Stephen Wolfram: ChatGPT and the Nature of Truth, Reality & Computation | Lex Fridman Podcast #376
- You know I can tell ChatGPT, create a piece of code and then
just run it on my computer. And I'm like, you know, that, that sort of personalizes
for me the what could, what could possibly go wrong, so to speak. - Was that exciting or
scary, that possibility? - [Wolfram] It was a
little bit scary actually, because it's kind of like,
if you do that right, What is the sandboxing
that you should have? And that's sort of a,
that's a, a version of, of that question for the world. That is, as soon as you put
the AIs in charge of things, you know, how much, how many
constraints should there be on these systems before
you put the AIs in charge of all the weapons and all these, you know, all these
different kinds of systems. - Well here's the fun
part about sandboxes, is the AI knows about them and
has the tools to crack them. The following is a conversation
with Steven Wolfram, his fourth time on this podcast. He's a computer scientist, mathematician, theoretical physicist, and the
founder of Wolfram Research, a company behind
Mathematica, Wolfram Alpha, Wolfram Language and the Wolfram physics and meta mathematics projects. He has been a pioneer in exploring the computational nature of reality. And so he's the perfect person
to explore with together the new quickly evolving
landscape of large language models as human civilization
journeys towards building super intelligent AGI. This is a Lex Fridman podcast. To support it, Please check out our
sponsors in the description. And now, dear friends
here's Stephen Wolfram. You've announced the
integration of ChatGPT and Wolfram Alpha and Wolfram Language. So let's talk about that integration. What are the key differences from the high philosophical level, maybe the technical level
between the capabilities of, broadly speaking, the
two kinds of systems, large language models, and this computational
gigantic computational system infrastructure that is Wolfram Alpha? - Yeah. So what does
something like ChatGPT do? It's, it's mostly
focused on make language, like the language that humans have made and put on the web and so on. Yeah, so, you know, it's, it's primary sort of
underlying technical thing is you've given a prompt, it's trying to continue
that prompt in a way that's somehow typical of what it's seen based on a trillion words of text that humans have written on the web. And the way it's doing
that is with something which is probably quite
similar to the way we humans do the first stages of that, using a neural net and so on, and just saying, given these,
given this piece of text, let's ripple through the
neural net one word and, and get one word at a time of output. And it's kind of a, a shallow computation on a large amount of kind of training data that is what we humans
have put on the web. That's a different thing from
sort of the computational stack that I've spent
the last, I don't know, 40 years or so building, which has to do with what
can you compute many steps, potentially a very deep computation. It's not sort of taking the statistics of what we humans have produced and trying to continue things
based on that statistics. Instead, it's trying to take
kind of the formal structure that we've created in our civilization, whether it's from mathematics,
or whether it's from kind of systematic knowledge of all kinds, and use that to do
arbitrarily deep computations to figure out things
that, that aren't just, let's match what's already
been kind of said on the web, but let's potentially be
able to compute something new and different that's never
been computed before. So as a, as a practical matter, you know, the, the what we're, you know, the, our goal is to have made as
much as possible of the world computable in the sense
that if there's a question that in principle is answerable from some sort of expert
knowledge that's been accumulated, we can compute the
answer to that question, and we can do it in a sort
of reliable way that's, that's the best one can do,
given what the expertise that our civilization has accumulated. It's a very, it's a,
it's a much more sort of labor intensive on the
side of kind of being, creating kind of the, the
computational system to do that. Obviously the in, in the, the
kind of the ChatGPT world, it's like take things which were produced for quite other purposes, namely the all the things
we've written out on the web and so on, and sort of forage
from that things which were, are like what's been written on the web. So I think, you know, as a,
as a practical point of view, I view sort of the ChatGPT
thing as being wide and shallow and what
we're trying to do with sort of building out computation
as being this sort of deep, also broad, but, but most importantly kind of deep type of thing. I think another way to
think about this is, if you go back in human
history, you know, I don't know, a thousand years or something, and you say, what, what,
what can the typical person, what's the typical person
going to figure out? Well, the answer is there's
certain kinds of things that we humans can quickly figure out. That's sort of what, what our, you know, other neural architecture
and the kinds of things we learn in our lives let us do. But then there's this whole
layer of kind of formalization that got developed in which is, you know, the kind of whole sort of
story of intellectual history and the whole kind of depth of learning that formalization turned
into things like logic, mathematics, science and so on. And that's the kind of
thing that allows one to kind of build these towers of, of, of, of sort of towers of things you work out. It's not just, I can immediately
figure this out, it's no, I can use this kind of
formalism to go step by step and work out something which was not immediately obvious to me. And that's kind of the story of what, what we're trying to do computationally, is to be able to build
those kind of tall towers of what implies, what
implies what and so on. And as opposed to kind of the yes, I can immediately figure it out, it's just like what I saw
somewhere else in something that I heard or remembered
or something like this. - What can you say about
the kind of formal structure or the kind of formal
foundation you can build such a formal structure on
about the kinds of things you would start on in order to build this kind of deep
computable knowledge trees. - So the question is sort of how do you, how do you think about
computation and there's, there's a couple of points here. One is what computation
intrinsically is like, and the other is what
aspects of computation we humans with our minds
and with the kinds of things we've learned can sort of relate to in that computational universe. So if we start on the kind of, what can computation be like, it's something I've spent some
big chunk of my life studying is imagine that you are,
you know, we, we usually, we write programs where we kind of know what we want the program to do and we carefully write, you
know, many lines of code and we hope that the program does what we, what we intended it to do. But the thing I've been
interested in is if you just look at the kind of
natural science of programs, so you just say, I'm gonna make this program,
it's a really tiny program. Maybe I even pick the pieces
of the program at random, but it's really tiny. And by really tiny, I mean, you know, less than a line of code type thing. You say, what does this
program do? And you run it. And big discovery that I
made in the early eighties is that even extremely simple
programs when you run them can do really complicated things. Really surprised me. It took me several years
to kind of realize that that was a thing, so to speak, but that that realization
that even very simple programs can do incredibly complicated things that we very much don't
expect that discovery, I mean, I realized that that's very
much, I think how nature works. That is nature has simple rules, but yet does all sorts
of complicated things that we might not expect, you know, as a big thing of the last few
years has been understanding that that's how the whole
universe and physics works. But that's a, a quite separate topic. But, so there's this
whole world of programs and what they do and very rich, sophisticated things that
these programs can do. But when we look at
many of these programs, we look at them and say,
well that's kind of, I don't really know what that's doing. It's not a very human kind of thing. So on the one hand we have
sort of what's possible in the computational universe. On the other hand, we have the kinds of things
that we humans think about, the kinds of things that have developed in kind of our intellectual history. And that's, and the, the really, the challenge to sort of
making things computational is to connect what's
computationally possible out in the computational
universe with the things that we humans sort of typically
think about with our minds. Now that's a complicated
kind of moving target because the things that we
think about change over time, we've learnt more stuff,
we've invented mathematics, we've invented various kinds of ideas and structures and so on. So it's, it's gradually expanding. We're kind of gradually
colonizing more and more of this kind of intellectual
space of possibilities. But the, the real thing, the real challenge is how do you take, what is computationally possible? How do you take, how do you encapsulate the kinds of things that we think about in a
way that kind of plugs into what's computationally possible. And and actually the, the,
the big sort of idea there is this idea of kind of
symbolic programming, symbolic representations of things. And so the, the question
is when you look at sort of everything in the world
and you kind of, you know, you take some visual scene or something you're looking at and then you say, well, how do I turn that into something that I can kind of stuff into my mind? You know, there are lots of
pixels in my visual scene, but the things that I remembered
from that visual scene are, you know, there's a chair in this place, it's a kind of a symbolic representation of the visual scene. There are two chairs on
a table or something, rather than there are
all these pixels arranged in all these detailed ways. And so the question then is how
do you take sort of all the, all the things in the world and make some kind of representation that corresponds to the types of ways that we think about things. And, and human language is, is sort of one form of
representation that we have. We talk about chairs, that's a word in human language and so on. How do we, how do we take, but human language is not
in and of itself something from that plugs in very
well to sort of computation. It's not something from which you can immediately compute
consequences and so on. And so you have to kind of
find a way to take sort of the, the, the stuff we understand
from human language and make it more precise. And that's really the story
of, of symbolic programming. And you know what, what that turns into is something which I didn't know at the time it was going to work as well as it has, but back in the 1979 or so, I was trying to build my
first big computer system and trying to figure out, you know, how should I represent
computations at a high level. And I kind of invented
this idea of using kind of symbolic expressions, you know, structured as it's kind of like a, a function and a bunch of arguments, but that function doesn't
necessarily evaluate to anything. It's just a, a thing that sits there representing a structure. And so building up that structure and it's turned out that
structure has been extremely, it's a, it's a good match
for the way that we humans, it seems to be a good match for the way that we humans kind of
conceptualize higher level things. And it's been for the last, I don't know, 45 years or something, it's
served me remarkably well. - So building up that structure using this kind of
symbolic representation. But what can you say
about abstractions here? Because you could just start
with your physics project. You could start at a hypergraph at a very, very low level and build
up everything from there, but you don't, right, you take shortcuts. - [Wolfram] Right? - You, you take the highest
level of abstraction, convert that, the kind of abstraction that's
convertible to something computable using symbolic
representation and then that, that's your new foundation for that little piece of knowledge. - [Wolfram] Yes. - Somehow all that is integrated. - Right? So the, the sort of
a very important phenomenon that is kind of a thing that
I've sort of realized is just, it's one of these things
