it's similar to Tesla. You can do over the air firmware updates, and now you have completely
new user interface. And all this bells and
whistles and improvements on everything like the latest, right? When we say generalized platform, that's what we're talking about. - Yeah, it's really cool how
the app that Noland is using, there's like calibration,
all that kind of stuff. And then there's update. You just click and get an update. What other future capabilities
are you kinda looking to? You said vision. That's a fascinating one. What about sort of
accelerated typing or speech, this kind of stuff? And what else is there? - Yeah, those are still in
the realm of movement program. So largely speaking, we have two programs. We have the movement program and we have the vision program. The movement program
currently is focused around the digital freedom. As you can easily guess,
if you can control 2D cursor in the digital space, you could move anything
in the physical space. So robotic arms, wheelchair,
your environment, or even really like, whether
it's through the phone or just like directly to those interfaces, so like to those machines. So we're looking at ways to kind of expand those types of capability,
even for Noland. That requires conversation with the FDA and kind of showing safety data for, if there's a robotic arm or a wheelchair that we can guarantee that
they're not gonna hurt themselves accidentally, right? It's very different if you're moving stuff in the digital domain versus
like in the physical space, you can actually potentially
cause harm to the participants. So we're working through that right now. Speech does involve
different areas of the brain. Speech prosthetic is
very, very fascinating, and there's actually been a
lot of really amazing work that's been happening in academia. Sergei Stavisky at UC
Davis, Jaimie Henderson, and late Krishna Shenoy at Stanford doing just some incredible amount of work in improving speech neuroprosthetics. And those are actually looking more at parts of the motor cortex that are controlling
these focal articulators. And being able to like, even by mouthing the
word or imagine speech, you can pick up those signals. The more sophisticated higher
level processing areas, like the Broca's area or Wernicke's area, those are still very, very big mystery in terms of the underlying mechanism of how all that stuff works. But yeah, I mean, I think
Neuralink's event goal is to kind of understand those things and be able to provide
a platform and tools to be able to understand
that and study that. - This is where I get to
the pothead questions. Do you think we can start getting insight into things like thought? So speech is, there's a muscular
component, like you said. There's like the act of producing sounds. But then what about the
internal things like cognition? Like low level thoughts
and high level thoughts. Do you think we'll start noticing kind of signals that could be picked up? They could be understood,
they could be maybe used in order to interact
with the outside world. - In some ways, like I guess this starts to kind of get into the heart
problem of consciousness. And I mean, on one hand, all of these are, at some point, set of electrical signals, that from there, maybe it in itself is giving you the
cognition or the meaning, or somehow, human mind
is incredibly amazing storytelling machine. So we're telling ourselves
and fooling ourselves that there's some
interesting meaning here. But I mean, I certainly think that BCI and really BCI at the end of the day is a set of tools that
help you kind of study the underlying mechanisms
in both like local but also broader sense. And whether there's some
interesting patterns of like electrical signal, that means like you're
thinking this versus, and you can either like learn from like many, many sets of
data to correlate some of that and be able to do mind reading or not. I'm not sure. I certainly would not
kind of rule that out as a possibility, but I think BCI alone probably can't do that. There's probably additional
set of tools and framework. And also, like just heart
problem of consciousness at the end of the day is rooted in this philosophical question of like, what's the meaning of it all? What's the nature of our existence? Where's the mind emerge
from this complex network? - Yeah, how does the
subjective experience emerge from just a bunch of
spikes, electrical spikes? - Yeah, yeah, I mean, we
do really think about BCI and what we're building as a tool for understanding the mind, the brain. The only question that matters. There's actually, there actually is some
biological existence proof of like what it would take
to kind of start to form some of these experiences
that may be unique. If you actually look at
every one of our brains, there are two hemispheres. There's a left-sided brain,
there's a right-sided brain. And I mean, unless you
have some other conditions, you normally don't feel like
left legs or right legs. Like you just feel like one legs, right? So what is happening there, right? If you actually look
at the two hemispheres, there's a structure that
kind of characterize the two called the corpus callosum
that is supposed to have around 200 to 300 million
connections or axons. So whether that means that's
the number of interface and electrodes that we need to create some sort of
mind meld, or from that, like whatever new conscious experience that you can experience. But yeah, I do think that there's like kind of an interesting existence
proof that we all have. - And that threshold is
unknown at this time. - Oh yeah, these things, everything in this domain
is speculation, right? - And then there would be, you'd be continuously
pleasantly surprised. Do you see a world where
there's millions of people, like tens of millions,
hundreds of millions of people walking around with a Neuralink device, or multiple Neuralink
devices in their brain? - I do. First of all, there are, like if you look at
worldwide, people suffering from movement disorders
and visual deficits. I mean, that's in the tens if not hundreds of millions of people. So that alone, I think,
there's a lot of benefit and potential good that we can do with this type of technology. And once you start to
get into kind of neuro, like psychiatric application, depression, anxiety, hunger, or obesity, right? Like mood, control of appetite, I mean, that starts to
become very real to everyone. - Not to mention that most people on earth have a smartphone. And once BCI starts
competing with a smartphone as a preferred methodology of interacting with the digital world, that also becomes an interesting thing. - Oh yeah, I mean, yeah. This is even before going to that, right? I mean, there's like almost, I mean, the entire world that could benefit from
these types of thing. And then, yeah, like
if we're talking about kind of next generation
of how we interface with machines or even ourselves, in many ways, I think BCI
can play a role in that. And some of the things
that I also talk about is I do think that there
is a real possibility that you could see eight billion people walking around with Neuralink. - Well, thank you so
much for pushing ahead. And I look forward to
that exciting feature. - Thanks for having me. - Thanks for listening to
this conversation with DJ Seo. And now, dear friends,
here's Matthew MacDougall, the head neurosurgeon at Neuralink. When did you first become
fascinated with the human brain? - Since forever. As far back as I can remember, I've been interested in the human brain. I mean, I was a thoughtful kid and a bit of an outsider. And you sit there thinking about what the most important
things in the world are in your little tiny adolescent brain. And the answer that I came to, that I converged on was
that all of the things you can possibly conceive of,
as things that are important for human beings to care about are literally contained in the skull. Both the perception of them
and their relative values. And the solutions to all our problems and all of our problems are
all contained in the skull. And if we knew more about how that worked, how the brain encodes information and generates desires and
generates agony and suffering, we could do more about it. You think about all the
really great triumphs in human history. You think about all the
really horrific tragedies. You think about the holocaust, you think about any prison
full of human stories, and all of those problems
boil down to neurochemistry. So if you get a little
bit of control over that, you provide people the
option to do better. And in the way I read history, the way people have dealt
with having better tools is that they most often
in the end do better, with huge asterisk. But I think it's an interesting, a worthy and noble pursuit to give people more options, more tools. - Yeah, that's a fascinating
way to look at human history. You just imagine all these
neurobiological mechanisms, Stalin, Hitler, all of these, Gengis Khan, all of them just had like a brain, just a bunch of neurons, like a few tons of billions of neurons gaining a bunch of information
over a period of time. They have a set of module that does language and
memory and all that. And from there, in the
case of those people, they're able to murder millions of people. - [Matthew] Yeah. - All that coming from, there's not some glorified
notion of a dictator of this enormous mind
or something like this. It's just the brain. - Yeah, yeah. I mean, a lot of that
has to do with how well people like that can
organize those around them. - Other brains. - Yeah, and so I always
find it interesting to look to primatology, look to our closest non-human relatives for clues as to how
humans are going to behave and what particular humans
are able to achieve. And so you look at chimpanzees and bonobos and they're similar but different in their social structures particularly. And I went to Emory in Atlanta and studied under Frans,
the great Frans de Waal, who was kind of the leading
primatologist who recently died, and his work at looking
at chimps through the lens of how you would watch
an episode of "Friends" and understand the motivations of the characters
interacting with each other. He would look at a chimp colony and basically apply that lens. I'm massively oversimplifying it. If you do that, instead of just saying, subject 473 through his
feces at subject 471, you talk about them in terms
of their human struggles, accord them the dignity
of themselves as actors with understandable goals and drives, what they want out of life. And primarily, it's the
things we want out of life: food, sex, companionship, power. You can understand chimp
and bonobo behavior in the same lights much more easily. And I think doing so gives
you the tools you need to reduce human behavior from
the kind of false complexity that we layer onto it with language and look at it in terms of, oh, well, these humans are
looking for companionship, sex, food, power. And I think that's a pretty powerful tool to have in understanding human behavior. - And I just went to the
Amazon jungle for a few weeks, and it's a very visceral reminder that a lot of life on earth
is just trying to get laid. They're all screaming at each other. Like I saw a lot of monkeys, and they're just trying
to impress each other, or maybe if there's a battle for power, but a lot of the battle for power has to do with them getting laid. - Right. Breeding rights often
go with alpha status. And so if you can get a piece of that, then you're gonna do okay. - And would like to think that we're somehow
fundamentally different, but especially when it comes to primates, we're really aren't, you know. We can use fancier poetic language, but maybe some of the underlying drives that motivate us are similar. - Yeah, I think that's true. - And all of that is coming
from this, the brain. - Yeah.
- So when did you first start studying the brain as I guess as a biological mechanism? - Basically, the moment I got to college, I started looking around for labs that I could do neuroscience work in. I originally approached
that from the angle of looking at interactions
between the brain and the immune system, which
isn't the most obvious place to start, but I had this idea at the time that the contents of your
thoughts would have an impact, a direct impact, maybe a powerful one on non-conscious systems in your body. The systems we think of as homeostatic, automatic mechanisms like
fighting off a virus, like repairing a wound. And sure enough, there are big
crossovers between the two. I mean, it gets to kind of a key point that I think goes under recognized. One of the things people don't recognize or appreciate about
the human brain enough, and that is that it basically controls or has a huge role in almost
everything that your body does. Like you try to name an example of something in your body
that isn't directly controlled or massively influenced by the
brain, and it's pretty hard. I mean, you might say like
bone healing or something, but even those systems, the
hypothalamus and pituitary end up playing a role in
coordinating the endocrine system that does have a direct influence on, say, the calcium level in your blood
that goes to bone healing. So non-obvious connections
between those things implicate the brain as really a potent prime mover in all of health. - One of the things I realized
in the other direction too, how most of the systems in the body integrated with the human brain, like they affect the brain
also, like the immune system. I think there's just people
who study Alzheimer's and those kinds of things. It's just surprising how
much you can understand of that from the immune
system, from the other systems that don't obviously seem
to have anything to do with sort of the nervous system. They all play together. - Yeah, you could
understand how that would be driven by evolution too,
just in some simple examples. If you get sick, if you
get a communicable disease, you get the flu, it's pretty advantageous for your immune system to tell your brain, "Hey, now be antisocial for a few days. Don't go be the life of the party tonight. In fact, maybe just
cuddle up somewhere warm under a blanket and just
stay there for a day or two." And sure enough, that
tends to be the behavior that you see both in
animals and in humans. If you get sick, elevated
levels of interleukins in your blood and TNF alpha in your blood ask the brain to cut
back on social activity. And even moving around, you have lower locomotor activity in animals that are infected with viruses. - So from there, the early days
in neuroscience to surgery, when did that step happen? - Yeah.
- This is a leap. - It was sort of an evolution of thought. I wanted to study the brain. I started studying the brain in undergrad in this neuroimmunology lab. I, from there, realized at some point that I didn't wanna
just generate knowledge. I wanted to effect real
changes in the actual world, in actual people's lives. And so after having not
really thought about going into medical school, I was on a track to go into a PhD program. I said, "Well, I'd like that option. I'd like to actually potentially help tangible people in front of me." And doing a little digging
found that there exists these MD-PhD programs where
you can choose not to choose between them and do both. And so I went to USC for medical school and had a joint PhD program with Caltech, where I actually chose
that program particularly because of a researcher at
Caltech named Richard Andersen, who's one of the godfathers
of primate neuroscience and has a MACAC lab where Utah arrays and other electrodes were being inserted into the brains of monkeys
to try to understand how intentions were being
encoded in the brain. So I ended up there with the idea that maybe I would be a neurologist and study the brain on the side, and then discovered that neurology, again, I'm gonna make
enemies by saying this, but neurology predominantly and distressingly to me is the practice of diagnosing a thing and then saying, "Good luck with that. There's not much we can do." And neurosurgery, very differently, it's a powerful lever on taking people that are headed in a bad direction
and changing their course in the sense of brain tumors that are potentially treatable
or curable with surgery. Even aneurysms in the brain, blood vessels that are gonna rupture, you can save lives really is at the end of the
day, what mattered to me. And so I was at USC, as I mentioned, that happens to be one of the
great neurosurgery programs. And so I met these truly
epic neurosurgeons, Alex Khalessi and Mike
Apuzzo and Steve Giannotta and Marty Weiss, these sort of epic people that were just human
beings in front of me. And so it kind of changed my thinking from neurosurgeons are distant gods that live on another planet and occasionally come and visit us to these are humans that
have problems and are people. And there's nothing
fundamentally preventing me from being one of them. And so at the last
minute in medical school, I changed gears from going
into a different specialty and switched into neurosurgery,
which cost me a year. I had to do another year of research, because I was so far along in the process to switch into neurosurgery. The deadlines had already passed. So it was a decision that cost time, but absolutely worth it. - What was the hardest
part of the training on the neurosurgeon track? - Yeah, two things. I think that residency in neurosurgery is sort of a competition of pain, of like how much pain
can you eat and smile. - Yeah.
- And so there's workout restrictions that are not really, they're viewed at, I think, internally among the residents as weakness. And so most neurosurgery residents try to work as hard as they can. And that I think necessarily
means working long hours, and sometimes, over the work hour limits. And we care about being compliant with whatever regulations
are in front of us. But I think more important than that, people wanna give their all in becoming a better neurosurgeon, because the stakes are so high. And so it's a real fight to get residents to say go home at the end of their shift and not stay and do more surgery. - Are you seriously saying
like one of the hardest things is literally like
forcing them to get sleep and rest and all this kind of stuff? - Historically, that was the case. I think the next generation, I think the next generation
is more compliant and more selfcare-
- Weaker is what you mean. All right, I'm just
kidding, I'm just kidding. - I didn't say it. - Now I'm making enemies. Okay, I get it. Wow, that's fascinating. So what was the second thing? - The personalities. And maybe the two are connected, but- - Was it pretty competitive? - It's competitive and it's also, as we touched on earlier,
primates like power, and I think neurosurgery
has long had this aura of mystique and excellence
and whatever about it. And so it's an invitation
I think for people that are cloaked in that authority. A board certified
neurosurgeon is basically a walking fallacious
appeal to authority, right? You have license to walk into any room and act like you're an expert on whatever. And fighting that tendency is not something that most
neurosurgeons do well. Humility isn't the forte. - Yeah, so I have friends who know you, and whenever they speak about you, that you have the surprising quality for a neurosurgeon of humility, which I think indicates
that it's not as common as perhaps in other professions, 'cause there is a kind of gigantic sort of heroic aspect to neurosurgery, and I think it gets to
people's head a little bit. - Yeah. Well, I think that allows me to play well at an Elon company. Because Elon, one of his strengths, I think, is to just instantly see through fallacy from authority. So nobody walks into a
room that he's in and says, "Well, goddamn it, you have to trust me. I'm the guy that built the
last 10 rockets or something." And he says, "Well, you did it wrong and we can do it better." Or, "I'm the guy that kept Ford alive for the last 50 years. You listen to me on how to build cars." And he says no. And so you don't walk
into a room that he's in and say, "Well, I'm a neurosurgeon. Let me tell you how to do it." He's gonna say, "Well, I'm a
human being that has a brain. I can think from first principles myself, thank you very much. And here's how I think
it ought to be done. Let's go try it and see who's right." And that's proven I think
over and over in his case to be a very powerful approach. - If we just take that tangent, there's a fascinating
interdisciplinary team at Neuralink that you get to interact
with, including Elon. What do you think is the
secret to a successful team? What have you learned from just getting to observe these folks? World experts in different
disciplines work together. - Yeah, there's a sweet
spot where people disagree and forcefully speak their mind and passionately defend their position, and yet are still able to
accept information from others and change their ideas when they're wrong. And so I like the analogy of sort of how you polish rocks. You put hard things in a
hard container and spin it. People bash against each other and outcome's a more refined product. And so to make a good team at Neuralink, we've tried to find
people that are not afraid to defend their ideas passionately. And occasionally, strongly
disagree with people that they're working with and have the best idea come out on top. It's not an easy balance, again, to refer back to the primate brain. It's not something that is inherently built into the primate brain to say, "I passionately put all
my chips on this position and now I'm just gonna walk away from it. Admit you were right." Part of our brains tell us
that that is a power loss. That is a loss of face, a loss of standing in the community. And now, you're a zeta chimp, 'cause your idea got trounced. And you just have to recognize
that that little voice in the back of your head is maladaptive and it's not helping the team win. - Yeah, you have to have the confidence to be able to walk away from
an idea that you hold onto. Yeah.
- Yeah. - And if you do that often enough, you're actually going to become the best in the world at your thing. I mean, that kind of that rapid iteration. - Yeah, you'll at least be
a member of a winning team. - Ride the wave. What did you learn? You mentioned there's a lot of
amazing neurosurgeons at USC. What lessons about surgery and life have you learned from those folks? - Yeah, I think working
your ass off, working hard while functioning as a member of a team, getting a job done that
is incredibly difficult, working incredibly long
hours, being up all night, taking care of someone that you think probably won't
survive no matter what you do. Working hard to make people
that you passionately dislike look good the next morning. These folks were
relentless in their pursuit of excellent neurosurgical
technique decade over decade. And I think we're well-recognized
for that excellence. Especially Marty Weiss,
Steve Giannotta, Mike Apuzzo, they made huge contributions
not only to surgical technique, but they built training
programs that trained dozens or hundreds of
amazing neurosurgeons. I was just lucky to kind
of be in their wake. - What's that like, you
mentioned doing a surgery where the person is likely not to survive. Does that wear on you? - Yeah. It's especially challenging when you, with all respect to our elders, it doesn't hit so much when you're taking care of an 80-year-old and something was going to
get them pretty soon anyway. And so you lose a patient like that, and it was part of the natural course of what is expected of them in
the coming years, regardless. Taking care of a father of
two or three, four young kids, someone in their 30s that
didn't have it coming, and they show up in your ER having their first seizure of their life, and lo and behold, they've
got a huge, malignant, inoperable or incurable brain tumor. You can only do that, I
think, a handful of times before it really starts
eating away at your armor. Or a young mother that shows up that has a giant hemorrhage in her brain that she's not gonna survive from. And they bring her four-year-old daughter to say goodbye one last time before they turn the ventilator off. The great Henry Marsh is
a English neurosurgeon who said it best. I think he says that every neurosurgeon carries with them a private graveyard, and I definitely feel that, especially with young parents. That kills me. They had a lot more to give. The loss of those people specifically has a knock on effect that's
going to make the world worse for people for a long time. And it's just hard to feel
powerless in the face of that. And that's where I think you
have to be borderline evil to fight against a company like Neuralink or to constantly be taking pot shots at us because what we're doing is
to try to fix that stuff. We're trying to give people options, to reduce suffering. We're trying to take the pain out of life that broken brains brings in. And yeah, this is just our little way that we're fighting back
against entropy, I guess. - Yeah, the amount of
suffering that's endured when some of the things
that we take for granted that our brain is able to
do is taken away is immense. And to be able to restore some of that functionality is a real gift. - Yeah, we're just starting. We're gonna do so much more. - Well, can you take me
through the full procedure of implanting, say, the
N1 chip in Neuralink? - Yeah, it's a really simple, really simple, straightforward procedure. The human part of the surgery
that I do is dead simple. It's one of the most basic neurosurgery procedures imaginable. And I think there's evidence
that some version of it has been done for thousands of years. That there are examples I
think from ancient Egypt of healed or partially healed
trephinations from Peru or ancient times in South America where these protosurgeons would drill holes in people's skulls, presumably to let out the evil spirits, but maybe to drain blood clots. And there's evidence of bone
healing around the edge, meaning the people at least survive some months after a procedure. And so what we're doing is that. We are making a cut in the
skin on the top of the head over the area of the brain
that is the most potent representation of hand intentions. And so if you are an
expert concert pianist, this part of your brain is lighting up the entire time you're playing. We call it the hand knob. - The hand knob.
- Yeah. - So it's all like the finger movement. All of that is just firing away. - Yep, there's a little squiggle
in the cortex right there. One of the folds in the brain is kind of doubly folded
right on that spot. So you can look at it on an MRI and say, "That's the hand knob." And then you do a functional test and a special kind of MRI called
an a functional MRI, fMRI. And this part of the brain
lights up when people, even quadriplegic people whose brains aren't connected to their
finger movements anymore. They imagine finger movements, and this part of the
brain still lights up. So we can ID that part of the brain in anyone who's preparing
to enter our trial and say, "Okay, that part of the brain, we confirm, is your hand intention area." And so I'll make a little cut in the skin, we'll flap the skin open, just like kind of opening
the hood of a car, only a lot smaller. Make a perfectly round one inch
diameter hole in the skull, remove that bit of skull, open the lining of the brain,
the covering of the brain. It's like a little bag of
water that the brain floats in. And then show that part
of the brain to our robot. And then this is where the robot shines. It can come in and take these tiny, much smaller than human hair electrodes and precisely insert them into the cortex, into the surface of the brain
to a very precise depth, in a very precise spot that
avoids all the blood vessels that are coating the surface of the brain. And after the robot's done with its part, then the human comes back
in and puts the implant into that hole in the
skull and covers it up, screwing it down to the skull and sewing the skin back together. So the whole thing is a few hours long. It's extremely low risk compared
to the average neurosurgery involving the brain that might say, open up a deep part of the brain or manipulate blood vessels in the brain. This opening on the surface of the brain, with only cortical micro insertions, carries significantly less risk than a lot of the tumor
or aneurysm surgeries that are routinely done. - So cortical micro
insertions that are via robot and computer vision are designed
to avoid the blood vessels. - Exactly. - So I know you're a bit biased here, but let's compare human and machine. - Sure.
- So what are human surgeons able to do well, and what are robot
surgeons able to do well at this stage of our human
civilization development? - Yeah, yeah, that's a good question. Humans are general purpose machines. We're able to adapt to unusual situations. We're able to change the plan on the fly. I remember well a surgery that
I was doing many years ago down in San Diego, where the
plan was to open a small hole behind the ear and go
reposition a blood vessel that had come to lay on the facial nerve, the trigeminal nerve, the
nerve that goes to the face. When that blood vessel lays on the nerve, it can cause just intolerable,
horrific shooting pain that people describe like being
zapped with a cattle prod. And so the beautiful, elegant surgery is to go move this blood
vessel off the nerve. The surgery team, we went in there and started moving this blood vessel and then found that there
was a giant aneurysm on that blood vessel that
was not easily visible on the pre-op scans. And so the plan had to dynamically change, and the human surgeons
had no problem with that. We're trained for all those things. Robots wouldn't do so
well in that situation, at least in their current incarnation, fully robotic surgery, like the electrode insertion portion of the Neuralink surgery. It goes according to a set plan. And so the humans can interrupt the flow and change the plan, but the robot can't really change the
plan midway through. It operates according
to how it was programmed and how it was asked to run. It does its job very precisely, but not with a wide degree of latitude and how to react to changing conditions. - So there could be just a
very large number of ways that you could be surprised as a surgeon when you enter a situation
that could be subtle things that you have to dynamically adjust to. - Correct.
- And robots are not good at that. - Currently.
- Currently. I think we are at the
dawn of a new era with AI of the parameters for robot responsiveness to be dramatically broadened, right? I mean, you can't look
at a self-driving car and say that it's operating
under very narrow parameters. If a chicken runs across the road, it wasn't necessarily
programmed to deal with that, specifically, but a Waymo
or a self-driving Tesla would have no problem reacting
to that appropriately. And so surgical robots aren't
there yet, but give it time. - And then there could be a lot of, sort of into like
semi-autonomous possibilities of maybe a robotic surgeon could say, this situation is perfectly familiar, or the situation is not familiar. And in the not familiar case,
a human could take over. But basically like be very
conservative in saying, "Okay, this for sure has
no issues, no surprises, and let the humans deal
with the surprises, with the edge cases, all that." That's one possibility. So you think eventually,
you'll be out of the job? Well, you being neurosurgeon, your job being neurosurgeon. Humans, there will not
be many neurosurgeons left on this earth. - I'm not worried about my job in the course of my professional life. I think I would tell my
my kids not necessarily to go in this line of work depending on how things look in 20 years. - It's so fascinating, 'cause I mean, if I have a line of work, I
would say it's programming. And if you ask me like for the
last, I don't know, 20 years, what I would recommend for people, I would tell 'em, yeah, go. You will always have a job
if you're a programmer, 'cause there's more and more computers and all this kind of
stuff and it pays well. But then you realize these
large language models come along and they're really
damn good at generating code. So overnight, you could be surprised like, "Wow, like what is the
contribution of the human really?" But then you start to think, "Okay, it does seem like
humans have ability, like you said, to deal
with novel situations." In the case of programming,
it's the ability to kinda come up with novel
ideas to solve problems. It seems like machines aren't
quite yet able to do that. And when the stakes are very high, when it's life critical,
as it is in surgery, especially in neurosurgery, the stakes are very high for a robot to actually replace a human. But it's fascinating that
in this case of Neuralink, there's a human-robot collaboration. - Yeah, yeah. I do the parts it can't do, and it does the parts I can't do. And we are friends. (Lex laughing) - I saw that there's a
lot of practice going on. So I mean, everything in Neuralink is tested extremely rigorously. But one of the things I
saw, that there's a proxy on which the surgeries are performed. - Yeah.
- So this is both for the robot and for the human, for everybody involved
in the entire pipeline. What's that like practicing the surgery? - It's pretty intense. So there's no analog to
this in human surgery. Human surgery is sort
of this artisanal craft that's handed down directly from master to pupil over the generations. I mean, literally the way you learn to be a surgeon on humans is
by doing surgery on humans. I mean, first, you watch your professors do a bunch of surgery, and then finally, they
put the trivial parts of the surgery into your hands, and then the more complex parts. And as your understanding of the point and the purposes of the surgery increases, you get more responsibility
in the perfect condition. Doesn't always go well. In Neuralink's case, the
approach is a bit different. We, of course, practiced as
far as we could on animals. We did hundreds of animal surgeries. And when it came time
to do the first human, we had just amazing
team of engineers build incredibly lifelike models. One of the engineers, Fran
Romano, in particular built a pulsating brain in a
custom 3D printed skull that matches exactly
the patient's anatomy, including their face and
scalp characteristics. And so when I was able to practice that, I mean, it's as close as it
really reasonably should get to being the real thing
and all the details, including the having a mannequin body attached to this custom head. And so when we were doing
the practice surgeries, we'd wheel that body into the CT scanner and take a mock CT scan
and wheel it back in and conduct all the normal
safety checks verbally. "Stop, this patient, we're
confirming his identification, is mannequin number blah, blah, blah." And then opening the brain
in exactly the right spot using standard operative
neuronavigation equipment, standard surgical drills in the same OR that we do all of our practice
surgeries in at Neuralink. And having the skull open
and have the brain pulse, which adds a degree of
difficulty for the robot to perfectly precisely plan
and insert those electrodes to the right depth and location. And so yeah, we kind of broke new ground on how extensively we
practiced for this surgery. - So there was a historic moment, a big milestone for Neuralink in part for humanity with the first human getting a Neuralink implant
in January of this year. Take me through the surgery on Noland. What did it feel like to be part of this? - Yeah. Well, we're lucky to have
just incredible partners at the Barrow Neurologic Institute. They are, I think, the premier neurosurgical hospital in the world. They made everything as easy as possible for the trial to get going
and helped us immensely with their expertise on
how to arrange the details. It was a much more high
pressure surgery in some ways. I mean, even though the
outcome wasn't particularly in question in terms of
our participant safety, the number of observers,
the number of people, there's conference rooms full of people watching live streams in the hospital, rooting for this to go perfectly, and that just adds pressure
that is not typical for even the most intense
production neurosurgery. Say, removing a tumor or placing deep brain stimulation electrodes. And it had never been
done on a human before. There were unknown unknowns. And so definitely, a
moderate pucker factor there for the whole team, not knowing if we were going to encounter, say, a degree of brain
movement that was unanticipated or a degree of brain
sag that took the brain far away from the skull and
made it difficult to insert or some other unknown unknown problem. Fortunately, everything went well and that surgery is one of the smoothest outcomes we could have imagined. - Were you nervous? I mean, you're a bit quarterback in the Super Bowl kind of situation. - Extremely nervous. Extremely. I was very pleased when it went well and when it was over. Looking forward to number two.
