Jeffrey Shainline: Neuromorphic Computing and Optoelectronic Intelligence | Lex Fridman Podcast #225
the following is a conversation with jeff shaneline a scientist at nist interested in opto electronic intelligence we have a deep technical dive into computing hardware that will make jim keller proud i urge you to hop on to this rollercoaster ride through neuromorphic computing and superconducting electronics and hold on for dear life jeff is a great communicator of technical information and so it was truly a pleasure to talk to him about some physics and engineering to support this podcast please check out our sponsors in the description this is the lex friedman podcast and here is my conversation with jeff shaneline i got a chance to read a fascinating paper you um authored called optoelectronic intelligence so maybe we could start by talking about this paper and start with the basic questions what is optoelectronic intelligence yeah so in that paper the the concept i was trying to describe is sort of an architecture for building brain-inspired computing that leverages light for communication in conjunction with electronic circuits for computation in that particular paper a lot of the work we're doing right now in our project at nist is focused on superconducting electronics for computation i'll go into why that is but that might make a little more sense in context if we first describe what that is in contrast to which is semiconducting electronics so is it worth taking a couple minutes to describe semiconducting electronics it might even be worthwhile to step back and uh talk about electricity and circuits and how circuits work right before we talk about super conductivity right okay how does the computer work jeff well i won't go into everything that makes a computer work but let's talk about the basic building blocks a transistor so and even more basic than that a semiconductor material silicon say so uh in silicon silicon is a semiconductor and what that means is at low temperature there are no free charges no free electrons that can move around so when you talk about electricity you're talking about predominantly electrons moving to establish electrical currents and they move under the influence of voltages so you apply voltages electrons move around those can be measured as currents and you can represent information in that way so semiconductors are special in the sense that they are really malleable so if you have a semiconductor material it you can change the number of free electrons that can move around by putting different elements different atoms in lattice sites so what is a lattice site well a semiconductor is a crystal which means all the atoms that comprise the material are at exact locations that are perfectly periodic in space so if you start at any one atom and you go along the what are called the lattice vectors you get to another atom and another atom and another atom and for high quality devices it's important that it's a a perfect crystal with very few defects but you can intentionally replace a silicon atom with say a phosphorus atom and then you can you can change the number of free electrons that are in a region of space that has that excess of what are called dopants so picture a device that has a left terminal and a right terminal and if you apply a voltage between those two you can cause electrical current to flow between them now we add a third terminal up on top there and depending on the voltage between the left and right terminal and that third voltage you can you can change that current so what's commonly done in digital electronic circuits is to leave a fixed voltage from left to right and then change that voltage that's applied at what's called the gate the gate of the transistor so what you do is you you make it to where there's an excess of electrons on the left excess of electrons on the right and very few electrons in the middle and you do this by changing the concentration of different dopants in the lattice spatially and then when you apply a voltage to that gate you can either cause current to flow or turn it off and so that's sort of your zero and one you if you apply voltage current can flow that current is representing a digital one and uh from that from that basic element you can build up all the complexity of digital electronic circuits that have really had a profound influence on our society now you're talking about electrons can you give a sense of what scale we're talking about when we're talking about in silicon uh being able to mass manufacture these kinds of uh gates yeah so scale in a number of different senses well at the scale of the silicon lattice the distance between two atoms there is half a nanometer so um people often like to compare these things to the the width of a human hair i think it's some six orders of magnitude smaller than the width of a human hair uh something on that order so remarkably small we're talking about individual atoms here and electrons are of that length scale when they're in that environment but there's another sense that scale matters in digital electronics this is perhaps the more important sense although they're related scale refers to a number of things it refers to the size of that transistor so for example i said you have a left contact a right contact and some space between them where the the gate electrode sits that that's called the the channel width uh or the channel length and um what has enabled what we think of as moore's law or the continued increased performance in silicon microelectronic circuits is the ability to make that size that feature size ever smaller ever smaller at a a really remarkable pace i mean that that feature size has decreased uh consistently every couple of years for the since the 1960s and that was that was what moore predicted in the 1960s he thought it would continue for at least two more decades and it's been much longer than that and so um that is why we've been able to fit ever more devices ever more transistors ever more computational power on essentially the same size of chip so a user sits back and does essentially nothing you're running the same computer program but those devices are getting smaller so they get faster they get more energy efficient and all of our computing performance just continues to improve and we don't have to think too hard about what we're what we're doing as say a software design or something like that i absolutely don't mean to say that there's no innovation in software that are the user side of things of course there is but from from the hardware perspective we just have been given this gift of continued performance improvement through this scaling that is ever smaller feature sizes with very similar um say power consumption that power consumption is has not continued to scale in the most recent decades but um nevertheless we had a really good run there for a while and now we're down to gates that are seven nanometers which is state of the art right now maybe global foundries is trying to push it even lower than that i can't keep up with where the predictions are that it's going to end but seven nanometer uh seven nanometer transistor has just just a few tens of atoms along the length of the conduction pathway so a naive semiconductor device physicist would think you can't go much further than that without some kind of revolution in the way we think about the physics of our devices is there something to be said about the mass manufacture of these devices right right so that's another thing so how have we been able to make those transistors smaller and smaller well companies like intel global foundries they invest a lot of money in the lithography so how are these chips actually made well one of the most important steps is this what's called ion implantation so you have you start with sort of a pristine silicon crystal and then using photolithography which is a technique where you can pattern different shapes using light you can define which regions of space you're going to implant with different uh different species of ions that are going to change the local electrical properties right there so by using ever shorter wavelengths of light and different kinds of optical techniques and different kinds of lithographic techniques things that go far beyond my knowledge base you can just simply shrink that feature size down and you say you're at seven nanometers well the wavelength of light that's being used is over 100 nanometers that's already deep in the uv so how how are those minut features patterned well there's there's an extraordinary amount of innovation that has gone into that but nevertheless it stayed very consistent in this ever-shrinking feature size and now the question is can you make it smaller and even if you do do you still continue to get performance improvements but that's another kind of scaling where these these companies have been able to so okay you you picture a chip that has a processor on it well that chip is not made as a chip it's made as a on a wafer and um using photolithography you basically print the same pattern on different dies all across the wafer multiple layers tens probably probably a hundred some layers in a mature foundry process and and you do this on ever bigger wafers too that's another aspect of scaling that's occurred in the last several decades so now you have this 300 millimeter wafer it's like as big as a pizza and it has maybe a thousand processors on it and then you dice that up using a saw and now you can sell these things so cheap because the the manufacturing process was so streamlined i think a technology as revolutionary as silicon microelectronics has to have that kind of manufacturing scalability which i will just emphasize i believe is enabled by physics it's not i mean that of course there's human ingenuity that goes into it but at least from my my side where where i sit it sure looks like the physics of our universe allows us to to produce that and we've we've discovered how more so than we've invented it although of course we have invented it humans have invented it but it was it's almost as if it was there waiting for us to to discover it you mean the entirety of it or are you specifically talking about the techniques of photo lithography like the optics involved i mean the entirety of the scaling down to the seven nanometers that you're able to have electrons not interfere with each other in such a way that you could still have gates like that's enabled to achieve that scale spatial and temporal seems to be very special and is enabled by the physics of our world all the things you just said so starting with the the silicon material itself silicon is a unique semiconductor it has essentially ideal properties for making a specific kind of transistor that's extraordinarily useful so i mentioned that silicon has uh well when you make a transistor you have this gate contact that sits on top of the conduction channel and depending on the voltage you apply there you pull more carriers into the conduction channel or push them away so it becomes more or less conductive in order to have that work without just sucking those carriers right into that contact you need a very thin insulator and and part of scaling has been to gradually decrease the thickness of that of that gate insulator so that you can use a roughly similar voltage and still have the same current voltage characteristics so the material that's used to do that or i should say was initially used to do that was a silicon dioxide which just naturally grows on the silicon surface so you expose silicon to the atmosphere that we breathe and uh well if you're manufacturing you're going to purify these gases but nevertheless that that what's called a native oxide will grow there there are essentially no other materials on the entire periodic table that have as good of a gate insulator as as that silicon dioxide and that that has to do with nothing but the physics of the interaction between silicon and oxygen and if it wasn't that way transistors could not they they could not perform in nearly the the degree of capability that they have and that that has to do with the way that the the oxide grows the reduced density of defects there it's it's insulation meaning essentially it's energy gaps you can apply a very large voltage there without having current leak through it so that's physics right there um there are other things too silicon is a semiconductor in in an elemental sense you you only need silicon atoms a lot of other semiconductors you need two different kinds of atoms like a compound from group three and a compound from group five that opens you up to lots of defects that can occur where one atom's not sitting quite at the lattice site it is and it's switched with another one that degrades performance but then also on the side that you mentioned with the the manufacturing we have access to light sources that can produce these very short wavelengths of light how does photolithography occur well you actually put this polymer on top of your wafer and you expose it to light and then you use a aqueous chemical processing to dissolve away the regions that were exposed to light and leave the regions that were not and we are blessed with these polymers that have the right property where they can um cause scission events where the polymer splits where a photon hits i mean you know maybe maybe that's not too surprising but i don't know it all it all comes together to have this really complex uh manufacturable ecosystem where very sophisticated technologies can be devised and it works quite well and amazingly like you said with a wavelength at like 100 nanometers or something like that you're still able to achieve on this polymer precision of whatever whatever we said seven nanometers yeah i think i've heard like four nanometers being talked about something like that yes i if we could just pause on this and we'll return to super connectivity but in this whole journey from a history perspective what what do you think is the most beautiful at the intersection of engineering and physics to you and this whole process that we talked about with silicon and photolithography things that people were able to achieve in order to uh push the moore's law forward is it the early days the the invention of the transistor itself is it uh some particular cool little thing that um maybe not many people know about like what do you think is most beautiful in this in this whole process journey the most beautiful is a little difficult to answer let me let me try and sidestep it a little bit and just say what strikes me about looking at the the history of silicon microelectronics is that uh so when when quantum mechanics was developed people quickly began applying it to semiconductors and it was broadly understood that these are fascinating systems and people cared about them for their basic physics but also their utility is devices and then the transistor was invented in the late 40s in a relatively crude experimental setup where you just crammed a metal electrode into the semiconductor and and that was that was ingenious these people were able to um make it work you know uh but so what what i want to get to that that really strikes me is that in those early days there were a number of different semiconductors that were being considered they had different properties different strengths different weaknesses most people thought germanium was the the way to go it it had some some nice properties uh related to things about how the electrons move inside the lattice but other people thought that compound semiconductors with group 3 and group 5 also had really really extraordinary um properties that might be conducive to to making the best devices so there were different groups exploring each of these and that's great that's how science works you have to cast a broad net but then what i what i find striking is why why is it that silicon won because it's not that it's not that germanium is a useless material and it's not present in technology or compound semiconductors they're both doing doing exciting and important things slightly more niche applications whereas silicon is the semiconductor material for microelectronics which is the platform for digital computing which has transformed our world why did silicon win it's because of a remarkable assemblage of qualities that no one of them was the clear winner but it it made these sort of compromises between a number of different influences it had that really excellent gate oxide that allowed it to that allowed us to make mosfets these high performance transistors so quickly and cheaply and easily without having to do a lot of materials development the the band gap of silicon um is actually so in a semiconductor there's there's an important parameter which is called the band gap which tells you uh if you they're they're sort of electrons that fill up to one level in in the energy diagram and then there's a gap where electrons aren't allowed to have an energy in a certain range and then there's another energy level above that and that that difference between the lower sort of filled level and the unoccupied level that tells you how much voltage you have to apply in order to induce a current to flow so with germanium that's about 0.75 electron volts that means you have to apply 0.75 volts to to get a current moving and it turns out that if you compare that to the the thermal excitations that are induced just by the temperature of our environment that gap's not quite big enough you start to use it to perform computations it gets a little hot and you get all these accidental carriers that are excited into the the conduction band and it causes errors in your computation silicon's band gap is just a little higher 1.1 electron volts but you have an exponential dependence on the the number of carriers that are present that can induce those errors uh it decays exponentially with that voltage so just that that slight extra energy in that band gap really puts it in an ideal position to be operated in the in the conditions of our of our ambient environment it's kind of fascinating that so like you mentioned air is um decrease exponentially uh with the voltage so it's funny because this error thing comes up you know when you start talking about quantum computing it's kind of amazing that everything we've been talking about the errors as we scale down seems to be extremely low yes and like all of our computation is based on the assumption that it's extremely low yes so it's not digital computation digital sorry digital computation so as opposed to our biological computation our brain is like the assumption is stuff is gonna fail all over the place and we somehow have to still be robust to that that's exactly right so this also this is gonna be the most controversial part of our conversation where you're gonna make some enemies so let me ask because we've been talking about physics and engineering a which group of people is smarter and more important for this one let me ask the question in a better way some of the big innovations some of the beautiful things that we've been talking about how much of it is physics how much of it is engineering my dad is a physicist and he talks down to all the amazing engineering that we're doing in the artificial intelligence and the computer science and the robotics and all that space so we argue about this all the time so what do you think who gets more credit i'm genuinely not trying to just be politically correct here i don't see how you would have any of the what we consider sort of the great accomplishments of society without both and you absolutely need both of those things physics tends to play a key role earlier in the development and then engineering optimization these things take over and uh i mean the invention of the transistor or actually even before that the understanding of semiconductor physics that allowed the invention of the transistor that's all physics so if you didn't have that physics you don't even get to get on the on the on the field but once you have understood and demonstrated that this is in principle possible moore's law is engineering that why we have uh computers more powerful than than old supercomputers in each of our phones is that's all engineering and i i think i would be quite foolish to say that that's i mean that that's not valuable if it's not a great