that sort of in the, in the future of kind
of everything is going to become more and more important as this phenomenon of
computational reducibility. And the, the question is, if you know the rules for
something, you have a program, you're gonna run it. You might say, I know the rules, great, I know everything about
what's gonna happen. Well in principle you do
because you can just run those rules out and just see what they do. You might run them a million
steps, you see what happens, et cetera. The question is, can you like immediately
jump ahead and say, I know what's gonna happen
after a million steps. And the answer is 13 or something. - [Fridman] Yes. - And the, the one of
the very critical things to realize is if you could
reduce that computation, there isn't a sense, no point in doing the computation. - [Fridman] Yeah. - The place where you really
get value outta doing the computation is when you
had to do the computation to find out the answer. But this phenomenon that you
have to do the computation to find out the answer, this phenomenon of computational
reducibility seems to be tremendously important for thinking about lots of kinds of things. So one of the things
that happens is, okay, you've got a model of the
universe at the low level in terms of atoms of space and hypergraphs and rewriting hypergraphs and so on. And it's happening, you know, 10 to the 100 times
every second, let's say. Well, you say great, then
we've, we've nailed it. We've, we've, we know
how the universe works. Well the problem is the
universe can figure out what it's gonna do. It does those 10 to the
100, you know, steps. But for us to work out what it's gonna do, we have no way to reduce that computation. The only way to do the computation, to see the result of the
computation is to do it. And if we're operating
within the universe, we're kind of, there's no, there's no opportunity to
do that because the universe is doing it as fast as
the universe can do it. And that's, you know,
that's what's happening. So what we're trying to do, and a lot of the story of science and a lot of other kinds of things is finding pockets of reducibility. That is, you could have a situation
where everything in the world is full of computational reducibility, we never know what's gonna happen next. The only way we can figure out
what's gonna happen next is just let the system run
and see what happens. So in a sense, the story of, of most kinds of science, inventions, a lot of kinds of things is
the story of finding these places where we can locally jump ahead. And one of the features of
computational reducibility is there are always pockets of reducibility. There are always places, there are always an
infinite number of places where you can jump ahead. There's no way where you
can jump completely ahead. But there are little, little patches, little places where you
can jump ahead a bit. And I think, you know, we can talk about physics
project and so on, but I think the thing we
realize is we kind of exist in a slice of all the
possible computational id reducibility in the universe. We exist in a slice where
there's a reasonable amount of predictability. And in a sense, as we try and construct these
kind of higher levels of, of abstraction symbolic
representations and so on, what we're doing is
we're finding these lumps of reducibility that we can
kind of attach ourselves to and about, which we can kind of have fairly simple narrative things to say. Because in principle, you know, I say what's gonna happen
in the next few seconds? You know, oh, there are these molecules
moving around in the air in this room and oh gosh, it's an incredibly complicated
story and that's a whole computational irreducible thing, most of which I don't care about. And most of it is, well, you know, the air's still gonna be here
and nothing much is going to be different about it. And that's a kind of reducible fact about what is ultimately
at an underlying level of computational irreducible process. - And life would not be possible
if we didn't have a large number of such reducible pockets. - [Wolfram] Yes. - Pockets amenable to reduction
into something symbolic. - Yes, I think so. Okay. I mean, life in, in the
way that we experience it, that, I mean, you know,
one might, you know, depending on what we mean by
life, so to speak, the, the, the experience that we have
of sort of consistent things happening in the world, the
idea of space, for example, where there's, you know, we
can just say you are here, you move there, it's
kind of the same thing. It's still you in that different
place even though you're made of different atoms
of space and so on. This is this idea that it's, that there's sort of this
level of predictability of what's going on. That's us finding a slice
of reducibility in what is underneath this computation
irreducible kind of system. And I think that's, that's sort of the, the thing which is actually
my favorite discovery of the last few years, is the realization that it
is sort of the interaction between this sort of
underlying computational irrereducibility and our
nature as kind of observers who sort of have to key into
computational reducibility. That fact leads to the main
laws of physics that we discovered in through 20th century. So this is, we talk about
this in, in in more detail, but this is, to me, it's kind
of our nature as observers. The fact that we are
computationally bounded observers. We don't get to follow
all those little pieces of computational irrereducibility to stuff. What is about out there in the
world into our minds requires that we are looking at
things that are reducible, we are compressing kind of, we are, we're extracting just some essence, some kind of symbolic essence of what's the detail of
what's going on in the world. That together with one other condition that first seems sort
of trivial but isn't, which is that we believe
we are persistent in time. That is. - Yes. - [Wolfram] You know. - So causality. - Here's the thing. At every
moment according to our theory, we are made of different atoms
of space at every moment. Sort of the microscopic detail
of what what the universe is made of is being rewritten. And that's, and in fact, the very fact that this coherence
between different parts of space is a consequence of the
fact that there are all these little processes going on
that kind of knit together the structure of space. It's kind of like if you wanted
to have a fluid with a bunch of molecules in it, if those
molecules weren't interacting, you wouldn't have this
fluid that would pour and do all these kinds of things. It would just be sort of
a free floating collection of molecules. So similar it is with space
that the fact that space is kind of knitted together as a consequence of all this activity in space. And the fact that kind of what, what we consist of sort of
this, this series of, of of, you know, we're, we're
continually being rewritten. And the question is, why is it the case that
we think of ourselves as being the same us through time? That's kind of a, a key assumption. I think it's a key aspect of
what we see as sort of our consciousness so to speak, is that we have this kind of consistent thread of experience. - Well isn't that just
another limitation of our mind that we wanna reduce- - [Wolfram] Yeah. - Reality into some that kind of temporal - [Wolfram] Yes. - Consistency is just a nice
narrative to tell ourselves. - [Wolfram] Right. Well, the fact is, I think it's critical to the
way we humans typically operate is that we have a single
thread of experience. You know, if you, if
you imagine sort of a, a mind where you have, you know, maybe that's what's happening
in various kinds of minds that aren't working the same
way other minds work is that you're splitting into multiple threads of experience. It's also, it's also
something where, you know, when you look at, I don't know, quantum mechanics for example, in the insides of quantum mechanics, it's splitting into many
threads of experience. But in order for us humans
to interact with it, you kind of have to have to
knit all those different threads together so that we say, oh yeah, a definite thing happened and now the next definite thing happens and so on. And I think, you know, sort of inside it, it's sort of interesting to
try and imagine what's it like to have kind of these
fundamentally multiple threads of experience going on. I mean, right now different human minds have different threads of experience. We just have a bunch of minds that are interacting with each other, but we don't have a a you know, within each mind there's a single thread. And that's a, that is indeed a simplification. I think it's a, it's a thing, you know, the general computational system does not have that simplification. And it's one of the things, you know, I I people often seem
to think that, you know, consciousness is the, the highest level of kind
of things that can happen in the universe, so to speak. But I think that's not true. I think it's actually a, a a specialization in
which among other things, you have this idea of a
single thread of experience, which is not a general feature
of anything that could kind of computationally happen in the universe. - So it's a feature of a
computationally limited system that's only able to
observe reducible pockets. - [Wolfram] Yeah. - So I mean this word observer, it, it means something in quantum mechanics, it means something in a lot of places. It means something to us humans. - [Wolfram] Right. - As conscious beings. So what, what's the importance of the
observer? What is the observer? What's the importance of the observer in the computational universe? - So this question of what is an observer? What's the general idea of an observer? It's actually one of my next
projects which got somewhat derailed by the, the current
sort of AI mania. But- - Is there a connection
there or is that, do you, do you think the observer
primarily a physics phenomena? - Is it related to the whole AI thing? - Yes. - [Wolfram] Yes, it is related. So one question is, what
is a general observer? So, you know, we know we have an idea what is a general computational system. We think about touring machines, we think about other
models of computation. There's a question, what is a
general model of an observer? And there there's kind
of observers like us, which is kind of the
observers we're interested in. You know, we could imagine an
alien observer that deals with computational I irrereducibility
and it has a mind that's utterly different from ours and, and completely incoherent
with what, what we are like. But the fact is that that, you know, if we are talking about observers like us, that one of the key things is
this idea of kind of taking all the detail of the world and being able to stuff it into a mind, being able to take all
the detail and kind of, you know, extract out
of it a smaller set of, of kind of degrees of freedom. A smaller number of, of elements that will
sort of fit in our minds. And I think this, this question, so I've been interested
in trying to characterize what is the general observer. And the general observer is, I think in part there are many, let let me give an example of
a, you know, you have a gas, it's got a bunch of molecules
bouncing around and the thing you are measuring about
the gas is it's pressure. And the only thing you as an observer care about is pressure. And that means you have a
piston on the side of this box and the piston is being pushed by the gas. And there are many, many different ways that
molecules can hit that piston. But all that matters is
the kind of aggregate of all those molecular impacts. Because that's what determines pressure. So there's a huge number
of different configurations of the gas, which are all equivalent. So I think one key aspect of
observers is this equivalent thing of many different
configurations of a system saying, all I care about is
this aggregate feature. All I care about is
this, this overall thing. And that's, that's sort
of one, one aspect. And we see that in lots
of different, again, it's the same story over
and over again that there's, there's a lot of detail in the world, but what we are extracting from it is something a sort of a thin, a thin summary of that, of that detail. - Is that thin summary nevertheless true. Is can it be a crappy
approximation that an average is, is correct. I mean, if we look at the observer, that's the human mind, it seems
like there's a lot of very, as represented by natural
language for example, there's a lot of really
crappy approximation. - [Wolfram] Sure. - And that could be maybe a feature of it. - [Wolfram] Well, yes. - But there's ambiguity. - [Wolfram] Right, right. You don't know, you know,