- Yeah. Even with all that practice, all of that, you've never been in a situation that's still high stakes in
terms of people watching. And we should also probably mention, given how the media
works, a lot of people, maybe in a dark kind of way,
hoping it doesn't go well. - Well, I think wealth is easy to hate or envy or whatever. And I think there's a whole
industry around driving clicks, and bad news is great for clicks. And so any way to take an
event and turn it into bad news is gonna be really good for clicks. - It just sucks because I think
it puts pressure on people. It discourages people from trying to solve really hard problems, because
to solve hard problems, you have to go into the unknown. You have to do things that
haven't been done before, and you have to take risks. - Yeah.
- Calculated risks. You have to do all kinds
of safety precautions, but risks nevertheless. And I just wish there would
be more celebration of that, of the risk taking versus
like people just waiting on the sidelines, like
waiting for failure, and then pointing out the failure. Yeah, it sucks. But in this case, it's
really great that everything went just flawlessly, but it's unnecessary
pressure, I would say. - Now that there's a human
with literal skin in the game, there's a participant whose wellbeing rides on this doing well, you have to be a pretty bad person to be rooting for that to go wrong. And so hopefully, people
look in the mirror and realize that at some point. - So did you get to
actually front row seat like watch the robot work? You get to see the whole thing? - Yeah, I mean, because an
MD needs to be in charge of all of the medical decision making throughout the process, I
unscrubbed from the surgery after exposing the brain and
presenting it to the robot and placed the targets on the robot software interface that tells the robot where it's going to insert
each thread that was done with my hand on the mouse,
for whatever that's worth. - So you were the one placing the targets? - Yeah.
- Oh, cool. So like the robot with a computer vision provides a bunch of candidates and you kinda finalize the decision. - Right. The software engineers
are amazing on this team. And so they actually provided an interface where you can essentially use a lasso tool and select a prime area
of brain real estate, and it will automatically
avoid the blood vessels in that region and automatically
place a bunch of targets. So that allows the human robot operator to select really good areas of brain and make dense applications
of targets in those regions, the regions we think are gonna have the most high fidelity representations of finger movements and
arm movement intentions. - I've seen like images of this. And for me, with OCD, it's for some reason a really pleasant, I think there's a subreddit
called oddly satisfying. - [Matthew] Yeah, love that subreddit. - It's oddly satisfying to
see the different target sites avoiding the blood vessels
and also maximizing like the usefulness of those
locations for the signal. It just feels good. It's like, ah. - As a person who has a visceral reaction to the brain bleeding, I can tell you it's extremely satisfying watching the electrodes
themselves go into the brain and not cause bleeding. - Yeah, yeah. So you said the feeling was of relief when everything went perfectly. - Yeah. - How deep in the brain
can you currently go and eventually go, let's
say, on the Neuralink side. It seems the deeper you go in the brain, the more challenging it becomes. - Yeah, so talking broadly
about neurosurgery, we can get anywhere. It's routine for me to put deep
brain stimulating electrodes near the very bottom of the brain, entering from the top and passing about a two millimeter wire all the way into the bottom of the brain. And that's not revolutionary. A lot of people do that. And we can do that with
very high precision. I use a robot from
Globus to do that surgery several times a month. It's pretty routine. - What are your eyes in that situation? What are you seeing? What kind of technology
can you use to visualize where you are to light your way? - Yeah, so it's a cool
process on the software side. You take a preoperative MRI that's extremely high resolution
data of the entire brain. You put the patient to sleep, put their head in a frame that holds the skull very rigidly, and then you take a CT scan of their head while they're asleep with that frame on, and then merge the MRI
and the CT in software. You have a plan based on the MRI where you can see these
nuclei deep in the brain. You can't see them on CT, but if you trust the
merging of the two images, then you indirectly know
on the CT where that is. And therefore, indirectly know where in reference to the titanium frame screwed to their head those targets are. And so this is '60s
technology to manually compute trajectories, given the
entry point and target and dial in some goofy
looking titanium actuators with a manual actuators with
little tick marks on them. The modern version of
that is to use a robot. Just like a little KUKA arm, you might see it building
cars at the Tesla factory. This small robot arm can
show you the trajectory that you intended from the pre-op MRI and establish a very rigid holder through which you can drill
a small hole in the skull and pass a small rigid wire
deep into that area of the brain that's hollow and put your electrode through that hollow wire and then remove all of
that except the electrode. So you end up with the electrode very, very precisely placed
far from the skull surface. Now that's standard
technology that's already, been out in the world for a while. Neuralink right now is focused
entirely on cortical targets, surface targets because
there's no trivial way to get, say, hundreds of wires
deep inside the brain without doing a lot of damage. So your question, what do you see? Well, I see an MRI on a screen. I can't see everything
that that DBS electrode is passing through on its
way to that deep target. And so it's accepted with this approach that there's gonna be about
what one in a hundred patients who have a bleed somewhere in the brain as a result of passing that wire blindly into the deep part of the brain. That's not an acceptable
safety profile for Neuralink. We start from the position that we want this to be dramatically maybe two or three orders of
magnitude safer than that. Safe enough really that you or I, without a profound medical problem, might on our lunch break someday say, "Yeah, sure, I'll get that. I'd be meaning to upgrade
to the latest version." And so the safety constraints
given that are high. And so we haven't settled
on a final solution for arbitrarily approaching
deep targets in the brain. - It's interesting 'cause
like you have to avoid blood vessels somehow. Maybe there's creative ways
of doing the same thing, like mapping out high resolution
geometry of blood vessels and then you can go in blind. But how do you map out that in a way that's like super stable? There's a lot of interesting
challenges there, right? - [Matthew] Yeah. - But there's a lot to do on the surface. Luckily.
- Exactly. So we've got vision on the surface. We actually have made a
huge amount of progress sewing electrodes into the spinal cord as a potential workaround
for a spinal cord injury that would allow a brain-mounted implant to translate motor intentions
to a spine-mounted implant that can effect muscle contractions in previously paralyzed arms and legs. - That's mind-blowing. That's just incredible. So like the effort there
is to try to bridge the brain to the spinal cord to the peripheral nervous system. So how hard is that to do? - We have that working in
very crude forms in animals. - That's amazing.
- Yeah, we've done- - So similar to like with Noland, where he's able to
digitally move the cursor, here you're doing the
same kind of communication but with the actual factors that you have. - Yeah. - [Lex] That's fascinating. - Yeah, so we have anesthetized
animals doing grasp and moving their legs in
a sort of walking pattern. Again, early days, but the future is bright
for this kind of thing. And people with paralysis should look forward to that bright future. They're gonna have options. - Yeah, and there's a lot
of sort of intermediate or extra options where you
take like an Optimus robot, like the arm, and to be
able to control the arm. The fingers and hands of
the arm as a prosthetic. - So skeletons are getting better too. - So skeletons. Yeah, so that goes hand in hand. Although I didn't quite understand until thinking about it deeply and doing more research about Neuralink, how much you can do on the digital side. So this digital telepathy, I didn't quite understand that you can really map the intention, as you described in the hand knob area, that you can map the intention. Just imagine it, think about it. That intention can be mapped to actual action in the digital world. And now more and more, so much can be done in the digital world
that it can reconnect you to the outside world. It can allow you to have freedom, have independence if
you're a quadriplegic. That's really powerful. Like you can go really far with that. - Yeah, our first
participant, he's incredible. He's breaking world
records left and right. - And he is having fun
with it, it's great. Just going back to the
surgery, your whole journey, you mentioned to me offline,
you have surgery on Monday. So you're like doing surgery all the time. - Yeah. - Maybe the ridiculous question, what does it take to get good at surgery? - Practice, repetitions. Same with anything else. There's a million ways of
people saying the same thing and selling books saying it, but you call it 10,000
hours, you call it, you know, spend some chunk of your life, some percentage of your
life focusing on this, obsessing about getting better at it. Repetitions, humility, recognizing
that you aren't perfect at any stage along the way. Recognizing you've got improvements to make in your technique. Being open to feedback and coaching from people with a different
perspective on how to do it. And then just the constant
will to do better. That fortunately, if
you're not a sociopath, I think your patients bring that with them to the office visits every day. They force you to wanna
do better all the time. - Yeah, to step up. I mean, it's a real human being, a real human being that you can help. - Yeah.
- So every surgery, even if it's the same exact surgery, is there a lot of variability between that surgery
and a different person? - Yeah, a fair bit. I mean, a good example for
us is the angle of the skull relative to the normal
plane of the body axis, of the skull over hand knob
is pretty wide variation. I mean, some people
have really flat skulls, and some people have really steeply angled skulls over that area. And that has consequences for
how their head can be fixed in sort of the frame that we use and how the robot has
to approach the skull. Yeah, people's bodies
are built as differently as the people you see
walking down the street, as much variability in body
shape and size as you see there. We see in brain anatomy and skull anatomy, there are some people who
we've had to kind of exclude from our trial for having skulls that are too thick or too thin or scalp that's too thick or too thin. I think we have like the
middle 97% or so of people, but you can't account for all
human anatomy variability. - How much like mushiness
and messes there, 'cause taking biology classes, the diagrams are always
really clean and crisp. Neuroscience, the pictures of neurons are always really nice and vary. But whenever I look at
pictures of like real brains, I don't know what is going on. - Yeah.
- So how much are biological systems in reality? Like how hard is it to
figure out what's going on? - Not too bad. Once you really get used to this, that's where experience
and skill and education really come into play is if
you stare at a thousand brains, it becomes easier to kind
of mentally peel back the, say, for instance, blood vessels that are obscuring the sulci and gyri, kind of the wrinkle pattern
of the surface of the brain. Occasionally, when you're
first starting to do this and you open the skull, it doesn't match what you thought you were
gonna see based on the MRI. And with more experience, you
learn to kind of peel back that layer of blood vessels and see the underlying pattern
of wrinkles in the brain and use that as a landmark
for where you are. - [Lex] The wrinkles are a landmark? So like-
- Yeah. So I was describing hand knob earlier. That's a pattern of the
wrinkles in the brain. It's sort of this sort of Greek letter, omega-shaped area of the brain. - So you could recognize
the hand knob area. Like if I show you a thousand brains and give you like one minute with each, you'd be like, "Yep, that's that?" - Sure.
- And so there is some uniqueness to that area of the brain, like in terms of the geometry,
the topology of the thing. - Yeah. - Where is it about in the- - So you have this strip of
brain running down the top called the primary motor area. And I'm sure you've seen this
picture of the homunculus laid over the surface of the brain, the weird little guy with
huge lips and giant hands. That guy sort of lays with his legs up at the top of the brain and
face, arm, areas farther down and then some kind of mouth,
lip, tongue areas farther down. And so the hand is right in there. And then the areas that control speech, at least on the left side of the brain in most people are just below that. And so any muscle that you
voluntarily move in your body, the vast majority of that
references that strip or those intentions come
from that strip of brain. And the wrinkle for hand knob is right in the middle of that. - And vision is back here.
- Back, yep. - Also, close to the surface? - Vision's a little deeper. And so this gets to your question about how deep can you get to do vision. We can't just do the surface of the brain. We have to be able to go in, not as deep as we have to go for DBS, but maybe a centimeter deeper than we're used to for hand insertions. And so that's work in progress. That's a new set of
challenges to overcome. - By the way, you
mentioned the Utah array. And I just saw a picture of that, and that thing looks terrifying. - [Matthew] Yeah, bed of nails. - It's because it's rigid. And then if you look at the
threads, they're flexible. What can you say that's interesting to you about the flexible, that kind of approach of the flexible threads to deliver the electrodes
next to the neurons? - Yeah, I mean, the goal
there comes from experience. I mean, we stand on
the shoulders of people that made Utah arrays and
used Utah arrays for decades before we ever even came along. Neuralink arose, partly this approach to technology arose out
of a need recognized after Utah arrays would fail routinely because the rigid electrodes, those spikes that are literally hammered using an air hammer into the brain, those spikes generate
a bad immune response that encapsulates the electrode spikes in scar tissue essentially. And so one of the projects
that was being worked on in the Andersen lab at
Caltech when I got there, was to see if you could use chemotherapy to prevent the formation of scar. Things are pretty bad when
you're jamming a bed of nails into the brain and then
treating that with chemotherapy to try to prevent scar tissue. It's like, maybe we've
gotten off track here, guys. Maybe there's a fundamental
redesign necessary. And so Neuralink's approach
of using highly flexible, tiny electrodes avoids
a lot of the bleeding, avoids a lot of the immune
response that ends up happening when rigid electrodes are
pounded into the brain. And so what we see is
our electrode longevity and functionality and the
health of the brain tissue immediately surrounding
the electrode is excellent. I mean, it goes on for years
now in our animal models. - What do most people not understand about the biology of the brain? We mention the vasculature. That's really interesting. - I think the most interesting
maybe underappreciated fact is that it really does
control almost everything. I mean, I don't know, for
out of the blue example, imagine you want a lever on fertility, you wanna be able to turn
fertility on and off. I mean, there are legitimate
targets in the brain itself to modulate fertility, say blood pressure. You wanna modulate blood pressure. There are legitimate targets
in the brain for doing that. Things that aren't immediately
obvious as brain problems are potentially solvable in the brain. And so I think it's an under-explored area for primary treatments of all the things that bother people. - That's a really fascinating
way to look at it. Like there's a lot of
conditions we might think have nothing to do with the brain, but they might just be symptoms of something that actually
started in the brain. The actual source of the problem. The primary source is
something in the brain. - Yeah, not always. I mean, kidney disease is real. But there are levers you
can pull in the brain that affect all of these systems. - There's knobs.
- Yeah. - On-off switches and knobs in the brain, from which this all originates. Would you have a Neuralink
chip implanted in your brain? - Yeah. I think use case right
now is use a mouse, right? I can already do that. And so there's no value proposition. On safety grounds alone, sure. I'll do it tomorrow. - You say the use case of the mouse, is it after like researching all this and part of it is just watching
Noland have so much fun? If you can get that bits per second look really high with a mouse, like being able to interact, 'cause if you think about it, the way, on the smartphone, the way you swipe, that was transformational how
you interact with a thing. It's subtle. You don't realize it, but
to able to touch a phone and to scroll with your
finger, that's like, that changed everything. People were sure you
need a keyboard to type. There's a lot of HCI aspects to that that changed how we
interact with computers. So there could be a certain
rate of speed with the mouse that would change everything. - Yeah.
- It's like you might be able to just click around a
screen extremely fast. And that, I can see myself getting a Neuralink for much more rapid interaction
with the digital devices. - Yeah, I think recording
speech intentions from the brain might change things as well. The value proposition
for the average person, a keyboard is a pretty
clunky human interface, requires a lot of training. It's highly variable in
the maximum performance that the average person can achieve. I think taking that out of the equation and just having a natural word to computer interface might change things for a lot of people. - It'd be hilarious if that
is the reason people do it. Even if you have speech to text, that's extremely accurate,
it currently isn't, but say it gotten super accurate, it'd be hilarious if
people went for Neuralink just so you avoid the
embarrassing aspect of speaking, like looking like a douche bag speaking to your phone in public, which is a real, like that's a real constraint. - Yeah. I mean, with a bone conducting case, that can be an invisible headphone, say, and the ability to think
words into software and have it respond to you, that starts to sound sort of like embedded super intelligence. If you can silently ask
for the Wikipedia article on any subject and have it read to you, without any observable change happening in the outside world, for one thing, standardized
testing is obsolete. (laughs) - Yeah. If it's done well in the
UX side, it could change. I don't know if it transforms society, but it really can create a kind of shift in the way we interact
with digital devices in the way that a smartphone did. Just having to look into the
safety of everything involved, I would totally try it
so it doesn't have to go to some like incredible
thing where you have, it connects your vision or to some other, like it connects all over your brain. That could be like just
connecting to the hand knob. You might have a lot of
interesting interaction, human-computer interaction possibilities. That's really interesting. - Yeah, and the technology
on the academic side is progressing at light speed here. I think there was a really
amazing paper out of UC Davis, Sergey Stavisky's lab that
basically made a initial solve of speech decode. It was something like 125,000 words that they we're getting with
very high accuracy, which is- - So you're just thinking the word? - Yeah.
- Thinking the word and you're able to get it? - Yeah.
- Oh boy. Like you have to have the
intention of speaking it. - Right. - So like do the inner voice. Man, it's so amazing to me
that you can do the intention, the signal mapping. All you have to do is just
imagine yourself doing it. And if you get the feedback
that it actually worked, you can get really good at that. Like your brain will first of all adjust and you develop it like any other skill. Like touch typing, you develop
in that same kind of way. To me, it's just really fascinating to be able to even to play with that. Honestly, like I would get a Neuralink just to be able to play with that. Just to play with the capacity, the capability of my
mind to learn this skill. It's like learning the skill of typing and learning the skill of moving a mouse. It's another skill of moving the mouse, not with my physical
body, but with my mind. - I can't wait to see
what people do with it. I feel like we're cavemen right now. We're like banging rocks with a stick and thinking that we're making music. At some point, when these
are more widespread, there's gonna be the equivalent of a piano that someone can make art with their brain in a way that we didn't even anticipate. I'm looking forward to it. - Give it to like a teenager. Like anytime I think
I'm good at something, I'll always go to like, I don't know. Even with the bit per second
of playing a video game, you realize you give it to a teenager, you've given your link to a teenager, just the large number of them, the kind of stuff, they get good at stuff. They're gonna get like
hundreds of bits per second. Even just with the current technology. - Probably. Probably. - 'Cause it's also addicting, the number go up aspect of it of like improving and training, 'cause it is almost like a skill. And plus, there's the
software on the other end that adapts to you. And especially if the
adapting procedure algorithm becomes better and better and better, you're like learning together. - Yeah, we're scratching the
surface on that right now. There's so much more to do. - So on the complete other side of it, you have an RFID chip implanted in you. - Yeah.
- So I hear, nice. - Little subtle thing. - It's a passive device that you use for unlocking like a
safe with top secrets, or what do you use it for? What's the story behind it? - I'm not the first one. There's this whole community
of weirdo biohackers that have done this stuff, and I think one of the
early use cases was storing private crypto wallet keys and whatever. I dabbled in that a bit
and had some fun with it. - You have some bitcoin
implanted in your body somewhere. You can't tell where, yeah. - Yeah, actually, yeah. (Lex laughing) It was the modern day equivalent of finding change in the sofa cushions after I put some orphan crypto on there that I thought was worthless and forgot about it for a few years. Went back and found that some
community of people loved it and had propped up the value of it. And so it had gone up 50 fold. - Wow.
- So there was a lot of change in those cushions. (Lex laughing) - That's hilarious. - But the primary use case was mostly as a tech demonstrator. It has my business card on it. You can scan that in by
touching it to your phone. It opens the front door to my house, whatever simple stuff. - It's a cool step. It's a cool leap to implant
something in your body. I mean, perhaps, it's a
similar leap to a Neuralink because for a lot of
people, that kind of notion of putting something inside
your body, something electronic inside a biological system is a big leap. - Yeah, we have a kind
of a mysticism around the barrier of our skin. We're completely fine
with knee replacements, hip replacements, dental implants. But there's a mysticism still around the inviable barrier that
the skull represents. And I think that needs to be treated like any other pragmatic barrier. The question isn't, how incredible
is it to open the skull? The question is, what
benefit can we provide? - So from all the surgeries you done, from everything you understand the brain, how much does neuroplasticity
come into play? How adaptable is the brain, for example, just even in the case
of healing from surgery or adapting to the post-surgery situation. - The answer that is sad for me and other people of my demographic is that plasticity decreases with age. Healing decreases with age. I have too much gray hair
to be optimistic about that. There are theoretical ways
to increase plasticity using electrical stimulation. Nothing that is totally proven out as a robust enough mechanism
to offer widely to people. But yeah, I think there's
cause for optimism that we might find something
useful in terms of, say, an implanted electrode
that improves learning. Certainly, there's been
some really amazing work recently from Nicholas
Schiff, Jonathan Baker, and others who have a cohort of patients with moderate traumatic brain injury who have had electrodes placed in the deep nucleus in the brain called the centromedian nucleus or just near central media nucleus. And when they apply small
amounts of electricity to that part of the brain, it's almost like electronic caffeine. They're able to improve
people's attention and focus. They're able to improve how
well people can perform a task. I think in one case, someone
who was unable to work after the device was turned on,
they were able to get a job. And that's sort of one of the holy grails for me with Neuralink and
other technologies like this is from a purely utilitarian standpoint, can we make people able
to take care of themselves and their families economically again? Can we make it so someone
who's fully dependent and even maybe requires a
lot of caregiver resources, can we put them in a position
to be fully independent, taking care of themselves,
giving back to their communities? I think that's a very
compelling proposition, and what motivates a lot of what I do and what a lot of the people
at Neuralink are working for. - It's just a cool possibility that if you put a Neuralink in
there, that the brain adapts, like the other part of
the brain adapts too. - Yeah.
- And integrates it. The capacity of the brain to
do that is really interesting. Probably unknown to the degree
to which you can do that, but you're now connecting
an external thing to it, especially once it's doing stimulation, like the biological brain
and the electronic brain outside of it working together. Like the possibilities there
are really interesting. It's still unknown but interesting. It feels like the brain is really good at adapting to whatever. - Yeah.