contribution uh it's a beautiful dance would you put like silicon the understanding of the material properties in the space of engineering like how does that whole process work to understand that it has all these nice properties or even the development of photolithography is is that basically would you put that in the category of engineering no i would say that it is basic physics it is applied physics it's material science it's um x-ray crystallography it's polymer chemistry it's it's everything i mean chemistry even is thrown in there absolutely yes yes absolutely just no biology okay we can get to biology right well the biology is in the humans that are engineering the system that's all integrated deeply okay so let's return you mentioned this uh word superconductivity so what does that have to do with what we're talking about right okay so in a semiconductor as i tried to describe a second ago you can sort of uh in induce currents by applying voltages and those have sort of typical properties that you would expect from some kind of a conductor those electrons they don't just flow perfectly without dissipation if an electron collides with an imperfection in the lattice or another electron it's going to slow down it's going to lose its momentum so you have to keep applying that voltage in order to keep the current flowing in a superconductor something different happens if you get a current to start flowing it will continue to flow indefinitely there's there's no dissipation so that's crazy how does that happen well it happens at low temperature and this is crucial it has to it has to be a quite low temperature and what what i'm talking about there i for essentially all of our conversation i'm going to be talking about conventional superconductors um sometimes called low tc superconductors low critical temperature superconductors and so those materials have to be in at a temperature around say around 4 kelvin i mean their critical temperature might be 10 kelvin something like that but you want to operate them at around 4 kelvin 4 degrees above absolute zero and what happens at that temperature at that very low temperatures in certain materials is that the the noise of atoms moving around the lattice vibrating electrons colliding with each other that becomes sufficiently low that the electrons can settle into this very special state it's sometimes referred to as a macroscopic quantum state because if i had a piece of superconducting material here let's say niobium is a very typical um superconductor if i if i had a block of niobium here and we cooled it below its critical temperature all of the electrons in that in that superconducting state would be in one coherent quantum state they would the the wave function of that state is described in terms of all of the particles simultaneously but it extends across macroscopic dimensions the size of a whatever material the size of whatever block of that material i have sitting here and the way that the way this occurs is that you know we let's try to be a little bit light on the technical details but essentially the electrons coordinate with each other they they are able to in this macroscopic quantum state they're able to sort of one can quickly take the place of the other you can't tell electrons apart they're they're what's known as identical particles so if this electron runs into a defect that would otherwise cause it to scatter it can just sort of um almost miraculously avoid that defect because it's not really in that location it's part of a macroscopic quantum state and the entire quantum state was not scattered by that defect so you can get a current that flows without dissipation and that's called a supercurrent that's uh sort of just very much scratching the surface of of superconductivity there's very deep and rich physics there which is probably not the main subject we need to go into right now but it turns out that when you have this material you can you can do usual things like make wires out of it so you can get current to flow in a straight line on a chip but you can also make other devices that perform different kinds of operations some of them are kind of logic operations like you like you'd get in a transistor the most common or most um i would say diverse in its utility the component is a joseph's injunction it's not analogous to a transistor in the sense that if you apply a voltage here it changes how much current flows from left to right but it is analogous in sort of a sense of it's the it's the go-to component that a that a circuit engineer is going to use to start to build up more complexity so these are uh these junctions serve as gates they can they can serve as gates they can so i'm not sure how house um concerned to be with semantics but let me just briefly say what a joseph's injunction is and we can talk about different ways that they can be used basically if you have a superconducting wire and then a small gap of a different material that's not superconducting an insulator or normal metal and then another superconducting wire on the other side that's a joseph's injunction so it's sometimes referred to as a superconducting weak link so you have this superconducting state on one side and on the other side and that the superconducting wave function actually tunnels across that gap and when you when you create such a physical entity it has very unusual um current voltage characteristics within in that gap like like weird stuff through the entire circuit so you can imagine suppose you had a loop set up that had one of those weak links in in the loop current would flow in that loop independent even if you hadn't applied a voltage to it and that's called the josephson effect so the fact that there's this phase difference in the quantum wave function from one side of the tunneling barrier to the other induces current to flow so how does you change state right exactly so how do you change state now picture if i have a current bias coming down this line in my circuit and there's a joseph's injunction right in in the middle of it and now i make another wire that goes around the joseph's injunction so i have a loop here a superconducting loop i can add current to that loop by exceeding the critical current of that joseph's injunction so like any superconducting material it can carry this supercurrent that i've described this current that can propagate without dissipation up to a certain level and if you try and pass more current than that through the material it's going to become a resistive material a normal normal material so in the in the joseph's injunction the same thing happens i can bias it above its critical current and then what it's going to do it's going to add a quantized amount of current into that loop and what i mean by quantized is it's going to come in discrete packets with a well-defined value of current so in the vernacular of of some people working in this community you would say you pop a flux on into the loop so a flux on you pop a flux on into the loop yeah so if that's a skateboarder sorry go ahead a flux on is one of these quantized uh sort of uh amounts of current that you can add to a loop and this is a cartoon picture but i think it's sufficient for our purposes so which uh maybe it's useful to say what is the speed at which these discrete packets of current travel because we'll be talking about light a little bit it seems like the speed is important the speed is important that's an excellent question sometimes i wonder where you how you became so astute but um so this uh matrix four is coming out so maybe that's related i'm not sure i'm dressed for the job i was trying to get to become an extra matrix for it didn't work out anyway uh so what's the speed of these packets you'll have to find another gig i know i'm sorry um so the speed of the pack is actually these flux ons these these uh sort of pulses of of um current that are generated by joseph's injunctions they can actually propagate very close to the speed of light uh maybe something like a third of the speed of light that's quite fast so one of the reasons why joseph's injunctions are appealing is because their signals can propagate quite fast and they can they can also switch very fast what i mean by switch is perform that operation that i described where you add current to the loop that can happen within um a few tens of picoseconds so you can get you can get devices that operate in the hundreds of gigahertz range and by comparison most processors in our in our conventional computers operate closer to the the one gigahertz range maybe three gigahertz seems to be kind of where where those speeds have have leveled out so the gamers listening to this are getting really excited that overclock their system to like what is it like four gigahertz or something 100 this sounds incredible uh can i just as a tiny tangent is the physics of this understood well how to do this stably oh yes the physics is understood well the physics of joseph's injunctions is understood well the technology's understood quite well too the reasons why it hasn't displaced silicon microelectronics in conventional digital computing i think are more related to what i was alluding to before about the the myriad practical almost mundane aspects of silicon that make it so useful you can make a transistor ever smaller and smaller and it will still perform its digital function quite well the same is not true of a joseph's injunction you really they don't they just it's not the same thing that there's this feature that you can keep making smaller and smaller and it'll keep performing the same operations this loop i described any joseph's in circuit well i i'm going to be careful i shouldn't say any joseph's in circuit but many josephs and circuits the way they process information or the way they perform whatever function it is they're trying to do maybe it's sensing a weak magnetic field it it depends on an interplay between the junction and that loop and you can't make that loop much smaller and it's not for practical reasons that have to do with lithography it's for fundamental physical reasons about the way the magnetic field interacts with that superconducting material there's there are physical limits that no matter how good our technology got those circuits would i think would never be able to be scaled down to the the densities that silicon microelectronics can i don't know if we mentioned is there something interesting about the various superconducting materials involved or is it all there's a lot of stuff that's interesting and it's not silicon it's not silicon no so like it's some materials that also required to be super cold for calvin yes so so let's dissect a couple of those different things the super cold part let me just mention for your gamers out there that are trying to clock it at four gigahertz and would love to go to what kind of cooling system can achieve exactly four kelvin you need liquid helium and so liquid helium is expensive it's inconvenient you need a cryostat that that sits there and the energy consumption of that cryostat is impracticable for it's not going in your cell phone you're not so you can picture holding your cell phone like this and then something the size of you know uh a keg of beer or something on your back to cool it like that makes no sense yeah so if you if you're trying to make this in consumer devices uh electronics that are ubiquitous across society superconductors are not in the race for that for now but you're saying so we're just to frame the conversation maybe the thing we're focused on is computing systems that serve as like as servers like large yes large systems so so then you can contrast what's going on in your cell phone with what's going on at one of the super computers um colleague katie schuman invited us out to oak ridge a few years ago so we got to see titan and that was when they were building summits so these are some high performance supercomputers out in tennessee and those are filling entire rooms the size of warehouses you know so once you're at that level okay there you're already putting a lot of power into cooling you need cooling is part of your engineering task that you have to deal with so there it's not entirely obvious that cooling to 4 kelvin is out of the question it's it has not happened yet and i can speak to why that is in the digital domain if you're interested i think it's not going to happen i don't think i don't think superconductors are going to replace semiconductors for digital computation um there are there are a lot of reasons for that but i think ultimately what it comes down to is all things considered cooling errors scaling down to feature sizes all that stuff semiconductors work better at the system level is there some aspect of uh just curious about the historical momentum of this is there some power to the momentum of an industry that's mass manufacturing using a certain material is this is like a titanic shifting like what's your sense when a good idea comes along how good does that idea need to be for the titanic to start shifting that's a that's an excellent question that's an excellent way to to frame it and you know i don't know the answer to that but what i think is okay so the the history of the superconducting logic goes back to the 70s ibm made a big push to do superconducting digital computing in the 70s and they made some choices about their devices and their architectures and things that in hindsight were kind of doomed to fail and i don't mean any disrespect for the people that did it it was hard to see at the time but then another generation of superconducting logic was introduced i want to say the 90s someone named likarev and seminov they propose an entire family of circuits based on joseph's injunctions that are doing digital computing based on logic gates and or not these kinds of things um and they showed how it could go hundreds of times faster than silicon microelectronics and it was it's extremely exciting i wasn't working in the field at that time but later when i went back and read the literature i was just like wow this is this is so awesome uh and so it you might think well the reason why it didn't displace silicon is because silicon already had so much momentum at that time but that was the 90s silicon kept that momentum because it had the simple way to keep getting better you just make features smaller and smaller so you know it would have to be i don't think it would have to be that much better than silicon to displace it but the problem is it's just not better than silicon it might be better than silicon in one metric speed of a switching operation or power consumption of a switching operation but building a digital computer is a lot more than just that elemental operation it's everything that goes into it including the manufacturing including the packaging including the um the you know various materials aspects of things so the reason why and even in even in some of those early papers i can't remember which one it was licorice said something along the lines of you can see how we could build an entire family of digital electronic circuits based on these components they could go 100 or more times faster than semiconductor logic gates but i don't think that's the right way to use superconducting electronic circuits he didn't say what the right way was but he basically said digital logic trying to steal the show from silicon is probably not what these circuits are are most suited to accomplish so if we can just linger and use the word computation when you talk about computation how do you think about it do you think purely on just um the the switching or do you think something a little bit larger scale a circuit taken together performing the basic arithmetic operations that are then required to do the kind of computation that makes up a computer because when we talk about the speed of computation is it boiled down to the basic switching or is there some bigger picture that you're thinking about well all right so maybe we should disambiguate there are a variety of different kinds of computation i don't pretend to be an expert in the theory of computation or anything like that i guess it's important to differentiate though between digital logic which represents information as a series of bits binary digits which you know uh you can think of them as zeros and ones or whatever usually they correspond to a physical system that has two very well separated states and then other kinds of computation like we'll get into more the way your brain works which it is i think indisputably processing information but where the computation begins and ends is not anywhere near as well defined it it doesn't depend on these two levels here's a zero here's a one it's there's a lot of gray area that's usually referred to as analog computing um also in in conventional digital computers or um digital computers in general you have a concept of what's called arithmetic depth which is jargon that basically means how many sequential operations are performed to turn an input into an output and those kinds of computations in in digital systems are highly serial meaning that data streams they don't branch off too far to the side you do you have to pull some information over there and access memory from here and stuff like that but by and large the the computation proceeds in a serial manner it's not that way in the brain in the brain you're always drawing information from different places it's much more network-based computing neurons don't wait for their turn they fire when they're ready to fire and so it's it's asynchronous so one of the other things about a digital system is you're performing these operations on a clock and that's a that's a crucial aspect of it get rid of a clock in a digital system nothing makes sense anymore the brain has no clock it builds its own time scales based on its internal activity so so you can think of the brain as kind of uh like this like network computation where it's actually really trivial simple computers uh just a huge number of them and they're networked i would say it is complex sophisticated little processors and there's a huge number of things neurons are not no offense i don't mean to offend sure no they're very complicated and beautiful and yeah but we often oversimplify them yes they're actually like there's computation happening within a neuron right so i i would say to think of a a transistor as the building block of a digital computer is accurate you use a few transistors to make your logic gates you build up more you build up processors from logic gates and things like that so you can think of a transistor as a fundamental building block or you can think of as we get into more highly parallelized architectures you can think of a processor as a fundamental building block to make the analogy to the neuro side of things a neuron is not a transistor a neuron is a is a processor it has synapses even synapses are not transistors but they are more um they're lower on the information processing hierarchy in a sense they do a bulk of the computation but neurons are entire processors in and of themselves that can take in many different kinds of inputs on many different spatial and temporal scales and produce many different kinds of outputs so that they can perform different computations in different contexts so this is where it enters this distinction between computation and communication so you can think of neurons performing computation and the inter networking the interconnectivity of neurons is communication routine neurons and you see this with very large server systems i've been i mentioned offline i've been talking to jim keller whose dream is to build giant computers that uh you know the bottom like there's often the communication between the different pieces of computing so in this paper that we mentioned optoelectronic intelligence you say electrons excel at computation while light is excellent for communication maybe you can linger and say in this context what do you mean by computation and communication what what are electrons what is light and why do they excel at those two tasks yeah just to to first speak to computation versus communication i would say computation is essentially taking in some information performing operations on that information and producing new hopefully more useful information so for example um imagine you have a