it could be the case you, you're just measuring
the aggregate impacts of these molecules,
but there is some tiny, tiny probability that molecules
will arrange themselves in some really funky way. And that just measuring that average isn't going to be the main point. - [Fridman] Yeah. - By the way, an awful lot of science is very
confused about this because, you know, you look at, you look at papers and
people are really keen, they draw this curve and
they have these, you know, these bars on the curve and things. And it's just this curve
and it's this one thing, and it's supposed to represent
some system that has all kinds of details in it. And this is a way that lots
of science has gotten wrong because people say, I remember years ago I was
studying snowflake growth that, you know, you have the
snowflake and it's growing, it has all these arms, it's
doing complicated things. But there was a literature
on this stuff and it talked about, you know, what's the
rate of snowflake growth? And you know, it, it got pretty good answers for the rate of the
growth of the snowflake. And they looked at it more carefully. And then they had these
nice curves of, you know, snowflake growth rates and so on. I looked at it more carefully
and I realized according to their models, the snowflake
will be spherical. And so they got the growth rate right. But the detail was just utterly wrong. And you know, the not
only the detail, the, the whole thing was, was
not capturing, you know, it was capturing this aspect of the system that was in a sense, missing the main point
of what was going on. - What is the geometric
shape of a snowflake? - Snowflakes start in, in the phase of water that's relevant. - [Fridman] Yeah. - To the formation of snowflakes. It's a phase of ice, which starts with a
hexagonal arrangement of, of water molecules. And so it starts off growing
as a hexagonal plate. And then what happens is- - Is the plate, oh, oh versus sphere. - Well, no, no, but it's,
it's much more than that. I mean, snowflakes are fluffy, you know, typical snowflakes have
little, little dendritic arms. - [Fridman] Okay, yeah, yeah, yeah. - And, and what actually happens is, it's kind of kind of cool
because you can make these very simple discrete models with
cellular automata and things that, that figure this out. You start off with this,
you know, hexagonal thing, and then the places it, it starts to grow little arms, and every time a little piece of ice, it adds itself to the snowflake. The fact that that ice condensed
from the water vapor heats the snowflake up locally. And so it makes it less likely for, for another piece of ice
to accumulate right nearby. So this leads to a kind
of growth inhibition. So you grow an arm and it, it is a, a separated arm because right
around the arm it got a little bit hot and it didn't add more ice there. So what happens is it
grows, you have a hexagon, it grows out arms, the arms grow arms, and then the arms grow arms grow arms. And eventually, actually
it's kind of cool because it, it actually fills in another
hexagon, a bigger hexagon. And when I first looked at this, you know, had a very simple model for
this, I realized, you know, when it fills in that hexagon, it actually leaves some holes behind. So I thought, well, you know,
that is that really right? So I look at these pictures
of snowflakes and sure enough they have these little holes
in them that are kind of scars of the way that these arms grow out. - So you can't fill in backfill
holes. So you keep going. - They don't backfill. Yeah, they don't backfill. - And, and presumably there's
a limitation on how big, like you can't arbitrarily grow. - I'm not sure. I mean, the
thing falls through the, the, I mean, I think it does, you know, it hits the ground at some point. I think you can grow. I I think you can grow in the lab. I think you can grow pretty big ones. I think you can grow
many, many iterations of, this kind of goes from
hexagon, it grows out arms, it turns back, it fills
back into a hexagon. It grows more arms again in. - In 3D. - No. It's flat usually. - Why is it flat? Why doesn't it spin out?
Okay, okay, wait a minute. You said it's fluffy and
fluffy is a three-dimensional property, no or. - No, it's, it's fluffy snow is. Okay. So you know what makes we're really, we're really in a- - [Fridman] Let's go there. Multiple snowflakes become fluffy. A single snowflake is not fluffy? - No, no. A single snowflake is fluffy. And what happens is, you know, if, if you have snow that
is just pure hexagons, they, they can, you know, they,
they fit together pretty well. It's not, it doesn't, it doesn't make, it doesn't have a lot of air in it. And they can also slide against
each other pretty easily. And so the snow can be
pretty, you know, can, I think avalanches happen sometimes when, when the things tend
to be these, you know, hexagonal plates and it kind of slides. But then when the thing has all these arms that have grown out, it's not, they don't
fit together very well. And that's why the snow
has lots of air in it. And if you look at one
of these snowflakes, and if you catch one, you'll
see it has these little arms. And people actually,
people often say, you know, no two snowflakes are alike. That's mostly because
as a snowflake grows, they do grow pretty consistently with these different arms and so on. But you capture them at
different times as they, you know, they fell through, through the air in a different way. You'll catch this one, this stage. And as it goes through different stages, they look really different. And so that's why, you know, it kinda looks like no two
snowflakes are alike because you caught them at different,
at different times. - So the rules under which
they grow are the same. - [Wolfram] Yes. - It's just the timing is- - [Wolfram] Yes. - Okay. So the point is, science is not able to describe the full complexity of snowflake growth. - Well science, if you, if you do what people might often do, which is say, okay,
let's make it scientific, let's turn into one number, and that one number is kind
of the growth rate of the arms or some such other thing, that fails to capture
sort of the detail of what's going on inside the system. And that's, in a sense, a big challenge for science
is how do you extract from the natural world, for example, those aspects of it
that you are interested in talking about. Now you might just say, I don't really care about the
fluffiness of the snowflakes. All I care about is the
growth rate of the arms. In which case, you know, you have, you can have a good model
without knowing anything about the fluffiness. But the fact is, as a practical,
you know, when if you, if you say what's the, what is the most obvious
feature of a snowflake? Oh, that it has this complicated shape, well then you've got a different
story about what you model. I mean, this, this is
one of the features of, of sort of modeling and
science that, you know, what is a model? A model is some way of reducing
the actuality of the world to something where you can
readily sort of give a narrative for what's happening, where you can basically make
some kind of abstraction of what's happening and answer questions that you care about answering. If you wanted to answer all possible questions about the system, you'd have to have the whole
system because you might care about this particular molecule. Where did it go? And you know, your model, which is some big abstraction of that has nothing to say about that. So, you know, one of the things that's, that's often confusing in sciences, people will have, I've got
a model, somebody says, somebody else will say, I don't believe in your model
because it doesn't capture the feature of the
system that I care about. You know, there's always
this controversy about, you know, is the, is it a correct model? Well, no model is a, except for the actual system itself is a correct model in the sense
that it captures everything. Question is, does it capture
what you care about capturing? Sometimes that's ultimately
defined by what you're going to build technology out of things like this. The one counterexample to
this is if you think you're modeling the whole
universe all the way down, then there is a notion of a correct model. But even that is more complicated
because it depends on kind of how observers sample things and so on. That's a, that's a separate story, but at least at the first
level to say, you know, this thing about, oh,
it's an approximation. You're capturing one aspect, you're not capturing other aspects. When you really think
you have a complete model for the whole universe, you better be capturing
ultimately everything, even though to actually run
that model is impossible because of computational reducibility. The only, the only thing that
successfully runs that model is the actual running of the universe. - Is the universe itself. But okay, so what you care about is
an interesting concept. So that's a, that's a human concept. So that's what you're
doing with Wolfram Alpha and Wolfram Language, is you're trying to come up
with symbolic representations. - [Wolfram] Yes. - As simple as possible. So a model that's as simple as
possible that fully captures stuff we care about. - [Wolfram] Yes. So I mean, for example, you
know, we could we'll have a, a thing about, you
know, data about movies, let's say we could be describing every individual pixel
in every movie and so on. But that's not the level
that people care about. And it's, yes, this is a, I mean, and, and that level that people
care about is somewhat related to what's described in natural language. But what what we're trying to
do is to find a way to sort of represent precisely so
you can compute things. See one thing when you say
you give a piece of a natural language question is you
feed it to a computer. You say, does the computer understand
this natural language? Well, you know, the computer processes it
in some way it does this, maybe it can make a continuation
of the natural language. You know, maybe it can go on from the prompt and say what it's gonna say. You say, does it really understand it? Hard to know. But for in this kind
of computational world, there is a very definite
definition of does it understand, which is could it be turned
into this symbolic computational thing from which you can compute
all kinds of consequences? And that's the, that's the sense in which
one has sort of a target for the understanding of natural language. And that's kind of our goal
is to have as much as possible about the world that can be computed in a, in a reasonable way, so to speak, be able to be sort of captured by this kind of computational language. That's, that's kind of the goal. And, and I think for us humans, the, the main thing that's important
is as we formalize what we're talking about, it gives us a way of kind of
building a structure where we can sort of build this tower
of consequences of things. So if we're just saying, well, let's talk about it a natural language, it doesn't really give us some
hard foundation that lets us, you know, build step by
step to work something out. I mean, it's kind of like
what happens in, in math, if we were just sort of
vaguely talking about math but didn't have the kind of
full structure of math and all that kind of thing, we wouldn't be able to build this kind of big tower of consequences. And so, you know, in, in a sense what we're
trying to do with the whole computational language effort
is to make a formalism for describing the world that makes
it possible to kind of build this, this tower of consequences. - Well can you talk about this dance between natural language
and Wolfram Language? So there's this gigantic
thing we call the internet where people post memes and diary type thoughts and very important sounding
articles and all of that. That makes up the
training data set for GPT, and then there's Wolfram Language. How can you map from the
natural language of the internet to the Wolfram language? Is there an manual? Is there
an automated way of doing that? As we look into the future? - Well, so Wolf and Alpha, what it does, it's kind of front end is
turning natural language into computational language, right? - What you mean by that
is there's a prompt, you ask a question, what is
the capital of some country. - And it, and it turns into, you know, what's the distance between, you know, Chicago and London or something. And that will turn into, you know, geo distance of entity city, you know, et cetera, et cetera, et cetera. Each one of those things is very, is very well defined. We know, you know, given that it's the entity
city, Chicago, et cetera, et cetera, et cetera, you