- But of course, it is a system that by itself is already, like everything serves a purpose and so you don't wanna
mess with it too much. - Yeah, it's like, eliminating a species from an ecology. You don't know what the
delicate interconnections and dependencies are. The brain is certainly a
delicate, complex beast. And we don't know every
potential downstream consequence of a single change that we make. - Do you see yourself
doing, so you mentioned P1, surgeries of P2, P3, P4, P5? Just more and more and more humans. - I think it's a certain
kind of brittleness or a failure on the company's side if we need me to do all the surgeries. I think something that I would very much like to work towards is a
process that is so simple and so robust on the surgery side that literally anyone could do it. We wanna get away from
requiring intense expertise or intense experience to
have this successfully done and make it as simple and
translatable as possible. I mean, I would love it if
every neurosurgeon on the planet had no problem doing this. I think we're probably far
from a regulatory environment that would allow people
that aren't neurosurgeons to do this, but not impossible. - All right, I'll sign up for that. Did you ever anthropomorphize
the robot R1? Like do you give it a name? Do you see it as like a friend, as like working together with you? - I mean, to a certain degree it's- - Or anatomy who's gonna get the gap. - To a certain degree, yeah,
it's complex relationship. - All the good relationships are. - It's funny when, in the
middle of the surgery, there's a part of it where I stand basically shoulder to
shoulder with the robot. And so if you're in the room
reading the body language, that's my brother in arms there. We're working together
on the same problem. Yeah, I'm not threatened by it. - Keep telling yourself that. (laughs) How have all the surgeries that
you've done over the years, the people you've helped and the stakes, the high stakes that you've mentioned, how has that changed your
understanding of life and death? - Yeah. It gives you a very visceral sense, and this makes sound trite, but it gives you a very visceral sense that death is inevitable. On one hand, you are, as a neurosurgeon, you're deeply involved in these like just hard to fathom tragedies: young parents dying, leaving
a four-year-old behind say. And on the other hand, it takes the sting out of it a bit because you see how just mind
numbingly universal death is. There's zero chance that
I'm going to avoid it. I know techno optimists right now and longevity buffs right now would disagree on that 0.0% estimate. But I don't see any chance that our generation is going to avoid it. Entropy is a powerful force, and we are very ornate,
delicate, brittle DNA machines that aren't up to the
cosmic ray bombardment that we're subjected to. So on the one hand, every human that has ever
lived died or will die. On the other hand, it's just
one of the hardest things to imagine inflicting on anyone that you love is having them gone. I'm sure you've had friends
that aren't living anymore and it's hard to even think about them. And so I wish I had arrived
at the point of nirvana where death doesn't have a sting. I'm not worried about it,
but I can at least say that I'm comfortable
with the certainty of it. If not, having found out how
to take the tragedy out of it when I think about my
kids either not having me or me not having them or my wife. - Maybe I have come to accepting intellectual certainty of
it, but it may be the pain that comes with losing
the people you love, I don't think I've come to understand the existential aspect of it. Like that this is gonna end. And I don't mean like in some trite way. I mean like, it certainly feels
like it's not going to end. Like you live life like
it's not going to end. - [Matthew] Right. - And the fact that this light that's shining this consciousness is going to no longer be,
in one moment, maybe today, it fills me when I really am able to load all that in with
Ernest Becker's terror. Like it's a real fear. I think people aren't always honest with how terrifying it is. I think the more you are able to really think through it,
the more terrifying it is. It's not such a simple thing. Oh well, it's the way life is. If you really can load that in, it's hard. But I think that's why the stoics did it, because it like helps you
get your shit together and be like, well, the moment, every single moment you're
alive is just beautiful. And it's terrifying that it's gonna end, like almost like you're
shivering in the cold a child helpless, this kind of feeling. And then it makes you,
when you have warmth, when you have the safety, when you have the love
to really appreciate it. I feel like sometimes, in your position, when you mentioned
armor, just to see death, it might make you not be able to see that, the finiteness of life, because if you kept looking
at that, it might break you. So it's good to know that you're kind of still struggling with that, that there's the neurosurgeon
and then there's a human. And the human is still
able to struggle with that and feel the fear of that
and the pain of that. - Yeah, it definitely
makes you ask the question of how long, how many
of these can you see? And not say, "I can't do this anymore." But I mean, you said it well. I think it gives you an
opportunity to just appreciate that you're alive today. And I've got three kids
and an amazing wife and I'm really happy. Things are good. I get to help on a project
that I think matters. I think it moves us forward. I'm a very lucky person. - It's the early steps of a potentially gigantic leap for humanity. It's a really interesting one. And it's cool 'cause like you, you read about all this stuff in history where it's like the early days. I've been reading, before
going to the Amazon, I would read about explorers
that would go and explore even the Amazon jungle for the first time. Those are the early steps. Or early steps into space, early steps in any discipline, in physics and mathematics. And it's cool 'cause this
is like, on the grand scale, these are the early steps into delving deep into the human brain. So not just observing the brain, but be able to interact
with the human brain. It's gonna help a lot of people, but it also might help us understand what the hell's going on in there. - Yeah, I think ultimately,
we wanna give people more levers that they can pull, right? Like you wanna give people options. If you can give someone
a dial that they can turn on how happy they are, I think that makes people
really uncomfortable. But now, talk about major
depressive disorder. Talk about people that
are committing suicide at an alarming rate in this country. And try to justify that queasiness in that light of you
can give people a knob to take away suicidal
ideation, suicidal intention. I would give them that knob. I don't know how you
justify not doing that. - Yeah, you can think about like all the suffering that's
going on in the world. Like every single human being that's suffering right now, it'll be a glowing red dot. The more suffering, the more it's glowing. And you just see the
map of human suffering, and any technology that
allows you to dim that light of suffering on a grand
scale is pretty exciting, because there's a lot of people suffering and most of them suffer quietly. We look away too often, and we should remember
those that are suffering, 'cause once again, most of
them are suffering quietly. - Well, and on a grander
scale, the fabric of society, people have a lot of complaints about how our social fabric is working or not working, how our politics
is working or not working. Those things are made
of neurochemistry too, in aggregate, right? Like our politics is
composed of individuals with human brains and, the
way it works or doesn't work is potentially tunable in
the sense that, I don't know, say remove our addictive behaviors or tune our addictive
behaviors for social media or our addiction to outrage,
our addiction to sharing the most angry political
tweet we can find. I don't think that leads
to a functional society. And if you had options for people to moderate
that maladaptive behavior, there could be huge benefits to society. Maybe we could all work together a little more harmoniously
toward useful ends. - There's a sweet spot,
like you mentioned, you don't wanna completely
remove all the dark sides of human nature 'cause those
kind of are somehow necessary to make the whole thing work. But there's a sweet spot. - Yeah, I agree. We gotta suffer a little, just not so much that you lose hope. - Yeah. We knew all the surgeries you've done. Have you seen consciousness in there ever? Was there like a glowing light? - I have this sense that I never found it. Never removed it, like a
dementor in Harry Potter. I have this sense that
consciousness is a lot less magical than our instincts wanna claim it is. It seems to me like a useful
analog for thinking about what consciousness is in the brain, is that we have a really
good intuitive understanding of what it means to, say, touch your skin and know what's being touched. I think consciousness is just that level of sensory mapping applied to the thought processes
in the brain itself. So what I'm saying is consciousness is the sensation of some part
of your brain being active. So you feel it working. You feel the part of your brain that thinks of red things
or winged creatures or the taste of coffee. You feel those parts of
your brain being active the way that I'm feeling my
palm being touched, right? And that sensory system
that feels the brain working is consciousness. - That's so brilliant. It's the same way, it's
the sensation of touch when you're touching a thing. Consciousness is the sensation of you feeling your brain working,
your brain thinking, your brain perceiving. - Which isn't like a
warping of space and time or some quantum field effect, right? It's nothing magical. People always wanna
ascribe to consciousness something truly different. And there's this awesome long history of people looking at whatever the latest discovery in physics is
to explain consciousness, because it's the most magical, the most out there thing
that you can think of. And people always wanna do
that with consciousness. I don't think that's necessary. It's just a very useful and gratifying way of
feeling your brain work. - And as we said, it's
one heck of a brain. - [Matthew] Yeah. - Everything we see around
us, everything we love, everything that's beautiful, it came from brains like these. - It's all electrical activity
happening inside your skull. - And I, for one, am grateful
that there's people like you that are exploring all
the ways that it works and all the ways it can be made better. - Thanks, Lex. - Thank you so much for talking today. - It's been a joy. Thanks for listening to this conversation with Matthew MacDougall. And now, dear friends,
here's Bliss Chapman, Brain Interface Software
lead at Neuralink. You told me that you've
met hundreds of people with spinal cord injuries or with ALS and that your motivation
for helping at Neuralink is grounded in wanting to help them. Can you describe this motivation? - Yeah. First, just a thank you to all the people I've gotten a chance to speak with, for sharing their stories with me. I don't think there's any
world really in which I can share their stories as
powerful way as they can. But just I think to summarize
at a very high level what I hear over and over
again is that people with ALS or severe spinal cord injury in a place where they basically can't
move physically anymore, really at the end of the day
are looking for independence. And that can mean different
things for different people. For some folks, it can mean
the ability just to be able to communicate again
independently without needing to wear something on their face,
without needing a caretaker to be able to put
something in their mouth. For some folks, it can mean
independence to be able to work again, to be able to
navigate a computer digitally, efficiently enough to
be able to get a job, to be able to support themself,
to be able to move out and ultimately be able to support themself after their family maybe
isn't there anymore to take care of them. And for some folks, it's as simple as just being able to respond
to their kid in time before they run away or get
interested in something else. And these are deeply personal and sort of very human problems. And what strikes me again and again when talking with these folks is that this is actually an engineering problem. This is a problem that
with the right resources, with the right team, we can
make a lot of progress on. And at the end of the day, I think that's a deeply inspiring message and something that makes me
excited to get up every day. - So it's both an engineering problem in terms of a BCI, for example, that can give them capabilities where they can interact with the world. But also on the other side,
it's an engineering problem for the rest of the world
to make it more accessible for people living with quadriplegia. - Yeah, and actually,
I'll take a broad view sort of lens on this for a second. I think I'm very in favor of anyone working in this problem space. So beyond BCI, I'm happy and excited and willing to support any way I can folks working on eye tracking systems, working on speech to text systems, working on head trackers or
mouse sticks or quad sticks. And I've met many engineers and folks in the community
that do exactly those things. And I think for the people
we're trying to help, it doesn't matter what the
complexity of the solution is as long as the problem is solved. And I wanna emphasize that there can be many solutions out there that can help with these problems. And BCI is one of a
collection of such solutions. So BCI, in particular, I think offers several advantages here. And I think the folks that
recognize this immediately are usually the people who
have spinal cord injury or some form of paralysis. Usually, you don't have to explain to them why this might be something
that could be helpful. It's usually pretty self-evident. But for the rest of us
folks that don't live with severe spinal cord injury or who don't know somebody with ALS, it's not often obvious why
you would want a brain implant to be able to connect
and navigate a computer. And it's surprisingly nuanced, and to the degree that
I've learned a huge amount just working with Noland in the first Neuralink clinical trial
and understanding from him in his words why this
device is impactful for him. And it's a nuanced topic. It can be the case that
even if you can achieve the same thing, for
example, with a mouse stick when navigating a computer,
he doesn't have access to that mouse stick every
single minute of the day. He only has access when
someone's available to put it in front of him. And so a BCI can really offer a level of independence and autonomy that if it wasn't literally
physically part of your body, it'd be hard to achieve in any other way. - So there's a lot of fascinating aspects to what it takes to get Noland to be able to control a cursor on
the screen with his mind. You texted me something that I just love. You said, "I was part of the team that interviewed and selected P1. I was in the operating room during the first human surgery monitoring live signals
coming out of the brain. I work with the user basically every day to develop new UX paradigm's
decoding strategies. And I was part of the
team that figured out how to recover useful BCI to new world record levels when the signal quality degraded." We'll talk about I think
every aspect of that, but just zooming out, what was it like to be part of that team
and part of that historic, I would say, historic first? - Yeah, I think for me, this is something I've been excited about for close to 10 years now. And so to be able to be
even just some small part of making it a reality
is extremely exciting. A couple maybe special moments
during that whole process that I'll never really truly forget, one of them is during the actual surgery, at that point in time, I
know Noland quite well. I know his family. And so I think the initial
reaction when Noland is rolled into the operating room is just a "oh shit" kind of reaction. But at that point, muscle memory kicks in and you sort of go into, you let your body just do all the talking. And I have the lucky job in
that particular procedure to just be in charge of
monitoring the implant. So my job is to sit there, to look at the signals
coming off the implant, to look at the live brain
data streaming off the device as threads are being
inserted into the brain and just to basically observe and make sure that nothing is going wrong or that there's no red
flags or fault conditions that we need to go and investigate or pause the surgery to debug. And because I had that
sort of spectator view of the surgery, I had a
slightly removed perspective than I think most folks in the room. I got to sit there and think to myself, "Wow, that brain is moving a lot." When you look into the
side look craniectomy, that we stick the threads in, one thing that most people don't
realize is the brain moves. The brain moves a lot when you breathe, when your heart beats, and
you can see it visibly. So that's something that I
think was a surprise to me and very, very exciting to be
able to see someone's brain who you physically know and
have talked with at length actually pulsing and
moving inside their skull. - And they use that brain
to talk to you previously, and now it's right there moving. - Yep.
- Actually, I didn't realize that in terms of the thread sending, so the Neuralink implant
is active during surgery, and one thread at a time, you're able to start seeing the signal? - Yeah.
- So that's part of the way you test that the thing is working? - Yeah, so actually in the operating room, right after we sort of finished
all the thread insertions, I started collecting what's
called broadband data. So broadband is basically
the most raw form of signal you can collect
from a Neuralink electrode. It's essentially a measurement of the local field potential or the, yeah, the voltage essentially measured by that electrode. And we have a certain
mode in our application that allows us to visualize
where detected spikes are. So it visualizes sort of where, in the broadband symbol, and it's very, very raw form of the data a neuron is actually spiking. And so one of these moments
that I'll never forget as part of this whole clinical trial is seeing live in the operating room, while he's still under anesthesia, beautiful spikes being
shown in the application, just streaming live to a
device I'm holding in my hand. - So this is no signal
processing the raw data and then the signals
processings on top of it, you're seeing the spikes detected? - [Bliss] Right, yeah. - And that's a UX too because-
- Yes. - That looks beautiful as well. - During that procedure, there was actually a lot
of cameramen in the room. So they also were curious
and wanted to see. There's several neurosurgeons in the room who are all just excited to
see robots taking their job and they're all crowded
around a small little iPhone watching this live brain
data stream out of his brain. - What was that like seeing the robot do some of the surgery? So the computer vision
aspect where it detects all the spots that avoid the blood vessels and then obviously with
the human supervision, then actually doing the
really high precision connection of the threads to the brain. - That's a good question. My answer's gonna be pretty
lame here, but it was boring. I've seen it so many times. Yeah, that's exactly how
you want surgery to be. You want it to be boring, because I've seen it so many times. I've seen the robot do the surgery literally hundreds of times, and so it was just one more time. - Yeah, all the practice
surgeries and the proxies and this is just another day. - Yep. - So what about when Noland woke up? Do you remember a moment where he was able to move the
cursor, not move the cursor, but get signal from the brain such that it was able to show
that there's a connection? - Yeah, yeah. So we are quite excited to
move as quickly as we can, and Noland was really, really
excited to get started. He wanted to get started
actually the day of surgery, but we waited till the next
morning very patiently. So a long night. And the next morning in the
ICU, where he was recovering, he wanted to get started and
actually start to understand what kind of signal we can
measure from his brain. And maybe for folks who are not familiar with the Neuralink system, we
implant the Neuralink system or the Neuralink implant
in the motor cortex. So the motor cortex is responsible for representing things like motor intent, sort of if you imagine
closing and opening your hand, that kind of signal representation would be present in the motor cortex. If you imagine moving
your arm back and forth or wiggling a pinky, this sort of signal can be present in the motor cortex. So one of the ways we
start to sort of map out, what kind of signal do we
actually have access to in any particular individual's brain is through this task called body mapping. And body mapping is where
you essentially present a visual to the user and you say, "Hey, imagine doing this." And that visual is a 3D
hand opening and closing, or index finger modulating up and down. And you ask the user to imagine that, and obviously, you can't see them do this, 'cause they're paralyzed
so you can't see them actually move their arm,
but while they do this task, you can record neural activity, and you can basically
offline model and check, can I predict or can I
detect the modulation corresponding with
those different actions? And so we did that task and we realized, hey, there's actually some modulation associated with some of his hand motion, which was the first indication that, okay, we can potentially use that modulation to do useful things in the world. For example, control a computer cursor. And he started playing with it, the first time we showed him it, and we actually just
took the same live view of his brain activity and
put it in front of him. And we said, "Hey, you
tell us what's going on. We're not you. You're able to imagine different things, and we know that it's
modulating some of these neurons so you figure out for us what that is actually representing." And so he played with it for a bit. He was like, "I don't quite get it yet." He played for a bit longer. And he said, "Oh, when I move this finger, I see this particular
neuron start to fire more." And I said, okay, "Prove it, do it again." And so he said, "Okay,
three, two, one, boom." And the minute he moved, you
can see like instantaneously this neuron is firing - single neuron. I can tell you the exact channel number if you're interested. It's stuck in my brain now forever. But that single channel firing
was a beautiful indication that it was behaved really
modulated neural activity that could then be used
for downstream tasks like decoding a computer cursor. - And when you say single channel, is that associated with
a single electrode? - Yeah, channel electrode
are interchangeable. - And there's 1,024 of those? - 1,024, yeah. - That's incredible that that works. When I was learning about all
this and like loading it in, it was just blowing my
mind that the intention, you can visualize yourself
moving the finger, that can turn into a signal, and the fact that you
can then skip that step and visualize the cursor moving or have the intention of the cursor moving and that leading to a signal that can then be used to move the cursor, there is so many exciting things there to learn about the brain,
about the way the brain works, the very fact of their existing signal that can be used is really powerful. But it feels like that's
just like the beginning of figuring out how that
signal could be used really, really effectively. I should also just, there's so many fascinating details here, but you mentioned the body mapping step. At least in the version I saw
that Noland was showing off, there's like a super nice interface, like a graphical interface. But like it just felt like
I was like in the future 'cause it like, you know, I guess it visualizes you moving the hand. And there's very like a
sexy, polished interface. Hello. I don't know if there's a voice component, but it just felt like when you wake up in a really nice video
game and this is a tutorial at the beginning of that video game. "This is what you're supposed to do." It's cool. - No, I mean, the future
should feel like the future. - But it's not easy to pull that off. I mean, it needs to be
simple but not too simple. - Yeah, and I think the
UX design component here is underrated for BCI
development in general. There's a whole interaction effect between the ways in which you visualize an instruction to the user and the kinds of signal you can get back. And that quality of sort of
your behavioral alignment to the neural signal is a function of how good you are at expressing to the user what you want them to do. And so yeah, we spend a lot
of time thinking about the UX, of how we build our applications, of how the decoder actually functions, the control surfaces it
provides to the user. All these little details matter a lot. - So maybe it'd be nice to get
into a little bit more detail of what the signal looks like and what the decoding looks like. So there's a N1 implant
that has, like we mentioned, 1,024 electrodes and that's
collecting raw data, raw signal. What does that signal look like, and what are the different
steps along the way before it's transmitted,
and what is transmitted, all that kind of stuff? - Yeah, yep. This is gonna be a fun one. Let's go. So maybe before diving into what we do, it's worth understanding
what we're trying to measure, because that dictates a
lot of the requirements for the system that we build. And what we're trying to measure is really individual neurons
producing action potentials. And action potential
is, you can think of it like a little electrical impulse that you can detect if
you're close enough. And by being close enough,
I mean, like within let's say 100 microns of that cell. And 100 microns is a
very, very tiny distance. And so the number of neurons
that you're gonna pick up with any given electrode is just a small radius
around that electrode. And the other thing worth understanding about the underlying biology here is that when neurons
produce an action potential, the width of that action potential
is about one millisecond. So from the start of the
spike to the end of the spike, that whole width of that sort
of characteristic feature of a neuron firing is
one millisecond wide. And if you want to detect
that an individual spike is occurring or not, you
need to sample that signal or sample the local full
potential nearby that neuron much more frequently
than once a millisecond. You need to sample many,
many times per millisecond to be able to detect that this is actually the characteristic waveform of a neuron producing an action potential. And so we sample across
all 1,024 electrodes about 20,000 times a second. 20,000 times a second means we've already given
one millisecond window. We have about 20 samples that tell us what that exact shape of that
action potential looks like. And once we've sort of
sampled at super high rate the underlying electrical
field nearby these cells, we can process that signal into just where do we detect a
spike or where do we not, sort of a binary signal one or zero. Do we detect a spike in
this one millisecond or not? And we do that because the
actual information carrying sort of subspace of neural activity is just when are spikes occurring. Essentially, everything that we care about for decoding can be
captured or represented in the frequency
characteristics of spike trains, meaning how often are spikes firing in any given window of time. And so that allows us to
do sort of a crazy amount of compression from this very
rich, high density signal to something that's much, much more sparse and compressible that can be
sent out over a wireless radio, like a Bluetooth
communication, for example. - Quick tangents here. You mentioned electrode neuron. There's a local neighborhood
of neurons nearby. How difficult is it to like isolate from where the spike came from? - Yeah, so there's a whole
field of sort of academic neuroscience work on exactly this problem, of basically given a single electrode or given a set of electrodes
measuring a set of neurons, how can you sort of sort, spike sort which spikes are
coming from what neuron? And this is a problem that's
pursued in academic work because you care about it for
understanding what's going on in the underlying sort of
neuroscience of the brain. If you care about understanding how the brain's representing information, how that's evolving through time, then that's a very, very
important question to understand. For sort of the
engineering side of things, at least at the current scale, if the number of neurons per
electrode is relatively small, you can get away with basically ignoring that problem completely. You can think of it like
sort of a random projection of neurons to electrodes, and there may be in some cases more than one neuron per electrode. But if that number is small enough, those signals can be thought of as sort of a union of the two. And for many applications,
that's a totally reasonable trade off to make and can
simplify the problem a lot. And as you sort of
scale out channel count, the relevance of distinguishing
individual neurons becomes less important, because
you have more overall signal and you can start to rely
on sort of correlations or covariance structure in
the data to help understand when that channel's firing, what does that actually represent? 'Cause you know that when
that channel's firing in concert with these other 50 channels, that means move left. But when that same channel's
firing with concert with these other 10 channels,
that means move right. - Okay, so you have to do
this kind of spike detection on board, and you have to
do that super efficiently, so fast and not use too much power, 'cause you don't wanna be
generating too much heat. So it has to be a super
simple signal processing step. - [Bliss] Yeah. - Is there some wisdom you can share about what it takes to overcome that challenge? - Yeah, so we've tried
many different versions of basically turning this raw signal into sort of a feature that you
might wanna send off the device. And I'll say that I don't think we're at the final step of this process. This is a long journey. We have something that
works clearly today, but there can be many
approaches that we find in the future that are much better than what we do right now. So some versions of what we do right now, and there's a lot of academic
heritage to these ideas, so I don't wanna claim
that these are original Neuralink ideas or anything like that. But one of these ideas
is basically to build a sort of like a convolutional
filter almost, if you will, that slides across the signal and looks for a certain
template to be matched. And that template consists
of sort of how deep the spike modulates, how much it recovers, and what the duration
and window of time is that the whole process takes. And if you can see in the
signal that that template is matched within certain bounds, then you can say, "Okay, that's a spike." One reason that approach
is super convenient is that you can actually implement that extremely efficiently in hardware, which means that you
can run it in low power across 1,024 channels all at once. Another approach that we've
recently started exploring, and this can be combined with
the spike detection approach, something called spike band power. And the benefits of that approach are that you may be able to
pick up some signal from neurons that are maybe too far away
to be detected as a spike, because the farther away
you are from an electrode, the weaker that actual spike waveform will look like on that electrode. So you might be able to pick up population level activity
of things that are maybe slightly outside the
normal recording radius, what neuroscientists
sometimes refer to as the hash of activity, the other
stuff that's going on, and you can look at sort
of across many channels how that sort of background
noise is behaving and you might be able to get more juice out of the signal that way. But it comes at a cost. That signal is now a floating
point representation, which means it's more expensive
to send out over a power. It means you have to find
different ways to compress it that are different than what you can apply to binary signals. So there's a lot of different challenges associated with these
different modalities. - So also, in terms of communication, you're limited by the
amount of data you can send. - [Bliss] Yeah. - And also, because you're currently using the Bluetooth protocol, you
have to batch stuff together. But you have to also do this
keeping the latency crazy low. Like crazy low. Anything to say about the latency? - Yeah, this is a passion project of mine, so I wanna build the
best mouse in the world. I don't wanna build like the, you know, the Chevrolet Spark or
whatever of electric cars. I wanna build like the Tesla
Roadster version of a mouse. And I really do think it's quite possible that within 5 to 10 years, that most eSports
competitions are dominated by people with paralysis. This is like a very real
possibility for number of reasons. One is that they'll have
access to the best technology to play video games effectively. The second is they have the time to do so. So those two factors together are particularly potent
for eSport competitors. - Unless people without paralysis are also allowed to implant. - (laughs) Right. - Which is, it is another way to interact with a digital device. And there's something to that, if it's a fundamentally
different experience, more efficient experience. Even if it's not like some kinda full on high bandwidth communication, if it's just the ability to
move the mouse 10x faster, like the bits per second, if I can achieve a bits per second, that 10x, what I can do with the mouse, that's a really interesting
possibility of what that can do, especially as you get really
good at it with training. - It's definitely the case that you have a higher
ceiling performance, because you don't have
to buffer your intention through your arm, through your muscle. You get just, by nature of
having a brain implant at all, like 75 millisecond
lead time on any action that you're actually trying to take. And there's some nuance to this, like there's evidence
that the motor cortex, you can sort of plan
out sequences of action so you may not get that
whole benefit all the time. But for sort of like
reaction time style games where you just wanna, somebody's over here, snipe
'em, that kind of thing. You actually do have just
an inherent advantage 'cause you don't need
to go through muscle. So the question is, just how
much faster can you make it? And we're already than
you what you would do if you're going through muscle
from a latency point of view, and we're in the early stage of that. I think we can push it
sort of our end-to-end latency right now from brain
spike to cursor movement, it's about 22 milliseconds. If you think about the
best mice in the world, the best gaming mice, that's
about five milliseconds-ish of latency, depending on how you measure. Depending how fast your screen refreshes, there's a lot of characteristics
that matter there. But yeah, and the rough
time for like a neuron in the brain to actually impact your command of your hand
is about 75 millisecond. So if you look at those numbers, you can see that we're
already like competitive and slightly faster than what you'd get by actually moving your hand. And this is something that,
if you ask Noland about it, when he moved the cursor
for the first time, we asked him about this. This was something I
was super curious about, like what does it feel like
when you're modulating, a click intention or when
you're trying to just move the cursor to the right. He said it moves before he is
like actually intending it to, which is kind of a surreal
thing and something that I would love to experience myself one day. What is that like to have that
thing just be so immediate, so fluid that it feels like it's happening before you're actually
intending it to move. - Yeah, I suppose we've
gotten used to that latency, that natural latency that happens. So is the currently the
bottleneck, the communication, so like the Bluetooth communication, what's the actual bottleneck? I mean, there's always
gonna be a bottleneck. What's the current bottleneck? - Yeah, a couple things. So kind of hilariously,
Bluetooth low energy protocol has some restrictions on how
fast you can communicate. So the protocol itself
establishes a standard of the most frequent sort
of updates you can send are on the order of 7.5 milliseconds. And as we push latency down to the level of sort of individual
spikes impacting control, that level of resolution,
that kind of protocol is gonna become a limiting
factor at some scale. Another sort of important nuance to this is that it's not just the Neuralink itself that's part of this equation. If you start pushing latency
sort of below the level of how fast screens refresh,
then you have another problem. Like you need your whole system
to be able to be as reactive as the sort of limits of what
the technology can offer. Like you need the screen like 120 hertz just doesn't work anymore if you're trying to have something respond at something that's at the
level of one millisecond. - That's a really cool challenge. I also like that for a T-shirt, the best mouse in the world. Tell me on the receiving
end, so the decoding step, now we figured out what the spikes are, we got them all together, now we're sending that over to the app. What's the decoding step look like? - Yeah, so maybe first, what is decoding? I think there's probably
a lot of folks listening that just have no clue what it means to decode brain activity. - Actually, even if we
zoom out beyond that, what is the app? So there's an implant that's
wirelessly communicating with any digital device
that has an app installed. So maybe can you tell me a
high level what the app is, what the software is outside of the brain? - Yeah, so maybe working
backwards from the goal, the goal is to help
someone with paralysis, in this case Noland, be able to navigate his
computer independently. And we think the best way to do that is to offer them the
same tools that we have to navigate our software
because we don't wanna have to rebuild an entire software
ecosystem for the brain. At least not yet. Maybe someday you can imagine there's UXs that are built natively for BCI. But in terms of what's
useful for people today, I think most people would prefer
to be able to just control mouse and keyboard inputs
to all the applications that they wanna use for their daily jobs, for communicating with
their friends, et cetera. And so the job of the application is really to translate
this wireless stream of brain data coming off the implant into control of the computer. And we do that by essentially building a mapping from brain activity
to sort of the HID inputs to the actual hardware. So HID is just the protocol for communicating like
input device events. So for example, move
mouse to this position or press this key down. And so that mapping is fundamentally what the app is responsible for. But there's a lot of nuance
of how that mapping works that we spend a lot of
time to try to get right and we're still in the early
stages of a long journey to figure out how to do that optimally. So one part of that process is decoding. So decoding is this process of taking the statistical
patterns of brain data that's being channeled across
this Bluetooth connection to the application and turning it into, for example, a mouse movement. And that decoding step,
you can think of it in a couple different parts. So similar to any
machine learning problem, there's a training step and
there's an inference step. The training step in our
case is a very intricate behavioral process where the user has to imagine doing different actions. So for example, they'll
be presented a screen with a cursor on it and they'll be asked to push that cursor to the right. Then imagine pushing that
cursor to the left, push it up, push it down, and we can
basically build up a pattern or using any sort of modern ML method, a mapping of given this brain data and that imagined behavior
map one to the other. And then at test time, you take that same
pattern matching system. In our case, it's a deep neural network, and you run it and you
take the live stream of brain data coming off
their implant, you decode it by pattern matching to what
you saw at calibration time, and you use that for a
control of the computer. Now, a couple like sort of rabbit holes that are I think are quite interesting, one of them has to do with how you build that best template matching system because there's a variety
of behavioral challenges and also debugging challenges when you're working with
someone who's paralyzed. Because again, fundamentally, you don't observe what
they're trying to do. You can't see them attempt
to move their hand. And so you have to figure out
a way to instruct the user to do something and validate
that they're doing it correctly such that then you can
downstream, build with confidence the mapping between the neural spikes and the intended action. And by doing the action correctly, what I really mean is at
this level of resolution of what neurons are doing. So if in ideal world,
you could get a signal of behavioral intent that
is ground truth accurate at the scale of sort of
one millisecond resolution, then with high confidence,
I could build a mapping from my neuro spikes to
that behavioral intention. But the challenge is, again, that you don't observe what
they're actually doing. And so there's a lot of nuance to how you build user experiences that give you more than
just sort of a course on average correct representation of what the user's intending to do. If you want to build
the world's best mouse, you really want it to be
as responsive as possible. You want it to be able to do exactly what the user's intending
at every sort of step along the way, not just
on average be correct when you're trying to move
it from left to right. And building a behavioral
sort of calibration game or sort of software experience
that gives you that level of resolution is what we
spend a lot of time working. - So the calibration process, the interface has to encourage precision, meaning like whatever it does, it should be super intuitive
that the next thing the human is going to likely
do is exactly that intention that you need and only that intention. And you don't have any feedback except that may be
speaking to you afterwards what they actually did. You can't, "Oh yeah."
- Right. - So that's fundamentally, that is a really exciting UX challenge, 'cause that's all on the UX. It's not just about being
friendly or nice or usable. - Yeah.
- It's like- - User experience is how it works. - It's how it works.
- Yeah. - For the calibration, and calibration, at least
at this stage of Neuralink, is like fundamental to
the operation of the thing and not just calibration but continued calibration essentially. - [Bliss] Yeah. - Wow, yeah.