picture in front of you and there is a key in it and that's what you're looking for for whatever reason you want to you want to find the key we all want to find the key so the input is that that entire picture and the output might be the coordinates where the key is so you've reduced the total amount of information you have but you found the useful information for you in that present moment that's the useful information you think about this computation as like controlled synchronous sequential not necessarily it could be that could be how your system is performing the computation or it could be asynchronous it there are lots of ways to find the key it depends it depends on the nature of the data depends on um that's a very simplified example a picture with a key in it what about if you're in the world and you're trying to decide the best way to live your life you know that it might be interactive it might be there might be some recurrence or some weird asynchrony i got it so but there's an input and there's an output and you do some stuff in the middle that yeah it goes from the input to the app you've taken in information and output different information hopefully reducing the total amount of information and extracting what's useful yeah communication is then getting that information from the location in which it's stored because information is physical as landauer emphasized and so it is more in one place and you need to get that information to another place so that something else can use it for whatever computation it's working on maybe it's part of the same network and you're all trying to solve the same problem but neuron a over here just deduced something based on its inputs and it's now sending that information across the network to another location so that would be the act of communication can you linger on landau and saying information is physical roth landauer not to be confused with lev landau yeah and he he made huge contributions to our our understanding of the reversibility of information in in this concept that energy has to be dissipated in computing when the computation is irreversible but if you can manage to make it reversible then you you don't need to expend energy but if you um if you do expend energy to perform a computation there's sort of a minimal amount that you have to do and it's kt log2 and it's all somehow related to the second law of thermodynamics and that the universe is an information process and then we're living in a simulation so okay sorry sorry for that tangent so information so that's the defining the the distinction between computation and communication let me say one more thing just to clarify communication ideally does not change the information it moves it from one place to another but it is preserved got it okay all right that's beautiful so uh then the an electron versus light distinction and why are electrons uh good at computation and light good at communication yes this is um there's a lot that goes into it i guess but just try to speak to the simplest part of it electrons interact strongly with one another they're charged particles so if i pile a bunch of them over here they're feeling a certain amount of force and they want to they want to move somewhere else they're strongly interactive you can also get them to sit still you can an electron has a mass so you can you can cause it to be spatially localized so for computation that's useful because now i can make these little devices that put a bunch of electrons over here and then i change the the state of a gate like i've been describing put a different voltage on this gate and now i move the electrons over here now they're sitting somewhere else i have a physical mechanism with which i can represent information it's spatially localized and have knobs that i can adjust to change where those electrons are or what they're doing light by contrast photons of light uh which are the discrete packets of energy that were identified by einstein they do not interact with each other um especially at low light levels if you're in a medium and you have a high a bright high light level you you can get them to interact with each other through the interaction with that medium that they're in but that's that's a little bit more exotic and for the purposes of this conversation we can assume that photons don't interact with each other so if you have a bunch of them all propagating in the same direction they don't interfere with each other if i want to send if i if i have a communication channel and i put one more photon on it it doesn't screw up with those other one it doesn't change what those other ones were doing at all so that's really useful for communication because that means you can sort of allow a lot of these photons to flow uh with without disruption of each other and they can they can branch really easily and things like that but it's not good for computation because it's very hard for this packet of light to change what this packet of light is doing they they pass right through each other so in computation you want to change information and if photons don't interact with each other it's difficult to get them to change the information represented by the others so that that's the fundamental difference is is there also something about the way they travel through different materials or is that just a particular engineering no it's not that's deep physics i think so this gets back to electrons interact with each other and photons don't so say say i'm trying to get a packet of information from me to you and we have a wire going between us in order for me to send electrons across that wire i first have to raise the voltage on my end of the wire and that means putting a bunch of charges on it and then that that charge packet has to propagate along the wire and it has to get all the way over to you there's that wire is going to have something that's called capacitance which basically tells you how much charge you need to put on the wire in order to raise the voltage on it and the capacitance is going to be proportional to the length of the wire so the longer the the length of the wire is the more charge i have to put on it and the energy required to charge up that line and move those electrons to you is also proportional to the capacitance and goes as the voltage squared so you get this huge penalty if you if you want to send electrons across a wire over appreciable distances so distance is an important thing here when you're doing communication distance is an important thing so is the number of connections i'm trying to make me to you okay one that's not so bad if i want to now send it to 10 000 other friends then then all of those wires are adding tons of extra capacitance now not only does it take forever to put the charge on that wire and raise the voltage on all those lines but it takes a ton of power and the number 10000 is not randomly chosen that's roughly how many connections each neuron in your brain makes so it a neuron in your brain needs to send 10 000 messages every time it has something to say you can't do that if you're trying to drive electrons from here to 10 000 different places the brain does it in a slightly different way which we can discuss how can light achieve the 10 000 connections and why is it um why is it better in terms of like the energy use uh required to use light for the communication of the ten thousand connections right right so now instead of trying to send electrons from me to you i'm trying to send photons so i can make what's called a guide which is just a simple piece of a material it could be glass like an optical fiber or silicon on a on a chip and i just have to i just have to inject photons into that waveguide and independent of how long it is independent of how many different connections i'm making it doesn't change the the voltage or anything like that that i have to raise up on the on the wire so if i have one more connection if i add additional connections i need to add more light to the waveguide because those photons need to split and go to different paths that makes sense but i don't have a capacitive penalty that sometimes these are called wiring parasitics there are no parasitics associated with light in that same sense so well just this might be a dumb question but how do i catch a photon on the other end uh what's is it material is it's with the polymer stuff you were talking about for the for a different application for photolithography like how do you catch photo there's a lot of ways to catch a photon it's not a dumb question it's a it's a deep and important question that basically defines a lot of the work that goes on in our group at nist one of my group leaders say woonam has built his career around these superconducting single photon detectors so if you're going to try to sort of reach a lower limit and detect just one particle of light superconductors come back into our conversation and just picture a simple device where you have current flowing through a superconducting wire and um a loop again or no let's say yes you have a loop so you have a superconducting wire that goes straight down like this and on on your loop branch you have a little ammeter something that measures current there's a resistor up there too go with me here so um your current biasing this so there's current flowing through that superconducting branch since there's a resistor over here all the current goes through the superconducting branch now a photon comes in strikes that superconductor we talked about this superconducting macroscopic quantum state that's going to be destroyed by the energy of that photon so now that branch of the circuit is resistive too and you've properly designed your circuit so that the resistance on that superconducting branch is much greater than the other resistance now all of your current's going to go that way your ammeter says oh i just got a pulse of current that must mean i detected a photon then where you broke that superconductivity in a matter of a few nanoseconds it cools back off dissipates that energy and the current flows back through that superconducting branch this is a very powerful superconducting device that allows us to understand quantum states of light i didn't realize a loop like that could be sensitive to a single photon i mean that um that seems strange to me because i mean so what happens when you just barrage it with photons if you put a bunch of photons in there essentially the same thing happens you just drive it into the normal state it becomes resistive and it's not particularly interesting so you have to be careful how many photons you send like you have to be very precise with your communication well it depends so i would say that that's actually in the in the application that we're trying to use these detectors for that's a feature because what we want is for uh if if a neuron sends one photon to a synaptic connection and one of these superconducting detectors is sitting there you get this pulse of current and that synapse says event then i'm going to do what i do when there's a synapse event i'm going to perform computations that kind of thing but if accidentally you send two there or three or five it does the exact same got it and so that's this is this is how in the system that we're devising here communication is entirely binary and that's what i tried to emphasize a second ago communication should not change the information you're not saying oh i got this kind of communication event four photons no we're not keeping track of that this neuron fired this synapse says that neuron fired that's it so that's a that's a noise filtering property of those detectors however there are other applications where you'd rather know the exact number of photons that can be very useful in quantum computing with light and our group does a lot of work around another kind of superconducting sensor called a transition edge sensor that uh adrian alita in our group does a lot of work on that and that can tell you based on the amplitude of the current pulse you divert exactly how many photons were in that pulse so what's that useful for just one way that you can encode information in quantum states of light is in the number of photons you can have what are called number states and a number state will have a well-defined number of photons and maybe the output of your quantum computation encodes its information in the number of photons that are generated so if you have a detector that is sensitive to that it's extremely useful can you achieve like a clock with photons or is that not important is there a synchronicity here in general it can be important uh clock distribution is a big challenge in especially large computational systems and so yes optical clocks optical clock distribution is a is a very powerful technology i i don't know the state of that field right now but i imagine that if you're trying to distribute a clock across any appreciable size computational system you you want to use light yeah i wonder how these giant systems work especially like uh super computers do they need to do clock distribution or are they doing more ad hoc parallel like concurrent programming like there's some kind of locking mechanisms or something that's the fascinating question but the let's zoom in at this very particular question of computation on a processor and communication between processors so what does this system look like that you're envisioning one of the places you're envisioning it is in the paper on optoelectronic intelligence so what are we talking about are we talking about something that starts to look a lot like the human brain or does it still look a lot like a computer what are the size of this thing is it go inside a smartphone or as you said does it go inside something that's more like a house like uh what should we be imagining what are you thinking about when you're thinking about these fundamental systems let me introduce the word neuromorphic there's this concept of neuromorphic computing where what that broadly refers to is um computing based on the information processing principles of the brain and as digital computing seems to be pushing towards some fundamental performance limits people are considering architectural advances drawing inspiration from the brain more distributed parallel network kind of architectures and stuff and so there's this continuum of neuromorphic from things that are pretty similar to digital computers but maybe there are more cores and the way they send messages is a little bit more like the way brain neurons send spikes but for the most part it's still digital electronics and then you know you have some things in between where maybe you're you're using transistors but now you're starting to use them instead of in a digital way in an analog way and so you're trying to get those circuits to behave more like neurons and then that's a little bit quite quite a bit more on the neuromorphic side of things you're trying to get your circuits although they're still based on silicon you're trying to get them to perform operations that are highly analogous to the operations in the brain and that's where a great deal of work is in neuromorphic computing people like yakimo and davari and gert kaunbergs jennifer hasler countless others it's it's a rich and exciting field uh going back to carver mead in the late 1980s and then all the way on the other extreme of the continuum is where you say i'll give up anything related to transistors or semiconductors or anything like that i'm not not starting with the assumption that i'm going to use any kind of conventional computing hardware and instead what i want to do is try and understand what makes the brain powerful at the kind of information processing it does and i want to think from first principles about what hardware is best going to enable us to capture those information processing principles in an artificial system and that's where i live that's where that's where i'm doing my exploration these days so uh what are the first principles of brain like computation communication right yeah this is this is so important and i'm glad we booked 14 hours for this because uh i only have 13 i'm sorry okay so the brain is notoriously complicated and i think that's a an important part of why it why it can do what it does but okay let me let me try to break it down uh starting with the devices neurons as i as i said before they're they're sophisticated devices in and of themselves and synapses are too they they can um change their state based on the activity so they they adapt over time that's crucial to the way the brain works they don't just adapt on one time scale they can adapt on myriad time scales from the the spacing between pulses the spacing between spikes that come from neurons all the way to the age of the organism um also relevant perhaps i think the most important thing that's guided my thinking is the the network structure of the brain so which can also be adjusted yeah in different scales absolutely yes so so you're you're making new con you're changing the strength of contacts you're changing the the spatial distribution of them although spatial distribution doesn't change that much once you're a mature organism but that network structure is is really crucial so let me dwell on that for a second um you can't talk about the brain without emphasizing that most of the neurons in the the neocortex or the prefrontal cortex the part of the brain that we think is most responsible for high-level reasoning and things like that those neurons make thousands of connections so you have this network that is highly interconnected and i think it's safe to say that one of the primary reasons that they make so many different connections is that allows information to be communicated very rapidly from any spot in the network to any other spot in the network so that's a that's a sort of spatial aspect of it you can quantify this in terms of concepts that are related to fractals and scale invariants which i think is is a very beautiful concept so what i mean by that is kind of no matter what spatial scale you're looking at in the brain within certain bounds you see the same general statistical pattern so if i draw a box around some region of my cortex most of the connections that those neurons within that box make are going to be within the box to each other in their local neighborhood and that's sort of called clustering loosely speaking but a non-negligible fraction is going to go outside of that box and then if i draw a bigger box the pattern is going to be exactly the same so you have the scale and variance and you also have a a non-vanishing probability of a neuron making connection very far away so suppose you you want to plot the probability of a neuron making a connection as a function of distance if that were an exponential function it would go e to the minus radius over some characteristic radius and it would it would drop off up to some certain radius the probability would be reasonable close to one and then a beyond that characteristic length r0 it would it would drop off sharply and so that would mean that the neurons in your brain are really localized and that's not what we observe in instead what you see is that the probability of making a longer distance connection it does drop off but it drops off as a power law so the probability that you're going to have a connection at some radius r goes as r to the minus some power and that's more that's what we see with with forces in nature like the electromagnetic force between two particles or gravity goes as one over the radius squared so you can see this in fractals i love that there's a like a fractal dynamics to the brain that if you zoom out you draw the box and you increase that box by certain step sizes you're gonna see the same statistics i think that's probably very important to the way the brain processes information it's not just in the spatial domain it's also in the temporal domain and what i mean by that is that's incredible that this emerged through the evolutionary process that potentially somehow connected to the way the physics of the universe works yeah i i couldn't agree more that it's it's a deep and fascinating subject that i i hope to be able to spend my life studying you think you need to solve understand this this fractal nature in order to understand intelligence and company i do think so i