know, Illinois, United States, you know, we know the geolocation of that. We know it's population, we know all kinds of things
about it, which we have, you know, curated that data to be able to, to know that with some degree
of certainty, so to speak. And then, then we can
compute things from this. And that's, that's kind of the yeah. That, that's, that's that's the idea. - But then something like
GPT large language models, do they allow you to make that conversion much more powerful? - Okay, so it's an interesting thing which we still don't know everything about. Okay. The, I mean this question of
going from natural language to computational language, yes. In Wolfram Alpha, we've now, you know, Wolfram Alpha has been
out and about for what, 13 and a half years now. And, you know, we've achieved,
I don't know what it is, 98%, 99% success on queries
that get put into it. Now, obviously there's
a sort of feedback loop because the things that work are things people go on putting into it. So yeah, that that, but you know, we've, we've got to a very high
success rate of the, the little fragments of natural
language that put people put in, you know, questions,
math calculations, chemistry calculations, whatever it is. You know, we can, we can,
we, we do very well at that, turning those things into
computational language. Now I, from the very
beginning of Wolfram Alpha, I thought about, for example, writing code with natural language. In fact, I had, I, I was just
looking at this recently. I had a post that I wrote in 2010, 2011 called something like
Programming with Natural Language is actually Going to Work. Okay. And so, you know, we had done a bunch of experiments
using methods that were yeah, a little bit. Some of them are little
bit machine learning like, but certainly not the same, you know, the same kind of idea of
vast training data and so on. That is the story of
large language models. Actually I know that that
post piece of utter trivia, but that, that post Steve Jobs forwarded
that post around to all kinds of people at Apple. And he, you know, that was because he never really liked programming languages. So he was very happy to see the
idea that that that that you could get rid of this kind of
layer of kind of engineering like structure he
would've liked, you know, I think what's happening now, because it really is the
case that you can, you know, this idea that you have to
kind of learn how the computer works to use a programming
language is something that is I think a, a a thing that, you know, just like you had to learn
the details of the op codes to know how similar
language worked and so on. It's kind of a thing that's, that's that's a limited time horizon. But, but kind of the, the, you know, so this idea of how elaborate can you make kind of the prompt, how elaborate can you
make the natural language and abstract from it
computational language. It's a very interesting question. And you know what, ChatGPT, you know, GBT four and so on can do is pretty good. It isn't, it's very interesting process. I mean I'm still trying to
understand this workflow. We've been working out a lot
of tooling around this workflow - The natural language to
computational language. - [Wolfram] Right. - And the process. Especially if it's
conversation like dialogue, it's like multiple queries kind of thing. - Yeah. Right. There's so many things that
are really interesting that, that, that work and so on. So first thing is, can you just walk up to the computer and expect to sort of
specify a computation? What one realizes is humans
have to have some idea of kind of this way of thinking
about things computationally. Without that you're kind of
out of luck because you just have no idea what you're going
to walk up to a computer. I remember when I, I should
tell a silly story about myself, the very first computer I saw, which is when I was 10 years
old and it was a big mainframe computer and so on, and I didn't really understand
what computers did and it's like somebody's showing me
this computer and it's like, you know, can the computer work out
the weight of a dinosaur? It's like, that isn't a
sensible thing to ask. That's kind of, you know,
you have to give it, that's not what computers do. I mean, and Wolfram Alpha for example, you could say what's the
typical weight of a stegosaurus? And we'll give you some answer, but that's a very different
kind of thing from what one thinks of computers as doing. And so the, the kind of the
the question of, you know, first thing is people have
to have an idea of what, what computation is about. You know, I think it's a very, you know, for education that is the
key thing. It's kind of this, this, this notion, not computer science, not so that the details are programming, but just this idea of how
do you think about the world computationally computation, thinking about the world
computationally is kind of this formal way of thinking about the world. We've had other ones, like logic
was a formal way, you know, as a way of sort of abstracting and formalizing some aspects of the world. Mathematics is another one. Computation is this very broad
way of sort of formalizing the way we think about the world. And the thing that's, that's cool about computation
is if we can successfully formalize things in terms of computation, computers can help us figure
out what the consequences are. It's not like you formalized it with math. Well that's nice, but now you
have to, if you're, you know, not using a computer to do the math, you have to go work out a
bunch of stuff yourself. So I think, but the, this idea,
let's see, I mean the, the, you know, we're trying
to take kind of the, we're talking about
sort of natural language and its relationship to
computational language. The, the thing, the sort of the typical workflow
I think is first human has to have some kind of idea of
what they're trying to do. That if it, if it's something that they
want to sort of build a tower of, of capabilities on something
that they want to sort of formalize and make computational, so then human can type
something into, you know, some LLM system and sort of say vaguely what they want in sort
of computational terms, then it does pretty well at synthesizing Wolfram Language code, and it'll probably do better in the future because we've got a huge
number of examples of, of natural language input
together with the Wolfram Language translation of that. So it's kind of a, a you know, that that's a thing where you
can kind of extrapolating from all those examples makes it
easier to do that, that task. - Is the prompter task
could also kind of debugging the Wolfram Language code? Or is your hope to not do that debugging? - Oh, no. No, no. I mean, so, so there are many steps here. Yeah. Okay, so first, the first thing is you
type natural language, it generates Wolfram Language. - Do you have examples by the way? Do you have, do you have an
example that is, is it the, the dinosaur example, do you have an example that jumps to mind that we should be thinking about? Some dumb example. - It's like, take my heart
rate data and you know, figure out whether I, you know, make a moving average every
seven days or something and work out what the, and and
make a a plot of the result. Okay. So that's a thing
which is, you know, about two-thirds of a line
of Wolfram Language code. I mean it's, you know, list plot of moving average
of some data bin or something of the, of the data and
then you'll get the result. And you know, the vague thing that I was just saying in natural language could, would almost certainly correctly
turn into that very simple piece of Wolfram Language code. - You start mumbling about heart rate. - [Wolfram] Yeah. - Kinda, you know, you arrive at the moving
average kind of idea. - Right? You say average over seven days, maybe it'll figure out that
that's a moving, you know, that that can be encapsulated
as this moving average idea. I'm not sure. But then the typical workflow, but I'm seeing is you generate this piece of Wolfram Language code,
it's pretty small usually. It's, and if it isn't small, it probably isn't right. But you know, if it's, it's
pretty small and, you know, Wolfram Language is, one of the ideas of Wolfram Language is, it's a language that humans can read. It's not a language which, you know, programming languages tend
to be this one way story of humans write them and
computers execute from them. Wolfram Language is
intended to be something which is sort of like math
notation something where, you know, humans write it and humans are supposed
to read it as well. And so kind of the workflow
that's emerging is kind of this, this human mumbles some things, you know, large language model produces a fragment of Wolfram Language code. Then you look at that, you say, yeah, that looks well, typically
you just run it first, you see does it produce the right thing? You look at what it produces. You might say, that's obviously crazy. You look at the code, you
see I see why it's crazy. You fix it. If you really care about the
results and you really wanna make sure it's right, you better look at that code
and understand it because that's the way you have the
sort of checkpoint of did it really do what I expected it to do? Now you go beyond that. I mean it's, it's, it's, you know, what we find is, for example, let's say the code does the wrong thing, then you can often say to
the large language model, can you adjust this to do this? And it's pretty good at doing that. - Interesting. So you're using the output
of the code to give you hints about the, the, the function of the code. So you're debugging based on
the output of the code itself. - And by the way, right, The plugin that we have
or the, the, you know, for ChatGPT, it does
that routinely, you know, it will send the thing
in, it will get a result. It will discover, the LLM will discover
itself that the result is not plausible and it will
go back and say, oh, I'm sorry, it's very polite and it it, you know, it, it goes back and says, I'll rewrite that piece of
code and then it will try it again and get the result. The other thing that's
pretty interesting is when you're just running, so one of the new concepts that we have, we invented this whole idea of notebooks back 36 years ago now. And so now there's the
question of sort of how do you combine this idea of notebooks
where you have, you know, text and code and output, how do you combine that
with the notion of, of chat and so on. And there's some really
interesting things there. Like for example, a very typical
thing now is we have these, these notebooks where as soon as the, if, if the thing produces
errors, if the, you know, run this code and it produces
messages and so on, the, the, the LLM automatically not
only looks at those messages, it can also see all kinds of
internal information about stack traces and things like this. And it can then, it does a remarkably good
job of guessing what's wrong and telling you. So in other words, it's, it's looking at things,
it's sort of interesting, it's kind of a, a typical sort of AI ish thing
that it's able to have more sensory data than we humans
are able to have cause able to look at a bunch of stuff that
we humans would kind of glaze over looking at. And it's able to then come up with, oh this is the explanation
of what's happening. - And, and what is the
data the stack trace, the, the code you've written previously, the natural language you've written. - Yeah. It's also what's happening is
one of the things that's is is for example, when there's these messages, there's documentation
about these messages, there's examples of where the messages have occurred otherwise. - [Fridman] Nice. - All these kinds of things. The other thing that's really
amusing with this is when it makes a mistake, one of the things that's in our
prompt when the code doesn't work is read the documentation
and we have a, you know, another piece of the plugin
that lets it read documentation. And that again is very, very
useful because it, it will, you know, it will figure
out, sometimes it'll get, it'll make up the name of some
option for some function that doesn't really exist. Read the documentation, it'll have, you know, some wrong structure
for the function and so on. It's, that's a powerful thing. I mean the thing that, you know, I've realized is we built this language over the course of all
these years to be nice and coherent and consistent and so on. So it's easy for humans to understand. Turns out there was a side
effect that I didn't anticipate, which is it makes it easier
for AIs to understand. - It's almost like another
natural language. But-. - [Wolfram] Yeah. - So, so Wolfram language is