- You said something that I think is worth
exploring there a little bit. You said it's primarily a UX challenge, and I think a large component of it is, but there is also a very interesting machine learning challenge here, which is given some data set, including some on average correct behavior of asking the user to
move up or move down, move right, move left, and given a data set of neural spikes, is there a way to infer in
some kind of semi-supervised or entirely unsupervised way what that high resolution
version of their intention is? And if you think about
it, like there probably is because there are enough
data points in the dataset, enough constraints on your
model that there should be a way with the right sort of formulation to let the model figure out itself. For example, at this millisecond, this is exactly how hard
they're pushing upwards. And at this millisecond, this is how hard they're trying to push upwards. - It's really important to
have very clean labels, yes. So like the problem becomes much harder from the machine learning perspective if the labels are noisy. - [Bliss] That's correct. - And then to get the clean
labels, that's a UX challenge. - Correct, although clean labels, I think maybe it's worth
exploring what that exactly means. I think any given labeling strategy will have some number
of assumptions it makes about what the user's attempting to do. Those assumptions can be
formulated in a loss function, or they can be formulated
in terms of heuristics that you might use to just try to estimate or guesstimate what the
user's trying to do. And what really matters is how accurate are those assumptions. For example, you might say, "Hey, user, push upwards and follow the speed of this cursor," and your heuristic might be
that they're trying to do it exactly what that cursor's trying to do. Another competing heuristic might be they're actually trying
to go slightly faster at the beginning of the movement and slightly slower at the end. And those competing heuristics
may or may not be accurate reflections of what the
user's trying to do. Another version of the task might be, "Hey, user, imagine moving
this cursor a fixed offset. So rather than follow the cursor, just try to move it exactly
200 pixels to the right." So here's the cursor, here's the target. Okay, cursor disappears. Try to move that now invisible cursor 200 pixels to the right. And the assumption in that
case would be that the user can actually modulate
correctly that position offset, but that position offset assumption might be a weaker
assumption, and therefore, potentially you can make it more accurate than these heuristics that
are trying to guesstimate at each millisecond what
the user's trying to do. So you can imagine different tasks that make different
assumptions about the nature of the user intention and
those assumptions being correct is what I would think of as a clean label. - For that step, what are we
supposed to be visualizing? There's a cursor and you
wanna move that cursor to the right or the left or up and down or maybe move them by a certain offset. So that's one way, is that the
best way to do calibration? So for example, an alternative crazy way that probably is playing a role here is a game like Webgrid, where you're just getting a
very large amount of data, the person playing a game, where if they're in a state of flow, maybe you can get clean
signal as a side effect. - [Bliss] Yep. - Is that not an effective
way for initial calibration? - Yeah, great question. There's a lot to unpack there. So the first thing I
would draw a distinction between a sort of open
loop, first closed loop. So open loop, what I mean by that is the user is sort of
going from zero to one. They have no model at all, and they're trying to get to the place where they have some
level of control at all. In that setup, you really
need to have some task that gives the user a hint
of what you want them to do such that you can build this mapping again from brain data to output. Then once they have a model,
you could imagine them using that model and
actually adapting to it and figuring out the right
way to use it themself and then retraining on that data to give you sort of a
boost in performance. There's a lot of challenges associated with both of these techniques
and we can sort of rabbit hole into both of 'em, if you're interested. But the sort of challenge
with the open loop task is that the user themself doesn't get proprioceptive feedback
about what they're doing. They don't necessarily perceive themself or feel the mouse under their hand when they're using an open, when they're trying to do
an open loop calibration. They're being asked to perform something. Like imagine if you sort of
had your whole right arm numbed and you stuck it in a box
and you couldn't see it. So you had no visual feedback and you had no proprioceptive feedback about what the position or
activity of your arm was. And now you're asked, okay,
given this thing on the screen that's moving from left to
right, match that speed. And you basically can
try your best to invoke whatever that imagined
action is in your brain that's moving the cursor
from left to right. But in any situation,
you're gonna be inaccurate and maybe inconsistent
in how you do that task. And so that's sort of the fundamental challenge of open loop. The challenge with closed loop is that, once the user's given a model, and they're able to start
moving the mouse on their own, they're going to very
naturally adapt to that model. And that co-adaptation
between the model learning, what they're doing, and the user learning how to use the model may
not find you the best sort of global minima. And maybe that your first
model was noisy in some ways or maybe just had some like quirk, like if there's some like
part of the data distribution that didn't cover super well,
and the user now figures out because they're a
brilliant user like Noland. They figured out the right
sequence of imagined motions or the right angle they
have to hold their hand at to get it to work. And they'll get it to work great, but then the next day, they
come back to their device and maybe they don't remember exactly all the tricks that they
used the previous day. And so there's a complicated
sort of feedback cycle here that can emerge and can make it a very, very difficult debugging process. - Okay, there's a lot of really
fascinating things there. Yeah, actually, just to
stay on the closed loop, I've seen situations,
this actually happened watching psychology grad students. They use piece of software
when they don't know how to program themselves. They use piece of software
that somebody else wrote, and it has a bunch of bugs. And they figure out like, and they've been using it for years. They figured out ways to work around it. Oh, that just happens. Like nobody like considers
maybe we should fix this. They just adapt. And that's a really interesting notion, that we were really good at
adapting, but you need to still, that might not be the optimal. Okay, so how do you solve that problem? Do you have to restart from scratch every once in a while kind of thing? - Yeah, it's a good question. First and foremost, I would say this is not a solved problem. And for anyone who's listening in academia who works on BCIs, I would
also say this is not a problem that's solved by simply
scaling channel account. Maybe that can help and you can get sort of richer covariance structures that you can use to exploit when trying to come up with
good labeling strategies. But if you're interested in problems, that aren't gonna be solved inherently by scaling channel account,
this is one of them. Yeah, so how do you solve it? It's not a solved problem. That's the first thing I
wanna make sure gets across. The second thing is, any solution
that involves closed loop is going to become a very
difficult debugging problem. And one of my sort of general
heuristics for choosing what prompts to tackle is
that you wanna choose the one that's gonna be the easiest to debug, 'cause if you can do that, even if the ceiling is
lower, you're gonna be able to move faster because you have a tighter iteration loop debugging the problem. And in the open loop setting, there's not a feedback cycle debug with the user in the loop. And so there's some reason to think that that should be an
easier debugging problem. The other thing that's worth understanding is that even in a closed loop setting, there's no special software
magic of how to infer what the user is truly attempting to do. In the closed loop setting,
although they're moving the cursor on the screen, they
may be attempting something different than what your
model is outputting. So what the model is
outputting is not a signal that you can use to retrain
if you want to be able to improve the model further. You still have this very
complicated guesstimation or unsupervised problem of figuring out what is the true user intention
underlying that signal. And so the open loop problem
has the nice property of being easy to debug. And the second nice property of, it has all the same
information and content as the closed loop scenario. Another thing I wanna mention and call out is that this problem
doesn't need to be solved in order to give useful control to people. Even today with the solutions we have now and that academia has
built up over decades, the level of control that can be given to a user today is quite useful. It doesn't need to be solved to get to that level of control. But again, I wanna build
the world's best mouse. I wanna make it so good that it's not even a
question that you want it. And to build the world's best
mouse, the superhuman version, you really need to nail that problem. And a couple maybe details
of previous studies that we've done internally that
I think are very interesting to understand when thinking
about how to solve this problem, the first is that even when
you have ground truth data of what the user's trying to do, and you can get this with
an able-bodied monkey, a monkey that has a
Neuralink device implanted and moving a mouse to control a computer, even with that ground truth dataset, it turns out that the
optimal thing to predict to produce high performance BCI is not just the direct
control of the mouse. You can imagine building dataset of what's going on in the brain and what is the mouse
exactly doing on the table. And it turns out that
if you build the mapping from neuro spikes to predict exactly what the mouse is doing,
that model will perform worse than a model that
is trained to predict sort of higher level assumptions about what the user might be trying to do. For example, assuming
that the monkey is trying to go in a straight line to the target, it turns out that making those assumptions is actually more effective in producing a model
than actually predicting the underlying hand movement. - So the intention, not like the physical
movement or whatever. - Yeah.
- There's obviously a really strong correlation
between the two, but the intention is a more
powerful thing to be chasing. - [Bliss] Right. - Well, that's also super interesting. I mean, the intention
itself is fascinating, because yes, with the BCI here, in this case, with a digital telepathy, you're acting on the
intention, not the action, which is why there's an experience of like feeling like it's happening before you meant for it to happen. That is so cool. And that is why you could achieve like superhuman performance problem in terms of the control of the mouse. So for open loop, just to clarify, so whenever the person is tasked to like move the mouse to the right, you said there's not feedback so they don't get to get that satisfaction of like actually getting
it to move, right? - You could imagine giving
the user feedback on a screen, but it's difficult, because at this point, you don't know what
they're attempting to do. So what can you show them
that would basically give them a signal of I'm doing this
correctly or not correctly. So let's take this very specific example. Like maybe your calibration
task looks like you're trying to move the cursor a
certain position offset. So your instructions to the user are, "Hey, the cursor's here. Now, when the cursor disappears, imagine moving it 200
pixels from where it was to the right to be over this target." In that kind of scenario,
you could imagine coming up with some sort of consistency
metric that you could display to the user of, "Okay, I
know what the spike train looks like on average when you
do this action to the right. Maybe I can produce some sort
of probabilistic estimate of how likely is that to
be the action you took given the latest trial or
trajectory that you imagined." And that could give the
user some sort of feedback of how consistent are they
across different trials. You could also imagine that
if the user is prompted with that kind of consistency metric, that maybe they just become more behaviorally engaged to begin with because the task is kind of boring when you don't have any feedback at all. And so there may be benefits
to the user experience of showing something on the screen, even if it's not accurate,
just because it keeps the user motivated to try to increase that number or push it upwards. - So there's a psychology element here. - Yeah, absolutely. - And again, all of that is UX challenge. How much signal drift is there,
hour to hour, day to day, week to week, month to month? How often do you have to recalibrate because of the signal drift? - Yeah, so this is a
problem we've worked on, both with NHP, non-human primates,
before our clinical trial and then also with Noland
during the clinical trial. Maybe the first thing that's worth stating is what the goal is here. So the goal is really to enable the user to have a plug and play experience where I guess they don't
have to plug anything in, but a play experience where they can use the device whenever they want
to, however they want to. And that's really what we're aiming for. And so there can be a set of solutions that get to that state without considering this non-stationary problem. So maybe the first solution
here that's important is that they can recalibrate
whenever they want. This is something that Noland
has the ability to do today. So he can recalibrate the system at 2:00 AM in the middle of the night, without his caretaker or
parents or friends around to help push a button for him. The other important part
of the solution is that when you have a good model calibrated, that you can continue using that without needing to recalibrate it. So how often he has to do
this recalibration today depends really on his
appetite for performance. We observe sort of a
degradation through time of how well any individual model works, but this can be mitigated behaviorally by the user adapting
their control strategy. It can also be mitigated
through a combination of sort of software features
that we provide to the user. For example, we let
the user adjust exactly how fast the cursor is moving. We call that the gain, for example, the gain of how fast the cursor reacts to any given input intention. They can also adjust the smoothing, how smooth the output of that
cursor intention actually is. They can also adjust the friction, which is how easy is it
to stop and hold still. And all these software tools
allow the user a great deal of flexibility and
troubleshooting mechanisms to be able to solve this problem for themselves.
- By the way, all of this is done by looking to the right side of the screen, selecting the mixer,
and the mixer you have- - It's like DJ mode. DJ mode for your VCI. - So I mean, it's a really
well done interface. It's really, really well done. And so yeah, there's that bias
that there's a cursor drift that Noland talked about in a stream, although he said that you
guys were just playing around with it with him and then
constantly improving. So that could have been just a snapshot of that particular
moment, a particular day. But he said that there
was this cursor drift and this bias that
could be removed by him, I guess looking to the
right side of the screen, the left side of the screen
to kind of adjust the bias. - Yeah, yeah.
- That's one interface action I guess to adjust the bias. - Yeah, so this is actually an idea that comes out of academia. There are some prior work
with sort of BrainGate clinical trial participants
where they pioneered this idea of bias correction. The way we've done it I think is, yeah, it's very prototized, very beautiful user experience where the user can essentially flash the cursor over to
the side of the screen and it opens up a window
where they can actually sort of adjust or tune exactly
the bias of the cursor. So bias maybe, for people
who aren't familiar, is just sort of what is the default motion of the cursor if you're imagining nothing. And it turns out that
that's one of the first sort of qualia of the
cursor control experience that's impacted by neuro non-stationarity. - Quality off the cursor experience. - I don't know how else to describe it. I'm not the guy moving- - It's very poetic, I love it. The quality of the cursor experience. Yeah, I mean, it sounds poetic but it is deeply true. There is an experience,
when it works well, it is a joyful, a really
pleasant experience. And when it doesn't work well, it's a very frustrating experience. That's actually the art of UX. It's like you have the
possibility to frustrate people or the possibility to give them joy, - And at the end of the day,
it really is truly the case that UX is how the thing works. And so it's not just like
what's showing on the screen, it's also what control surfaces does a decode provide the user? Like we want them to feel
like they're in the F1 card, not like some like minivan, right? And that really truly is
how we think about it. Noland himself is an F1 fan, so we refer to ourself as a pit crew. He really is truly the F1 driver, and there's different control surfaces that different kinds of cars
and airplanes provide the user. And we take a lot of inspiration
from that when designing how the cursor should behave.. And what maybe one nuance of this is, even details like when you move a mouse on a MacBook track pad,
the sort of response curve of how that input that
you give the track pad translates to cursor movement is different than how it works with a mouse. When you move on the track pad, there's a different response function, a different curve to how much
a movement translates to input to the computer than when you
do it physically with a mouse. And that's because somebody
sat down a long time ago when they're designed
the initial input systems to any computer and they
thought through exactly how it feels to use
these different systems. And now we're designing
sort of the next generation of this input system to a
computer, which is entirely done via the brain, and there's
no proprioceptive feedback. Again, you don't feel
the mouse in your hand, you don't feel the keys
under your fingertips, and you want a control surface
that still makes it easy and intuitive for the user to understand the state of the system and how to achieve what
they wanna achieve. And ultimately, the end goal
is that that UX is completely, it fades into the background. It becomes something that's
so natural and intuitive that it's subconscious to the user, and they just should feel
like they have basically direct control over the cursor. It just does what they want it to do. They're not thinking
about the implementation of how to make it do
what they want it to do. It's just doing what they want it to do. - Is there some kind of things along the lines of like Fitts' law where you should move the
mouse in a certain kind of way that maximizes your
chance to hit the target? I don't even know what I'm asking, but I'm hoping the
intention of my question will land on a profound answer. No, is there some kind of understanding of the laws of UX when
it comes to the context of somebody using their
brain to control it? Like that's different
than actual with a mouse? - I think we're in the early stages of discovering those
laws, so I wouldn't claim to have solved that problem yet. But there's definitely
some things we've learned that make it easier for
the user to get stuff done. And it's pretty straightforward
when you verbalize it, but it takes a while to
actually get to that point when you're in the process of debugging the stuff in the trenches. One of those things is that
any machine learning system you build has some number of errors, and it matters how those errors translate to the downstream user experience. For example, if you're
developing a search algorithm in your photos, if you
search for your friend Joe and it pulls up a photo
of your friend Josephine, maybe that's not a big deal
because the cost of an error is not that high. In a different scenario where you're trying to
detect insurance fraud or something like this and
you're directly sending someone to court because of some
machine learning model output, then the errors make a lot
more sense to be careful about. You wanna be very thoughtful about how those errors translate
to downstream effects. The same is true in BCI. So for example, if you're building a model that's decoding a velocity
output from the brain versus an output where
you're trying to modulate the left click, for example. These have sort of different trade-offs of how precise you need to be before it becomes useful to the end user. For velocity, it's okay
to be on average correct, because the output of the model
is integrated through time. So if the user's trying
to click at position A, and they're currently in position B, they're trying to navigate over time to get between those two points. And as long as the output of the model is on average correct,
they can sort of steer it through time with the user
control loop in the mix. They can get to the
point they wanna get to. The same is not true of a click. For a click, you're
performing it almost instantly at the scale of neurons firing. And so you want to be very
sure that that click is correct because a false click can be
very destructive to the user. They might accidentally close
the tab that they're trying to do something and
lose all their progress. They might accidentally like hit some Send button on some text that it's only like half-composed
and reads funny after. So there's different
sort of cost functions associated with errors in this space. And part of the UX design is understanding how to build a solution
that is when it's wrong, still useful to the end user. - It's so fascinating that assigning cost to every action when an error occurs. So every action, if an error
occurs, has a certain cost, and incorporating that into how
you interpret the intention, mapping it to the action
is really important. I didn't quite until you said it realize there's a cost to like
sending the text early. It's like very expensive cost.
- Yeah. It's super annoying if you accidentally, like if you're a cursor,
imagine if your cursor misclick every once in a while,
that's like super obnoxious. And the worst part of it is, usually, when the user's trying to click,
they're also holding still because they're over the
target they wanna hit and they're getting ready to click, which means that in the data
sets that we build, on average, it's the case that sort of low speeds or desire to hold still. It's correlated with when the
user's attempting to click. - Wow, that is really fascinating. - It's also not the case. People think that, "Oh, a
click is a binary signal. This must be super easy to decode." Well, yes it is, but the bar is so much higher for it to become a useful thing for the user, and there's ways to solve this. I mean, you can sort of take
the compound approach of, well, let's just give the like, let's take five seconds to click. Let's take a huge window of time so it can be very
confident about the answer. But again, world's best mouse. The world's best mouse
doesn't take a second to click or 500 milliseconds to click. It takes five milliseconds
to click or less. And so if you're aiming
for that kind of high bar, then you really wanna solve
the underlying problem. - So maybe this is a
good place to ask about how to measure performance,
this whole bits per second. Can you like explain
what you mean by that? Maybe a good place to start is to talk about Webgrid as a game, as a good illustration of the
measurement of performance. - Yeah, maybe I'll take
one zoom out step there, which is just explaining why
we care to measure this at all. So again, our goal is to
provide the user the ability to control the computer as well as I can and hopefully better. And that means that they can do it at the same speed as what I can do. It means that they have access to all the same functionality that I have, including all those little
details like command tab, command space, all this stuff. They need to be able to
do it with their brain and with the same level of reliability as what I can do with my muscles. And that's a high bar. And so we intend to measure and quantify every aspect of that to understand how we're progressing towards that goal. There's many ways to
measure BPS, by the way. This isn't the only way,
but we present the user a creative targets, and basically, we compute a score which
is dependent on how fast and accurate they can select, and then how small are the targets. And the more targets
that are on the screen, the smaller they are, the more information
you present per click. And so if you think about
it from information theory point of view, you can communicate across different information
theoretic channels. And one such channel is a typing interface you could imagine that's
built out of a grid, just like a software
keyboard on the screen. And bits per second is a
measure that's computed by taking the log of the number
of targets on the screen. You can subtract one if you
care to model a keyboard because you have to subtract one for the Delete key on the keyboard, but log of the number
of targets on the screen times the number of correct
selections minus incorrect, divided by some time window. For example, 60 seconds. And that's sort of the standard way to measure a cursor
control task in academia. And all credit in the world
goes to this great professor, Dr. Shenoy of Stanford who
came up with that task. And he's also one of my
inspirations for being in the field. So all the credit in the world to him for coming up with a standardized metric to facilitate this kind of
bragging rights that we have now, to say that Noland is
the best in the world at this task with his BCI. It's very important for progress that you have standardized
metrics that people can compare across different
techniques and approaches. How well does this do? So yeah, big kudos to him and
to all the team at Stanford. Yeah, so for Noland, and
for me playing this task, there's also different modes that you can configure this task. So the Webgrid task can be presented as just sort of a left
click on the screen, or you could have targets
that you just dwell over, or you could have targets
that you left, right click on. You could have targets
that are left, right click, middle click, scrolling,
clicking, and dragging. You could do all sorts of things within this general framework. But the simplest, purest
form is just blue targets show up on the screen. Blue means left click. That's the simplest form of the game. And the sort of prior
records here in academic work and at Neuralink internally with sort of NPS have all been matched or beaten by Noland with
his Neuralink device. So sort of prior to Neuralink,
the sort of world record for a human using device
is somewhere between 4.2 to 4.6 BPS, depending on exactly what paper you read and
how you interpret it. Noland's current record is 8.5 BPS. And again, this sort of median Neuralink performance is 10 BPS. So you can think of it roughly as he's 85% the level of control
of a median Neuralinker using their cursor to select
blue targets on the screen. And yeah, I think there's a
very interesting journey ahead to get us to that same
level of 10 BPS performance. It's not the case that sort
of the tricks that got us from four to six BPS,
and then six to eight BPS are gonna be the ones that
get us from eight to 10. And in my view, the core challenge here is really the labeling problem. It's how do you understand at
a very, very fine resolution what the user's attempting to do. And yeah, I highly encourage folks in academia to work on this problem. - What's the journey
with Noland on that quest of increasing the BPS on Webgrid? In March, you said that he selected 89,285 targets in Webgrid. - Yep.
- So he loves this game. He's really serious about improving his performance in this game. So what is that journey
of trying to figure out how to improve that performance? How much can that be done
on the decoding side? How much can that be done
on the calibration side? How much can that be
done on the Noland side of like figuring out how to convey his intention more cleanly? - Yeah, no, this is a great question. So in my view, one of
the primary reasons why Noland's performance is so
good is because of Noland. Noland is extremely
focused and very energetic. He'll play Webgrid sometimes
for like four hours in the middle of the night. Like from 2:00 AM to 6:00
AM, he'll be playing Webgrid, just because he wants to push it to the limits of what he can do. And this is not us like
asking him to do that. I wanna be clear. Like we're not saying, "Hey, you should play Webgrid tonight." We just gave him the game
as part of our research, and he is able to play it independently and practice whenever he wants, and he really pushes hard to push it, the technology's the absolute limit. And he views that as like his job really to make us be the bottleneck. And boy, has he done that well. And so the first thing
to acknowledge is that he's extremely motivated
to make this work. I've also had the privilege to meet other clinical trial participants
from BrainGate and other trials, and they very much
shared the same attitude of like they view this
as their life's work to advance the technology
as much as they can. And if that means selecting
targets on the screen for four hours from 2:00 AM
to 6:00 AM, then so be it. And there's something
extremely admirable about that that's worth calling out. Okay, so then how do you sort of get from where he started, which is
no cursor control to a BPS? So I mean, when he started, there's a huge amount of learning to do on his side and our side to figure out what's the most intuitive control for him. And the most intuitive
control for him is sort of, you have to find the set intersection of what do we have this signal to decode. So we don't pick up every single
neuron in the motor cortex, which means we don't have representation for every part of the body. So there may be some
signals that we have better sort of decode performance on than others. For example, on his left hand,
we have a lot of difficulty distinguishing his left ring finger from his left middle finger. But on his right hand,
we have a good control and good modulation
detected from the neurons that we're able to record for his pinky, his thumb, and his index finger. So you can imagine how these different sub spaces of modulated activity intersect with what's the
most intuitive for him. And this has evolved over time. So once we gave him the ability to calibrate models on his own, he was able to go and explore
various different ways to imagine controlling the cursor. For example, he could imagine
controlling the cursor by wiggling his wrist side to side, or by moving his entire arm. I think at one point, he did his feet. He tried like a whole bunch
of stuff to explore the space of what is the most natural way for him to control the cursor. That at the same time, it's
easy for us to decode rules. - Just to clarify, it's through
the body mapping procedure that you're able to figure
out which finger he can move? - Yes, yeah, that's one way to do it. Maybe one nuance of when he's doing it, he can imagine many more things than we represent in that
visual on the screen. So we show him sort of
abstractly, "Here's a cursor. You figure out what
works the best for you." And we obviously have hints
about what will work best from that body mapping procedure of we know that this particular action, we can represent well. But it's really up to
him to go and explore and figure out what works the best. - But at which point does
he no longer visualize the movement of his body
and is just visualizing the movement of the cursor? - Yeah.
- How quickly does he go from, how quickly does he get there? - So this happened on a Tuesday. I remember this day very clearly, because at some point during the day, it looked like he wasn't doing super well. It looked like the model
wasn't performing super well and he was like getting distracted. But he actually, it wasn't the case. Like what actually happened
was he was trying something new where he was just controlling the cursor. So he wasn't imagining
moving his hand anymore. He was just imagining,
I don't know what it is, some like abstract intention to move the cursor on the screen. And I cannot tell you what the difference between those two things are. I really truly cannot. He's tried to explain it to me before. I cannot give a first person
account of what that's like. But the expletives that
he uttered in that moment were enough to suggest that it was a very qualitatively
different experience for him to just have direct
neural control over a cursor. - I wonder if there's a way through UX to encourage a human
being to discover that, because he discovered
it, like you said to me, that he's a pioneer. So he discovered that on
his own through all of this, the process of trying to move the cursor with different kinds of intentions. But that is clearly a really
powerful thing to arrive at, which is to let go of trying
to control the fingers and the hand and control the actual digital device with your mind. - That's right, UX is how it works. And the ideal UX is one
that the user doesn't have to think about what they need to do in order to get it done. It just does it. - That is so fascinating. But I wonder on the biological side how long it takes for the brain to adapt. - Yeah.
- So is it just simply learning like high level software, or is there like a
neuroplasticity component where like the brain is adjusting slowly? - Yeah, the truth is, I don't know. I'm very excited to see with
sort of the second participant that we implant what the
journey is like for them, because we'll have learned a lot more. Potentially, we can help them understand and explore that direction more quickly. This is something I didn't know. This wasn't me prompting
Noland to go try this. He was just exploring
how to use his device and figure it out himself. But now that we know that
that's a possibility, that maybe there's a way to, for example, hint the user, "Don't try
super hard during calibration. Just do something that feels natural, or just directly control the cursor. Don't imagine explicit action." And from there, we should be
able to hopefully understand how this is for somebody who has not experienced that before. Maybe that's the default
mode of operation for them. You don't have to go through this intermediate phase of explicit motions. - Or maybe if that naturally
happens for people, you can just occasionally encourage them to allow themselves to move the cursor. Actually sometimes, just
like with a four minute mile, just the knowledge that that's possible. - Pushes you to do it. - Yeah, enables you to do it, and then it becomes trivial. And then it also makes you wonder, it's the cool thing about humans. Once there's a lot more
human participants, they will discover
things that are possible. - Yes, and share their experiences. - Yeah, and share.