think they're deeply intertwined yes i think power laws are right at the heart of it so just to just to push that one through the same thing happens in the temporal domain so suppose you had um suppose your neurons in your brain were always oscillating at the same frequency then the probability of finding a neuron oscillating as a function of frequency would be this narrowly peaked function around that certain characteristic frequency that's not at all what we see the probability of finding neurons oscillating or pulsing producing spikes at a certain frequency is again a power law which means there's no there's no defined scale of the temporal activity in the brain it's you don't what at what speed do your thoughts occur well there's a there's a fastest speed that can occur and that is limited by communication and other other things but there's not a characteristic scale we have thoughts on all temporal scales from you know a few tens of milliseconds which is physiologically limited by our devices compare that to tens of picoseconds that i talked about in superconductors all the way up to the lifetime of the organism you can still think about things that happened to you when you were a kid well if you want to be really trippy then across multiple organisms in the entirety of human civilization you have thoughts that span organisms right yes taking it to that level if you're willing to see the entirety of the human species as a single organism with the collective intelligence and that too on a spatial and temporal scale there's thoughts occurring and then if you look at not just the human species but the entirety of life on earth is as an organism with thoughts that occurring that are greater and greater sophisticated thoughts there's a different spatial and temporal skill there this is getting very suspicious hold on though before we're done i just want to just tie the bow yes and say that the the spatial and temporal aspects are intimately interrelated with each other so activity between neurons that are very close to each other is more likely to happen on this this faster time scale and information is going to propagate and encompass more of the brain more of your cortices different modules in the brain are going to be engaged in information processing on longer time scales so there's this concept of information integration where most neurons are neurons are specialized any given neuron or any cluster of neuron has its specific purpose but they're also they're also very much integrated so you you have neurons that specialize but share their information and so that happens through these fractal nested oscillations that occur across spatial and temporal scales i think capturing those dynamics in hardware to me that's the goal of of neuromorphic computing so does it need to look so first of all that's fascinating we stated some clear principles here now does it have to look like the brain outside of those principles as well like what other characteristics have to look like the human brain or can it be something very different well it depends on what you're trying to use it for and so i i think a lot of the community asks that question a lot what are you going to do with it and i i completely get it i think that's a very important question and it's also sometimes not the most helpful question what if what you want to do with it is study it what if you just want to see um what does it what do you have to build into your hardware in order to observe these dynamical principles so and also i ask sometimes i ask myself that question every day and i'm not sure i'm able to answer that it's like what are you what are you gonna do with this particular neuromorphic machine so suppose what we're trying to do with it is build something that thinks we're not trying to get it to make us any money or drive a car maybe we'll be able to do that but that's not our goal our goal is to see if we can get the same types of behaviors that we observe in our own brain and by behaviors in this sense what i mean the behaviors of the components the neurons the network that kind of stuff i think there's another element that i didn't really hit on that that you also have to build into this and those are architectural principles they have to do with the hierarchical modular construction of the network and without getting too lost in jargon the the main point that i think is relevant there let me try and illustrate it with a cartoon picture of the architecture of the brain so in the brain you have the the cortex which is sort of this outer sheet um it's actually a you can it's a layered structure you can if you could take it out of your brain you could unroll it on the table and it would be about the size of a of a pizza sitting there and um that's a module it it does certain things it it processes as yorgi buzaki would say it processes the what of of what's going on around you but you have another really crucial module that's called the hippocampus and that that network is structured entirely differently first of all this this cortex that had described 10 billion neurons in there so numbers matter here and they're they're organized in that sort of power law distribution where the probability of making a connection drops off as a power law in space the hippocampus is another module that's important for understanding how where you are when you are um keeping track of of your your position in space and time and that network is very much random so the probability of making a connection it it almost doesn't even drop off as a function of distance it's the same probability that you'll make it here to over there but there are only about 100 million neurons there so you can have that huge densely connected module because it's not so big and the neocortex or the cortex and the hippocampus they talk to each other constantly and that communication is largely facilitated by what's called the thalamus i'm not a neuroscientist here i'm trying to do my best to recite this cartoon picture of the brain i gotcha yeah something like that so this thalamus is is coordinating the activity between the neocortex and the hippocampus and making sure that they they talk to each other at the right time and send messages that would be useful to one another so this all taken together is called the thalamocortical complex and it seems like building something like that is going to be crucial to capturing the types of activity we're looking for because though those responsibilities those separate modules they do different things that's got to be central to um achieving these states of efficient information integration across space and time by the way i am able to achieve this state by watching uh simulations visualizations of the thelma cortical complex there's a few people i forget from where they've created these incredible visual illustrations of like visual stimulation from the eye or something like that it this in this image like flowing through the brain wow i haven't seen that i gotta check that out so it's one of those things you you find this stuff in the world and you see like on youtube it has like 1000 views these like these visualizations of the human brain processing information and like because there's uh there's chemistry there like because this is act from actual human brains i don't know how they're doing the coloring but they're able to actually trace the uh like different the the chemical and the electrical signals throughout the brain and the visual thing it's like whoa because it looks kind of like the universe i mean the whole thing is just incred i recommend it highly i'll probably post a link to it but you can just look for uh um one of the things they simulate is the uh thelma cortical uh complex and just visualization you can find that yourself on youtube but it's it's beautiful um the other question i have for you is um how does memory play into all of this because all the signals sending back and forth that's kind of like uh that's computation and communication but that's kind of like uh you know processing of inputs and outputs uh to produce outputs in the system that's kind of like maybe reasoning maybe there's some kind of recurrence but like is there a storage mechanism that you think about in the context of neuromorphic computing yeah absolutely so that's got to be central you have to have a way that you can store memories and there are a lot of different kinds of memory in the brain that's yet another example of how it's it's not a simple system so there's one kind of memory one way of talking about memory uh usually starts in the context of hopfield networks you were lucky to talk to john hopfield on this program but the the basic idea there is uh working memory is stored in the dynamical patterns of activity between neurons and you can you can think of a certain pattern of activity as an attractor meaning if you put in some signal that's similar enough to other previously experienced signals like that then you're going to converge to the same network dynamics and you will see these neurons participate in the same network patterns of activity that they have in the past so you can talk about the probability that different inputs will allow you to converge to different basins of attraction and you might think of that as oh i saw this face and then i excited this network pattern of activity because last time i saw that face i was at you know what some movie and that that's a famous person on the screen or something like that so so that's one memory storage mechanism but crucial to the ability to imprint those memories in your brain is the ability to change the strength of connection between one neuron and another that synaptic connection between them so synaptic weight update is a massive field of neuroscience and neuromorphic computing as well so there are two poles to that on that spectrum one in okay so in more in the language of machine learning we would talk about supervised and unsupervised learning in when i'm trying to tie that down to neuromorphic computing i will use a definition of supervised learning which basically means the external user the person who's controlling this hardware has some knob that they can tune to change each of the synaptic weights depending on whether or not the network's doing what you want it to do whereas what i mean in this conversation when i say unsupervised learning is that those synaptic weights are are dynamically changing in your network based on nothing that the user is doing nothing that there's no wire from the outside going into any of those synapses the network itself is reconfiguring those synaptic weights based on physical properties that you've built into the devices so if if the synapse receives a pulse from here and that causes the neuron to spike some circuit built in there with no help from me or anybody else adjusts the weight in a way that makes it more likely to store the useful information and excite the useful network patterns and makes it less likely that random noise useless communication events will have an important uh effect on the network activity so there's memory encoded in the weights uh the synaptic weights yeah what about the formation of something that's not often done in machine learning the formation of new synaptic connections right well that seems to so again not not a neuroscientist here but my reading of the literature is that that's particularly crucial in early stages of brain development where a newborn is uh born with tons of extra synaptic connections and it's actually pruned over time so the number of synapses decreases as opposed to growing new long-distance connections it is possible in the brain to grow new neurons and um assign new synaptic connections but it doesn't seem to be the primary mechanism by which the brain is learning so for example like right now sitting here talking to you you say lots of interesting things and i learn what i learn from you and i can remember things that you just said and i didn't grow new axonal connections down to new synapses to to enable those it's plasticity mechanisms in the between the synaptic connections between neurons that enable me to learn on that time scale so at the very least that you can sufficiently approximate that with just weight updates you don't need to form new connections i would say weight updates are a big part of it i also think there's more because broadly speaking when we're doing machine learning our networks say we're talking about feed forward deep neural networks the temporal domain is not really part of it okay you're gonna put in an image and you're gonna get out of classification and you're gonna do that as fast as possible so you care about time but time is not part of the essence of this thing really um whereas in spiking neural networks what we see in the brain time is as crucial as space and they're intimately intertwined as i've tried to say and so adaptation on on different time scales is important not not just in memory for formation although it plays a key role there but also in just keeping the activity in a useful dynamic range so you have other plasticity mechanisms not just weight update or at least not on the time scale of many action potentials but even on the shorter time scale so a synapse can become much less efficacious it can it can transmit a weaker signal after the second third fourth that can second third fourth action potential to occur in a sequence so that's what's called short-term synaptic plasticity which is a form of learning you're learning that i'm getting too much stimulus from looking at something bright right now so i need to tone that down you know there's also another really important mechanism in learning it's called metaplasticity what that seems to be is a a way that you change not the weights themselves but the rate at which the weights change so when i am in say a lecture hall and my this is a potentially terrible cartoon example but let's say i'm in a lecture hall and uh it's time to learn right so my brain will release more perhaps dopamine or some neuromodulator that's going to change the the rate at which synaptic plasticity occurs so that can make me more sensitive to learning at certain times more sensitive to overwriting previous information and less sensitive at other times and finally as long as i'm rattling off the list i think another concept that falls in the category of learning or memory adaptation is homeostasis or homeostatic adaptation where neurons have the ability to control their firing rate so if if one neuron is just like blasting way too much it will naturally tone itself down it's its threshold will adjust so that it's it stays in a useful dynamical range and we see that that's that's captured in in deep neural networks where you don't just change the synaptic weights but you can also move the thresholds of simple neurons in those models and so to uh to achieve this spiking neural networks you want to use like you want to implement the first principles that you mentioned of the temporal and the spatial fractal dynamics here so you can you can communicate locally you can communicate across much greater distances and do the same thing in space and do the same thing in time now you have like a chapter called superconducting hardware for neuromorphic computing so what are some ideas that integrates some of the things we've been talking about in terms of the first principles of neuromorphic computing and the ideas that you outline in uh optoelectronic intelligence yeah so let me start i guess on the communication side of things because that's what led us down this track in the first place by us i'm talking about my my team of colleagues at nist you know saeed han bryce primavera sonia buckley jeff chiles adam mcconnel to name alex tate name a few our group leaders cew nam and rich mirin we've all contributed to this so this is not this is not me saying necessarily just the things that that i've proposed but sort of where our team's thinking has evolved over the years can i can quickly ask what is nist and where is this amazing group of people located nist is the national institute of standards and technology the the the larger facility is out in gaithersburg maryland our team is located in boulder colorado um we nist is a is a federal agency under the department of commerce we do a lot with by we i mean other people at nist would do a lot with standards you know um making sure that we understand the system of units international system of units uh precision measurements there's a lot going on in uh electrical engineering material science and it's historic i mean i mean it's like it's one of those it's like mit or something like that it has a reputation over many decades of just being this really um a place where there's a lot of brilliant people have done a lot of amazing things but in terms of the people in your team in this team of people involved in the concept we're talking about now i'm just curious what kind of disciplines are we talking about what is it mostly physicists and electrical engineers some material scientists but i would say yeah i think physicists and electrical engineers my background is in photonics the use of light for technology so coming from there i i tend to have found colleagues that are more from that background although uh adam akan more of a superconducting electronics background we need a diversity of folks this project is sort of cross-disciplinary i would love to be working more with neuroscientists and things um but we haven't we haven't reached that scale yet but yeah you're focused on the hardware side which requires all the disciplines that you mentioned yes and then of course neuroscience may be a source of inspiration for some of the the the long-term vision i would actually call it more than inspiration i would call it sort of um a road map you know we're not trying to to build exactly the brain but i don't think it's enough to just say oh neurons kind of work like that let's kind of do that thing i mean we're very much following the concepts that the cognitive sciences have laid out for us which i believe is is a really robust road map i mean just on a little bit of a tangent it's often stated that we just don't understand the brain and so it's really hard to replicate it because we just don't know what's going on and i maybe five or seven years ago i would have said that but as i got more interested in the subject i had read more of the neuroscience literature and i was just taken by the exact opposite sense i can't believe how much they know about this i can't believe how mathematically rigorous and um sort of theoretically complete a lot of the concepts are that's not to say we understand consciousness or we understand the self or anything like that but why is the brain what is the brain doing and why is it doing those things we have a neuroscientists have a lot of answers to those questions so there's a lot if you're a hardware designer that just wants to get going whoa it's pretty clear which direction to go in i think okay uh so i love i love the the optimism behind that but um in the implementation of these systems that uh uses supercontext super connectivity how do you make it happen so to me it starts with thinking about the communication network you know for sure that the ability of each neuron to communicate to many thousands of colleagues across the network is indispensable i take that as a core principle of my my architecture my thinking on the subject so coming from coming from a background in photonics it was very natural to say okay we're going to use light for communication just in case listeners may not know light is often used in communication i mean if you think about radio that's light it's long wavelengths but it's electromagnetic radiation it's the same it's the same physical phenomenon obeying exactly the same maxwell's equations and then all the way down to uh fiber fiber optics now you're using visible or near-infrared wavelengths of light but the way you send messages across the ocean is now contemporary over optical fibers so using light for communication is not a stretch it makes perfect sense so you might ask well why don't you use light for communication in a conventional microchip and the answer to that is i believe physical it's v we a light source on a silicon chip that was as simple as a transistor we would there would not be a processor