a kind of foreign language. - [Wolfram] Yes, yes. - You have a lineup. English, French, Japanese,
Wolfram language and then I don't know Spanish, and then the system is not
gonna notice. Hopefully. - Well, yes, I mean maybe, you know, that's an interesting question because it really depends on
what I see as being a, a an important piece of
fundamental science that basically just jumped out at us with ChatGPT. Because I think, you know, the, the real question is
why does ChatGPT work? How is it possible to
encapsulate, you know, to successfully reproduce
all these kinds of things in natural language, you
know, with a, you know, a comparatively small, he says, you know, couple hundred billion, you know, weights of neural nets and so on. And I think that, you know, that that relates to kind of a, a fundamental fact about language, which, you know, the, the
main, the main thing is that I think there's a
structure to language that we haven't kind of
really explored very well. It's kind of this semantic
grammar I I'm talking about, about, about language. I mean, we, we kind of know that when we, when we set up human language, we know that it has certain regularities. We know that it has a certain
grammatical structure, you know, noun followed by verb, followed by noun, adjectives, et cetera, et cetera, et cetera. That's, its kind of grammatical structure. But I think the thing that
ChatGPT is showing us is that there's an additional kind
of regularity to language, which has to do with the
meaning of the language beyond just this pure, you know, part of speech combination type of thing. And I, I think the, the, the kind of the, the one example of that that
we've had in the past is logic. And you know, I, I think my, my sort of kind of picture
of how was logic invented? How was logic discovered? It really was a thing that was discovered in its original conception. It was discovered presumably by Aristotle who kind of listened to a
bunch of people orators, you know, giving speeches. And this one made sense,
that one doesn't make sense, this one, and you know, you see
these patterns of, you know, if the, you know, I don't
know what, you know, if the, if the Persians do this,
then the, this does that, et cetera, et cetera, et cetera. And what, what Aristotle realized is there's a structure to those sentences, there's a structure to that
rhetoric that doesn't matter whether it's the Persians
and the Greeks or whether it's the cats and the dogs. It's just, you know, p and q
you can abstract from this, the, the details of these
particular sentences. You can lift out this
kind of formal structure. And that's what logic is. - That's a heck of a discovery by the way. Logic, you're making me realize now. - [Wolfram] Yeah. - It's not obvious. - The fact that there is an abstraction from natural language that has, where you can fill in any word you want. - [Fridman] Yeah. - Is a very interesting discovery. Now it took a long time to mature. I mean, Aristotle had this
idea of silogistic logic where there were these particular patterns of how you could argue
things, so to speak. And you know, in the Middle Ages part of education was you memorized the syllogisms, I forget how many there were, but 15 of them or something. And they all had names, they all had mnemonics. Like I think Barbara and
Celarent were two of the, the mnemonics for the silogisms. And people would kind of, this is a valid argument
because it follows the Barbara of Syllogism, so to speak and, and it
took until 1830, you know, with George Bule to kind of
get beyond that and kind of see that there was a, a level of abstraction
that was beyond the, this particular template
of a sentence, so to speak. And that's, you know, what, what's interesting there
is in a sense, you know, ChatGPT is operating at
the Aristotelian level. It's essentially dealing
with templates of sentences. By the time you get to
Bule and Bule and algebra and this idea of, you know, you can have arbitrary depth
nested collections of ans and awes and knots, and you
can resolve what they mean. That's the kind of thing
that's a computation story. That's, you know, you've gone beyond the pure
sort of templates of natural language to something, which is an arbitrarily deep computation. But the thing that I
think we realized from, from ChatGPT is, you know, Aristotle stopped too quickly
and there was more that you could have lifted out of
language as formal structures. And I think there's, you know, in a sense we've captured
some of that in, in, you know, some of what, what is in language there. There's, there's a, there's a lot of kind of little calculate, little algebras of, of what you can say, what language talks about. I mean, whether it's, I don't know, if you say I
go from place A to place B, place B to place C, then I know I've gone
from place A to place C. If A is a friend of B
and B is a friend of C, it doesn't necessarily follow
that A is a friend of C. These are things that are, and
you know that there are, if, if you go from from place A
to place B place B to place C, it doesn't matter how
you went, like logic. It doesn't matter whether
you flew there, walked there, swam there, whatever you
still, this transitivity of, of where you go is still valid. And there are, there are many kinds of kind of features, I think of the way the world
works that are captured in these aspects of, of
language, so to speak. And I think what, what
ChatGPT effectively has found, just like it discovered logic, you know, people are really
surprised it can do these, these logical inferences. It discovered logic the same
way Aristotle discovered logic by looking at a lot of sentences
effectively and noticing the patterns in those sentences. - But it feels like it's
discovering something much more complicated than logic. So this kind of semantic grammar, I think you wrote about this, maybe we can call it the laws of language, I believe you call, or which
I like the laws of thought. - Yes. That was the title that
George Bule had for his, his Bule in algebra back in 1830. But yes. - [Fridman] Laws of thought. - Yes. That was what he said. - [Fridman] Woo. All right. - So he thought, he thought he nailed it with Bule algebra. - [Fridman] Yeah. - There's more to it. - I, and it's a good question,
how much more is there to it? And it seems like one of the reasons, as you imply that the reason
GPT works, ChatGPT works, is that there's a finite
number of things to it. - [Wolfram] Yeah. I mean, it's, it's. - Like, it's discovering
the laws in some sense, GPTs discovering this laws
of semantic grammar that underlies language. - [Wolfram] Yes. And what, what's sort of interesting is
in the computational universe, there's a lot of other
kinds of computation that you could do. They're just not ones that we
humans have cared about and, and operate with. And that's probably because
our brains are built in a certain way. And, you know, the neural nets of our brains
are not that different in some sense from the neural nets of, of, of a large language model. And that's kind of, and and so when we think
about, and you know, maybe we can talk about this some more, but when we think about sort
of what will AIs ultimately do, the answer is insofar as AI
are just doing computation, they can run off and do all these kinds
of crazy computations. But the ones that we sort of have, have decided we care about
are there is this kind of very limited set. - That's where the reinforcement learning with human feedback seems to come in. The more the AI say the stuff
that kind of interests us, the more we're impressed by it. So they can do a lot of
interesting, intelligent things, but we're only interested
in the AI systems when they communicate human in a human-like way. - [Wolfram] Yes. - About human-like topics. - [Wolfram] Yes. - Well, it's, it's like technology. I mean, in a sense, the physical world provides
all kinds of things. You know, there are all kinds of processes going on in physics only
a limited set of those are ones that we capture
and use for technology. Cause they're only a
limited set where we say, you know, this is a thing that we can
sort of apply to the human purposes we currently care about. I mean, you might have said, okay, you pick up a piece of, of rock, you say, okay, there's a nice sillacate, it contains all kinds of
sillicon, I don't care. Then you realize, oh, we could
actually turn this into a, a, you know, semiconductor wafer and make
it microprocessor out of it, and then we care a lot about it. - [Fridman] Yes. - And it's, it's, you know, it's this thing about what do we, you know, in the evolution
of our civilization, what things do we identify as
being things we care about? I mean, it's, it's like, you know, when when there was a little
announcement recently of a possibility of a high
temperature superconductor that involved, you know, the element
lutetium, which, you know, generally nobody has cared about. - Fridman] Yes. - And, you know, it, it's kind of, but suddenly if there's this
application that relates to kind of human purposes,
we start to care a lot. - So given your thinking
that GPT may have discovered inklings of laws of thought,
do you think such laws exist? Can we linger on that? Yeah. What's your intuition here? - Oh, definitely. I mean, the fact is,
look, the, the logic is, but the first step, there are many other kinds of
calculate about things that we consider, you know, the, the about sort of things
that happen in the world or things that are meaningful. - Well, how do you know
logic's not the last step? You know what I mean? So. - Well, because we can plainly
see that there thing, I mean, if, if you say, here's a sentence that
is syntactically correct. Okay. You look at it and
you're like, you know, the happy electron, you know, ate, I don't know what some
something that it just, it, you look at it and it's
like, this is meaningless. It's just a bunch of words. It's syntactically correct, the nouns and the verbs
are in the right place, but it just doesn't mean anything. And so there clearly is some
rules that there are rules that determine when a sentence is, has the potential to be
meaningful that go beyond the pure parts of speech syntax. And so the question is,
what are those rules? And are there a fairly
finite set of those rules? My guess is that there's a
fairly finite set of those rules and they, you know, once
you have those rules, you have a kind of a
construction kit, just like the, the rules of syntactic grammar
give you a construction kit for making syntactically
correct sentences. So you can also have a
construction kit for making semantically correct sentences. Those sentences may not
be realized in the world. I mean, I think, you know,
the elephant flew to the moon. - [Fridman] Yeah. - A syntactic, a a semantically, you know, we know we have an idea. If I say that to you, you
kind of know what that means. But the fact is it hasn't
been realized in the world, so to speak. - So semantically correct
perhaps as things that can be imagined with a human mind, no. Things that are consistent
with both our imagination and our understanding of physical reality. I don't know. - [Wolfram] Yeah. It's a good question. I
mean, it's a good question. It it, it's a good question. I mean, I think it is, it is given the way we
have constructed language, it is things which, which fit with the things
we're describing in language. It's a bit circular in the end
because, you know, you can, and, and the, and the, the sort of boundaries of
what is physically realizable. Okay, let, let's take
the example of motion. Okay? Motion is a complicated concept. It might seem like it's a
concept that should have been figured out by the Greeks,
you know, long ago. But it's actually a really
pretty complicated concept because what is motion? Motion is you can go from place
A to place B and it's still you when you get to the other end, right? You, you take an object, you move it, and it's still the same object, but it's in a different place. Now, even in ordinary physics, that doesn't always work that way. If you're near a space time
singularity in a black hole, for example, and you take
your teapot or something, you don't have much of
a teapot by the time it's near the space time singularity. It's been completely, you know, deformed beyond recognition. But, so that's a case where
pure motion doesn't really work. You can't have a, a thing stay the same. But, so this idea of motion is, is something that sort of is a, is a slightly complicated idea. But once you have the idea
of motion, you can start, once you have the idea that
you're gonna describe things as being the same thing,
but in a different place, that sort of abstracted