- With each other. - And that because of them sharing it, they'll be able to do it. All of a sudden, that's
unlocked for everybody, because just the knowledge sometimes is the thing that enables it to do it. - Yeah, I mean, and just
to comment on that too, we've probably tried like
a thousand different ways to do various aspects of decoding, and now we know like what
the right subspace is to continue exploring further. Again, thanks to Noland and the many hours he's put into this. And so even just that help, like help constraints
sort of the beam search of different approaches
that we could explore really helps accelerate
for the next person the set of things that
we'll get to try on day one, how fast we hope to get
them to useful control, how fast we can enable 'em
to use it independently, and to get value out of the system. So yeah, massive hats off to Noland and all the participants
that came before him to make this technology a reality. - So how often are the
updates to the decoder? 'Cause Noland mentioned like, okay, there's a new update
that we're working on, and that in the stream, he said he plays the snake game
because it's like super hard. It's a good way for him to test
like how good the update is. And he says like sometimes the
update is a step backwards. It's a constant like iteration. Like what does the update entail? Is it mostly on the decoder side? - Yeah, a couple comments. So one is it's probably
worth drawing distinction between sort of research sessions where we're actively
trying different things to understand like what
the best approach is versus sort of independent
use where we wanted to have an ability to just go use the device, how anybody would wanna use their MacBook. And so what he's referring to is, I think usually in the
context of a research session, where we're trying many,
many different approaches to even unsupervised approaches like we talked about earlier to try to come up with better ways to estimate his true intention and more accurately decode it. And in those scenarios, I mean we try, in any given session, he'll sometimes work for like eight hours a day. And so that can be hundreds
of different models that we would try in that day. Like a lot of different things. Now, it's also worth noting that we update the application he uses quite frequently. I think sometimes, up to like
four or five times a day. We'll update his application
with different features or bug fixes or feedback
that he's given us. He's a very articulate person
who is part of the solution. He's not a complaining person. He says, "Hey, here's this
thing that I've discovered is not optimal in my flow. Here's some ideas how to fix it. Let me know what your thoughts are. Let's figure out how to solve it." And it often happens that
those things are addressed within a couple hours of
him giving us his feedback. That's the kind of
iteration cycle we'll have. And so sometimes, at the
beginning of the session, he'll give us feedback, and
at the end of the session, he's giving us feedback
on the next iteration of that process or that set up. - That's fascinating,
because one of the things you mentioned, that there
was 271 pages of notes taken from the BCI sessions,
and this was just in March. So one of the amazing
things about human beings that they can provide,
especially ones who are smart and excited and all like positive
and good vibes like Nolan, that they can provide
feedback, continuous feedback. - Yeah, it also requires, just to brag on the team a little bit, I work with a lot of exceptional people, and it requires the team
being absolutely laser focused on the user and what will
be the best for them. And it requires like a
level of commitment of, "Okay, this is what the user feedback was. I have all these meetings. We're gonna skip that today
and we're gonna do this." That level of focus
commitment is, I would say, underappreciated in the world. And also, you obviously
have to have the talent to be able to execute on
these things effectively. And yeah, we have that in loads. - Yeah, and this is such a
interesting space of UX design because there's so many unknowns here. And I can tell UX is difficult because of how many people do it poorly. It's just not a trivial thing. - Yeah, it's also, you know, UX is not something that
you can always solve by just constant iterating
on different things. Like sometimes, you
really need to step back and think globally, am
I even in like the right sort of minima to be
chasing down for a solution? Like there's a lot of problems in which sort of fast iteration
cycle is the predictor of how successful you will be. As a good example, like in
RL simulation, for example, the more frequently you get reward, the faster you can progress. It's just an easier learning prompt, the more frequently you get feedback. But UX is not that way. I mean, users are
actually quite often wrong about what the right solution is, and it requires a deep understanding
of the technical system and what's possible, combined with what the problem
is you're trying to solve. Not just how the user expressed it, but what the true underlying problem is to actually get to the right place. - Yeah, that's the old like stories of Steve Jobs like rolling in there, like, yeah, the user is a useful signal, but it's not a perfect signal. And sometimes, you have to
remove the floppy disk drive or whatever the, I forgot
all the crazy stories of Steve Jobs like making
wild design decisions. But there, some of it is aesthetic, that some of it is about the
love you put into the design, which is very much a Steve
Jobs-Jony Ive type of thing. But when you have a human
being using their brain to interact with it, it also
is deeply about function. It's not just aesthetic. And that you have to empathize with a human being before you, while not always listening
to them directly. Like you have to deeply empathize. It's fascinating. It's really, really fascinating. And at the same time, iterate. But not iterate in small ways. Sometimes, a complete like
rebuilding the design. He said that, Noland said in the early
days, the UX sucked. But you improved quickly. What was that journey like? - Yeah, I mean, I'll give
one concrete example. So he really wanted to
be able to read manga. This is something that he, I mean, it sounds like a simple thing, but it's actually a
really big deal for him. And he couldn't do it
with his mouth stick. It wasn't accessible. You can't scroll with a
mouse stick on his iPad on the website that he
wanted to be able to use to read the newest manga. - Might be a good quick pause to say the mouth stick
is the thing he's using, holding a stick in his
mouth to scroll on a tablet. - Right, yeah, it's basically, you can imagine it's a stylus that you hold between your teeth. Yeah, it's basically a very long stylus. - And it's exhausting, it hurts, and it's inefficient. - Yeah, and maybe it's
also worth calling out, there are other alternative
assisted technologies, but the particular situation Noland's in, and this is not uncommon, and I think it's also not
well understood by folks, is that he's relatively spastic so he'll have muscle
spasms from time to time. And so any assistive
technology that requires him to be positioned directly
in front of a camera, for example, an eye tracker, or anything that requires him
to put something in his mouth just is a no go, 'cause he'll either be shifted out of frame
when he has a spasm, or if he has something in his mouth, it'll stab him in the face
if he spasms too hard. So these kind of
considerations are important when thinking about what advantages a BCI has in someone's life. If it fits ergonomically into your life in a way that you can use it independently when your caretaker's not
there, wherever you want to, either in the bed or in the chair, depending on your comfort level and your desire to have pressure sores, all these factors matter a lot in how good the solution
is in that user's life. So one of these very
fun examples is scroll. So again, manga is something
he wanted to be able to read, and there's many ways
to do scroll with a BCI. You can imagine like different
gestures, for example. The user could do that,
would move the page. But scroll is a very
fascinating control surface, because it's a huge thing on
the screen in front of you. So any sort of jitter in the model output, any sort of error in the model output causes like an earthquake on the screen. Like you really don't
wanna have your manga page you're trying to read be
shifted up and down a few pixels just because your scroll decoder
is not completely accurate. And so this was an example
where we had to figure out how to formulate the problem in a way that the errors of the system,
whenever they do occur, and we'll do our best to minimize them, whenever those errors do occur, that it doesn't interrupt the qualia again of the experience that the user is having. It doesn't interrupt their
flow of reading their book. And so what we ended up building is this really brilliant feature. This is teammate named Ruse, who worked on this really brilliant work called quick scroll. And quick scroll basically
looks at the screen, and it identifies where on
the screen are scroll bars. And it does this by deeply
integrated with MacOs to understand where are the
scroll bars actively present on the screen using the
sort of accessibility tool that's available to MacOs apps. And we identified where
those scroll bars are and we provided a BCI scroll bar. And the BCI scroll bar looks
similar to a normal scroll bar but it behaves very differently in that once you sort of move over to it, your cursor sort of morphs onto it. It sort of attaches or latches onto it. And then once you push up or down in the same way that you'd
use a push to control the normal cursor, it actually
moves the screen for you. So it's basically like
remapping the velocity to a scroll action. And the reason that feels so
natural and intuitive is that, when you move over to attach
to it, it feels like magnetic. So you're like sort of stuck onto it. And then it's one continuous action. You don't have to like switch
your imagined movement. You sort of snap onto it
and then you're good to go. You just immediately can start pulling the page down or pushing it up. And even once you get that right, there's so many little nuances of how the scroll behavior works to make it natural and intuitive. So one example is momentum. Like when you scroll a page
with your fingers on the screen, you actually have some like flow. Like it doesn't just stop right when you lift your finger up. The same is true with BCI scroll. So we had to spend some time to figure out what are the right nuances. When you don't feel the screen
under your fingertip anymore, what is the right sort of dynamic or what's the right amount
of page give, if you will, when you push it to make
it flow the right amount for the user to have a natural experience reading their book. And there's a million, I mean, I could tell you like there's
so many little minutia of how exactly that scroll works
that we spent probably like a month getting right to make
that feel extremely natural and easy for the user to navigate. - I mean, even to scroll on
a smartphone with your finger feels extremely natural and pleasant. And it probably takes
a extremely long time to get that right. And actually, the same
kind of visionary UX design that we're talking about, don't
always listen to the users but also listen to them, and
also have like visionary big, like throw everything out, think from first principles but also not. Yeah, yeah, by the way,
it just makes me think that scroll bars on the desktop probably have stagnated
and never taken that like, 'cause the snap, same
as like snap the grid, snap the scroll bar action
you're talking about is something that could
potentially be extremely useful in the desktop setting, even just for users to just
improve the experience, 'cause the current scroll bar experience in the desktop is horrible.
- Yeah, agreed. - It's hard to find, hard to control. There's not a momentum. And the intention should be clear. When I start moving towards a scroll bar, there should be a snapping
to the scroll bar action. But of course, maybe I'm
okay paying that cost, but there's hundreds of millions of people paying that cost nonstop. But anyway, but in this case, this is necessary because
there's an extra cost paid by Noland for the jitteriness. So you have to switch between
the scrolling and the reading. There has to be a phase
shift between the two. Like when you're scrolling,
you're scrolling. - Right, right, so that is one drawback of the current approach. Maybe one other just
sort of case study here, so again, UX is how it works, and we think about that
holistically from like the, even the feature detection level of what we detect in the brain
to how we design the decoder, what we choose to decode,
to then how it works once it's being used by the user. So another good example in
the sort of how it works once they're actually using the decoder, the output that's displayed on the screen is not just what the decoder says. It's also a function of
what's going on on the screen. So we can understand, for example, that when you're trying to close a tab, that very small, stupid little
X that's extremely tiny, which is hard to get precisely hit if you're dealing with sort of a noisy output of the decoder, we can understand that that is a small little
X you might be trying to hit and actually make it a
bigger target for you. Similar to how when you're
typing on your phone, if you are used to like the
iOS keyboard, for example, it actually adapts the target size of individual keys based on
an underlying language model. So it'll actually
understand if I'm typing, "Hey, I'm going to see L." It'll make the E key bigger, because in those Lex is the
person I'm gonna go see. And so that kind of predictiveness
can make the experience much more smooth even without improvements to the underlying decoder or feature detection part of the stack. So we do that with a feature
called magnetic targets. We actually indexed the
screen and we understand, okay, these are the places
that are very small targets that might be difficult to hit. Here's the kind of cursor
dynamics around that location that might be indicative of
the user trying to select it. Let's make it easier. Let's blow up the size of it in a way that makes it easier for the user to sort of snap onto that target. So all these little
details, they matter a lot in helping the user be independent in their day-to-day living. - So how much of the work on the decoder is generalizable to P2, P3, P4, P5, PM? How do you improve the decoder in a way that's generalizable? - Yeah, great question. So the underlying signal
we're trying to decode is gonna look very
different in P2 than in P1. For example, channel
number 345 is gonna mean something different in user
one than it will in user two, just because that
electrode that corresponds with channel 345 is gonna be
next to a different neuron in user one versus user two. But the approach is the methods, the user experience of
how do you get the right sort of behavioral pattern from the user to associate with that neural signal, we hope that will translate over multiple generations of users. And beyond that, it's very, very possible. In fact, quite likely that we've overfit to sort of Noland's user
experience desires and preferences. And so what I hope to see is that when we get second,
third, fourth participant, that we find sort of what
the right wide minimums are that cover all the cases, that make it more intuitive for everyone. And hopefully, there's a
crosspollination of things where, "Oh, we didn't think
about that with this user, because they can speak. But with this user who
just can fundamentally not speak at all, this user
experience is not optimal." And that will actually, those improvements that we make there should hopefully translate then to even people who can't speak but don't feel comfortable doing so because we're in a public setting, like their doctor's office. - So the actual mechanism
of open loop labeling and then closed loop
labeling will be the same, and hopefully, can generalize
across the different users as they're doing the calibration step. And the calibration step is pretty cool. I mean, that in itself, the interesting thing about Webgrid, which is like closed loop, it's like fun. I love it when there's like, they used to be kind of
idea of human computation, which is using actions a
human would want to do anyway to get a lot of signal from. - Yeah.
- And like Webgrid is that, like a nice video game that also serves as great calibration. - It's so funny. I've heard this reaction so many times. Before sort of the first
user was implanted, we had an internal perception that the first user
would not find this fun. And so we thought really quite
a bit actually about like, should we build other games that like are more
interesting for the user so we can get this kind of data and help facilitate research for long duration and stuff like this? Turns out that like people love this game. - Yeah.
- I always loved it, but I didn't know that that
was a shared perception. - Yeah, and just in case
it's not clear, Webgrid is, there's a grid of, let's
say 35 by 35 cells, and one of them lights up blue and you have to move your mouse
over that and click on it. And if you miss it and it's red- - [Bliss] I played this
game for so many hours. So many hours. - And what's your record, you said? - I think I have the highest at Neuralink. Right now, my record's 17 BPS. - 17 BPS.
- Which is about, if you imagine that 35 by 35 grid, you're hitting about 100
trials per minute in. So 100 correct selections
in that one minute window. So you're averaging about, between 500, 600
milliseconds per selection. - So one of the reasons
that I think I struggle with that game is I'm
such a keyboard person, so everything is done with via keyboard. If I can avoid touching
the mouse, it's great. So how can you explain
your high performance? - I have like a whole ritual I go through when I play Webgrid. So it's actually like a diet
plan associated with this. Like it's a whole thing, so great. - The first thing-
- You have to fast for five days, have to
go up to the mountains. - Actually, it kind of, I mean, the fasting thing is important. So this is like, you know- - Focuses the mind, yeah?
- Yeah, it's true. So what I do is actually, I don't eat for a little bit beforehand. And then I'll actually eat
like a ton of peanut butter right before, and I get like- - This is a real thing?
- This is a real thing, yeah. And then it has to be
really late at night. This is again a night owl
thing I think we share, but it has to be like midnight,
2:00 AM kind of time window. And I have a very specific like physical position I'll sit in, which is, I used to be, I
was homeschooled growing up and so I did most of my
work like on the floor, just like in my bedroom or whatever. And so I have a very specific
situation on the floor. On the floor, I sit and play, and then you have to make sure like there's not a lot
of weight on your elbow when you're playing so
that you can move quickly. And then I turn the gain of the cursor, so the speed of the cursor way, way up. So it's like small motions
that actually move the cursor. - Are you moving with your wrist or you're never moving on- - I move my fingers. So my wrist is almost completely still. I'm just moving my fingers. - Yeah. You know those, just on a small tangent, which I've been meaning to
go down this rabbit hole of people that set the
world record in Tetris. Those folks, they're playing, there's a way to, did you see this? - I see like the three, like
all the fingers are moving. - Yeah, you could find a way to do it where like it's using a loophole, like a bug that you can do
some incredibly fast stuff. So it's along that line but not quite. But you do realize there'll
be like a few programmers right now listening to this
cool fast and eat peanut butter and be like-
- Yeah, please, please trade my record. I mean, the reason I did this, literally, was just because I wanted
the bar to be high. The team, like I wanted
the number that we aim for should not be like the median performance. It should be able to
beat all of us at least. Like that should be the minimum bar. - What do you think is possible, like 20 scrapes?
- Yeah, I don't know what the limits. I mean, the limits you can calculate just in terms of like screen refresh rate and like cursor immediately
jumping to the next target. But I mean, I'm sure
there's limits before that with just sort of reaction time and visual perception
and things like this. I would guess it's in the
below 40 but above 20, somewhere in there is probably the right that I'd never be thinking about. It also matters like how
difficult the task is. You could imagine like
some people might be able to do like 10,000 targets on the screen and maybe they can do better that way. There's some like task optimizations you could do to try to boost
your performance as well. - What do you think it takes for Noland to be able to do above 8.5? To keep increasing that number? You said like every increase in the number might require different- - [Bliss] Yeah. - Different improvements in the system. - Yeah, I think the
nature of this work is, I mean, the first answer
that's important to say is, I don't know. This is edge of the research. So again, nobody's gotten
to that number before. So what's next is gonna be a
heuristic guess from my part. What we've seen historically
is that different parts of the stack follow
next to different time points. So when I first joined Neuralink, like three years ago or so, one of the major problems
was just the latency of the Bluetooth connection. It was just like the radial
device wasn't super good. It was an early revision of the implant, and it just like, no matter
how good your decoder was, if your thing is updating
every 30 milliseconds or 50 milliseconds, it's
just gonna be choppy. And no matter how good you are, that's gonna be frustrating
and lead to challenges. So at that point, it was very
clear that the main challenge is just get the data off the
device in a very reliable way, such that you can enable the
next challenge to be tackled. And then at some point, it was, actually, the modeling challenge of how do you just build a good mapping, like the supervised learning problem of you have a bunch of data and you have a label
you're trying to predict, just what is the right like
neuro decoder architecture and hyper parameters to optimize that? And that was a problem for a bit. And once you solve that, it
became a different bottleneck. I think the next bottleneck
after that was actually just sort of software
stability and reliability. If you have widely varying
sort of inference latency in your system, or your app just lags out every once in a while, it
decreases your ability to maintain and get in a state of flow, and it basically just disrupts
your control experience. And so there's a variety
of different software bugs and improvements we made
that basically increased the performance of the system, made it much more
reliable, much more stable, and led to a state where we
could reliably collect data to build better models with. So that was a bottleneck for a while. It's just sort of like
the software stack itself. If I were to guess right now, there's sort of two major directions you could think about for
improving BPS further. The first major direction is labeling. So labeling is again this
fundamental challenge of given a window of time where the user is expressing some behavioral intent. What are they really trying to do at the granularity of every millisecond? And that, again, is a task design problem. It's a UX problem. It's a machine learning problem. It's a software problem. Sort of touches all
those different domains. The second thing you can think about to improve BPS further is
either completely changing the thing you're decoding
or just extending the number of things that you're decoding. So this is serving the direction
of functionality, okay? So you can imagine giving more clicks. For example, a left click, a
right click, a middle click. Different actions like
click and drag, for example. And that can improve the effective bit rate of your communication prosthesis. If you're trying to allow the
user to express themselves through any given communication channel, you can measure that with bits per second. But what action measures
at the end of the day is how effective are they at
navigating their computer. And so from the perspective
of the downstream tasks that you care about, functionality and extending functionality is something we're very interested in,
because not only can it improve the sort of number of BPS, but it can also improve the downstream sort of independence that the user has and the skill and efficiency with which they can
operate their computer. - Would the number of threads increasing also potentially help? - Yes, short answer is yes. It's a bit nuanced how that curve or how that manifests in the numbers. So what you'll see is
that if you sort of plot a curve of number of channels that you're using for decode versus either the offline metric of how good you are at decoding, or the online metric of
sort of, in practice, how good is the user using this device, you see roughly a log curve. So as you move further
out in number of channels, you get a corresponding sort
of logarithmic improvement in control quality and
offline validation metrics. The important nuance
here is that each channel corresponds with a specific represented intention in the brain. So for example, if you have a channel 254, it might correspond with
moving to the right. Channel 256 might mean move to the left. If you want to expand
the number of functions you want to control,
you really want to have a broader set of channels that covers a broader set
of imagined movements. You can think of it kinda
like Mr. Potato man actually. Like if you had a bunch of different imagined movements you could do, how would you map those imagined movements to input to a computer? You can imagine handwriting to output characters on the screen. You can imagine just
typing with your fingers and have that output text on the screen. You can imagine different finger modulations for different clicks. You can imagine wiggling your big nose or opening some menu or
wiggling your big toe to have like command tab
occur or something like this. So it's really, the amount of different actions
you can take in the world depends on how many channels you have on the information
content that they carry. - Right, so that's more
about the number of actions. So actually, as you increase
the number of threads, that's more about increasing the number of actions
you're able to perform. - One other nuance there
that is worth mentioning, so again, our goal is really
to enable a user with process to control their computer
as fast as I can. So that's BPS. With all the same functionality I have, which is what we just talked about, but then also as reliably as I can. And that last point is very related to channel account discussion. So as you scale out number of channels, the relative importance
of any particular feature of your model input to the output control of the user diminishes, which means that if the sort
of neural non-stationary effect is per channel, or if
the noise is independent, such that more channels means, on average, less output effect, then your reliability of
your system will improve. So one sort of core thesis
that at least I have is that scaling channel
account should improve the reliability system without any work on the decoder itself. - Can you linger on the reliability here? So first of all, when you see non-stationarity of the signal, which aspect are you referring to? - Yeah, so maybe let's talk briefly what the actual underlying
signal looks like. So again, I spoke very
briefly at the beginning about how when you imagine
moving to the right or imagine moving to the left, neurons might fire more or less. And their frequency
content of that signal, at least in the motor cortex, it's very correlated with
the output intention, the behavioral task
that the user is doing. You can imagine actually,
this is not obvious, that rate coding, which is
the name of that phenomenon is like the only way the brain
could represent information. You can imagine many different ways in which the brain could encode intention. And there's actually evidence
like in bats, for example, that there's temporal codes. So timing codes of like exactly
when particular neurons fire is the mechanism of
information representation. But at least in the motor cortex, there's a substantial evidence
that it's rate coding, or at least one, like first order of fact is that it's rate coding. So then if the brain is
representing information by changing the sort of
frequency of a neuron firing, what really matters is sort of the delta between sort of the
baseline state of the neuron and what it looks like
when it's modulated. And what we've observed and
what has also been observed in academic work is
that that baseline rate, sort of the, if you're
to target the scale, if you imagine that
analogy for like measuring flour or something when you're baking, that baseline state of
how much the pot weighs is actually different day to day. And so if what you're trying to measure is how much rice is in the pot, you're gonna get a different
measurement different days, because you're measuring
with different pots. So that baseline rate shifting
is really the thing that, at least from a first order
description of the problem, is what's causing this downstream bias. There can be other effects, non-linear effects on top of that, but at least, at a very first order description of the problem, that's what we observe day to day is that the baseline firing
rate of any particular neuron are observed on a
particular channel is changing. - So can you just adjust to the baseline to make it relative to
the baseline nonstop? - Yeah, this is a great question. So with monkeys, we have
found various ways to do this. One example way to do this is you ask them to do some behavioral task, like play the game with a joystick, you measure what's going on in the brain, you compute some mean of what's going on across
all the input features, and you subtract it in the input when you're doing your BCI session. Works super well. For whatever reason, that doesn't work super well with Noland. I actually don't know the full reason why, but I can imagine several explanations. One such explanation could
be that the context effect difference between some open loop task and some closed loop task
is much more significant with Noland than it is with a monkey. Maybe in this open loop task, he's watching the Lex Fridman podcast while he's doing the task, or he's whistling and listening to music and talking with his
friend and ask his mom what's for dinner while
he's doing this task. And so the exact sort
of difference in context between those two states
may be much larger, and thus lead to a bigger
sort of generalization gap between the features
that you're normalizing at sort of open loop time and what you're trying to
use at close loop time. - That's interesting. Just on that point,
it's kind of incredible to watch Noland be able
to do, to multitask, to do multiple tasks at the same time, to be able to move the
mouse courser effectively while talking and while being nervous, because he's talking in front of- - Kicking my ass and chest too, yeah. - Kicking your ass. And talk trash while doing it. So all at the same time. And yes, if you are trying
to normalize to the baseline, that might throw everything off. Boy is that interesting. - Maybe one comment on that too. For folks that aren't familiar
with assisted technology, I think there's a common belief that, well, why can't you
just use an eye tracker or something like this
for helping somebody move a mouse on the screen? And it's a really a fair question, and one that I actually did was not confident before Noland, that this was gonna be a profoundly transformative technology
for people like him. And I'm very confident
now that it will be, but the reasons are subtle. It really has to do with ergonomically how it fits into their life. Even if you can just offer
the same level of control as what they would have
with an eye tracker or with a mouse stick, but you don't need to have
that thing in your face. You don't need to be
positioned a certain way. You don't need your caretaker to be around to set it up for you. You can activate it when you want, how you want, wherever you want. That level of independence is
so game changing for people. It means that they can text
a friend at night privately without their mom needing
to be in the loop. It means that they can like open up and browse the internet at
2:00 AM when nobody's around to set their iPad up for them. This is like a profoundly
game changing thing for folks in that situation. And this is even before we
start talking about folks that may not be able to communicate at all or ask for help when they want to. This can be potentially the only link that they have to the outside world. And yeah, that one doesn't
I think need explanation of why that's so impactful. - You mentioned neural decoder. How much machine learning
is in the decoder? How much magic, how much
science, how much art, how difficult is it to
come up with a decoder that figures out what these
sequence of spikes mean? - Yeah, good question. There's a couple different
ways to answer this. So maybe I'll zoom out briefly first, and then I'll go down
one of the rabbit holes. So the zoomed out view is that building the decoder is really the process of building the dataset, plus
compiling it into the weights. And each of those steps is important. The direction I think
of further improvement is primarily going to
be in the dataset side of how do you construct the
optimal labels for the model. But there's an entirely separate challenge of then how do you compile the best model. And so I'll go briefly
down the second one, down the second rabbit hole. One of the main challenges with designing the optimal model for BCI
is that offline metrics don't necessarily correspond
to online metrics. It's fundamentally a control problem. The user is trying to control
something on the screen, and the exact sort of user
experience of how you output the intention impacts
their ability to control. So for example, if you just
look at validation loss, as predicted by your model,
there can be multiple ways to achieve the same validation loss. Not all of them are equally
controllable by the end user. And it might be as simple as saying, "Oh, you could just add
auxiliary loss terms that like help you capture the
thing that actually matters." But this is a very
complex nuanced process. So how you turn the labels into the model is more of a nuanced process than just like a standard
supervised learning problem. One very fascinating anecdote here, we've tried many different
sort of neural network architectures that translate brain data to velocity outputs, for example. And one example that's stuck in my brain from a couple years ago
now is, at one point, we were using just
fully connected networks to decode the brain activity. We tried A/B test where we were measuring the relative performance
in online control sessions of sort of 1D convolution
over the input signal. So if you imagine per channel,
you have a sliding window that's producing some Commvault feature for each of those input sequences for every single channel simultaneously. You can actually get
better validation metrics, meaning you're fitting the data better, and it's generalizing
better on offline data if you use this
convolutional architecture. You're reducing parameters. It's sort of a standard procedure when you're dealing with time series data. Now it turns out that when
using that model online, the controllability was worse, was far worse, even though the
offline metrics were better. And there can be many
ways to interpret that, but what that taught me at least was that, hey, it's at least the case right now that if you were to just throw a bunch of computer at this problem, and you were trying to sort
of hyper parameter optimize, or let some GPT model hard code or come up with or invent
many different solutions, if you were just optimizing for loss, it would not be sufficient, which means that there's still some inherent modeling gap here. There's still some artistry
left to be uncovered here of how to get your model
to scale with more compute. And that may be fundamentally
labeling problem, but there may be other
components to this as well. - Is it data constrained at this time? Which is what it sounds like. How do you get a lot of good labels? - Yeah, I think it's
data quality constrained, not necessarily data quantity constrained. - But even like even just a quantity. I mean, 'cause it has to be
trained on the interactions. I guess there's not
that many interactions. - Yeah, so it depends what version of this you're talking about. So if you're talking about like, let's say the simplest
example of just 2D velocity, then I think, yeah, data
quality is the main thing. If you're talking about how to build a sort of multifunction output that lets you do all the inputs, the computer that you and I can do, then it's actually a much more sophisticated nuanced modeling challenge, because now you need to think about not just when the user's left clicking, but when you're building
the left click model, you also need to be thinking about how to make sure it doesn't fire when they're trying to right click or when they're trying to move the mouse. So one example of an interesting bug from like sort of week one
of a BCI with Nolan was, when he moved the mouse, the click signal sort
of dropped off a cliff, and when he stopped, the
click signal went up. So again, there's a contamination
between the two inputs. Another good example was, at
one point, he was trying to do sort of a left click and drag. And the minute he started moving, the left click signal dropped off a cliff. So again, 'cause there's
some contamination between the two signals, you
need to come up with some way to either in the dataset or in the model, build robustness against this kind of, you think of it like
overfitting, but really, it's just that the model has not seen this kind of variability before. So you need to find some way
to help the model with that. - This is super cool because it feels like all of this is very
solvable, but it's hard. - Yes, it is fundamentally
an engineering challenge. This is important to emphasize, and it's also important to emphasize that it may not need
fundamentally new techniques, which means that people
who work on, let's say, unsupervised speech classification using CTC loss, for example,
with internal to Siri, they could potentially have
very applicable skills to this. - So what things are you excited about in the future development of the software stack on Neuralink? So everything we've been talking about, the decoding, the UX- - I think there's some I'm excited about, like something I'm excited
about from the technology side, and some I'm excited
about for understanding how this technology is
going to be best situated for entering the world. So I'll work backwards. On the technology entering
the world side of things, I'm really excited to
understand how this device works for folks that cannot speak at all. They have no ability to
sort of bootstrap themselves into useful control by
voice command, for example, and are extremely limited in
their current capabilities. I think that will be an
incredibly useful signal for us to understand, I mean, really
what is an existential threat for all startups, which
is product market fit. Does this device have the capacity and potential to transform people's lives in the current state? And if not, what are the gaps? And if there are gaps, how do we solve them most efficiently? So that's what I'm very
excited about for the next sort of year or so of
clinical trial operations. The technology side, I'm quite excited about
basically everything we're doing. I think it's gonna be awesome. The most prominent one, I would say, is scaling channel count. So right now, we have a
thousand channel device. The next version, we'll have
between 3 and 6,000 channels. And I would expect that curve
to continue in the future. And it's unclear what set of problems will just disappear
completely at that scale, and what set of problems will remain and require for their focus. And so I'm excited about
the clarity of gradient that that gives us in terms
of the user experiences we choose to focus our
time and resources on. And also in terms of the, yeah, even things as simple as not stationary. Like does that problem just completely go away at that scale, or do we need to come
up with new creative UXs still even at that point? And also, when we get to that time point, when we start expanding out dramatically the set of functions that you
can output from one brain, how to deal with all the nuances
of both the user experience of not being able to
feel the different keys under your fingertips, but
still needing to be able to modulate all of them in synchrony to achieve the thing you want. And again, you don't have that appropriate set to feedback loop,
so how can you make that intuitive for a user to control a high dimensional control surface without feeling the thing physically? I think that's gonna be a
super interesting problem. I'm also quite excited to understand, do these scaling laws continue? Like as you scale channel count, how much further out do you go before that saturation point is truly hit? And it's not obvious today. I think we only know what's in the sort of interpolation space. We only know what's
between zero and 1,024. We don't know what's beyond that. And then there's a whole sort of like range of interesting sort of neuroscience and brain questions, which
is when you stick more stuff in the brain, in more places, you get to learn much more quickly about what those brain regions represent. And so I'm excited about that fundamental neuroscience learning,
which is also important for figuring out how,
and to most efficiently, insert electrodes in the future. So yeah, I think all those dimensions, I'm really, really excited about. And that doesn't even
get close to touching the sort of software stack that
we work on every single day and what we're working on right now. - Yeah, it seems
virtually impossible to me that a thousand electrodes
is where it saturates. It feels like this would be one of those silly notions in the
future, where obviously, you should have millions of electrodes, and this is where like the
true breakthroughs happen. - Yeah. - You tweeted-
- Oh. - "Some thoughts are most
precisely described in poetry." Why do you think that is? - I think it's because
the information bottleneck of language is pretty steep. And yet you're able to reconstruct in the other person's
brain more effectively without being literal. If you can express a sentiment
such that in their brain, they can reconstruct the
actual true underlying meaning and beauty of the thing that you're trying to get across. The sort of the generator function in their brain's more powerful than what language can express. And so the mechanism of
poetry is really just to feed or seed that generator function. - So being literal sometimes
is a suboptimal compression for the thing you're trying to convey. - And it's actually in
the process of the user going through that generation that they understand what you mean. That's the beautiful part. It's also like when you look
at a beautiful painting, like it's not the pixels of the
painting that are beautiful, it's the thought process that
occurs when you see that, the experience of that. That actually is a thing that matters. - Yeah, it's resonating with some deep- - [Bliss] Yeah. - Thing within you that
the artist also experienced and was able to convey
that through the pixels. And that's actually gonna be
relevant for full on telepathy. It's like if you just read
the poetry, literally, that doesn't say much
of anything interesting. It requires a human to interpret it. So it's the combination of the human mind and all the experiences
that human being has within the context of the
collective intelligence of the human species that
makes that poem make sense. And they load that in. And so in that same way,
the signal that carries from human to human
meaning may seem trivial, but may actually carry a lot of power because of the complexity of the human mind on the receiving end. Yeah, that's interesting. Poetry still doesn't, who is it? I think Yoshi Bako first said something about all the people that think we've achieved AGI explain
why humans like music. - [Bliss] Oh yeah. - And until the AGI likes music, you haven't achieved AGI or something like this.