in the world that didn't use light for communication at least above some distance how many light sources are needed oh you need a light source at every single point a light source per neuron per neuron per per liter but then if you could have a really small and nice light source you can your definition of neuron could be flexible could be yes yes sometimes it's helpful to me to say in this hardware a neuron is that entity which has a light source that and i i can and then there was light yeah i mean i can explain more about that but um somehow this like rhymes with consciousness because the the people will often say the light of consciousness so that consciousness is that which is conscious i got it that's not my quote that's me that's my quote uh you see that quote comes from my background yours is in optics mine in light mine is in darkness so go ahead so what the point i was making there is that if it was easy to manufacture light sources along with transistors on a silicon chip they would be everywhere yeah and it's not easy it's there people have been trying for decades and it's actually extremely difficult i think an important part of our research is is dwelling right at that at that spot there so is it physics or engineering so okay so it's it's physics i think so and what i mean by that is as as we discussed silicon is the material of choice for transistors and it it's very difficult to imagine that that's going to change anytime soon silicon is notoriously bad at emitting light and that has to do with the immutable properties of silicon itself the way that the energy bands are structured in silicon you're never going to make silicon efficient as a light source at room temperature without doing very exotic things that degrade its ability to interface nicely with those transistors in the first place so that's that's like one of these things where it's why why is nature dealing us that blow you give us these beautiful transistors and you give us all the motivation to use light for communication but then you don't give us a light source so well okay you do give us a light source compound semiconductors like we talked about back at the beginning an element from group three and an element from group five from an alloy where every other lattice site switches which element it is those have much better properties for generating light you put electrons in light comes out almost 100 percent of the the electron hole it can be made efficiently i'll take your warfare okay however i say it's physics not engineering because it's very difficult to get those compound semiconductor light sources situated with your silicon in order to do that ion implantation that i talked about at the beginning high temperatures are required so you you got to make all of your transistors first and then put the compound semiconductors on top of there you can't grow them afterwards because that requires high temperature it screws up all your transistors you try and stick them on there they don't have the same lattice constant the spacing between atoms is different enough that it just doesn't work so nature does not seem to be telling us that hey go ahead and combine light sources with your digital switches for conventional digital computing and conventional digital computing will often require smaller scale i guess in terms of like smartphone like so in which kind of systems can does nature hint that we can use um light and photons for communication well so let me just try and be clear you can use light for communication in digital systems just the light sources are not intimately integrated with the silicon you you manufacture all the silicon you have your microchip plunk it down and then you manufacture your light sources separate chip completely different process made in a different foundry and then you put those together at the package level got it so now you have you have some i would say a great deal of architectural limitations that are introduced by that sort of package level integration as opposed to monolithic on the same chip integration but it's still a very useful thing to do and that's where i had done some work previously before i came to nist there's a project led by vladimir stoyanovic that now spun out into a company called ir labs led by mark wade and chen sun where they're doing exactly that so you have your light source chip your silicon chip whatever it may be doing maybe it's digital electronics maybe it's some other control purpose something and the the silicon chip drives the the light source chip and modulates the intensity of the light so you can get data out of the package on an optical fiber and that still gives you tremendous advantages in bandwidth as opposed to sending those signals out over electrical lines but it is somewhat peculiar to my eye that they have to be integrated at this package level and those those people i mean they're so smart those are my my colleagues that i respect a great deal so it's it's very clear that it's not just they're making a bad choice this is what physics is telling us it just wouldn't make any sense to to try to stick them together yeah so there even if it's difficult um it's uh easier than the alternative unfortunately i think so yes and i again i need to go back and and make sure that i'm not taking the wrong way i'm not saying that the pursuit of integrating compound semiconductors with silicon is fruitless and shouldn't be pursued it should and people are doing great work kaimei lao and john bowers others they're they're doing it and they're making progress but to my eye it doesn't look like that's ever going to be just the standard monolithic light source on silicon process i i just don't see it it's yeah so nature kind of points the way usually and if you resist nature it's just you're gonna have to do a lot more work and it's gonna be expensive and not scalable got it but okay so let me uh let's go like far into the future let's imagine this gigantic neuromorphic computing system that uh simulates all of our realities it currently is mentioned matrix four so this thing uh this powerful computer how does it operate like so so what what are the neurons what is the communication what's your sense all right so let me let me now after spending 45 minutes trashing light source integration with silicon let me now say why i'm basing my entire life yeah professional life on integrating light sources with electronics i think the game is completely different when you're talking about superconducting electronics for several reasons um let me try to go through them one is that as i mentioned it's difficult to integrate those compound semiconductor light sources with silicon with silicon is a requirement that is introduced by the fact that using semiconducting electronics in superconducting electronics you're still going to start with a silicon wafer but it's it's just the bread for your sandwich in a lot of ways you're not using that silicon in precisely the same way for the electronics you're now depositing superconducting materials on top of that the prospects for integrating light sources with that kind of an electronic process are certainly less explored but i think much more promising because you don't need those light sources to be intimately integrated with the transistors that's where the problems come up they don't need to be lattice matched to the silicon all that kind of stuff instead it seems possible that you can take those compound semiconductor light sources stick them on the silicon wafer and then grow your superconducting electronics on the top of that it's at least not obviously going to fail so the computation would be done on the superconductive material as well yes the computation is done in the superconducting electronics and the light sources receive signals that say hey a neuron reach threshold produce a pulse of light send it out to all your downstream synaptic connections those are again super conductive elect superconducting electronics perform your computation and you're off to the races your network works so then if we can rewind real quick so what are the limitations of the challenges of super conducting electronics when we think about constructing these kinds of systems so actually let me let me say uh one other thing about the light sources yes please and then i'll then i'll move on i promise because this is this is probably tedious first this is super exciting okay one other thing about the light sources i said that silicon is terrible at emitting photons it's just not what it's meant to do however the game is different when you're at low temperature if you're working with superconductors you have to be at low temperature because they don't work otherwise when you're at 4 kelvin silicon is not obviously a terrible light source it's still not as efficient as compound semiconductors but it might be good enough for this application the final thing that i'll mention about that is again leveraging superconductors as i said in in a different context superconducting detectors can receive one single photon in that conversation i failed to mention that semiconductors can also receive photons that's the primary mechanism by which it's done a camera in your phone that's receptive to visible light it's is receiving photons it's based on silicon or you can make it in different semiconductors for different wavelengths but it requires on the order of a thousand a few thousand photons to receive a pulse now when you're using a superconducting detector you need one photon exactly one i mean one or more so the fact that your synapses can now be based on superconducting detectors instead of semiconducting detectors brings the light levels that are required down by some three orders of magnitude so now you don't need good light sources you can have the world's worst light sources as long as they spit out maybe a few thousand photons every time a neuron fires you have the heart you have the hardware principles in place that you might be able to do perform this optoelectronic integration to me optoelectronic integration is it's just so enticing we want to be able to leverage electronics for computation light for communication working with silicon microelectronics at room temperature that has been exceedingly difficult and i hope that when we move to the superconducting domain target a different application space that is neuromorphic instead of digital and use superconducting detectors maybe optoelectronic integration comes to us okay so there's a bunch of questions so one is temperature so in these kind of hybrid heterogeneous systems what's the temperature what are some of the constraints of the operation here does it all have to be a four kelvin as well four kelvin everything has to be at four kelvin okay so what are the other engineering challenges of making this kind of optoelectronic systems let me just dwell on that four kelvin for a second because some people hear four kelvin and they just get up and leave they just say i don't i'm not doing it you know and to me that's very earth-centric species-centric we live in 300 kelvin so we want our technologies to operate there too i totally get it yeah what's zero celsius zero celsius is 273 kelvin so we're talking very very cold here this is this is even boston cold this is real cold yeah siberia no okay so just for reference the the temperature of the cosmic microwave background is about 2.7 kelvin so we're still warmer than deep space good so that when the universe dies out it'll be colder than 4k it's already colder than 4k in the in the expanses you know you don't have to get that far away from the earth in order to to drop down to not far from what you're saying is the aliens that live at the edge of the observable universe are using superconductive material for their computation they don't have to live at the edge of the universe the aliens that are more advanced than us in their solar system are doing this in their asteroid belt we can get to that oh because of the they can get that to that temperature easier there sure yeah all you have to do is reflect the sunlight away and you have a huge head start oh so the sun is the problem here yeah like it's warm here on earth yeah okay okay so can you uh so how do we get to 4k what's well okay so very different kind of 4k temperature yeah what i want to say about temperature is that if you can swallow that if you can say all right i give up applications that have to do with my cell phone and the convenience of you know a laptop on a train and you instead for me i'm i'm very much in the scientific head space i'm not looking at products i'm not looking at what this will be useful to sell to consumers instead i'm thinking about scientific questions well it's just not that bad to have to work at 4 kelvin we do it all the time in our labs at nist and so does i mean for reference the entire quantum computing sector usually has to work at something like 100 millikelvin 50 millikelvin so now you're talking another factor of 100 even colder than that a fraction of a degree and everybody seems to think quantum computing is going to take over the world that it's so much more expensive to have to get that extra factor of 10 or whatever colder and yet it's not stopping people from investing in in that area and by investing i i mean putting their research into it as well as venture capital or whatever so oh so so based on the energy of what you're commenting on i'm getting a sense that's one of the criticism of this approach is 4k for kelvin is uh is a big negative it is the show stopper for a lot of people they just i mean and understandably i i'm not saying that that that's not a consideration of course it is for for some okay so different motivations for different people in the academic world suppose you spent your whole life learning about silicon microelectronic circuits you you send a design to a foundry they send you back a chip and you go test it at your tabletop and now i'm saying here now learn how to use all these cryogenics so you can do that at 4 kelvin no come on man i don't want to do that that sounds it's the old momentum the titanic or the turning yeah kind of but you're saying that's not that too much of a finding when we're looking at large systems and the gain you can potentially get from them that's not that much of a cost and when you want to answer the scientific question about what are the physical limits of cognition well the physical limits they don't care if you're at 4 kelvin if you can perform cognition at a scale orders of magnitude beyond any room temperature technology but you got to get cold to do it you're going to do it and to me that's the interesting application space it's not even an application space that's the interesting scientific paradigm so i i personally am not going to let low temperature stop me from realizing a technological domain or or realm that is achieving in most ways everything else that i that i'm looking for in my hardware so that okay that's a big one is there other kind of engineering challenges that you envision yeah yeah yeah so let me take a moment here because i haven't really described what i mean by a neuron or a network in this particular hardware yeah do you want to talk about loop neurons and there's so many fascinating but you just have so many amazing papers that people should definitely check out and uh the titles alone are just killers so anyway go ahead right so let me say big picture based on optics photonics for communication superconducting electronics for computation how how does this all work so a neuron in this in this hardware platform can be thought of as circuits that are based on joseph's injunctions like we talked about before where every time a photon comes in so let's start by talking about a synapse a synapse receives a photon one or more from a from a different neuron and it converts that optical signal to an electrical signal the amount of current that that adds to a loop is controlled by the synaptic weight so as i said before you're popping fluxons into a loop right so a photon comes in it hits a superconducting single photon detector one photon the the absolute physical minimum that you can communicate from one place to another with light and that detector then converts that to an electrical signal and the amount of signal is uh correlated with some kind of weight yeah so the synaptic weight will tell you how many fluxons you pop into the loop it's an analog number we're doing analog computation now well can you just linger on that what the heck is the flux on are we supposed to know this or is this is this a funny uh it's like the big bang is this is this a funny word for something deeply technical no let's let's try to avoid using the word flux line because it's not actually necessary when a when a photo is fun to say though so uh so it's very necessary i would say when when a photon hits that superconducting single photon detector current is added to a superconducting loop and the the amount of current that you add can is an analog value can have eight bit equivalent resolution something like that 10 bits maybe that's amazing by the way this is starting to make a lot more sense when you're using superconductors for this the energy of that circulating current is is less than the energy of that photon so your your energy budget is not destroyed by doing this analog computation so now in the language of a neuroscientist you would say that's your post-synaptic signal you have this current being stored in a loop you can decide what you want to do with it most likely you're going to have it decay exponentially so every single synapse is going to have some given time constant and that's determined by set by putting some resistor in that in that superconducting loop so a synapse a synapse event occurs when a photon strikes a detector adds current to that loop it decays over time that's the postsynaptic signal then you can process that in a dendritic tree bryce primavera and i have a paper that we've submitted about that for the more neuroscience oriented people there's a lot of dendritic processing a lot of plasticity mechanisms you can implement with essentially exactly the same circuits you have this one simple building block circuit that you can use for a synapse for a dendrite for the neuron cell body for all the plasticity functions it's all based on the same building block just tweaking a couple parameters so this basic building block has both an optical and an electrical component and then you can just build arbitrary large systems with that close you're not at fault for thinking that that's what i meant what i what i should say is that if you want it to be a synapse you tack a detector a superconducting detector onto the front of it and if you want it to be anything else there's no optical component got it so at the front optics in the front uh electrical stuff in the back electrical yeah in the processing and in the the output signal that it sends to the next stage of processing further so the dendritic trees is electrical it's all electrical it's all electrical in the super domain for anybody who's up on their superconducting circuits it's just based on a dc squid the most ubiquitous it's which is a circuit composed of two joseph's injunctions so it's it's a very bread and butter kind of thing and then the only place where you go beyond that is the neuron cell body itself it's receiving all these electrical inputs from the synapses or dendrites or however you've structured that particular unique neuron and when it reaches its threshold which occurs by driving a joseph's injunction above its critical current it produces a pulse of current which starts an amplification sequence voltage amplification that produces light out of a transmitter so one of one of our colleagues adam akan and sonia buckley as well did a lot of work on the the light sources and the amplifiers that drive the current and produce sufficient voltage to drive current through that now semiconducting part so that light source is the semiconducting part of a neuron and that so the neuron has reached threshold it produces a pulse of light that light then fans out across a network of wave guides to reach all the downstream synaptic terminals that do perform this process themselves so it's probably worth explaining what a network of wave guides is because a lot of listeners aren't going to know