idea then has, you know, that has all sorts of consequences. Like this transitivity of
motion go from A to B, B to C. You've gone from A to C. And that's so that level of description, you can have what are sort
of inevitable consequences. They're inevitable features of the way you've sort of set things up. And that's, I think what
this sort of semantic grammar is capturing is things, things like that. And I, you know, I think that it's a question
of what does the word mean when you say I go from, I
move from here to there. Well, it's complicated
to say what that means. This is this whole issue of, you know, is pure motion possible, et
cetera, et cetera, et cetera. But once you have kind of got
an idea of what that means, then there are inevitable
consequences of that idea. - But the very idea of meaning, it seems like there's
some words that become, it's like there's a
latent ambiguity to them. I mean, it is the word like emotionally loaded words like hate and love, right? It's like, what, what do
they, what do they mean? Exactly? What, what? So especially when you
have relationships between complicated objects, we seem
to take this kind of shortcut, descriptive shortcut of, to describe like object
A hates object B, what's, what's that really mean? - [Wolfram] Right. Well, words are defined by
kind of our social use of them. I mean, it's not, you know, a word in computational
language, for example, when we say we have a, a construct there, we expect that that construct
is a building block from which we can construct an arbitrary tall tower. So we have to have a very
solid building block. And you know, we have to, it
turns into a piece of code, it has documentation, it's,
you know, it's a whole, it's a whole thing. But the word hate, you know, the documentation for that word. Well, there isn't a standard
documentation for that word, so to speak. It's a complicated thing
defined by kind of how we use it when, you know, if it wasn't for the fact
that we were using language, I mean, so, so what is
language at some level, language is a way of packaging
thoughts so that we can communicate them to another mind. - Can these complicated words be converted into something that a
computation engine can use? - Right? So, so I think
the answer to that is that, that what one can do in
computational language is define, make a def- make a specific definition. And if you have a complicated word, like let's say the word eat, okay, you'd think that's a simple word. It's, you know, animals
eat things, whatever else. But you know, you do programming, you say this function eats arguments, which is sort of poetically similar to the animal eating things. But if you start to say, well, what are the implications of, you know, the function eating something,
you know, does it can, can the function be poisoned?
Well, maybe it can actually, but you know, if there's a tight
mismatch or something in a, in some language. But, but you know, in what, how far does that
analogy go? And it, it's, it's just an analogy. Yeah. Whereas if you use the word
eat in a computational language level, you would define there
isn't a thing which you, you anchor to the kind of
natural language concept eat. But it is now some precise
definition of that, that then you can compute things from. - But don't you think the
analogy is also precise software eats the world, huh? Don't, don't you think there's, there is something concrete in terms of meaning about analogies. - Sure. But the thing that sort
of is the first target for computational language is
to take sort of the ordinary meaning of things and
try and make it precise, make it sufficiently precise. You can build these towers
of computation on top of it. - [Fridman] Yeah. - So it's kinda like if you
start with a piece of poetry and you say, I'm going to define my program
with this piece of poetry. It's kind of like, that's,
that's a difficult thing. It's better to say, I'm gonna just have this
boring piece of prose and it's using words in the ordinary way. - [Fridman] Yeah. - And that's how I'm
communicating with my computer. And that's how I'm going to
build the solid building block from which I can construct
this whole kind of computational tower. - So there is some sense
where if you take a poem and reduce it to something computable, you're gonna have very few things left. So maybe there's a bunch of
human interaction that's just poetic aimless nonsense. - [Wolfram] Well. - That's just like recreational,
like hamster in a wheel. It's not actually producing anything. - Well, I, I I, I think that that's a complicated
thing because in a sense, human linguistic communication
is, there's one mind, it's producing language
that language is having an effect on another mind. - [Fridman] Yeah. - And the question of
there's sort of a, a, a type of effect that is well
defined, let's say where, where, for example, it's very independent of the
two minds that the, it doesn't, you know, there, there, there's communication where it
can matter a lot sort of what the experience of, of, of one mind is versus
another one and so on. - [Fridman] Yeah. But what is the purpose of
natural language communication? - [Wolfram] I think, I think the- - Versus, so computation
computational language somehow feels more amenable
to the definition of purpose. It's like, yeah, you're given two clean
representations of a concept and you can build a tower based on that. - [Wolfram] Right. - Is natural language the same
thing but more fuzzy or what? - Well, I think the, the story
of natural language, right? And the, the, that's the great
invention of our species. We don't know whether it
exists in other species, but we know it exists in our species. It's the thing that allows
you to sort of communicate abstractly from like one generation of the species to another. You can, you know, there is an abstract version of knowledge that can be passed down. It doesn't have to be, you know, genetics. It doesn't have to be, you know, you don't have to apprentice
the next species, you know, the next generation of birds
to the previous one to show them how something works. - [Fridman] Yeah. - There is this abstracted
version of knowledge that can be kind of passed down. Now that, you know, it relies on, it still tends to rely
because language is fuzzy. It does tend to rely on
the fact that, you know, if we look at the, you know,
some ancient language that, where we don't have a chain
of translations from it until what we have today, we may not understand
that ancient language. And we may not understand, you know, its concepts may be different from the ones that we have today. We still have to have
something of a chain. But it is something where we
can realistically expect to communicate abstract ideas. And that's, you know, that's
one of the big, big roles of, of a language I think, you know, in, in, it's, you know, and that that's been this, this ability to sort of
concrete-ify abstract things is what, what language has provided. - Do you see natural language
and thought as the same, the stuff that's going inside your mind? - Well, that's been a
long debate in philosophy. - It seems to be become more important now when we think about
how intelligent GPT is. - [Wolfram] Whatever that means. - Whatever that means. But it seems like the stuff
that's going on in the human mind seems something like intelligence. - [Wolfram] Yes. - And is the language- - Well, we call it intelligence. Yeah. - We call it. Well, yes. - And so you, you start to think of, okay, what's the relationship between thought, the language of thought,
the laws of thought, the laws of the words like reasoning and the laws of language and how that has to do with computation, which seems like more rigorous,
precise ways of reasoning. - [Wolfram] Right. Which are beyond human. I mean, much of what computers
do, humans do not do. I mean, you, you might say- - Humans are a subset. - [Wolfram[ Yes.
- [Fridman] Presumably. - [Wolfram] Yes.
- [Fridman] Hopefully. - [Wolfram] Yes. The, the yes. Right. You know, you might say, who needs computation when we have large, large language models? Large language models can just, you know, eventually you'll have
a big enough NeuroNet can do anything, but they're really doing
the kinds of things that humans quickly do. And there're plenty of sort
of formal things that humans never quickly do. For example, I don't know, I, you know, you can, some people can
do mental arithmetic. They can do a certain
amount of math in their, in their minds. I don't think many people can
run a program in their minds of any sophistication. It's just not something people do. It's not something people
have even thought of doing. Cause it just, it's kind of
a, it's kind of not, you know, you can easily run it on a computer. - We're an arbitrary program.
- [Wolfram] Yeah. - Aren't we running specialized programs? - [Wolfram] Yeah, yeah. But if I say to you- - Run this program- - [Wolfram] Here's a
touring machine. Yeah. You know, tell me what
it does after 50 steps. And you're like, trying to
think about that in your mind. That's really hard to do.
It's not what people do. I mean it- - [Fridman] Well, in some sense, people program, they build
a computer, they program it. Just to answer your question
about what the system does after 50 steps. I mean, humans build computers. - Yes. Yes, yes, that's right. But they've created something
which is then, you know, then when they run it, it's doing something different than what's happening in their minds. I mean, they've outsourced that, that piece of computation
from something that is in internally happening in their
minds to something that is now a tool that's external to their minds. - [Fridman] So by the way, humans to you didn't invent computers. They discovered them. - They discovered computation. - [Fridman] Which- - They invented the
technology of computers. - This, the computer is just a kind of way to plug into this whole, this stream of computation. There's probably other, are other ways. There's probably a lot of ways. - [Wolfram] For sure. I
mean the, the, you know, the particular ways that
we make computers out of semiconductors and electronics and so on. That's the particular
technology stack we've built. I mean, the story of a
lot of what people try to do with quantum computing
is finding different sort of underlying physical, you know, infrastructure
for doing computation. You know, biology does
lots of computation. It does it using an infrastructure
that's different from semiconductors and electronics. It's a, you know, it's a molecular scale, sort of computational
process that hopefully we'll understand more about. I have some ideas about
understanding more about that. But you know, that's a,
that's another ins you know, it's another representation
of computation, things that happen in the
physical universe at the level of, you know, these evolving
hypergraphs and so on. That's another sort of
implementation layer for this abstract idea of computation. - So if GPT or large language
models are starting to form, starting to develop or
implicitly understand the laws of language and thought, do you think they can be made explicit? - [Wolfram] Yes. - How. - [Wolfram] With a bunch of effort? - [Fridman] I mean, so do, do they have- - It's like doing natural science. I mean, what is happening
in natural science? You have the world that's doing
all these complicated things and then you discover, you know,
Newton's laws, for example. This is how motion works. This is the way that this particular sort of idealization of the world, this is how we describe it in a simple computation reducible way. And I think it's the same thing here. It's, there are sort of
computationally reducible aspects of what's happening that you can
get a kind of narrative theory for just as we've got narrative theories in physics and so on. - Do you think it will
be depressing or exciting when all the laws of
thought are made explicit human thought are made explicit? - I think that once you understand computational reducibility, it is, it's neither of those things. Because the fact is people say
for example, people will say, oh, but you know, I have free
will I, I kind of, you know, I operate in a way that is,
you know, you, you, they, they have the idea that they're
doing something that is sort of, of internal to them, that
they're figuring out what's, what's happening. But in fact, we think there are laws
of physics that ultimately determine, you know, every,
every nerve, you know, every electrical impulse and
a nerve and things like this. So you might say, isn't it depressing that we
are ultimately just determined by the rules of physics, so to speak? It's the same thing.