- Do you not think that's like some next
token entropy surprise kind of thing going on there? - I don't know.
- I don't know either. I listen to a lot of classical music and also read a lot of poetry. And yeah, I do wonder if
like there is some element of the next token surprise
factor going on there. - Yeah, maybe.
- Because I mean, like a lot of the tricks
in both poetry and music are like basically, you have
some repeated structure. And then you do like a twist. It's like, okay, verse or
like clause one, two, three is one thing, and then
clause four is like, okay, now we're onto the next theme. And they kind of play with
exactly when the surprise happens and the expectation of the user. And that's even true like, through history as musicians evolve music, they take like some nuanced structure that people are familiar with and they just tweak it a little bit. Like they tweak it and
add a surprising element. This is especially true in
classical music heritage. But that's what I'm wondering,
like is it all just entropy- - So breaking structure
or breaking symmetry is something that humans seem to like. Maybe as simple as that. - Yeah, and I mean, great artists copy, and they also, knowing
which rules to break is the important part. And that fundamentally, it must be about the listener of the piece. Like which rule is the right one to break, it's about the user or the audience member perceiving that as interesting. - What do you think is the
meaning of human existence? - There's a TV show I really
like called "The West Wing." And in "The West Wing,"
there's a character. He's the president of the United States who's having a discussion about the Bible with one of their colleagues. And the colleague says something about, "The Bible says X, Y, and Z." And the President says, "Yeah,
but it also says A, B, C." And the person says, "Do you believe the Bible
to be literally true?" And the President says,
"Yes, but I also think that neither of us are smart
enough to understand it." I think the analogy here
for the meaning of life is that largely, we don't know
the right question to ask. And so I think I'm very aligned with sort of "The Hitchhiker's
Guide to the Galaxy" version of this question, which is basically, if we can ask the right questions, it's much more likely we find the meaning of human existence. And so in the short term, as a heuristic in the sort
of search policy space, we should try to increase the diversity of people asking such questions, or generally of consciousness and conscious beings
asking such questions. So again, I think I'll take
the 'I don't know card' here, but say I do think there are
meaningful things we can do that improve the likelihood
of answering that question. - It's interesting how
much value you assign to the task of asking the right questions. That's the main thing is not the answers, it's the questions. - This point by the way is driven home in a very painful way when
you try to communicate with someone who cannot speak,
because a lot of the time, the last thing to go is
they have the ability to somehow wiggle a lip or move something that allows them to say yes or no. And in that situation, it's very obvious that what matters is, are you asking them the right question to be
able to say yes or no to? - Wow, that's powerful. Well, Bliss, thank you
for everything you do, and thank you for being you, and thank you for talking today. - Thank you. - Thanks for listening to this conversation with Bliss Chapman. And now, dear friends,
here's Noland Arbaugh, the first human being to
have a Neuralink device implanted in his brain. You had a diving accident in
2016 that left you paralyzed with no feeling from the shoulders down. How did that accident change your life? - That's sort of a freak
thing that happened. Imagine you're running into the ocean, although this is a lake, but
you're running into the ocean and you get to about waist high, and then you kind of like dive in, take the rest of the plunge
under the wave or something. That's what I did. And then I just never came back up. Not sure what happened. I did it running into the
water with a couple of guys. And so my idea of what happened is really just that I
took like a stray fist, elbow, knee, foot, something
to the side of my head. The left side of my head was sore for about a month afterwards. So must must've taken a pretty big knock. And then they both came up, and I didn't. And so I was face down
in the water for a while. I was conscious. And then eventually just realized I couldn't hold my breath any longer. And I keep saying, "Took a big drink." People, I don't know if
they like that I say that. It seems like I'm making light of it all, but it's just kind of how I am. And I don't know, like I'm a very relaxed sort of stress-free person. I rolled with the punches
for a lot of this. I kind of took it in stride. It's like, "All right,
well what can I do next? How can I improve my
life even a little bit on a day-to-day basis?" At first, just trying
to find some way to heal as much of my body as possible, to try to get healed, to
try to get off a ventilator, learn as much as I could
so I could somehow survive once I left the hospital. And then thank God I had
like my family around me. If I didn't have my parents, my siblings, then I would've never made it this far. They've done so much for me, more than like I can ever
thank them for, honestly. And a lot of people don't have that. A lot of people in my situation, their families either aren't
capable of providing for them, or honestly, just don't want to. And so they get placed
somewhere in some sort of home. So thankfully I had my family. I have a great group of friends, a great group of buddies from college who have all rallied around me, and we're all still incredibly close. People always say, "If you're lucky, you'll end up with one or
two friends from high school that you keep throughout your life." I have about 10 or 12 from high school that have all stuck around, and we still get together,
all of us twice a year. We call it the spring
series and the fall series. This last one we all did,
we dressed up like X-Men. So I did a Professor Xavier,
and it was freaking awesome. It was so good. So yeah, I have such a great
support system around me. And so being a quadriplegic
isn't that bad. I get waited on all the time. People bring me food and
drinks, and I get to sit around and watch as much TV and
movies and anime as I want. I get to read as much as I want. I mean, it's great. - It's beautiful to see that you see the silver lining in all of this. Just going back, do
you remember the moment when you first realized you were paralyzed from the neck down?
- Yeah, yep. I was face down in the water right when whatever something hit my head. I tried to get up and I
realized I couldn't move and it just sort of clicked. I'm like, "All right, I'm paralyzed. Can't move. What do I do? If I can't get up, I can't flip over, can't do anything, then I'm
gonna drown eventually." And I knew I couldn't
hold my breath forever, so I just held my breath
and thought about it for maybe 10, 15 seconds. I've heard from other people
that like look on liquors, I guess the two girls that
pulled me out of the water were two of my best friends. They're lifeguards. And one of them said that
it looked like my body was sort of shaking in the water, like I was trying to flip over and stuff. But I knew, I knew immediately. And I just kind of, I realized that that's
what my situation was from here on out. Maybe if I got to the hospital, they'd be able to do something. When I was in the hospital, like right before surgery, I was trying to calm
one of my friends down. I had like brought her with
me from college to camp, and she was just bawling
over me, and I was like, "Hey, it's gonna be fine. Like don't worry." I was cracking some jokes
to try to lighten the mood. The nurse had called
my mom, and I was like, "Don't tell my mom. She's just gonna be stressed out. Call her after I'm out of surgery," 'cause at least she'll
have some answers then, like whether I live or not, really. And I didn't want her to be stressed through the whole thing. But I knew. And then when I first
woke up after surgery, I was super drugged up. They had me on fentanyl like
three ways, which was awesome. I don't recommend it, but
I saw some crazy stuff on that fentanyl, and it was still the best I've ever felt on drugs. Medication, sorry, on medication. And I remember the first time
I saw my mom in the hospital. I was just bawling. I had like ventilator in, like I couldn't talk or anything, and I just started crying, because it was more like seeing her. I mean, the whole situation
obviously was pretty rough, but it was just like seeing her face for the first time was pretty hard. But yeah, I never had like a moment of, "Man, I'm paralyzed. This sucks. I don't wanna like be around anymore." It was always just, "I hate
that I have to do this, but like sitting here and
wallowing isn't gonna help." - So immediate acceptance. - [Noland] Yeah, yeah. - Has there been low points along the way? - Yeah, yeah, sure. I mean, there are days
when I don't really feel like doing anything. Not so much anymore. Like not for the last couple years, I don't really feel that way. I've more so just wanted to
try to do anything possible to make my life better at this point. But at the beginning, there
were some ups and downs. There were some really
hard things to adjust to. First off, just like
the first couple months, the amount of pain I was
in was really, really hard. I mean, I remember screaming at the top of my lungs in the hospital, because I thought my legs were on fire. And obviously, I can't feel anything, but it's all nerve pain. And so that was a really hard night. I asked them to give me as
much pain meds as possible. They're like, "You've had
as much as you can have, so just kind of deal with it. Go to a happy place sort of thing." So that was a pretty low point. And then every now and again, it's hard, like realizing things that
I wanted to do in my life that I won't be able to do anymore. I always wanted to be
a husband and father, and I just don't think
that I could do it now as a quadriplegic. Maybe it's possible, but
I'm not sure I would ever put someone I love through that, like having to take care of me and stuff. Not being able to go out and play sports. I was a huge athlete growing
up, so that was pretty hard. Little things too, when I
realized I can't do them anymore. Like there's something really special about being able to hold
a book and smell a book. Like the feel, the texture, the smell, like as you turn the
pages, like I just love it. I can't do it anymore. And it's little things like that. The two-year mark was pretty rough. Two years is when they say
you will get back basically as much as you're ever gonna get back, as far as movement and sensation goes. And so for the first two years, that was the only thing
on my mind was like try as much as I can to move my fingers, my hands, my feet, everything possible to try to get sensation and movement back. And then when the two-year mark hit, so June 30th, 2018, I was really sad that
that's kind of where I was. And then just randomly here and there, but I was never like depressed
for long periods of time. Just it never seemed worthwhile to me. - What gave you strength? - My faith. My faith in God was a big one. My understanding that it
was all for a purpose. And even if that purpose wasn't anything involving Neuralink,
even if that purpose was, there's a story in the Bible about Job, and I think it's a really,
really popular story, about how Job has all
of these terrible things happen to him, and he praises God throughout the whole situation. I thought, and I think
a lot of people think for most of their lives that they are Job, that they're the ones going
through something terrible, and they just need to praise
God through the whole thing and everything will work out. At some point after my accident, I realized that I might not be Job, that I might be one of his children that gets killed or
kidnapped or taken from him. And so it's about terrible
things that happen to those around you who you love. So maybe, in this case,
my mom would be Job, and she has to get through
something extraordinarily hard and I just need to try and make it as best as possible for her, because she's the one that's really going through this massive trial. And that gave me a lot of strength. And obviously, my family. My family and my friends, they give me all the strength that I need on a day-to-day basis. So it makes things a lot easier having that great
support system around me. - From everything I've seen of you online, your streams and the way you
are today, I really admire, let's say, your unwavering
positive outlook on life. Has that always been this way? - Yeah, yeah. I mean, I've just always thought I could do anything I ever wanted to do. There was never anything too big. Like whatever I set my mind
to, I felt like I could do it. I didn't wanna do a lot. I wanted to like travel around and be sort of like a gypsy
and like go work odd jobs. I had this dream of
traveling around Europe and being like, I don't know, a shepherd in like Wales or Ireland. And then going and being
a fisherman in Italy, doing all these things for like a year. Like it's such like cliche things, but I just thought it would be so much fun to go and travel and do different things. And so I've always just seen the best in people around me too. And I've always tried
to be good to people. And growing up with my mom too, she's like the most positive
energetic person in the world. And we're all just people people. I just get along great with people. I really enjoy meeting new people, and so I just wanted to do everything. This is just kind of just how I've been. - It's just great to see that
cynicism didn't take over, given everything you've been through. - Yeah. - Was that like a
deliberate choice you made that you're not gonna
let this keep you down? - Yeah, a bit. Also, like it's just kind of how I am. Like I said, I roll with
the punches with everything. I always used to tell people, like I don't stress about things much. And whenever I'd see
people getting stressed, I would just say, "It's not hard. Just don't stress about it." And that's all you need to do. And they're like, "That's
not how that works." I'm like, "It works for me. Like just don't stress, and
everything will be fine. Like everything will work out." Obviously, not everything
always goes well, and it's not like it all works out for the best all the time, but I just don't think stress has had any place in my life since I was a kid. - What was the experience like of you being selected to
be the first human being to have a Neuralink device
implanted in your brain? Were you scared, excited?
- No, no, it was cool. (Lex laughing) Like I was never afraid of it. I had to think through a lot. Should I do this? Like be the first person? I could wait until number two or three and get a better version of the Neuralink. Like the first one might not work. Maybe it's actually gonna kind of suck it. It's gonna be the worst
version ever in a person. So why would I do the first one? Like I've already kind of been selected. I could just tell them, like,
"Okay, find someone else, and then I'll do number two or three." Like I'm sure they would let me. They're looking for a few people anyways. But ultimately, I was like, I don't know, there's something about being
the first one to do something. It's pretty cool. I always thought that if I had the chance that I would like to do
something for the first time, this seemed like a
pretty good opportunity, and I was never scared. I think my like faith
had a huge part in that. I always felt like God was
preparing me for something. I almost wish it wasn't this, because I had many conversations with God about not wanting to do any
of this as a quadriplegic. I told him, "I'll go
out and talk to people. I'll go out and travel the world and talk to stadiums, thousands of people, give my testimony, I'll do all of it, but like heal me first. Don't make me do all of this in a chair. That sucks." And I guess he won that argument. I didn't really have much of a choice. I always felt like there
was something going on. And to see how, I guess,
easily I made it through the interview process and how quickly everything happened, how the stars sort of
aligned with all of this, it just told me like, as the
surgery was getting closer, it just told me that it
was all meant to happen. It was all meant to be. And so I shouldn't be afraid
of anything that's to come. And so I wasn't. I kept telling myself like, "You say that now, but as
soon as the surgery comes, you're probably gonna be freaking out. Like you're about to have brain surgery." And brain surgery is a big
deal for a lot of people, but it's a even bigger deal for me. Like it's all I have left. The amount of times I've been like, "Thank you God that you
didn't take my brain and my personality and
my ability to think, my like love of learning, like my character, everything,
like thank you so much. Like as long as you left me that, then I think I can get by." And I was about to let
people go like root around, and they're like, "Hey, we're gonna go put some stuff in your brain. Hopefully, it works out." And so it was something
that gave me pause. But like I said, how
smoothly everything went, I never expected for a second
that anything would go wrong. Plus, the more people I
met on the Barrow's side and on the Neuralink side, they're just the most
impressive people in the world. Like I can't speak enough to how much I trust
these people with my life and how impressed I am with all of them. And to see the excitement on their faces, to like walk into a room
and roll into a room and see all of these people looking at me, like we're so excited. Like we've been working so hard on this, and it's finally happening. It's super infectious, and it just makes me wanna do it even more and to help them achieve their dreams. Like I don't know, it's so rewarding. And I'm so happy for
all of them, honestly. - What was the day of surgery like? When did you wake up? What'd you feel? - Yeah.
- Minute by minute. - Yeah.
- Were you freaking out? - No, no. I thought I was going to, but as surgery approached
the night before, the morning of, I was just excited. Let's make this happen. I think I said something like that to Elon on the phone beforehand. We were like FaceTiming, and I was like, "Let's rock and roll." And he's like, "Let's do it." I don't know, I wasn't scared. So we woke up. I think we had to be at the
hospital at like 5:30 AM. I think surgery was at like 7:00 AM. So we woke up pretty early. I'm not sure much of us slept that night. Got to the hospital 5:30, went through like all the pre-op stuff. Everyone was super nice. Elon was supposed to be
there in the morning, but something went wrong with his plane, so we ended up FaceTiming. That was cool. Had one of the greatest
one-liners of my life. After that phone call, hung up with him. There were like 20 people
around me, and I was like, I just hope he wasn't too
starstruck talking to me. - Nice.
- Yeah, it was good. - Well done.
- Yeah, yeah. - Did you write that ahead of
time, or it just came to you? - No, it just came to me. I was like, "This seems right." When in surgery, I asked if I could pray right beforehand. So I like prayed over the room. I asked God if you would
like be with my mom in case anything happened to me. And just to like calm
her nerves out there. Woke up and played a bit
of a prank on my mom. I don't know if you've heard about it. - Yeah, I read about it. - Yeah, she was not happy. - Can you take me through the prank - Yeah, this is something-
- Do you regret doing that now?
- No, no, not one bit. It was something I had talked about ahead of time with my buddy, Bain. I was like, "I would really
like to play a prank on my mom." Very specifically, my mom. She's very gullible. I think she had knee surgery once even. And after she came out of knee surgery, she was super groggy. She's like, "I can't feel my legs." And my dad looked at her, he was like, "You don't have any legs. Like they had to amputate both your legs." We just do very mean
things to her all the time. I'm so surprised that she still loves us. But right after surgery,
I was really worried that I was going to be too like
groggy, like not all there. I had anesthesia once
before, and it messed me up. Like I could not function
for a while afterwards. And I like said a lot of
things that I was like, I was really worried
that I was gonna start, I don't know, like dropping some bombs. And I wouldn't even know,
I wouldn't remember. So I was like, "Please
God, don't let that happen. And please let me be there
enough to do this to my mom." And so she walked in after surgery. It was like the first time they had been able to
see me after surgery. And she just looked at me, she said, "Hi, like, how are you? How are you doing? How do you feel?" And I looked at her in this very, I think the anesthesia helped, very like groggy, sort of
confused look on my face. It's like, "Who are you?" And she just started
looking around the room, like at the surgeons,
at the doctors, like, "What did you do to my son? You need to fix this right now." Tears started streaming. I saw how much she was freaking out. I was like, "I can't let this go on." And so I was like, "Mom, I'm fine. Like it's all right." And still, she was not happy about it. She still says she's
gonna get me back someday. But I mean, I don't know. I don't know what that's gonna look like. - It's a lifelong battle.
- Yeah, it was good. - In some sense, it was a demonstration that you still got- - That's all I wanted it to be
- Sense of humor. - That's all I wanted it to be. And I knew that doing
something super mean to her like that would show her-
- - - Yeah.
- To show that you're still there, that you love her. - [Noland] Yeah, exactly, exactly. - It's a dark way to do it, but I love it. What was the first time
you were able to feel that you can use the Neuralink device to effect the world around you? - Yeah, the first little taste I got of it was actually not too long after surgery. Some of the Neuralink team had brought in like a little iPad, a
little tablet screen, and they had put up
eight different channels that were recording some
of my neuron spikes. They put it in front of me. Like this is like real
time your brain firing. That's super cool. My first thought was, "I
mean, if they're firing now, let's see if I can
affect them in some way." So I started trying to
like wiggle my fingers and I just started like
scanning through the channels, and one of the things I was doing was like moving my index
finger up and down. And I just saw this yellow
spike on like top row, like third box over or something. I saw this yellow spike
every time I did it. And I was like, "Oh, that's cool." And everyone around me was just like, "Well, what are you seeing?" I was like, "Look, look at this one. Look at like this top row, third
box over this yellow spike. Like that's me right there, there, there." And everyone was freaking out. They started like clapping. I was like, "That's super unnecessary." - That's awesome.
- This is what's supposed to happen, right? - So you're imagining yourself moving each individual finger one at a time, and then seeing like that
you can notice something, and then when you did the index
finger, you're like, "Oh." - Yeah, I was wiggling
kind of all of my fingers to see if anything would happen. There was a lot of other things going on, but that big yellow spike was
the one that stood out to me. Like I'm sure that if I would've
stared at it long enough, I could have mapped out
maybe 100 different things. But the big yellow spike
was the one that I noticed. - Maybe you could speak to what it's like to sort of wiggle your fingers, to imagine that the mental,
the cognitive effort required to sort of wiggle your
index finger, for example. How easy is that to do? - Pretty easy for me. It's something that,
at the very beginning, after my accident, they told me to try and move my body as much as possible. Even if you can't, just keep trying, because that's going to create
new like neural pathways or pathways in my spinal cord
to like reconnect these things to hopefully regain some movement someday. - That's fascinating.
- Yeah, I know. It's bizarre, but I- - So that's part of the recovery process is to keep trying to move your body? - Yep, as much as you can.
- And that's, and the nervous system does its thing. It starts reconnecting.
- Yeah. It'll start reconnecting for some people. Some people, it never works. Some people, they'll do it. Like for me, I got some
bicep control back, and that's about it. I can, if I try enough, I can wiggle some of my fingers. Not like on command. It's more like if I try to move, say my right pinky and I
just keep trying to move it, after a few seconds, it'll wiggle. So I know there's stuff there. Like I know, and that happens with a few - -different of my fingers and stuff. But yeah, that's what they tell you to do. One of the people at the time when I was in the hospital
came in and told me, for one guy who had recovered
most of his control, what he thought about every
day was actually walking, like the act of walking
just over and over again. So I tried that for years. I tried just imagining
walking, which it's hard. It's hard to imagine like all
of the steps that go into, well, taking a step, like all of the things that have to move, like all of the activations
that have to happen along your leg in order
for one step to occur. - But you're not just imagining. You're like doing it, right? - I'm trying, yeah. So it's like, it's imagining over again what I had to do to take a step, because it's not something
any of us think about. You wanna walk and you take a step. You don't think about all
of the different things that are going on in your body. So I had to recreate that in
my head as much as I could. And then I practice it
over and over and over. - So it's not like a
third person perspective. It's a first person perspective. You're like, it's not like you're imagining yourself walking. You're like literally
doing this, everything, all the same stuff as if you're walking. - Yeah, which was hard. It was hard at the beginning. - Like frustrating hard, or like actually cognitively hard? Like which way?
- It was both. There's a scene in one of the
"Kill Bill" movies actually, oddly enough, where she is like paralyzed, I don't know, from like a
drug that was in her system. And then she like finds some way to get into the back of a truck or something, and she stares at her
toe and she says, "Move." Like move your big toe. And after a few seconds
on screen, she does it. And she did that with every
one of her like body parts until she can move again. I did that for years, just
stared at my body and said, "Move your index finger. Move your big toe." Sometimes, vocalizing it like out loud, sometimes just thinking it. I tried every different way to do this to try to get some movement back. And it's hard because it
actually is like taxing, like physically taxing on my body, which is something I
would've never expected, 'cause it's not like I'm moving, but it feels like there's
a buildup of, I don't know, the only way I can describe
it is there are like signals that aren't getting
through from my brain down, 'cause there's that gap in my spinal cord. So brain down, and then from
my hand back up to the brain. And so it feels like those signals get stuck in whatever body
part that I'm trying to move. And they just build up and build up and build up until they burst. And then once they burst, I
get like this really weird sensation of everything
sort of like dissipating back out to level, and then I do it again. It's also just like a fatigue
thing, like a muscle fatigue, but without actually moving your muscles. It's very, very bizarre. And then if you try to
stare at a body part or think about a body part and move for two, three,
four, sometimes eight hours, it's very taxing on your mind. It's takes a lot of focus. It was a lot easier at the beginning because I wasn't able to like control a TV in my room or anything. I wasn't able to control
any of my environment. So for the first few years, a lot of what I was doing
was staring at walls. And so obviously, I did a lot of thinking, and I tried to move a lot just
over and over and over again. - So you never gave up sort of hope there? - No.
- Just training hard essentially?
- Yep, and I still do it. I do it like subconsciously. And I think that that helped a lot with things with Neuralink, honestly. It's something that I
talked about the other day at the all hands that I did at
Neuralink's Austin facility. - Welcome to Austin, by the way. - Yeah, hey, thanks man. I went to school-
- Nice hat. - Hey, thanks, thanks man. The gigafactory was super cool. I went to school at Texas A&M, so I've been around before. - So you should be saying welcome to me. - Yeah.