that look up the papers by jeff chiles on this one but basically light can be guided in a a simple basically wire of usually an insulating material so silicon silicon nitride different different kinds of glass just like in a fiber optic it's glass silicon dioxide that makes it a little bit big we want to bring these down so we use different materials like silicon nitride but basically just imagine a rectangle of some material that just goes and branches forms different different branch points that target different sub-regions of the network you can transition between layers of these so now we're talking about building in the third dimension which is absolutely crucial so that's what waveguides are so yeah that's great uh what why the third dimension is crucial okay so yes you were talking about what are some of the technical limitations one of the things that i believe we have to grapple with is that our brains are miraculously compact for the number of neurons that are in our brain it sure does fit in a small volume as it would have to if we're going to be biological organisms that are resource limited and things like that any kind of hardware neuron is almost certainly going to be much bigger than that if it is of comparable complexity even whether it's based on silicon transistors okay a transistor seven nanometers that doesn't mean a semiconductor based neuron is seven nanometers they they're big they they require many transistors different other things like capacitors and things that store charge they end up being on the order of a hundred microns by a hundred microns and it's difficult to get them down any smaller than that the same is true for superconducting neurons and the same is true if we're trying to use light for communication even if you're using electrons for communication you you have these wires where okay the elect the size of an electron might be angstroms but the size of a wire is not angstroms and if you try and make it narrower the resistance just goes up so it you don't actually win to communicate over long distances you need your wires to be microns wide and it's the same thing for wave guides waveguides are essentially limited by the wavelength of light and that's going to be about a micron so whereas compare that to an axon the analogous component in the brain which is 10 nanometers in diameter something like that they they're bigger when they need to communicate over long distances but grappling with the size of these structures is inevitable and crucial and so in order to make systems of comparable scale to the human brain by scale here i mean number of interconnected neurons you absolutely have to be using the third spatial dimension and that means on the wafer you need multiple layers of both active and passive components active i mean superconducting electronic circuits that are performing computations and passive i mean these wave guides that are routing the optical signals to different places you have to be able to stack those if you can get to something like 10 planes of each of those or maybe not even 10 maybe 5 6 something like that then you're in business now you can get millions of neurons on a wafer but that's not that's not anywhere close to the brain scale in order to get to the scale of the human brain you're going to have to also use the third dimension in the sense that entire wafers need to be stacked on top of each other with fiber optic communication between them and we need to be able to fill a space the size of this table with stacked wafers and that's when you can get to some 10 billion neurons like your human brain and i i don't think that's specific to the optoelectronic approach that we're taking i think that applies to any hardware where you're trying to reach commensurate scale and complexity as the human brain so you need that fractal stacking so stacking on the wafer and stacking of the wafers and then whatever the system that combined this stacking of the tables with the wafers and it has to be fractal all the way you're exactly right because that's the only way that you can efficiently get information from a small point to across that whole network it has to have the the power law connecting and photons are like uh optics throughout yeah absolutely once you're at this scale to me it's just obvious of course you're using light for communication you have fiber optics given to us you know from nature so simple the the thought of even trying to this any kind of electrical communication just doesn't it doesn't make sense to me i'm not saying it's wrong i don't know but that's where i'm coming from so let's return to loop neurons why are they called loop neurons yeah the term loop neurons comes from the fact like we've been talking about that they rely heavily on these superconducting loops so even in a lot of forms of digital computing with superconductors storing uh a signal in a superconducting loop is a a primary technique in this particular case it's it's just loops everywhere you look so the the strength of a synaptic weight is going to be set by the state the amount of current circulating in a loop that is coupled to the synapse so memory is implemented as current circulating in a superconducting loop the the coupling between say a synapse and a dendrite or a synapse in the neuron cell body occurs through loop loop coupling through transformer so current circulating in a synapse is going to induce current in a in a different loop a receiving loop in the in the neuron cell body so since all of the computation is happening in these flux storage loops and they play such a central role in in how the information is processed how memories are formed all that stuff i didn't think too much about it just call them loop neurons because it rolls off the tongue a little bit better than superconducting optoelectronic neurons okay so uh how do you design circuits for these loop neurons that's a great question there's a lot of different scales of design so at the level of just one synapse you can use conventional methods they don't they're not that complicated as as far as superconducting electronics goes it's just for joseph's injunctions or something like that depending on how much complexity you want to add so you can just directly simulate each component in in spice we've been what it's standard electrical simulation software okay basically so you're just you're just explicitly solving the differential equations that describe the circuit elements and then you can stack these things together in that simulation software to then build circuits you can but that becomes computationally expensive so one of the things when when covet hit we knew we had to turn some attention to more things you can do at home in your basement or whatever and one of them was was computational modeling so we started working on adapting abstracting out the circuit performance so that you don't have to explicitly solve the circuit equations which for joseph's injunctions usually needs to be done on like a picosecond time scale and you have a lot of nodes in your circuit so it results in a lot of differential equations that need to be solved simultaneously we were looking for a way to simulate these circuits that is scalable up to networks of millions or so neurons is sort of where we're targeting right now so we were able to analyze the behavior of these circuits and as i said there it's based on these simple building blocks so you really only need to understand this one building block and if you get a good model of that boom it tiles and you you can change the parameters in there to get different behaviors and stuff but it's all based on now it's one differential equation that you need to solve so one differential equation for every synapse dendrite or neuron in your in your system and for the neuroscientists out there it's just a simple leaky integrated fire model leaky integrator basically the synapse is a leaky integrator a dendrite is a leaky integrator so i'm really fascinated by how this one simple component can be used to achieve lots of different types of dynamical activity and to me that's where scalability comes from and also complexity as well complexity is often characterized by relatively simple building blocks connected in potentially simple or sometimes complicated ways and then emergent new behavior that was hard to predict from those simple simple elements and that's exactly what we're working with here so it's a very exciting platform both from a modeling perspective and from a hardware manifestation perspective where we can hopefully start to to have this uh test bed where we can explore things not just related to neuroscience but also related to other things that connected to other physics like critical phenomenon icing models things like that so you were asking how we simulate these circuits it's it's at different levels and we've got the simple spice circuit stuff that's no problem and now we're building these network models based on this more efficient leaky integrator so we can actually reduce every element to one differential equation and then we can also step through it on a much coarser time grid so it ends up being something like a factor of a thousand to ten thousand speed improvement which allows us to simulate but hopefully up to up to millions of neurons um whereas before we would have been limited to tens 100 something like that and just like uh simulating quantum mechanical systems with a quantum computer so the goal here is to understand such systems for me the goal is to study this as a scientific physical system it i'm not drawn towards turning this into an enterprise at this point i feel short-term applications that obviously make a lot of money is not necessarily a curiosity driver for you at the moment absolutely not if you're interested in short-term making money go with deep learning use silicon microelectronics if you want to understand things like the the physics of a fascinating system or if you want to understand something more along the lines of the physical limits of what can be achieved then i think single photon communication superconducting electronics is extremely exciting what if i want to use superconducting hardware at 4 kelvin to mine bitcoin that's my main interest that's that's the reason i wanted to talk to you today i want us not i don't know what's what's bitcoin [Laughter] it's a look it up on the internet somebody somebody told me about it i'm not sure exactly what it is uh so but let me ask nevertheless about applications to machine learning okay so what like if you look at the scale of 5 10 20 years is the is it possible to uh before we understand the nature of human intelligence and general intelligence do you think we'll start falling out of this exploration of neuromorphic systems ability to solve some of the problems that the machine learning systems of today can't solve i'm i'm really hesitant to over promise so i i really don't know i also i i don't really understand machine learning in a lot of senses i mean machine learning from my perspective appears to require that you know precisely what your input is and also what your goal is you usually have some objective function or something like that and that's just that's very limiting i mean of course a lot of times that that's the case you know there's a there's a picture and there's a horse in it so you're done but that's not a very interesting problem i think when i when i think about intelligence it's it's almost defined by the ability to handle problems where you don't know what your inputs are going to be and you don't even necessarily know what you're trying to accomplish i mean i'm not sure what i'm trying to accomplish in this world but at all scales yeah at all scales right i mean so and sometimes so i i'm i'm more drawn to the underlying phenomena the the the critical dynamics of this of this system trying to understand how elements that you build into your hardware result in emergent fascinating activity that was very difficult to predict things like that so but but but i got to be really careful because i think a lot of other people who if they found themselves working on this project in my shoes they would say all right what are what are all the different ways we can use this for machine learning actually let me let me just definitely mention colleague nist mike schneider he's also very much interested particularly in the super conducting side of things using the incredible speed power efficiency also ken segall at colgate other people working on specifically the superconducting side of this for machine learning and deep feed-forward neural networks there the advantages are obvious it's extremely fast yeah so that's less on the nature of intelligences and more on various characteristics of this hardware right yes you can use for the basic computation as we know it today yeah and communication one of the things that mike schneider's working on right now is an image classifier at a relatively small scale i think he's targeting that nine pixel problem where you can have three different characters and um you just you put in a nine pixel image and you classify it as one of these three three uh categories and that's going to be really interesting to see what happens there because if you can show that even at that scale you just put these images in and you get it out and you can he thinks he can do it i forgot if it's a nanosecond or some extra extremely fast classification time it's probably less it's probably 100 picoseconds or something there you have challenges though because the joseph's injunctions themselves the electronic circuit is extremely power efficient some orders of magnitude for something more than a transistor doing the same thing but when you have to cool it down to 4 kelvin you pay a huge overhead just for keeping it cold even if it's not doing anything so it's it you ha it has to work at big at large scale in order to overcome that power penalty but that's possible it's just it's gonna have to get that performance and this is sort of what you were asking about before is like how much better than silicon would it need to be and the answer is i don't know i think if it's if it's just overall better than silicon at a problem that a lot of people care about maybe it's image classification maybe it's face recognition maybe it's monitoring credit transactions i don't know then i think it will have a place it's not going to be in your cell phone but it could be in your data center uh so what about in terms of the data center i don't know if you're paying attention to the various systems like um tesla recently now announced uh dojo which is a large scale machine learning training system that again the bottom like there is probably going to be communication between those systems um is there something from your work on um the everything we've been talking about in terms of superconductive hardware that could be useful there oh i mean i okay tomorrow no in the long term it could be the whole thing it could be nothing i i don't know but definitely definitely um when you look at the so i don't know that much about dojo my understanding is that that's new right that's just just coming online well i don't i don't even know where uh it's it hasn't come online and and when you announce big sexy so let me explain to you the way things work in the in the world out there in the word of business and marketing it's not always clear where you are on the coming online part of that so i don't know where they are exactly but the vision is from ground up to build up you know a very very large scale modular machine learning asic basically hardware that's optimized for training neural networks and of course there's a lot of companies that are small and big working on this kind of problem the question is how to do it in a modular way that uh this has very fast communication the interesting aspect of tesla is you have a company that at least at this time is so singularly focused on solving a particular machine learning problem and is making obviously a lot of money doing so because the machine learning problem happens to be involved with autonomous driving so you have a system that's um driven by an application and that's really interesting because you know uh you have maybe google working on tpus and so on you have all these other companies with asics they're usually more kind of always thinking general so i like it when it's driven by a particular application because then you can really get to the it's like it's somehow if you just talk broadly about intelligence you may not always get to the right solutions it's nice to couple that sometimes a specific clear illustration of something that requires general intelligence which for me driving is one such case i think you're exactly right sometimes just having that focus on that application brings a lot of people focuses their energy and attention i think that so one of the things that's appealing about what you're saying is not just that the application is specific but also that the scale is big big and that the benefit is is also huge so financial and to humanity right right right yeah so i guess let me just try to understand is the point of this dojo system to figure out the the parameters that then plug into neural networks and then you don't need to retrain you you just make copies of a certain chip that has all the all the parameters established or no it's straight up retraining a large neural network over and over and over so you have to do it once for every new car no no you have to uh so they do this interesting process which i think is a process for machine learning supervised machine learning systems you're going to have to do which is you uh have a system you train your network once it takes a long time i don't know how long but maybe a week okay the train and then you deploy it on let's say about a million cars i don't know what the number is that part you just write software that updates some weights in a table and yeah okay but there's a loop back yeah yeah okay each of those cars run into trouble rarely but like they they uh they catch the edge cases of the performance of that particular system and then send that data back and either automatically or by humans that weird edge case data is annotated and then the network has to become smart enough to now be able to perform in those edge cases so has to get retrained there's clever ways of retraining different parts of that network but for the most part i think they prefer to retrain the entire thing so you have this giant monster that kind of has to be trained regularly i think the vision with uh dojo is to have a very large machine learning focused driving focused super computer that then is sufficiently modular they could be scaled to other machine learning applications right but like so they're not limiting themselves completely to this particular application but is this application is the way they kind of test this iterative process of machine learning is you make a system that's very dumb deploy it get the edge cases where it fails make it a little smarter it becomes a little less dumb and that where that iterative process achieves something that you can call intelligent or as smart enough to be able to solve this particular application so it has to do with uh training neural networks fast and training neural networks that are large but also based on an extraordinary amount of diverse input data yeah and that's one of the things so this does seem like one of those spaces where the the scale of superconducting optoelectronics the way that um so when you talk about the weaknesses like i said okay well you have to cool it down at this scale that's fine that's because that's not that's not an too much of an added cost most of your power is being dissipated by the circuits themselves not the cooling and also you have one centralized kind of cognitive hub if you will and so when if we're talking about putting a superconducting system in a car that's a that's questionable do you really want to cryostat in the trunk of everyone your car it'll fit it's not that big of a deal but hopefully there's a better way right but since this is sort of a central supreme intelligence or something like that and it's it needs to really have this massive data acquisition massive data integration i would think that that's where large-scale spiking neural networks