It's at a higher level. It's like, it's, it's, it's a shorter distance to get
from kind of semantic grammar to the way that we might
construct a piece of text than it is to get from individual nerve firings to how we construct a piece of text. But it's not fundamentally different. And by the way, as soon as we
have this kind of level of, you know, this other level
of description, it's kind of, it helps us to go even further. So we'll end up being able
to produce more and more complicated kinds of kinds of
things that just like when we, you know, if we didn't have a computer and we knew certain rules, we could write them down. We go a certain distance. But once we have a computer,
we can go vastly further. And this is the same kind of thing. - You wrote a blog post titled, what is ChatGPT doing
and why does it work? We've been talking about this, but can we just step back
and linger on this question? What, what's it, what's ChatGPT doing? What, what are these, A bunch of billion parameters trained on a large number of words. Like, why does it seem to work again? Is it, is it because to the point
you made that there's laws of language that can be
discovered by such a process? Is there something-? - Well, let, let's, let's talk about sort of the low level of what ChatGPT is doing. I mean, ultimately you give it a prompt. It's trying to work out, you know, what should the next word be? - Right. Which is wild. Isn't that, isn't that surprising to you
that this kind of low level dumb training procedure
can create something syntactically correct first and then semantically correct second. - [Wolfram] You know, the thing that has been sort
of a story of my life is realizing that simple rules
can do much more complicated things than you imagine. That something that starts simple and starts simple to describe can grow a thing that is, you know, vastly more complicated
than you can imagine. And, and honestly, it, it's
taken me, I mean, I don't know, I've sort of been thinking
about this now 40 years or so, and it always surprises me.
I mean, even for example, in our physics project, sort of thinking about the whole
universe growing from these simple rules, I still resist
because I keep on thinking, you know, how can something really complicated arise from something that simple? It just seems, you know, it
seems wrong, but yet, you know, the majority of my life,
I've kind of known from, from things I've studied, that
this is the way things work. So yes, I, it is wild that it's possible
to write a word at a time and produce a coherent essay, for example. But it's worth understanding
kind of how that's working. I mean, it's kind of like,
if, if it was going to say, you know, the cat sat on
the, what's the next word? Okay, so how does it
figure out the next word? Well, it's seen a trillion
words written on the internet, and it's seen the cat sat on the floor, the cat sat on the sofa,
the cat sat on the whatever. So it's minimal thing to do is just say, let's look at what we saw on the internet. We saw, you know, 10,000
examples of the cat sat on the, what was the most probable next word. Let's just pick that out and
say that's the next word. And that's, that's kind of what it at
some level is trying to do. Now, the problem is
there isn't enough text on the internet to, for, if you have a reasonable
length of prompt to that, that that specific prompt will never have occurred on the internet. And as you, as you kind of go further, there just won't be a place
where you could have trained, you know, where you could just, just worked out probabilities
from what was already there. You know, like if you say two plus two, there'll be a zillion examples of two plus two equaling four, and a very small number of examples of two plus two equals five and so on. And you can pretty much
know what's going to happen. So then the question is, well, if you can't just work out from examples, what's gonna happen? Just
no probabilistic for, for examples, what's gonna
happen, you have to have a model. And this kind of an idea, this idea of making models of
things is an idea that really, I don't know, I think Galileo probably was
one of the first people who sort of worked this out. I mean, it's kind of like, like, you know, I think I
gave an example of that, the little book I wrote
about, about ChatGPT where it's kind of like, you know, Galileo was dropping cannon balls off the, off the different floors of
the, of the tower of Piza. And it's like, okay, you drop
a cannonball off this floor, you drop a cannonball off this floor, you miss floor five or
something for whatever reason. But you know, the time it took the cannon
ball to fall to the ground from floors one, two, three, four, six, seven, eight, for example. Then the question is, can
you work out, can you, can you make a model
which figures out how long did it take the ball? How long would it have take
the ball to fall to the ground from the floor you didn't
explicitly measure. And the thing Galileo realized
is that you can use math, you can use mathematical
formulas to make a model for how long it will take the ball to fall. So now the question is, well, okay, you want to make a model for, for example, something much more elaborate, like you've got this arrangement
of pixels and is this arrangement of pixels an A or a B? Does it correspond to
something we'd recognize as an A or a B? And you can make a similar kind, you know, each pixel is like a
parameter in some equation. And you could write down
this giant equation where the answer is either, you know, A or you know, one or two A or B. And the question is then what
kind of a model successfully reproduces the way that we humans would, would conclude that this is an
A, and this is a B, you know, if if there's a, a complicated extra tail
on the top of the A, would we then conclude
something different? What is the type of model that
maps well into the way that we humans make distinctions about things? And the big kind of meta discovery is neural nets are such a model. It's not obvious they
would be such a model. It could be that human
distinctions are not captured. You know, we could try searching
around for a type of model that could be a mathematical model, it could be some model based
on something else that captures kind of typical human
distinctions about things. It turns out this model that
actually is very much the way that we think the
architecture of brains works, that perhaps, not surprisingly, that model actually corresponds to the way we make these distinctions. And so, you know, the, the, the core next point is that
the, the kind of model, this neural net model
makes sort of distinctions and generalizes things
in sort of the same way that we humans do it. And that's why when you say, you know, the cat sat on the green blank, even though it never didn't see
many examples of the cat sat on the green, whatever, it can make a, or the aardvark sat on
the green, whatever. I'm sure that particular sentence does not occur on the internet. And so it has to make a
model that concludes what, you know, it has to kind of
generalize from what it's, from the actual examples that it's seen. And so, so you know that that's
the factors that neural nets generalized in the same kind
of a way that we humans do. If, if we were, you know, the aliens might look at our
neural net generalizations and say, that's crazy. You know, that thing when you put that
extra little dot on the A, that isn't an A anymore. That's, you know, that messed the whole thing up. But for us humans, we make distinctions, which seem to correspond to
the kinds of distinctions that neural nets make. So then, you know, the, the thing that is just
amazing to me about ChatGPT is how similar the structure
it has is to the very original way people imagine
neural nets might work back in 1943. And, you know, there's a
lot of detailed engineering, you know, great cleverness,
but it's really the same idea. And in fact, even the sort of elaboration
of that idea where people said, let's put in some actual
particular structure to try and make the neural net more elaborate, to be very clever about it,
most of that didn't matter. I mean, there's some things
that seem to, you know, when you, when you train
this neural net, you know, the one thing, this kind of
transformer architecture, this attention idea that
really has to do with, does every one of these
neurons connect to every other neuron or is it somehow
causally localized, so to speak? Does it, like, we're making a sequence of
words and the words depend on previous words rather than just, everything can depend on everything. And that seems to be important. And just organizing things
so that you don't have a, a sort of a giant mess. But the thing, you know, the thing worth understanding about what is ChatGPT in the end? I mean, what is a neural net in the end? A neural net in the end is
each neuron has a, it, it, it's taking inputs from
a bunch of other neurons. It's, it's, eventually
it's going to have a, it's going to have a, a numerical value. It's going to compute some number. And it's, it's saying,
I'm gonna look at the, the neurons above me. It's kind of a, a series of layers. It's gonna look at the neurons above me and it's going to say, what are the values of all those neurons? Then it's gonna add those
up and multiply them by these weights, and then it's going to apply
some function that says if it's bigger than zero or something, then make it one or, and
otherwise make it zero or some slightly more
complicated function. You know very well how this works. - It's a giant equation
with a lot of variables. You mentioned figuring out
where the ball falls when you don't have data on the fourth floor. This, the equation here
is not as simple as- - [Wolfram] It's an equation
with 175 billion terms. - And it's quite surprising
that in some sense, a simple procedure of training
such an equation can lead to - [Wolfram] Well I think- - A good representation
of natural language. - Right? The, the real issue is, you know, this architecture of a neural net where, where what's happening is,
you know, you've, you've, you've turned so neural nets
always just deal with numbers. And so, you know, you've turned the sentence
that you started with into a bunch of numbers. Like let's say by mapping, you know, each word of the 50,000 words in English, you just map each word
or each part of a word into some number. You feed all those numbers in, and then the thing is going to, and then those numbers
just go into the values of these neurons. And then what happens is it's
just rippling down going layer to layer until it gets to the end. I think ChatGPT has about
400 layers, and you're just, you know, it just goes once through it. Just, every, every new word
it's gonna compute just says, here are the, here are the
numbers from the words before, let's compute the, what is it compute. It computes the probabilities
that it estimates for each of the possible 50,000 words
that could come next. And then it decides sometimes it will use the most probable word. Sometimes it will use not
the most probable word. It's an interesting fact
that there's this so-called temperature parameter, which,
you know, at temperature zero, it's always using the most
probable word that it, that it estimated was the, the most
probable thing to come next. You know, if you increase the temperature, it'll be more and more kind of random in its selection of words. It'll go down to the lower
and lower priority words. The thing I was just playing
with actually recently was the transition that happens as
you increase the temperature, the thing goes bonkers at
a particular, you know, sometimes at a particular temperature. I think maybe about 1.2 is