- Welcome to Texas, Lex. Yeah, I get you. - But yeah, I was talking about how a lot of what they've had me do, especially at the beginning, well, I still do it now is body mapping. So like there will be a
visualization of a hand or an arm on the screen and
I have to do that motion, and that's how they sort
of train the algorithm to like understand what I'm trying to do. And so it made things very
seamless for me, I think. - That's really, really cool. So yeah, it's amazing to know 'cause I've learned a lot about
the body mapping procedure with the interface and
everything like that. It's cool to know that
you've been essentially like training to be like
world class at that task. - Yeah, yeah. I don't know if other quadriplegics, like other paralyzed people give up. I hope they don't. I hope they keep trying, because I've heard other
paralyzed people say, like don't ever stop. They tell you two years,
but you just never know. The human body is capable
of amazing things. So I've heard other people
say, "Don't give up." Like I think one girl had spoken to me through some family members and said that she had been
paralyzed for 18 years, and she'd been trying to
like wiggle her index finger for all that time, and
she finally got it back like 18 years later. So like I know that it's possible, and I'll never give up doing it. I do it when I'm lying down. Like watching TV, I'll
find myself doing it, kind of just almost like on its own. It's just something I've
gotten so used to doing that I don't know, I don't
think I'll ever stop. - That's really awesome to hear, 'cause I think it's one of those things that can really pay off, in the long term. 'Cause like that is training. You're not visibly seeing the results of that training at the moment, but like, there's that like Olympic
level nervous system getting ready for something.
- Which honestly was like something that
I think Neuralink gave me that I can't thank them enough for it. Like I can't show my
appreciation for it enough was being able to visually see that what I'm doing is
actually having some effect. It's a huge part of the reason why, like I know now that I'm
gonna keep doing it forever, because before Neuralink,
I was doing it every day, and I was just assuming
that things were happening. Like it's not like I knew. I wasn't getting back any
mobility or sensation or anything. So I could have been running up against a brick wall for all I knew. And with Neuralink, I get to see like all the signals happen in real time, and I get to see that what I'm doing can actually be mapped. When we started doing like
click calibrations and stuff, when I go to click my index
finger for a left click, that it actually recognizes that. Like it changed how I
think about what's possible with like retraining my body to move. And so yeah, I'll never give up now. - And also, just the signal
that there's still a powerhouse of a brain there that's like- - Exactly.
- And as the technology develops, that brain is, I mean, that's the most important thing about the human body is the brain. And it can do a lot of the control. So what did it feel like when you first, could wiggle the index finger and saw the environment respond? Like that little-
- Yeah. - Wherever, just being way
too dramatic according to you. - Yeah, it was very cool. I mean, it was cool, but I
keep telling this to people, it made sense to me. Like it made sense that like there are signals
still happening in my brain, and that as long as you
had something near it that could measure those,
that could record those, then you should be able to
visualize it in some way. Like see it happen. And so that was not very surprising to me. I was just like, "Oh, cool." We found one. Like we found something that works. It was cool to see that
their technology worked, and that everything that
they had worked so hard for was like going to pay off. But I hadn't like moved a cursor
or anything at that point. I had like interacted with a computer or anything at that point. So it just made sense, it was cool. I didn't really know much
about BCI at that point either, so I didn't know like what sort of step this was actually making. Like I didn't know if
this was like a huge deal, or if this was just like, okay, it's cool that we got this far, but we're actually hoping for something like much
better down the road. It's like, okay. I just thought that they
knew that it turned on. So I was like, cool. Like this is cool. - Well, did you like read up on the specs of the hardware you're getting installed? Like the number of threads, this kind of stuff?
- Yeah, I do all of that, but it's all Greek to me. I was like, okay, threads, 64 threads, 16 electrodes, 1,024 channels. Okay. Like that math checks out. - Sounds right.
- Yeah. - When was the first time you were able to move a mouse cursor? - I know it must have been
within the first maybe week, a week or two weeks that I was able to like first move the cursor. And again, like it kind
of made sense to me. It didn't seem like that big of a deal. It was like, okay, well, hmm, how do I explain this? When everyone around you starts clapping for something that you've
done, it's easy to say, "Okay, like I did something cool. Like that was impressive in some way." What exactly that meant, what it was hadn't really like set in for me. So again, I knew that me
trying to move a body part and then that being mapped
in some sort of like machine learning algorithm
to be able to identify like my brain signals and then take that and give me cursor control, that all kind of made sense to me. I don't know like all
the ins and outs of it, but I was like, there are still
signals in my brain firing. They just can't get through because there's like a
gap in my spinal cord. And so they can't get all
the way down and back up, but they're still there. So when I moved the cursor for
the first time, I was like, "That's cool, but I expected
that that should happen." Like it made sense to me. When I moved the cursor for the first time with just my mind, without
like physically trying to move, so I guess I can get into
that just a little bit. Like the difference between attempted movement and imagined movement. - Yeah, that's a fascinating difference. - Yeah.
- From one to the other. - Yeah, yeah, yeah. So like attempted movement
is me physically trying to attempt to move, say, my hand. I try to attempt to move
my hand to the right, to the left, forward and back. And that's all attempted. Attempt to like lift
my finger up and down. Attempt to kick or something. I'm physically trying to
do all of those things, even if you can't see it. This would be like me attempting to like shrug my shoulders or something. That's all attempted movement. That's what I was doing for
the first couple of weeks when they were going to
give me cursor control. When I was doing body mapping, it was attempt to do
this, attempt to do that. When Nir was telling me
to like imagine doing it, it like kind of made sense to me, but it's not something
that people practice. Like if you started school
as a child and they said, "Okay, write your name with this pencil." And so you do that. "Okay, now imagine writing
your name with that pencil." Kids would think like, I guess like that kind of makes sense. And they would do it. But that's not something we're taught. It's all like how to do things physically. We think about like thought
experiments and things, but that's not like a physical
action of doing things. It's more like what you would
do in certain situations. So imagined movement, it never
really connected with me. Like I guess you could maybe describe it as like a professional athlete, like swinging a baseball bat
or swinging like a golf club. Like imagine what you're supposed to do. But then you go right to
that and physically do it, then you get a bat in your hand and then you do what
you've been imagining. And so I don't have that like connection. So telling me to imagine
something versus attempting it, there wasn't a lot that I
could do there mentally. I just kind of had to accept
what was going on and try. But the attempted moving
thing, it all made sense to me. Like if I try to move, then there's a signal
being sent in my brain. And as long as they can pick that up, then they should be able to map
it to what I'm trying to do. And so when I first moved
the cursor like that, it was just like, "Yes,
this should happen." Like I'm not surprised by that. - But can you clarify, is there
supposed to be a difference between imagined movement
and attempted movement? - Yeah, just that in imagined movement, you're not attempting to move at all. - You're like visualizing doing. And then theoretically, is
that supposed to be a different part of the brain that lights up in those two different situations? - [Bliss] Yeah, not necessarily. I think all these signals
can still be represented in motor cortex, but the
difference I think has to do with the naturalness
of imagining something versus attempting it-
- Got it. - [Bliss] And sort of the
fatigue of that over time. - And by the way, on the mic is Bliss. So this is just different
ways to prompt you to kind of get to the
thing that you arrived at. - [Noland] Yeah, yeah. - Attempted movement does sound
like the right thing - try. - Yeah, I mean, it makes sense to me. - 'Cause imagine for me,
I would start visualizing, like in my mind, visualizing. Attempted, I would actually
start trying to like, there's, I mean, I did like combat sports my
whole life, like wrestling. When I'm imagining a move,
see, I'm like moving my muscle. - Exactly.
- Like there is a bit of an activation almost,
versus like visualizing yourself like a picture doing it. - Yeah, it's something that I feel like naturally, anyone would do. If you try to tell someone
to imagine doing something, they might close their eyes and then start physically doing it. But it's just-
- Just didn't click. - Yeah. It's hard. It was very hard at the beginning. - But attempted worked.
- Attempted worked. It worked just like it should. Worked like a charm. - [Bliss] I remember
there was like one Tuesday we were messing around, and I think, I forget what swear word you used, but there's a swear word
that came out of your mouth when you figured out you could just do the direct cursor control. - Yeah, that's it. It blew my mind. Like no pun intended,
blew my mind when I first moved the cursor just with my thoughts and not attempting to move. It's something that I've found like over the couple of weeks,
like building up to that, that as I get better cursor controls, like the model gets better, then it gets easier for me to like, like I don't have to
attempt as much to move it. And part of that is something
that I'd even talked with them about when I
was watching the signals of my brain one day, I was watching, when I like attempted to move to the right and I watched the screen,
it's like I saw the spikes. It's like I was seeing the
spike, the signals being sent before I was actually attempting to move. I imagined just because when
you go to say move your hand or any body part, that signal gets sent before you're actually moving has to make it all the
way down and back up before you actually do
any sort of movement. So there's a delay there. And I noticed that there was
something going on in my brain before I was actually attempting to move, that my brain was like
anticipating what I wanted to do. And that all started sort of, I don't know, like
percolating in my brain. It was just sort of there, like always in the back. Like that's so weird
that it could do that. It kind of makes sense, but
I wonder what that means as far as like using the Neuralink. And then as I was playing around
with the attempted movement and playing around with the cursor, and I saw that like, as the
cursor control got better, that it was anticipating my movements and what I wanted it to do. Like cursor movements, what I wanted to do a bit
better and a bit better. And then one day, I just randomly, as I was playing Webgrid,
I like looked at a target before I had started
like attempting to move. I was just trying to like get over, like train my eyes to start looking ahead. Like, okay, this is the target I'm on, but if I look over here to this target, I know I can like maybe be
a bit quicker getting there. And I looked over and the
cursor just shot over it. It was wild. I had to take a step back. Like I was like, "This
should not be happening." All day, I was just
smiling, I was so giddy. I was like, "Guys, do
you know that this works? Like I can just think it and it happens," which like they'd all been
saying this entire time. like I can't believe like you're doing all this with your mind. I'm like, "Yeah, but is
it really with my mind?" Like I'm attempting to move, and it's just picking that up so it doesn't feel like it's with my mind. When I moved it for the
first time like that, it was, oh man. It made me think that this technology, that what I'm doing is actually way, way more impressive than I ever thought. It was way cooler than I ever thought. And it just opened up a whole
new world of possibilities of like what could possibly
happen with this technology and what I might be able
to be capable of with it. - Because you had felt for the first time, like this was digital telepathy. Like you're controlling a
digital device with your mind. - [Noland] Yep. - I mean, that's a real
moment of discovery. That's really cool. Like you've discovered something. I've seen like scientists talk
about like a big aha moment. Like Nobel Prize winning, they'll have this like holy crap. - Yeah.
- Like whoa. - That's what it felt. I didn't feel like, like I felt like I had
discovered something but for me. Maybe not necessarily for
like the world at large or like this field at large. It just felt like an aha moment for me. Like, "Oh this works." Like obviously, it works. And so that's what I do
like all the time now. I kind of intermix the attempted movement and imagined movement. I do it all like together, because I've found that there
is some interplay with it that maximizes efficiency with the cursor. So it's not all like one or the other. It's not all just, I only use attempted or I only use like imagined movements. It's more I use them in parallel, and I can do one or the other. I can just completely think
about whatever I'm doing. But I don't know. I like to play around with it. I also like to just
experiment with these things. Like every now and again, I'll get this idea in my head like, "Hmm, I wonder if this works." And I'll just start doing it, and then afterwards, I'll tell them, "By the way, I wasn't doing
that like you guys wanted me to. I thought of something and I
wanted to try it and so I did. It seems like it works, so maybe we should like
explore that a little bit." - So I think that discovery
is not just for you, at least from my perspective,
that's the discovery for everyone else who
ever uses a Neuralink that this is possible. Like I don't think this an obvious thing that this is even possible. It's like, I was saying to Bliss earlier, it's like the four-minute mile. People thought it was
impossible to run a mile in four minutes, and once
the first person did it, then everyone just started doing it. So like just to show that it's possible, that paves the way to
like anyone can not do it. That's the thing that's actually possible. You don't need to do
the attempted movement. You can just go direct. That's crazy. - It is crazy, it's crazy. - For people who don't know, can you explain how the Link app works? You have an amazing stream on the topic. Your first stream, I think,
on X describing the app. Can you just describe how it works? - Yeah, so it's just an
app that Neuralink created to help me interact with the computer. So on the Link app, there
are a few different settings and different modes and
things I can do on it. So there's like the body mapping, which we kind of touched on. There's a calibration. Calibration is how I
actually get cursor control. So calibrating what's going on in my brain to translate that into cursor control. So it will pop out models. What they use I think is like time. So it would be five minutes, and calibration will give
me so good of a model. And then if I'm in it for
10 minutes and 15 minutes, the models will progressively get better. And so the longer I'm in it generally, the better the models will get. - That's really cool, 'cause
you often refer to the models. The model's the thing that's constructed once you go through the calibration step. And then you also talked about, sometimes you'll play like
a really difficult game, like Snake, just to see
how good the model is. - Yeah, yeah, so Snake is kind of like my litmus test for models. If I can control Snake decently well, then I know I have a pretty good model. So yeah, the Link app has all of those. It has Webgrid in it now. It's also how I like connect to the computer just in general. So they've given me a lot
of like voice controls with it at this point,
so I can say like connect or implant disconnect. And as long as I have that charger handy, then I can connect to it. So the charger is also how
I connect to the Link app, to connect to the computer. I have to have the implant
charger over my head when I wanna connect to have it wake up, 'cause the implant's in hibernation mode, like always when I'm not using it. I think there's a setting to
like wake it up every so long. So we could set it to
half an hour or five hours or something if I just want
it to wake up periodically. So yeah, I'll like
connect to the Link app, and then go through all sorts of things. Calibration for the
day, maybe body mapping. I made them give me like
a little homework tab, because I am very forgetful and I forget to do things a lot. So I have like a lot of
data collection things that they want me to do. - Is the body mapping part
of the data collection, or is that also part of the- - Yeah, it is. It's something that they
want me to do daily, which I've been slacking on, 'cause I've been doing so much media and traveling so much. So I've been-
- You've been super famous. - Yeah, I've been a
terrible first candidate for how much I've been
slacking on my homework. But yeah, it's just
something that they want me to do every day to track how well the Neuralink
is performing over time and to have something to give. I imagine to give to the FDA to create all sorts of fancy charts
and stuff and show like, "Hey, this is what the Neuralink, this is how it's performing
day one versus day 90 versus day 180 and things like that. - What's the calibration step like? Is it like move left, move right? - It's a bubble game. So there will be like yellow bubbles that pop up on the screen. At first, it is open loop. So open loop, this is something that I still don't fully understand, the open loop and closed loop thing. - And me and Bliss talked for a long time about the difference between
the two on the technical side. - Okay.
- So it'd be great to hear your side of the story. - Open loop is basically, I have no control over the cursor. The cursor will be moving on
its own across the screen, and I am following by intention the cursor to different bubbles. And then the algorithm is training off of what like the signals it's
getting are as I'm doing this. There are a couple different
ways that they've done it. They call it center out target. So there will be a bubble in the middle and then eight bubbles around that. And the cursor will go from
the middle to one side. So say middle to left, back to middle, to up, to middle, like up, right. And they'll do that all
the way around the circle. And I will follow that
cursor the whole time, and then it will train
off of my intentions, what it is expecting my intentions to be throughout the whole process. - Can you actually speak to, when you say follow-
- Yes. - You don't mean with your eyes. You mean with your intentions. - Yeah, so generally for calibration, I'm doing attempted movements, 'cause I think it works better. I think the better models, as I progress through calibration, make it easier to use imagined movements. - Wait, wait, wait, wait. So calibrated on attempted movement will create a model that
makes it really effective for you to then use the force? - Yes, I've tried doing calibration with imagined movement, and it just doesn't work
as well for some reason. So that was the center out targets. There's also one where a random target will pop up on the
screen and it's the same. I just like move, I follow along with wherever the cursor
is to that target, all across the screen. I've tried those with imagined movement, and for some reason,
the models just don't, they don't give as high level as quality when we get into closed loop. I haven't played around with it a ton, so maybe like the different ways that we're doing calibration
now might make it a bit better. But what I've found is there
will be a point in calibration where I can use imagined movement. Before that point, it doesn't really work. So if I do calibration for 45 minutes, the first 15 minutes, I
can't use imagined movement. It just like doesn't work for some reason. And after a certain point, I can just sort of feel it. I can tell it moves different. That's the best way I can describe it. Like it's almost as if it is anticipating what I am going to do
again before I go to do it. And so using attempted
movement for 15 minutes, at some point, I can kind of tell when I like move my
eyes to the next target that the cursor is
starting to like pick up. Like it's starting to understand, it's learning like what I'm going to do. - So first of all, it's really cool that, I mean, you are a true
pioneer in all of this. You're like exploring how to do every aspect of this most
effectively, and there's just, I imagine so many lessons
learned from this. So thank you for being a pioneer in all these kinds of different
like super technical ways. And it's also cool to hear
that there's like a different like feeling to the experience when it's calibrated in different ways, 'cause I mean, I imagine your brain is doing something different, and that's why there's a
different feeling to it. And then trying to find the
words and the measurements to those feelings would
be also interesting. But at the end of the
day, you can also measure that your actual performance on whether it's Snake or Webgrid, you could see like what
actually works well. And you're saying, for
the open loop calibration, the attempted movement works best for now. - Yep, yep. - So the open loop, you
don't get the feedback that you did something. - Yeah-
- Is that frustrating? - No, no, it makes sense to me. Like we've done it with a cursor and without a cursor in open loop. So sometimes, it's just,
say, for like the center out, you'll start calibration
with a bubble lighting up, and I push towards that bubble. And then when that bubble, when it's pushed towards
that bubble for say, three seconds a bubble will pop and then I come back to the middle. So I'm doing it all just by my intentions. Like that's what it's learning anyways. So it makes sense that as long as I follow what they want me to do, like
follow the yellow brick road, that it'll all work out. - You're full of great references. Is the bubble game fun? - Yeah, they always feel so
bad making me do calibration. Like, "Oh, we're about to
do a 40-minute calibration." I'm like, "All right, do you
guys wanna do two of them?" Like I'm always asking to, like whatever they need,
I'm more than happy to do. And it's not bad. Like I get to lie there or sit in my chair and like do these things
with some great people. I get to have great conversations. I can give them feedback. I can talk about all sorts of things. I could throw something on
on my TV in the background and kinda like split my
attention between them. Like it's not bad at all. I don't mind it.
- Is there a score that you get? Like can you do better on the bubble game? - No, I would love that. I would love-
- Yeah. Writing down suggestions from Noland. - That's-
- Make it more fun. Gamified. - Yeah, that's one thing that I really, really enjoy about Webgrid is 'cause I'm so competitive. Like the higher the BPS,
the higher the score, I know the better I'm doing. I think I've asked at one
point one of the guys, like if he could give me some
sort of numerical feedback for calibration, like I would like to know what they're looking at. Like, "Oh, we see like this number while you're doing calibration, and that means, at least on our end, that we think calibration is going well." And I would love that,
because I would like to know if what I'm doing is going well or not. But then they've also told me like, "Yeah, not necessarily like one-to-one." It doesn't actually mean that calibration is
going well in some ways. So it's not like 100%, and they don't wanna like
skew what I'm experiencing or want me to change things based on that. If that number isn't always accurate to like how the model will turn out or how like the end result, that's at least what I got from it. One thing I do that I have asked them and something that I
really enjoy striving for is towards the end of calibration, there is like a time between targets. And so I like to keep, like at the end, that
number as low as possible. So at the beginning,
it can be four or five, six seconds between me popping bubbles. But towards the end, I like
to keep it below like 1.5. Or if I could, get it to like one second between like bubbles, because in my mind, that translates really nicely
to something like Webgrid where I know if I can hit
a target one every second that I'm doing real, real well. - There you go, that's a way to get a score on the calibrations. Like the speed, how quickly can you get from bubble to bubble.
- Yeah. - So there's the open loop, and then it goes to the closed loop. - Closed loop.
- The closed loop can already start giving you a sense, 'cause you're getting feedback of like how good the model is. - Yeah, so closed loop is when
I first get cursor control and how they've described it to me, someone who does not
understand this stuff, I am the dumbest person in the room every time I'm with-
- I love the humility. - Yeah, is that I am closing the loop. So I am actually now the
one that is like finishing the loop of whatever this loop is. I don't even know what the
loop is, they've never told me. They just say there is a loop. And at one point, it's
open and I can't control. And then I get control and it's closed. So I'm finishing the loop. - So how long the
calibration usually take? You said like 10, 15 minutes. - Well, yeah, they're trying to get that number down pretty low. That's what we've been
working on a lot recently is getting that down as low
as possible, so that way, if this is something that people
need to do on a daily basis or if some people need to do on a like every other
day basis or once a week, they don't want people to
be sitting in calibration for long periods of time. I think they've wanted to get it down seven minutes or below, at
least where we're at right now. It'd be nice if you never
had to do calibration. So we'll get there at
some point, I'm sure, the more we learn about the brain and like I think that's the dream. I think right now, for me to get like really,
really good models, I am in calibration 40 or 45 minutes. And I don't mind. Like I said, they always feel really bad, but if it's gonna get me a model that can like break
these records on Webgrid, I'll stay in it for flipping two hours. - Let's talk business. So Webgrid. I saw a presentation
where Bliss said by March, you selected 89,000 targets in Webgrid. Can you explain this game? What is Webgrid, and what does it take to be a world class performer in Webgrid, as you continue to break world records? - Yeah. - It's like a gold medalist, like well. - Yeah, I'd like to thank, I'd like to thank everyone
who's helped me get here, my coaches, my parents for
driving me to practice every day at five in the morning. Like to thank God. And just overall, my
dedication to my craft. - The interviews with athletes, they're always like that, it's like that template. - Yeah. - So Webgrid is a grid that sells.
- Webgrid is, yeah. It's literally just a grid. They can make it as big or
small as you can make a grid. A single box on that grid will light up and you go and click it. And it is a way for them to
benchmark how good a BCI is. So it's pretty straightforward. You just click targets. - [Lex] Only one blue cell appears, and you're supposed to move the mouse to there and click on it.
- Yep. So I like playing on like bigger grids, 'cause the bigger the
grid, the like more BPS. It's bits per second that you
get every time you click one. So I'll say, I'll play
on like a 35 by 35 grid, and then one of those little squares cell, we call it target, whatever, will light up and you
move the cursor there and you click it and
then you do that forever. - And you've been able to achieve at first eight bits per second. And then you recently broke that. - Yeah, I'm at 8.5 right now. I would've beaten that literally the day before I came to Austin. But I had like, I don't know, like a five second lag right at the end. And I just had to wait until
the latency calmed down and then I kept clicking. But I was at like 8.01 and
then five seconds of lag, and then the next like
three targets I clicked all stayed at 8.01. So if I would've been able to
click during that time of lag, I probably would've hit, I
don't know, I might've hit nine. So I'm there. I'm really close. And then this whole Austin trip has really gotten in the way of my Webgrid playing ability.
- It's frustrating. - Yeah, it's-
- So that's all you've been thinking about right now? - Yeah, I know. I want to do better at nine. I want to do better. I wanna hit nine, I think. Well, I know nine is
very, very achievable. I'm right there. I think 10 I could hit
maybe in the next month. Like I could do it probably in the next few weeks if I really push it. - I think you and Elon are
basically the same person, 'cause last time I did a podcast with him, he came in extremely frustrated that he can't beat Uber Lilith as a droid. That was like a year ago I
think, I forget, like solo. And I could just tell, there's
some percentage of his brain the entire time was thinking like, "I wish I was right now attempting." - I think he did it. - He did it that night.
- Yeah. - He stayed up and did it that night. It's just crazy to me. I mean, in a fundamental
way, it's really inspiring. And what you're doing is
inspiring in that way, 'cause I mean, it's not
just about the game. Everything you're doing there has impact. By striving to do well on Webgrid, you're helping everybody figure out how to create the system all along, like the decoding, the
software, the hardware, the calibration, all of it, how to make all of that work so you can do everything else really well. - Yeah, it's just really fun. - Well, that's also,
that's part of the thing is like making it fun. - Yeah, it's addicting. I've joked about like
what they actually did when they went in and put
this thing in my brain. They must have flipped a switch to make me more susceptible
to these kinds of games, to make me addicted to
like Webgrid or something. Do you know Bliss's high score? - Yeah, he said like 14 or something. - 17.
- Oh boy. - 17.1 or something, 17.01. - 17 dot, 17.01.
- Yeah. - He told me he like does it on the floor with peanut butter and he like fasts. It's weird. That sounds like cheating. Sounds like performance enhancing. - [Bliss] No, like the first
time Noland played this game, he asked, "How good are we at this game?" And I think you told me right then, "You're gonna try to beat me on that." - I'm gonna get there someday.
- Yeah. I fully believe you.
- I think I can. - I'm excited for that.
- Yeah. So I've been playing, first
off, with the dwell cursor, which really hampers my
Webgrid playing ability. Basically, I have to wait
0.3 seconds for every click. - Oh, so you can't do the clicks. So you click by dwelling, you said 0.3? - 0.3 seconds, which sucks. It really slows down how much I'm able to, like how high I'm able to get. I still hit like 50, I think I hit like 50 something trials, net
trials per minute in that, which was pretty good,
'cause I'm able to like, there's one of the settings is also like how slow you need to be moving in order to initiate a
click, to start a click. So I can tell sort of
when I'm on that threshold to start initiating a
click just a bit early, so I'm not fully stopped over
the target when I go to click. I'm doing it like on my
way to the targets a little to try to time it just right.
- Oh wow. So you're slowing down. - Yeah, just a hair
right before the targets. (Lex laughing) - This is like elite performance, okay. But that's still, it sucks that there's
a ceiling of the 0.3. - Well, I can get down to 0.2 and 0.1. Point one's what-
- I get it. - Yeah, and I've played
with that a little bit too. I have to adjust a ton
of different parameters in order to play with 0.1, and I don't have control
over all that on my end yet. It also changes like how
the models are trained. Like if I train a model like in Webgrid, like I bootstrap on a model,
which basically is them training models as I'm playing Webgrid based off of like the Webgrid data, so like if I play Webgrid for 10 minutes, they can train off that data specifically in order to get me a better model. If I do that with 0.3 versus 0.1, the models come out different. The way that they interact
is just much, much different. So I have to be really careful. I found that doing it with 0.3 is actually better in some ways, unless I can do it with 0.1 and change all of the
different parameters, then that's more ideal, 'cause obviously, 0.3 is faster than 0.1. So I could get there. I can get there. - Can you click using your brain? - For right now, it's the hover clicking with the dwell cursor. Before all the thread
retraction stuff happened, we were calibrating clicks
- left click, right click. That was my previous ceiling. Before I broke the record
again with the dwell cursor was I think on a 35 by 35 grid
with left and right click. And you get more BPS, more bits per second using multiple clicks
'cause it's more difficult. - Oh, because what is it, you're supposed to do either a left click or a like right click? You use a different color
for stuff like this? - Yeah, blue targets for left click; orange targets for right
click is what they had done. - Got it.