with vast communication and all these things would would have something pretty tremendous to offer it's not going to happen tomorrow there's a lot of development that needs to be done but you know we have to be patient with self-driving cars for a lot of reasons we were all optimistic that they would be here by now and okay they are to some extent but if we're thinking five or ten years down the line it it's it's not unreasonable one other thing i'll just let me just mention that getting into self-driving cars and technologies that are using ai out in the world this is something nist cares a lot about elham dabasi is leading up a much larger effort in ai at nist than than my my little project and really central to that mission is this concept of trustworthiness so when you're going to deploy this neural network in every single automobile with so much on the line you have to be able to trust that so now how do we know how do we know that we can trust that how do we know that we can trust the self-driving car or the the supercomputer that that trained it there's a lot of work there and there's a lot of that going on at nist and we're it's still early days i mean you're familiar with the yeah yeah and all that but there's a fascinating dance in engineering with like safety critical systems there's a desire in computer science i just recently talked to don knuth to to uh you know for algorithms and for systems for them to be provably correct or provably safe and you know this is one other difference between humans and biological systems is we're not provably anything right right and so there's uh some aspect of uh imperfection yes that we need to have built in like robustness to imperfection um be part of our systems which is a difficult thing for engineers to contend with they're very uncomfortable with the idea that you have to be okay with failure and almost engineer failure into the system mathematicians hate it too but i think it was i think it was turing who said something along the lines of i can give you an intelligent system or i can give you a flawless system but i can't give you both and it's in sort of creativity and abstract thinking seem to rely somewhat on stochasticity and um not having components that perform exactly the same way every time this is where like the disagreement i have with not disagreement but a different view on the world i'm with touring when i talk to robotic uh robot colleagues that sounds like i'm talking to robots colleagues that are roboticists the goal is perfection and to me is like no i think the goal should be um imperfection that's communicated and through the interaction between humans and robots that imperfection becomes um a feature not a bug right like together as a seen as a system the human and the robot together are better than any either of them individually but the robot itself is not perfect any in in any way of course there's had a bunch of disagreements including with mr elon about uh to me autonomous driving is fundamentally a human robot interaction problem not a robotics problem right elon is a robotics problem that's actually an open and fascinating question whether humans can be removed from the loop completely we've talked about a lot of fascinating chemistry and physics and engineering and we're always running up against this issue that nature seems to dictate what's easy and what's hard so you have this uh cool little paper that i'd love to just ask you about it's titled does cosmological evolution select for technology so in physics there's uh parameters that seem to define the way our universe works that physics works that if it worked any differently we would get a very different world so it seems like the parameters are very fine-tuned to the kind of physics that we see all the beautiful e equals mc squared that would get these nice beautiful laws it seems like very fine-tuned for that so what you argue in this article is uh it may be that the universe has also fine-tuned its parameters that enable the kind of technological innovation that we see that technology that we see can you can you explain this idea yeah i think you've introduced it nicely let me let me just try to say a few things in in my language leia what is what is this fine-tuning problem so physicists have spent centuries trying to understand the the system of equations that govern the way nature behaves the way particles move and interact with each other and as that understanding has become more clear over time it it it became sort of evident that it's all it's all well adjusted to allow a universe like like we see very complex this this large long-lived universe and so one one answer to that is well of course it is because we wouldn't be here otherwise but um i don't know that's not very satisfying that's sort of that's what's known as the weak anthropic principle it's a statement of selection bias we can only observe a universe that is fit for us to to live in so what does it mean for a universe to be fit for us to live in well the pursuit of physics it is based partially on coming up with equations that describe how things behave and interact with each other but in all those equations you have so there's the form of the equation sort of how how different fields or particles um move in space and time but then they're also the the parameters that just tell you sort of the strength of different couplings how strongly does a charged particle couple to the electromagnetic field or masses how how strongly does a a particle couple to the higgs field or something like that and those parameters that define that not not the general structure of the equations but the relative importance of different terms they seem to be every bit as important as the the structure of the equations themselves and so i forget who it was somebody when they were working through this and trying to see okay if i adjust the parameter this parameter over here call it the say the fine structure constant which tells us the strength of the electromagnetic interaction oh boy i can't change it very much otherwise nothing works the universe sort of doesn't it just pops into existence and goes away in a nanosecond or something like that and and somebody had the phrase this looks like a put up job meaning every one of these parameters was dialed in it's arguable how precisely they have to be dialed in but dialed in to some extent not just in order to enable our existence that's a very anthropocentric view but to enable a universe like this one so okay maybe i think the majority position of working physicists in the field is it has to be that way in order for us to exist we're here we shouldn't be surprised that that's the way the universe is and i don't know for a while that never sat well with me but i just kind of moved on because there are things to do and a lot of exciting work doesn't depend on resolving this puzzle but as i started working more with technology getting into the more recent years of my careers particularly when i started after having worked with silicon for a long time which was kind of eerie on its own but then when i switched over to superconductor i was just like this is crazy it's just absolutely um astonishing that our universe gives us super conductivity it's one of the most beautiful physical phenomena and it's also extraordinarily useful for technology so you can argue that the universe has to have the parameters it does for us to exist because we couldn't be here otherwise but why does it give us technology why does it give us silicon that has this ideal oxide that allows us to make a transistor without trying that hard that can't be explained by the same anthropic reasoning yeah so it's asking the why question i mean the a slight natural extension of that question is i wonder if the parameters were different if we would simply have just another set of paint brushes to create totally other things that wouldn't like like that wouldn't look like anything like the technology of today but would nevertheless have incredible complexity which is if you sort of zoom out and start defining things not by uh like how many batteries it needs and whether it can make toast but more like how much complexities within the system or something like that right well yeah you can you can start to quantify things like you're exactly right so nowhere am i arguing that in all of the vast parameter space of everything that could conceivably exist in the multiverse of nature there is this one point in parameter space where complexity arises i i doubt it it that would be a shameful waste of resources it seems yeah but it might be that we reside at one place in parameter space that has been um adapted through an evolutionary process to allow us to make certain technologies that that allow our our particular kind of universe to arise and sort of achieve the things it does see i wonder if nature in this kind of discussion if nature is a catalyst for innovation or if it's a ceiling for innovation so like is it going to always limit us like what you're talking about silicon is it just make it super easy to do awesome stuff in a certain dimension but we could still do awesome stuff in other ways it'll just be harder or it doesn't really set like the maximum we can do this that's a good thing to that's a good subject to discuss i guess i feel like we need to lay a little bit more groundwork so i want to make sure that um i introduce this in the context of lee smolin's previous idea so who's lee smolin and what kind of ideas does he have okay lee smolin is a theoretical physicist who back in the late 1980s published a paper in the early 1990s introduced this idea of cosmological natural selection which argues that the universe did evolve so his paper was called did the universe evolve and i gave myself the liberty of titling my paper does cosmological selection or just cosmological evolution select for technology in reference to that so he introduced that idea decades ago now he primarily works on um quantum gravity loop quantum gravity other approaches to um unifying quantum mechanics with general relativity as you can read about in his most recent book i believe and he's been on your show as well so but i i want to introduce this idea of cosmological natural selection because i think that is one of the core ideas that could change our understanding of how the universe got here our role in it what technology's doing here but there's a couple more pieces that need to be set up first so the beginning of our universe is largely accepted to be the big bang and what that means is if you look back in time by looking far away in space you see that um everything used to be at at one point and it expanded away from there there was a uh era in the evolutionary process of our universe that was called inflation and this idea was developed primarily by alan guth and and others andre linde and other others in the 80s and this idea of inflation is is basically that when a a singularity uh begins this process of of growth there can be a temporary stage where it just accelerates incredibly rapidly and based on quantum field theory this tells us that this should produce matter in precisely the proportions that we find of hydrogen and helium and the big bang lithium-2 lithium also um and other things too so so the predictions that come out of big bang inflationary cosmology have stood up extremely well to empirical verification the cosmic microwave background things like this so most scientists working in the field think that the origin of our universe is the big bang and i i base all my thinking on that as well i'm just laying this out there so that people understand that where i'm coming from is an extension not a replacement of of existing well-founded ideas in a paper i believe it was 1986 with uh alan guth and um another author farhy they they wrote that a a big bang i don't remember the exact quote a big bang is inextric inextricably linked with a black hole this singularity that we call our origin is mathematically indistinguishable from a black hole they're they're the same thing and lee smolin based his thinking uh on that idea i i believe i don't mean to speak for him but this is my reading of it so what lee smullen will say is that a black hole in one universe is a big bang in another universe and this allows us to have progeny offspring so uh a universe can be said to have come before another universe and very crucially small and arguably argues i i think this is potentially one of the great ideas of all time that's my opinion that when a black hole forms it's not a classical entity it's a quantum gravitational entity so there it is subject to the fluctuations that are inherent in quantum mechanics the the properties that what we're calling the parameters that describe the physics of that system are subject to slight mutations so that the offspring universe does not have the exact same parameters defining its physics as its parent universe they're close but they're a little bit different and so now you have a mechanism for evolution for natural selection so this mutation so there's and then if you think about the the dna of the universe are the basic parameters that govern its laws exactly so so that so what smallin said is our universe results from an evolutionary process that can be traced back some he estimated 200 million generations initially there was something like a vacuum fluctuation that produced through through random chance uh a universe that was able to reproduce just once so now it had one offspring and then over time it was able to make more and more until it evolved into a highly structured universe with a very long lifetime with a great deal of complexity and importantly especially importantly for lee smolin stars stars make black holes therefore we should expect our universe to be optimized have its physical parameters optimized to make very large numbers of stars because that's how you make black holes and black holes make offspring so we expect our the physics of our universe to have evolved to maximize fecundity the number of offspring and the way lee smolin argues you do that is through stars that the biggest ones die in these core collapse supernova that make a black hole and a child okay first of all i agree with you that this is back to our fractal view of everything from intelligence to our universe that is very compelling and a very powerful idea that um unites the origin of life and perhaps the origin of ideas and intelligence so from a dawkins perspective here on earth the evolution of those and then the evolution of the laws of physics that led to us i mean it's beautiful and then you stacking on top of that that maybe we are one of the offspring right okay so before getting into where i'd like to take that idea let me just a little bit more groundwork there is this concept of the multiverse and it it can be confusing different people use the word multiverse in different ways in in the multiverse that i think is relevant to picture when trying to grasp lee smolin's idea essentially every every vacuum fluctuation can be referred to as a universe it it occurs it borrows energy from the vacuum for some finite amount of time and it evanesces back into the quantum vacuum and ideas of uh guth before that and and andre linday with uh eternal inflation aren't that different that you would expect nature due to the the quantum properties of the vacuum which we we know exists they're they're measurable through things like the casimir effect and others you know that there are these fluctuations that are occurring what what smallin is arguing is that there is this extensive multiverse that we this universe what we can measure and interact with is not unique in nature it's just our residence it's it's where we reside and there are countless potentially infinity other universes other entire evolutionary trajectories that have evolved into things like what you were mentioning a second ago with different parameters and different ways of achieving complexity and reproduction and all that stuff so it's not that the evolutionary process is a funnel towards this end point not at all just like the biological evolutionary process that has occurred within our universe is not a unique route toward achieving one specific chosen kind of species no we we have extraordinary diversity around us that's what evolution does and for any one species like us it might feel like we're at the center of the process we're the destination of this process but we're just one of the many uh nearly infinite branches of this process and i suspect it is exactly infinite i mean i just can't understand how with this idea you can never draw a boundary around and say no the uni the universe i mean the multiverse has ten to the one quadrillion components but not infinity i don't know that that's well yeah i have uh cognitively in incapable as i think all of us are and truly understanding the concept of infinity and the concept of nothing as well and nothing but also the concept of a lot is pretty difficult i could just i can count i run out of fingers yeah at a certain point and then you're screwed and when you're wearing shoes and you can't even get down to your toes it's like it's like a thousand fine a million is that what and then it gets crazier and crazier right right so this particular so when we say technology by the way i mean there's some not to over romanticize the thing but there is some aspect about this branch of ours that allows us to um for the universe to know itself yes yes so to be to to have like little conscious cognitive fingers they're able to feel like to scratch the head right right right uh to to be able to construct e equals something squared and to introspect to have to start to gain some understanding of the laws that govern it isn't that um isn't that kind of uh amazing you know okay i'm just human but it feels like that if i were to build a system that does this kind of thing that involves laws of physics that evolves life that involves intelligence that my goal would be to come up with things that are able to think about itself right aren't we kind of close to the the the design specs the destination we're pretty close i don't know i mean i'm spending my career designing things that i hope will think about themselves exactly you and i aren't too far apart on that one but then maybe that problem is a lot harder than we imagined maybe we need to let's not get let's not get too far because i want to emphasize something that what you're saying is isn't it fascinating that the universe evolved something that can be conscious reflect on itself but lee smolin's idea didn't take us there remember it took us to stars lee smullen has argued i think right on almost every single way that cosmological natural selection could lead to a universe with rich structure and he argued that the structure the physics of our universe is designed to make a lot of stars so that they can make black holes but that doesn't explain what we're doing here in order to in order for that to be an explanation of us what you have to assume is that once you made that universe that was capable of producing stars life planets all these other things were along for the ride they got lucky we're we're kind of arising growing up in the cracks but the universe isn't here for us we're still kind of a fluke in that picture and i can't i don't i don't necessarily have like a philosophical opposition to that stance it's just not um okay so i don't think it's complete so it seems like whatever we got going on here to you it seems like whatever we have here on earth seems like a thing you might want to select for in this whole big process exactly so if what you are truly if your entire evolutionary process only cares about fecundity it only cares about making offspring universes because then there's going to be the most of them in that local region of of hyperspace which is the set of all possible universes let's let's say um you don't care how those universes are made you know they have to be made by black holes this is what this is what inflationary theory tells us the big bang tells us that black holes make universes but what if there was a technological means to make universes stars require a ton of matter because they're they're not thinking very carefully about how you make a black hole they're just using gravity you know um but if we devise