- So my previous record of 7.5 was with the blue and
the orange targets, yeah, which I think if I went back to that now doing the click calibration, and being able to like
initiate clicks on my own, I think I would would
break that 10 ceiling like in a couple days max. - Like yeah, you would
start making Bliss nervous about his 17-
- You should be. - Why do you think we
haven't given him the- - Yeah, exactly. So what did it feel like
with the retractions? That some of the threads retracted? - It sucked. It was really, really hard. The day they told me was the
day of my big Neuralink tour at their Fremont
facility, and they told me like right before we went over there. It was really hard to hear. My initial reaction was,
"Alright, go in, fix it. Like go in, take it out, and fix it." The first surgery was so easy. Like I went to sleep. A couple hours later, I
woke up and here we are. I didn't feel any pain, didn't take like any
pain pills or anything. So I just knew that if they
wanted to, they could go in and put in a new one like next
day if that's what it took, 'cause I wanted it to be better and I wanted not to lose the capability. I had so much fun playing with it for a few weeks, for a month. It had opened up so many doors for me. It had opened up so
many more possibilities that I didn't want to
lose it after a month. I thought it would've
been a cruel twist of fate if I had gotten to see the view from like the top of this mountain and then have it all come
crashing down after a month. And I knew like, say,
the top of the mountain, but how I saw it was I was just now starting to climb the mountain. There was so much more
that I knew was possible. And so to have all of that be taken away was really, really hard. But then on the drive
over to the facility, I don't know, like five
minute drive, whatever it is, I talked with my parents about it. I prayed about it. I was just like, "I'm not
gonna let this ruin my day. I'm not gonna let this
ruin this amazing like tour that they have set up for me. Like I wanna go show everyone how much I appreciate all
the work they're doing. I wanna go like meet all of the people who have made this possible, and I wanna go have one of
the best days of my life." And I did, and it was amazing, and it absolutely was one of the best days I've ever been privileged to experience. And then for a few days, I
was pretty down in the dumps. But for like the first few days
afterwards, I was just like, I didn't know if it was
gonna ever gonna work again. I made the decision that
even if I lost the ability to use the Neuralink, even if I lost, even if I like lost out
on everything to come, if I could keep giving
them data in any way, then I would do that. If I needed to just do like some of the data collection every day or body mapping every day for
a year, then I would do it, because I know that everything I'm doing helps everyone to come after me. And that's all I wanted. I guess the whole reason that
I did this was to help people, and I knew that anything
I could do to help, I would continue to do, even if I never got to
use the cursor again, then I was just happy to be a part of it. And everything that I'd
done was just a perk. It was something that I got to experience, and I know how amazing it's gonna be for everyone to come after me. So might as well just keep trucking along. - Well, that said, you were able to get to work your way up, to
get the performance back. So this is like going from
rocky one to rocky two. So when did you first realize
that this is possible, and what gave you sort of the strength and motivation, determination to do it? To increase back up and
beat your previous record? - Yeah, it was within a couple weeks. - Again, this feels like I'm interviewing an athlete. (laughs) This is great. I like to thank my parents. - The road back was long and hard, fraught many difficulties. There were dark days. It was a couple weeks, I think. And then there was just a turning point. I think they had switched
how they were measuring the neuron spikes in my brain. Bliss, help me out. - [Bliss] Yeah, the way
in which we're measuring the behavior of individual neurons. - Yeah.
- So we're switching from sort of individual spike detection to something called spike band power, which if you watch the previous segments with either me or DJ, you
probably have some content. - Yeah, okay, so when they
did that, it was kind of like, a light over the head,
like light bulb moment. Like, "Oh, this works." And this seems like we can run with this. And I saw the uptick in
performance immediately. Like I could feel it
when they switched over. I was like, "This is better. Like this is good." Like everything up till this point for the last few weeks, last like whatever, three or four weeks, 'cause it was before they even told me, like everything before this sucked. Like let's keep doing
what we're doing now. And at that point, it was not like, "Oh, I know I'm still only at, like saying Webgrid terms, like four or five BPS
compared to my 7.5 before. But I know that if we keep doing this, then like I can get back there." And then they gave me the dwell cursor, and the dwell cursor sucked at first. It's not, obviously, not what I want, but it gave me a path forward to be able to continue using it, and hopefully, to continue to help out. And so I just ran with
it, never looked back. Like I said, I'm just kind of person that roll with the punches anyways. - What was the process? What was the feedback
loop on the figuring out how to do the spike detection in a way that would actually
work well for Noland? - Yeah, it's a great question. So maybe just describe first
how the actual update worked. It was basically an
update to your implant. So we just did an over
the air software update to his implants, same way
you'd update your Tesla or your iPhone, and that
firmware change enabled us to record sort of averages of populations of neurons nearby individual electrodes. So we have sort of less resolution about which individual
neuron is doing what, but we have a broader
picture of what's going on nearby an electrode overall. And that feedback, I mean, basically, Noland described it was immediate when we flipped that switch. I think the first day we did that, you had three or four
BPS right out of the box. And that was a light bulb moment for, "Okay, this is the right path to go down." And from there, there's
a lot of feedback around like how to make this
useful for independent use. So what we care about ultimately is that you can use it independently
to do whatever you want. And to get to that point, it required us to re-engineer the UX, as you
talked about the dwell cursor, to make it something that
you can use independently without us needing to be
involved all the time. And yeah, this is obviously the
start of this journey still. Hopefully, we get back to
the places where you're doing multiple clicks and using that to control much more fluidly, everything, and much more naturally, the applications that you're trying to interface with. - And most importantly,
get that Webgrid number up. - Yes.
- Yeah. So how is the, on the hover click, do you accidentally click cells sometimes? Like how hard is it to
avoid accidentally clicking? - I have to continuously
keep it moving, basically. So like I said, there's a threshold where it will initiate a click. So if I ever drop below that, it'll start, and I have 0.3 seconds to move
it before it clicks anything. And if I don't want it to ever get there, I just keep it moving at a certain speed, and like just constantly
like doing circles on screen, moving it back and forth to
keep it from clicking stuff. I actually noticed a couple weeks back, when I was not using the implant, I was just moving my hand
back and forth or in circles. Like I was trying to keep
the cursor from clicking, and I was just doing it
like while I was trying to go to sleep, and I was like,
"Okay, this is a problem." (both laughing) - To avoid the clicking. I guess, does that create
problems like when you're gaming accidentally click a thing? - Yeah, yeah, it happens in chess. I've lost a number of games because I'll accidentally click something. - [Bliss] I think the
first time I ever beat you was because of an accident.
- Yeah, I misclicked, yeah. - It's a nice excuse, right? - Yeah.
- You can always, anytime you lose-
- You could just say- - That was accidental.
- Yeah. - You said the app improved
a lot from version one. When you first started using
it, it was very different. So can you just talk
about the trial and error that you went through with the team? Like 200 plus pages of notes. What's that process like of- - Yeah.
- Going back and forth and working together to improve the thing? - It's a lot of me just using
it like day in and day out and saying, like, "Hey, can
you guys do this for me? Like give me this. I wanna be able to do that. I need this." I think a lot of it just
doesn't occur to them maybe until someone is actually using the app, using the implant. It's just something that they just never would've thought of. Or it's very specific to even like me, maybe what I want. It's something I'm a little worried about with the next people that come is maybe they will want
things much different than how I've set it up, or what the advice I've given the team. And they're gonna look
at some of the things they've added for me. Like that's a dumb idea. Like why would he ask for that? And so I'm really looking
forward to get the next people on because I guarantee that
they're going to think of things that I've never thought of. They're gonna think of improvements. I'm like, "Wow, that's a really good idea. Like I wish I would've thought of that." And then they're also
gonna give me some pushback about like, "Yeah, what you
are asking them to do here, that's a bad idea. Let's do it this way." And I'm more than happy
to have that happen. But it's just a lot of
like different interactions with different games or applications, the internet, just with
the computer in general. There's tons of bugs that end up popping up
left, right, center. So it's just me trying to
use it as much as possible and showing them what
works and what doesn't work and what I would like to be better. And then they take that feedback, and they usually create
amazing things for me. They solve these problems in
ways I would've never imagined. They're so good at everything they do. And so I'm just really thankful that I'm able to give them feedback and they can make something of it, 'cause a lot of my feedback
is like really dumb. It's just like, "I want this. Please do something about it." And we'll come back and
super well thought out, and it's way better than anything I could have ever thought
of or implemented myself. So they're just great. They're really, really cool. - As the BCI community grows,
would you like to hang out with the other folks with Neuralink? What relationship, if any,
would you wanna have with them? Because you said like they
might have a different set of like ideas of how to use the thing. - Yeah.
- Would you be intimidated by their Webgrid performance? - No, no, I hope compete. I hope day one, they like
wipe the floor with me. I hope they beat it and they crush it. Double it if they can. Just because, on one hand, it's only gonna push me to be better, 'cause I'm super competitive. I want other people to push me. I think that is important
for anyone trying to achieve greatness is they need
other people around them who are going to push them to be better. And I even made a joke about it on X once. Like once the next people get chosen, like qubadi cot music, like
I'm just excited to have other people to do this with and to like share experiences with. I'm more than happy to interact with them as much as they want, more than happy to give them advice. I don't know what kind of
advice I could give them, but if they have questions,
I'm more than happy. - What advice would you have for the next participant
in the clinical trial? - That they should have fun with this, because it is a lot of fun. And that I hope they
work really, really hard, because it's not just for us, it's for everyone that comes after us. And come to me if they need anything. And to go to Neuralink
if they need anything. Man, Neuralink moves mountains. Like they do absolutely
anything for me that they can. And it's an amazing
support system to have. It puts my mind at ease
for like so many things that I have had like questions about, or so many things I wanna do. And they're always there, and
that's really, really nice. I would tell them not to be
afraid to go to Neuralink with any questions that
they have, any concerns, anything that they're
looking to do with this. And any help that Neuralink
is capable of providing, I know they will. And I don't know, I don't know. Just work your ass off,
because it's really important that we try to give our all to this. - So have fun and work hard. - Yeah, yeah, there we go. Maybe that's what I'll just
start saying to people: have fun, work hard. - Now, you're a real pro athlete. Just keep it short.
(Noland laughing) Maybe it's good to talk about
what you've been able to do now that you have a Neuralink implant. Like the freedom you gain from this way of interacting
with the outside world. Like you play video games all night. And you do that by yourself. And that's a kind of freedom. Can you speak to that
freedom that you gain? - Yeah, it's what all, I don't know, people in my position want. They just want more independence. The more load that I can take away from people around me, the better. If I'm able to interact with the world without using my family, without going through any of my friends, like needing them to help
me with things, the better. If I'm able to sit up
on my computer all night and not need someone to like sit me up, say like on my iPad, like in
a position where I can use it and then have to have them wait up for me all night until I'm ready
to be done using it, it takes a load off of all of us. And it's really like all I can ask for. It's something that I could never thank Neuralink enough for. And I know my family feels the same way. Just being able to have the
freedom to do things on my own at any hour of the day or
night, it means the world to me. And I don't know. - When you're up at 2:00 AM
playing Webgrid by yourself, I just imagine like it's darkness and there's just a light glowing. And you're just focused. What's going through your mind? (Noland laughing) Or you were like in a state of flow where it's like the mind is empty, like those like Zen masters? - Yeah, generally, it is me
playing music of some sort. I have a massive playlist, and so I'm just like rocking out to music. And then it's also just
like a race against time, 'cause I'm constantly looking at how much battery percentage
I have left on my implant. Like, all right, I have 30%, which equates to x amount of time, which means I have to break this record in the next hour and a half, or else, it's not happening tonight. And so it's a little
stressful when that happens. When it's above 50%, I'm
like, "Okay, like I got time." It starts getting down to 30 and then 20. It's like, all right, 10%, a little popup is gonna pop up right here, and it's gonna really
screw my Webgrid flow. It's gonna tell me that
there's like the low battery, low battery popup comes up, and I'm like, it's really gonna screw me over. So if I have to, if I'm
gonna break this record, I have to do it in the
next like 30 seconds, or else, that popup is
gonna get in the way, like cover my Webgrid. After that, I go click on
it, go back into Webgrid. And I'm like, "All
right, that means I have 10 minutes left before this thing's dead." That's what's going on
in my head generally, that and whatever song's playing. I want to break those records so bad. Like it's all I want
when I'm playing Webgrid. It has become less of like, "Oh, this is just a leisurely activity." Like I just enjoy doing this, because it just feels so
nice and it puts me at ease. No, once I'm in Webgrid,
you better break this record or you're gonna waste like five hours of your life right now. And I don't know, it's just fun. It's fun, man. - Have you ever tried Webgrid with like two targets and three targets? Can you get higher BPS with that? - Can you do that? - [Bliss] You mean, like
different color targets, or you mean-
- Oh, multiple targets, 'cause that change the thing. - Yeah, so BPS is a log
of number of targets times correct minus
incorrect divided by time. And so you can think of
like different clicks as basically doubling the
number of active targets. - Got it.
- So you know, you get basically higher BPS,
the more options there are, the more difficult to task. And there's also like zen
mode you've played in before, which is like-
- Yeah. Yeah, it covers the
whole screen with a grid. And I don't know. - [Lex] Yeah, and so you can go like, that's insane.
- Yeah. - [Bliss] He doesn't like it
'cause it didn't show BPS. - I had them put in a giant
BPS in the background, so now it's like the opposite of Zen mode. It's like super hard mode,
like just metal mode. It's just like a giant
number in the back counter. - [Bliss] We should rename that. Metal mode is a much better name now. - So you also play Civilization VI? - I love Civ VI, yeah. - [Lex] You usually go with Korea, you said?
- I do, yeah. So the great part about
Korea is they focus on like science tech victories,
which was not planned. Like I've been playing Korea for years, and then all of the
Neuralink stuff happened. So it kind of aligned. But what I've noticed with tech victories is if you can just rush
tech, rush science, then you can do anything. Like at one point in the game, you'll be so far ahead of
everyone technologically that you'll have like
musket men, infantry men, plane sometimes, and people
will still be fighting with like bows and arrows. And so if you want to
win a domination victory, you just get to a certain
point with the science and then go and wipe out
the rest of the world. Or you can just take science
all the way and win that way, and you're gonna be so
far ahead of everyone 'cause you're producing so much science that it's not even close. I've accidentally won in different ways just by focusing on science. - Accidentally won by focusing on science. - I was playing only science, obviously. Like just science all the way, just tech. And I was trying to get like every tech in the tech tree and stuff. And then I accidentally won
through a diplomatic victory, and I was so mad.
(Lex laughing) I was so mad, 'cause it just like ends the game. One turn, it was like, "Oh, you won, you're so diplomatic." I'm like, "I don't wanna do this. I should have declared war
on more people or something." It was terrible, but you don't need like giant civilizations with
tech, especially with Korea. You can keep it pretty small. So I generally just get
to a certain military unit and put them all around my
border to keep everyone out, and then I will just build up. So very isolationist. - Nice.
- Yeah. - Just work on the science and the tech. - [Noland] Yep, that's it. - You're making it sound so fun. - It's so much fun.
- And I also saw Civilization VII trailer. - Oh man, I'm so pumped.
- Yeah. And that's probably coming out- - Come on, Civ VII, hit me up. Alpha, beta tests, whatever. - Wait, when is it coming out? - 2025.
- Yeah, yeah, next year, yeah. What other stuff would
you like to see improved about the Neuralink app and
just the entire experience? - I would like to, like I said, get back to the like click on demand, like the regular clicks. That would be great. I would like to be able to
connect to more devices. Right now, it's just the computer. I'd like to be able to use it on my phone or use it on different
consoles, different platforms. I'd like to be able to control as much stuff as possible, honestly. Like an Optimus robot
would be pretty cool. That would be sick if I could
control an Optimus robot. The Link app itself, it
seems like we are getting pretty dialed in to what it
might look like down the road. Seems like we've gotten through a lot of what I want from it at least. The only other thing I would say is like more control
over all the parameters that I can tweak with my
like cursor and stuff. There's a lot of things that go into how the cursor moves in certain ways. And I have, I don't
know, like three or four of those parameters and there might- - Like gain and friction and all that? - Gain and friction, yeah. And there's maybe double
the amount of those with just like velocity and then with the actual dwell cursor. So I would like all of it. I want as much control over
my environment as possible, especially-
- So you want like advanced mode? Like there's menus usually,
there's basic mode. And you're like one of those folks like- - I go the-
- Power user advanced. - Yeah, yeah.
- Got it. - That's what I want. I want as much control
over this as possible. So yeah, that's really all I can ask for. Just give me everything. - Has speech been useful? Like just being able to talk also in addition to everything else? - Yeah, you mean like while I'm using it? - While you're using
it, like speech to text? - Oh yeah.
- Or do you type, or like, 'cause there's also a keyboard? That's really nice.
- Yeah, yeah. So there's a virtual keyboard. That's another thing I
would like to work more on is finding some way to type
or text in a different way. Right now, it is like
a dictation basically and a virtual keyboard that
I can use with the cursor. But we've played around
with like finger spelling, like sign language, finger spelling. And that seems really promising. So I have this thought in my head that it's going to be a
very similar learning curve that I had with the cursor, where I went from attempted movement to imagined movement at one point. I have a feeling, this
is just my intuition, that at some point, I'm going
to be doing finger spelling and I won't need to actually attempt to finger spell anymore, that I'll just be able to think the like letter that I
want and it'll pop up. - That would be epic.
- Yeah. - That's challenging, that's hard. That's a lot of work for
you to kinda take that leap. But that would be awesome. - And then like going from
letters to words is another step. Like you would go from, right now, it's finger spelling of like just the sign language alphabet. But if it's able to pick that up, then it should be able to pick up like the whole sign
language like language. And so then if I could do
something along those lines, or just the sign language spelled word, if I can spell it at a reasonable speed and it can pick that up, then I would just be able
to think that through and it would do the same thing. I don't see why not. After what I saw with the cursor control, I don't see why it wouldn't work, but we'd have to play around with it more. - What was the process in
terms of like training yourself to go from attempted movement
to imagined movement? How long did that take? So how long would this
kind of process take? - Well, it was a couple weeks before it just like happened upon me. But now that I know
that that was possible, I think I could make it
happen with other things. I think it would be much, much simpler. - Would you get an
upgraded implant device? - Sure, absolutely. Whenever they'll let me. - So you don't have any concerns for you with the surgery experience? All of it was like no regrets? - No. - So everything's been good so far? - Yep. - You just keep getting upgrades. - Yeah, I mean, why not? I've seen how much it's
impacted my life already. And I know that everything
from here on out, shit's gonna get better and better. So I would love to. I would love to get the upgrade. - What future capabilities
are you excited about sort of beyond this kind of telepathy? Is vision interesting? So for folks who, for
example, who are blind, so you're like enabling
people to see, or for speech. - Yeah, there's a lot that's
very, very cool about this. I mean, we're talking about the brain, so like this is just motor cortex stuff. There's so much more that can be done. The vision one is fascinating to me. I think that is going
to be very, very cool. To give someone the ability to see for the first time in their
life would just be, I mean it, it might be more amazing than
even helping someone like me. Like that just sounds incredible. The speech thing is really interesting, being able to have some sort
of like real time translation and cut away that language
barrier would be really cool. Any sort of like actual
impairments that it could solve, like with speech, would
be very, very cool. And then also, there are a
lot of different disabilities that all originate in the brain. And you would be able to, hopefully be able to solve a lot of those. I know there's already stuff
to help people with seizures that can be implanted in the brain. This would do, I imagine, the same thing. And so you could do something like that. I know that even someone like
Joe Rogan has talked about the possibilities with being able to stimulate the brain in different ways. I'm not sure. I'm not sure how ethical
a lot of that would be. That's beyond me honestly. But I know that there's
a lot that can be done when we're talking about the brain and being able to go in and physically make changes to help people
or to improve their lives. So I'm really looking forward to everything that comes from this. And I don't think it's all that far off. I think a lot of this can be
implemented within my lifetime, assuming that I live a long life. - What you were referring
to is things like people suffering from depression or things of that nature
potentially getting help. - Yeah, flip a switch like
that, make someone happy. I know, I think Joe has
talked about it more in terms of like you want to experience like what a drug trip feels like. Like you wanna experience
what it'd be like to be on- - Of course.
- Yeah, mushrooms or something like that, DMT. Like you can just flip
that switch in the brain. My buddy Bain has talked about
being able to like wipe parts of your memory and
re-experience things that, like for the first time, like your favorite movie
or your favorite book. Like just wipe that out real quick, and then re-fall in love with
Harry Potter or something. I told him, I was like, "I don't know how I feel about like people being able to just
wipe parts of your memory. That seems a little sketchy to me." He's like, "They're already doing it." - Sounds legit. Yeah, I would love memory replay. Just like actually high
resolution replay of old memories. - Yeah, I saw an episode of
"Black Mirror" about that once. I don't think I want it. - Yeah, so "Black Mirror"
always kind of considers the worst case, which is important. I think people don't
consider the best case or the average case enough. I don't know what it is about us humans. We wanna think about the
worst possible thing. We love drama. - [Noland] Yeah. (laughs) - It's like, how's this new
technology gonna kill everybody? We just love that. Again like, yes, let's watch. - Hopefully, people don't think
about that too much with me. It'll ruin a lot of my plans. - Yeah, yeah. I assume you're gonna have
to take over the world. I mean, I loved your Twitter. You tweeted, "I'd like to make jokes about hearing voices in my head since getting the Neuralink, but I feel like people
would take it the wrong way. Plus, the voices in my
head told me not to." - Yeah.
- Yeah. - Yeah.
- Please never stop. So you're talking about Optimus. Is that something you would
love to be able to do, to control the robotic arm
or the entirety of Optimus? - Oh yeah, for sure. For sure, absolutely. - You think there's something
like fundamentally different about just being able to
physically interact with the world? - Yeah, oh, 100%. I know another thing with like being able to like give people the
ability to like feel sensation and stuff too by going in with the brain and having the Neuralink maybe do that. That could be something that
could be translated through, transferred through the Optimus as well. Like there's all sorts of really cool interplay between that. And then also, like you said,
just physically interacting. I mean, 99% of the things that I can't do myself obviously need, I need a caretaker for, someone to physically do things for me. If an Optimus robot could
do that, like I could live an incredibly independent life and not be such a burden
on those around me and it would change the
way people like me live, at least until whatever
this is gets cured. But being able to interact
with the world physically, like that would just be amazing. And they're not just like for being, for having to be a caretaker or something, but something like I talked about, just being able to read a book. Imagine an Optimus robot just being able to hold a
book open in front of me, like get that smell again. I might not be able to
feel it at that point. Or maybe I could again with
the sensation and stuff. But there's something
different about reading like a physical book
than staring at a screen or listening to an audio book. I actually don't like audio books. I've listened to a ton
of them at this point, but I don't really like 'em. I would much rather like
read a physical copy. - So one of the things you would love to be able to experience
is opening the book, bringing it up to you. And to feel the touch of the paper. - Yeah. Oh man, the touch, the smell. I mean, it's just like something about the words on the page, and they've replicated that page color on like the Kindle and stuff. Yeah, it's just not the same, yeah. So just something as simple as that. - So one of the things you miss is touch. - I do, yeah. - A lot of things that I
interact with in the world, like clothes or literally
any physical thing that I interact with in
the world, a lot of times, what people around me will
do is they'll just come like, rub it on my face. They'll like lay something on
me so I can feel the weight. They will rub a shirt on
me so I can feel fabric. Like there's something
very profound about touch, and it's something that I miss a lot, and something I would love
to do again, but we'll see. - What would be the first thing you do with a hand that can touch? Give your mom a hug after that, right? - Yeah, I know. It's one thing that I've asked like God for basically every day since my accident was just being able to like one day move, even if it was only like my hand. So that way, like I could
squeeze my mom's hand or something just to like show her that, like how much I care and how
much I love her and everything. Something along those lines. Being able to just interact
with the people around me, handshake, give someone a hug. I don't know, anything like that. Being able to help me eat, like
I'd probably get really fat, which would be a terrible, terrible thing. - Also beat Bliss in chess
on a physical chess board. - Yeah, yeah. I mean, there are just
so many upsides. (laughs) And any way to find some way to feel like I'm bringing Bliss down to my level. - Yeah.
- Because- - Yeah.
- He's just such an amazing guy, and everything about him is just so above and beyond
that anything I can do to take him down a notch,
I'm more than happy. - Yeah, humble him a bit, he needs it. - [Noland] Yeah. (laughs) - Okay. As he's sitting next to me. Did you ever make sense of why God puts good people through such hardship? - Oh, man. I think it's all about understanding how much we need God. And I don't think that there's
any light without the dark. I think that if all of us
were happy all the time, there would be no reason
to turn to God ever. I feel like there would be
no concept of good or bad. And I think that as much
of like the darkness and the evil that's in the
world, it makes us all appreciate the good and the things
we have so much more. And I think, like when I had my accident, one of the first things I said to one of my best friends was,
and this was within like the first month or two
after my accident, I said, "Everything about this
accident has just made me understand and believe
that like God is real and that there really is a God, basically. And that like my interactions with him have all been real and worthwhile." And he said, if anything, seeing me go through this accident, he believes that there isn't a God. And it's a very different reaction. But I believe that it is
a way for God to test us, to build our character, to send us through trials and tribulations, to make sure that we
understand how precious he is, and the things that he's given us and the time that he's given us. And then to hopefully
grow from all of that. I think that's a huge part of being here is to not just have an easy life and do everything that's easy, but to step out of our comfort zones and really challenge ourselves, because I think that's how we grow. - What gives you hope
about this whole thing we have going on, human civilization? - Oh, man. I think people are my biggest inspiration. Even just being at
Neuralink for a few months, looking people in the eyes
and hearing their motivations for why they're doing
this, it's so inspiring. And I know that they
could be other places, cushier jobs, working somewhere else, doing X, Y, or Z that doesn't
really mean that much. But instead they're here, and
they want to better humanity and they want a better,
just the people around them, the people that they've
interacted with in their life, they wanna make better lives
for their own family members who might have disabilities,
or they look at someone like me and they say, "I can
do something about that so I'm going to." And it's always been what I've connected with most in the world are people. I've always been a people person and I love learning about people, and I love learning like
how people developed and where they came from. And to see like how much
people are willing to do for someone like me
when they don't have to, and they're going out of their
way to make my life better. It gives me a lot of hope
for just humanity in general, how much we care and how
much we're capable of when we all kind of get together and try to make a difference. And I know there's a lot of
bad out there in the world, but there always has been
and there always will be. And I think that that is, it shows human resiliency and it shows what we're able to endure, and how much we just want to
be there and help each other, and how much satisfaction
we get from that, because I think that's one of the reasons that we're here is just
to help each other. And I don't know, that
always gives me hope. It's just realizing that
there are people out there who still care and who wanna help. - And thank you for being
one such human being and continuing to be a great human being through everything you've been through and being an inspiration to many people, to myself, for many reasons,
including your epic, unbelievably great performance on Webgrid. I'll be training all night
tonight to try to catch up. - You can do it. - And I believe in you, that you can once you come back, so sorry to interrupt
with the Austin trip, once you come back, eventually beat Bliss. - Yeah, yeah, for sure. Absolutely.
- I'm rooting for you. The whole world is rooting for you. - Thank you.
- Thank you for everything you've done, man. - Thanks, thanks man. - Thanks for listening
to this conversation with Nolan Arbaugh, and
before that, with Elon Musk, DJ Seo, Matthew MacDougall,
and Bliss Chapman. To support this podcast, please check out our sponsors in the description. And now, let me leave you with
some words from Aldous Huxley in "The Doors of Perception." "We live together. We act on and react to one another, but always, and in all
circumstances, we are by ourselves. The martyrs go hand in
hand into the arena. They are crucified alone. Embraced, the lovers
desperately try to fuse their insulated ecstasies into a single self-transcendence; in vain. By its very nature, every embodied spirit is doomed to suffer and enjoy in solitude. Sensations, feelings, insights, fancies, all these are private and,
except through symbols and at second hand, incommunicable. We can pool information about experiences, but never the experiences themselves. From family to nation, every human group is a society of island universes. Thank you for listening, and hope to see you next time.