technologies that can efficiently compress matter into a singularity it turns out that if you can compress about 10 kilograms into a very small volume that will make a black hole that is likely highly probable to inflate into its own offspring universe this is according to calculations done by other people who are professional quantum theorists quantum field theorists and i hope i am grasping what they're telling me correctly i'm somewhat of a of a translator here but so so that's that's the position that is particularly intriguing to me which is that what might have happened is that okay this particular branch on the vast tree of evolution cosmological evolution now we're talking about not biological evolution within our universe but cosmological evolution went through exactly the process that elise mullin described got to the stage where stars were making lots of black holes but then continued to evolve and somehow bridged that gap and made intelligence and intelligence capable of devising technologies because technologies in intelligent species working in conjunction with technologies could then produce even more more efficiently more like faster and better and more different then you start to have different kind of mechanisms of mutation perhaps all that kind of stuff and so if you do a simple calculation that says all right if i want to we know roughly how many um core collapse supernova supernovae have resulted in black holes in our galaxy since the beginning of the universe and it's something like a billion so then you would have to estimate that it would be possible for a technological civilization to produce more than a billion black holes with the energy and matter at their disposal and so one of the calculations in that paper back of the envelope but i think revealing nonetheless is that if you take a relatively um common asteroid something that's about a kilometer in diameter what i'm thinking of is just scrap material laying around in our solar system and break it up into 10 kilogram chunks and turn each of those into a universe then you would have made at least a trillion black holes outpacing the star production rate by some three orders of magnitude that's one asteroid so now if you envision an intelligent species that would potentially have been devised initially by humans but then based on superconducting optoelectronic networks no doubt and they go out and populate they don't they don't have to fill the galaxy they just have to get out to the asteroid belt they could potentially dramatically outpace the rate at which stars are producing offspring universes and then wouldn't you expect that that's where we came from instead of a star yeah so you have to somehow become masters of gravity so like or just necessarily gravity so stars make black holes with gravity but any force that can make the energy density can compactify matter to produce a great enough energy density can form a singularity it doesn't it would not likely be gravity it's the weakest force you're more likely to use something like the technologies that we're developing for fusion for example so i don't know um the large ignition facility recently blasted a pellet with a 100 really bright lasers and caused that to get dense enough to engage in nuclear fusion so something more like that or a tokamak with a really hot plasma i'm not sure something i don't know exactly how it would be done i do like the idea that um especially just been reading a lot about gravitational waves and you know the fact that us humans with our technological capabilities one of the most impressive uh technological accomplishments of human history is ligo being able to precisely detect gravitational waves i'm particularly find appealing the idea that other alien civilizations from very far distances communicate with gravity with gravitational waves because as you become greater and greater master of gravity which seems way out of reach for us right now maybe that seems like a effective way of sending signals especially if your job is to manufacture black holes right so that that so let me ask there whatever i mean broadly thinking because we tend to think other alien civilizations would be very human-like but if we think of alien civilizations out there as basically generators of black holes however they do it because they get stars do you think there's a lot of them in our particular universe out there in our universe well okay let me ask okay this is great let me ask a very generic question and then let's see how you answer it which is uh how many alien civilizations are out there if the hypothesis that i just described is on the right track yes it would mean that the parameters of our universe have been selected so that intelligent civilizations will occur in sufficient numbers so that the if they reach something like supreme technological maturity let's define that as the ability to produce black holes then that's not a highly improbable event it it doesn't need to happen often because as i just described if you get one of them in a galaxy you're going to make more black holes than the stars in that galaxy but there's also not a super strong motivation well it's not obvious that you need them to be ubiquitous throughout the galaxy right so so one of the things that's that i try to emphasize in that paper is that given this idea of of how our parameters might have been selected it's clear that it's a it's a series of trade-offs right if you make i mean in order for intelligent life of our variety or anything resembling us to occur you need a bunch of stuff you need stars so that's right back to smolin's roots of this idea but you also need water to have certain certain properties you need you need things like the the rocky planets like the earth to be within the habitable zone all these things that you start talking about in the um the field of astrobiology trying to understand life in the universe but you can't over emphasize you can't tune the parameters so precisely to maximize the number of stars or to to give water exactly the properties or or to make rocky planets like earth the most numerous you have to compromise on all these things and so i think the way to test this idea is to look at what parameters are necessary for for each of these different subsystems and i've laid out a few that i think are promising there there could be countless others and see how changing the parameters makes it more or less likely that stars would form and have long lifetimes or that or that rocky planets in the habitable zone are likely to form all these different things so we can test how how much these things are in a tug of war with each other and the prediction would be that we kind of sit at this central point where if you if you move the parameters too much stars aren't stable or life doesn't form or technology's infeasible because because life alone at least the kind of life that we know of cannot make black holes we don't have this well i'm speaking for myself you're a very fit strong person but it might be possible for you but not for me to compress matter so we need these technologies but we don't know we have not been able to quantify yet how um finely adjusted the parameters would need to be in order for silicon to have the properties it does okay this is not directly speaking to what you're saying you're getting to the fermi paradox which is where are they where are the the life forms out there how numerous are they that sort of thing what i'm trying to argue is that if this framework is is on the right track a potentially correct explanation for our existence we don't it doesn't necessarily predict that intelligent civilizations are just everywhere because even if you just get one of them in a galaxy which is quite rare it could be enough to dramatically uh increase the fecundity of the universe as a whole yeah and i wonder once you start generating the offspring for universes black holes how that has effect on the what kind of effect does it have on the other uh candidates civilizations within that universe maybe it has a destructive aspect or there could be some arguments about once you have a lot of offspring that that just quickly accelerates to where the other ones can't even catch up it could but i guess if you want me to put my chips on the table or whatever i think i come down more on the side that intelligent life civilizations are rare and um i guess i follow max tegmark here and also there's there's a lot of papers coming out recently in the field of astrobiology that are seeming to say all right you just worked through the numbers on on some modified drake equation or something like that and it looks like it's not improbable you wouldn't you shouldn't be surprised that an intelligent species has arisen in our galaxy but if you think there's one the next solar system over it's it's highly improbable so i can see that the number the probability of finding a a civilization in a galaxy maybe it's most likely that you're gonna find one to a hundred or something but okay now it's it's really important to put a time window on that i think because does that mean in the entire lifetime of the galaxy before it it um so for in our case before we run into andromeda i think it's highly probable i shouldn't say i think it's tempting to believe that it's highly probable that in that entire lifetime of your galaxy you're going to get at least one intelligent species maybe thousands or something like that but it's also i think um a little bit naive to think that they're going to coincide in time and we'll be able to observe them and also if you look at the span of life on earth the earth earth history it was surprising to me to kind of look at the amount of time well first of all the the short amount of time there's no life is surprising life sprang up pretty quickly it's cellular single cell so but that was that's the point i'm trying to make is like so much with what of life on earth was just like single cell organisms like most of it most of us like boring bacteria type of stuff well bacteria are fascinating but i take your point no i get it i mean no offense to them this kind of speaking from the perspective of your paper of something that's able to generate technology as we kind of understand it that's a very short moment in time relative to that that full history of life on earth and maybe our universe is just saturated with bacteria like humans right but not the special extra agi super humans that those are very rare and once those spring up everything just goes to like it accelerates very quickly yeah it's it's we just don't have enough data to really say but i find this whole subject extremely engaging i mean there's this concept i think it's called the rare earth hypothesis which is that basically stating that okay microbes were here right away after the haitian era where we were being bombarded well after yeah bombarded by comets asteroids things like that and also after the moon formed so once things settled down a little bit in a few hundred million years you have microbes everywhere and it could have been we don't know exactly when it could have been remarkably brief that that took so it does indicate that okay life forms relatively easily i think that alone is sort of a checker on the scale for the argument that the parameters that allow even microbial life to form are not just a fluke but anyway that aside yes then there was this long dormant period not dormant things were happening but um important things were happening for some two and a half billion years or something after um the metabolic process that releases oxygen was developed then basically the plant is just sitting there getting more and more oxygenated more and more oxygenated until it's enough that you can build these large complex organisms and so the rare earth hypothesis would argue that the microbes are common in everywhere in any planet that's like roughly in the habitable zone and has some water on it's probably gonna have those but then getting to this cambrian explosion that happened some between five and six hundred million years ago that's that's rare you know and i i buy that i think that is rare so if you say how much life is in our galaxy i think that's probably the right answer is that microbes are everywhere cambrian explosion is extremely rare and then but the cambrian explosion kind of went like that where um within a couple tens or 100 million years all of these body plans came into existence and and basically all of the body plans that are now in existence on the on the planet were formed in that brief window and we've just been shuffling around since then so then what what caused humans to pop out of that i mean that could be another extremely rare threshold that a planet roughly in the habitable zone with water is not guaranteed to cross you know to me it's fascinating for being humble like the humans cannot possibly be the most amazing thing that such if you look at the entirety of the system that lease mola and you paint that cannot possibly be the most amazing thing that process generates so like if you look at the evolution what's the equivalent in the cosmological evolution and its selection for technology the equivalent of the human eye or the human brain universes that are able to do some like they don't need the damn stars they they're able to just do some incredible generation of complexity fast on like much more than if you think about it's like most of our universes are pretty freaking boring there's not much going on there's a few rocks flying around and there's some like apes that are just like um doing podcasts on some weird planet it just seems very inefficient if you think about like the the amazing thing the human eye the visual cortex can do the the brain the nervous everything that makes us more powerful than single cell organisms like if there's an equivalent of that for universes they're like the richness of physics that could be uh they could be expressed through a particular set of parameters like i mean that like for me i'm uh so from a computer science perspective a huge fan of cellular automata which is a nice sort of pretty visual way to illustrate how different laws can result in uh drastically different levels of complexity so like it's like yeah okay so we're all like celebrating look our little cellular automata is able to generate pretty triangles and squares and therefore we achieve general intelligence and then there'll be like some badass chuck norris type like uh universal touring machine type of cellular automata they're able to generate other cellular automata that does any arbitrary level of computation off the bat it like those have to then exist and then we're just like this we're just we'll be forgotten is this the story this is uh this podcast just entertains a few other apes for for a few months well i i'm kind of surprised to hear your cynicism be no i'm very up i i usually think of you as like a one who celebrates humanity in all its forms and things like that and i i guess i just i don't i see it the way you just described i mean okay fif we've been here for 13.7 billion years and you're saying gosh that's a long time let's get on with the show already some other universe could have kicked our butt by now but that's putting a characteristic time i mean why is 13.7 billion a long time i mean compared to compared to what i guess so when i look at our universe i see this extraordinary hierarchy that has developed over that time so at the beginning it was a chaotic mess of you know some plasma and nothing interesting going on there and even for the first stars to form that a lot of really interesting uh evolutionary processes had to occur by evolutionary in that sense i just mean um taking place over extended periods of time and structures are forming then and then it took that first generation of stars in order to produce the metals that then can more efficiently produce another generation of stars we're only the third generation of stars so we might still be pretty quick to the to the game here so i but i don't think i don't okay so then so then you have these stars now you have solar systems on those solar systems you have um rocky worlds you have gas giants like all this complexity and then you start getting life and the the complexity that's evolved through the evolutionary process in in life forms is just it's not a a letdown to me just no no and there's some of it is like some some of the planets is like icy it's like different flavors of ice cream they're icy but there might be water under yeah all kinds of life forms with some volcanoes right all kinds of weird stuff no no i i don't uh i think it's beautiful i think our life is beautiful and i think it was uh designed that by by design the scarcity of the whole thing i think mortality as terrifying as it is is fundamental to the whole reason we enjoy everything no i think it's beautiful i just think that all of us um conscious beings in the grand scheme of basically every at every scale will be completely forgotten well that's true i think everything is transient and that would go back to maybe something more like lao tzu the dao de jing or something where it's like yes there is nothing but change there is nothing but emergence and dissolve and that that's it but i just in this picture of this hierarchy that's developed i don't mean to say that now it gets to us and that's the pinnacle in fact i think the at a high level the story i'm trying to tease out in my research is about okay well so then what's the next level of hierarchy and if in if it's okay we're we're kind of pretty smart i mean talking about people like lee small and alan guth max tegmark okay we're really smart talking about me okay we're kind of we're we can find our way to the grocery store or whatever but sometimes but what's next you know i mean what if what if there's another level of hierarchy that grows on top of us that is even more profoundly capable and i mean we've talked a lot about superconducting sensors imagine these uh cognitive systems far more capable than us residing somewhere else in the solar system off of the surface of the earth where it's much darker much colder much more naturally suited to them and they have these sensors that can detect single photons of light uh from radio waves out to all across the spectrum to gamma rays and just see the whole universe and they just live in space with these massive um collection optics so that they what what did they do they just look out and and experience that that vast array of of what's being developed and if you're such a system presumably you would do some things for fun and the kind of fun thing i would do somebody who likes video games is i would create and maintain and observe something like earth and so in some sense we're like all what players on a stage for this uh superconducting um cold computing system out there i mean all of this is fascinating to think the the fact that you're actually designing systems here on earth that are trying to push this technological at the very cutting edge and also thinking about how does the like the evolution of physical laws lead us to the way we are it's fascinating that that coupling is fascinating it's like the ultimate rigorous application of philosophy to the rigorous application of engineering so i um jeff you're one of the most fascinating i'm i'm so glad i did not know much about you accept through your work and i'm so glad we got this um chance to talk you're you're one of the best explainers of exceptionally difficult concepts um and you're also the the speaking of like fractal you're able to function intellectually at all levels of the stack which which i deeply appreciate this was really fun you're a great educator a great scientist it's it's an honor that you spend your valuable time with me it's an honor that you would spend your time with me as well thanks jeff thanks for listening to this conversation with jeff shaneline to support this podcast please check out our sponsors in the description and now let me leave you with some words from the great john carmack who surely will be a guest on this podcast soon because of the nature of moore's law anything that an extremely clever graphics programmer can do at one point can be replicated by a merely competent programmer some number of years later thank you for listening and hope to see you next time you