the thing I was noticing from yesterday actually. That, you know, usually it's giving reasonable
answers and then at that temperature with some probability, it just starts spouting nonsense. And, you know, nobody
knows why this happens. I mean, it's, it's,
and by the way, I mean, the thing to understand is it's putting down one word at a time, but the outer loop of the
fact that it says, okay, I put down a word. Now let's take the whole
thing I wrote so far, let's feed that back in. Let's put down another word. That outer loop, which
seems almost trivial, is really important to the
operation of the thing. And, and for example, one of the things that is
kind of funky is it'll give an answer and you say to it,
is that answer correct? And it'll say no. And why is that happening? - [Fridman] It's fascinating. Right? - Right. Why can't it do that? Well, the answer is because it, it is going one word at
a time sort of forwards. And it didn't, you know, it it, it came along with some sort of chain of, of thought in a sense, and it, it came up with completely
the wrong answer. But as soon as you feed it, the whole thing that it came up with, it immediately knows
that that isn't right. It immediately can recognize
that was a, you know, a bad syllogism or something,
and can see what happened. Even though as it was being
led down this garden path, so to speak, it didn't, it
came to the wrong place. - But it's fascinating that
this kind of procedure converges to something that forms
a pretty good compressed representation of
language on the internet. - [Wolfram] Yeah. - That, that's quite. - [Wolfram] Right. Right, right. - I'm not sure what to make of it. - Well, look, I think, you know, there are many things we
don't understand. Okay. So for example, you know,
175 billion weights, it's maybe about a trillion
bites of information, which is very comparable to
the training set that was used. And you know, why that, why kind, it sort of stands to some kind
of reason that the number of weights in the neural
net, I don't know where, I can't really argue that I
can't really give you a good, you know, in a sense the
very fact that, you know, the insofar as there are definite
rules of what's going on, you might expect that eventually
we'll have a much smaller neural net that will successfully
capture what's happening. I, I don't think the best way to do it is probably a neural net. I think a neural net is what
you do when you don't know any other way to structure the thing. And it's a very good thing to
do if you don't know any other way to structure the thing. And for the last 2000 years, we haven't known any
other way to structure it. So this is a pretty good way to start. But that doesn't mean you
can't find sort of, in a sense, more symbolic rules for
what's going on that you know, much of which will then be, you can kind of get rid of
much of the structure of the neural net and replace it by
things which are sort of pure steps of computation, so to speak, sort of with neural net
stuff around the edges. And that becomes just a, you know, it's just a much simpler way to do it. - So the neural net you hope
will reveal to us good symbolic rules that make the
needs of the neural net less and less and less. - Right. And there will still be some
stuff that's kind of fuzzy, just like, you know, there
there're things that it, it's like this question
of what can we formalize, what can we turn into
computational language? What is just sort of, oh, it happens that way just because
brains are set up that way. - What do you think are the
limitations of large language models just to make it explicit? - Well, I mean, I think that deep computation
is not what large language models do. I mean, that's just, it's a different kind of thing, you know, the outer loop of a large language model. If, if you are trying to do
many steps in a computation, the only way you get to do
that right now is by spooling out, you know, all the, the whole chain of thought as
a bunch of words basically. And, you know, you can make a touring machine
out of that if you want to. I just was make doing that
construction, you know, in principle you can make
an arbitrary computation by just spooling out the words. But it's an, it's a bizarre and
inefficient way to do it. But it's something where the,
you know, I, I think that's, you know, sort of the, the
deep computation is it's, it's really what a humans can do quickly. Large language models will
probably be able to do well. Anything that you can do kind
of off the top of your head type thing is a, is really, you know, is good for large language
models and the things you do off the top of your head, you may
not get them always right, but you know, you'll, it, it's, it's thinking it through
the same way we do. - But I wonder if there's an
automated way to do something that humans do well much
faster to where it like loops. So generate arbitrary large
code bases off Wolfram Language for example. - [Wolfram] Well, the
question is what does he, what do you want the code base to do? - Escape control and take over the world? - [Wolfram] Okay, so, you know, the thing is when people say, you know, we, we want to build
this giant thing, right? A giant piece of computational
language in a sense, it's sort of a failure
of computational language if the thing you have to
build, in other words, if we have a description, if, if you have a small description, that's the thing that you
represent in computational language and then the computer
can compute from that. - [Fridman] Yes. - So in a sense in, you know, when, as soon as you're giving a
description, the, you know, if you have to somehow make
that description something, you know, definite something
formal and once and, and to say, to say, okay, I'm gonna give this
piece of natural language and then it's gonna split out this giant formal structure that
in a sense that doesn't, that that doesn't really make
sense because except insofar as that piece of natural language
kind of plugs into what we socially know, so to speak, plugs into kind of our corpus
of knowledge, then, you know, that's a way we are
capturing a piece of that corpus of knowledge. But hopefully we will have
done that in computational language. How do you make
it do something that's big? Well, you know, you have to have a way to
describe what you want. - Okay. I can make it
more explicit if you want. How about I just pop into my head, iterate through all
the members of Congress and figure out how to convince them that they have to let me, the meaning the system become president, pass all the laws that allows
AI systems to take control and be the president. I don't know. So that's a very explicit
like figure out the individual life story of each congressman
that each senator, anybody, I don't know, what's required to really
kind of pass legislation and figure out how to control them
and manipulate them, right. Get all the information. What would be the biggest
fear of this congressman? And in such a way that you
can take action on it in the digital space. So maybe threaten the
destruction reputation or something like this. - Right. If I can describe what I want. You know, to what extent can a large
language model automate that? - Would the help with the
help of the conqurization of something like Wolfram language that makes it more yeah, grounded. - [Wolfram] I think it
can go rather a long way. - I'm also surprised how
quickly I was able to generate. - Yeah, yeah. Right. - [Wolfram] That's a, an attack. - That, that's a, you know, I, I swear, I swear I did not think about
this before and it's funny how quickly, which is a very
concerning thing because that, that probably this idea will
probably do quite a bit of damage and there might
be a very large number of other such ideas. - Well, I'll give you a, a much more benign version of that idea. Okay. You're gonna make an AI
tutoring system and you know, that is a, that's a, a benign version of what you're
saying is I want this person to understand this point. You know, you are essentially doing
machine learning where the, where the, where the, you know,
the, the loss function, the, the thing you're trying to
get to is get the human to understand this point and, and when you do a test on
the human that they yes, they correctly understand
how this or that works. And I, I am confident that, you know, sort of a large language model
type technology combined with computational language is
going to be able to do pretty, pretty well at teaching us humans things. And it's gonna be an
interesting phenomenon because, you know, sort of
individualized teaching is, is a thing that has been
kind of a, you know, a goal for a long time. I think we're gonna get that
and I think more, you know, that, that it has many
consequences for, you know, like, like just, you know, if
you know me as an, if you, the AI know me, tell me
I'm about to do this thing, what is the, what are the
three things I need to know, you know, given what I already
know, you know, what's the, what's, let's say I'm, I'm looking at some paper
or something, right? it's like there's a version
of the summary of that paper that is optimized for me, so to speak, and where it really is. And I think that's really going to work. - It could understand the
major gaps in your knowledge - [Wolfram] Yes. - That if filled would actually give you a deeper understanding of the topic. - [Wolfram] Yeah. Right. And that's a, you know, that's an important thing because it, it really changes actually.
I think, you know, when, when you think about education and so on, it really changes kind
of what's worth doing, what's not worth doing and so on. It makes, you know, I know in my life I've learned
lots of different fields and you know, so I, yeah, I don't know. I have every time, I'm always think this is
the one that's going to, I'm not gonna be able to learn. But turns out sort of, there are sort of meta
methods for learning these things in the end. And, you know, I think this, this idea that it becomes
easier to, you know, it, it becomes easier to be
fed knowledge, so to speak. And it becomes, you know, if you need to know this
particular thing, you can, you know, you can get taught it in a, in an efficient way is something
I think is sort of a, a, an interesting feature. And I think it makes the, you know, things like the value of, of big towers of specialized
knowledge become less significant compared to the
kind of meta knowledge of sort of understanding kind of the, the big picture and being able
to connect things together. I think that, you know, there's
been this huge trend of, of let's be more and more
specialized because we have to, you know, we, we have to sort of ascend
these towers of knowledge, but by the time you can get, you know, more automation of being able
to get to that place on the tower without having to go
through all those steps, I think it, it sort of
changes that picture. - Interesting. So your intuition is that
in terms of the, the, the collective intelligence
of the species and the individual minds that
make up that collective, there'll be more, there will trend towards being generalists and being kind of philosophers. - That's what I think, I think that's where the
humans are gonna be useful. I think that a lot of these
kind of the drilling, the, the, the, the mechanical working out of things is much more automatable. It's much more AI, AI
territory, so to speak. - [Fridman] No more PhDs. - Well that's, it's interesting. Yes. I mean that, you know, the, the, the kind of the specialization, this kind of tower of specialization, which has been a feature of, you know, we've accumulated lots
of knowledge in our, in our species and, and you
know, in a sense, every time we, every time we have an
a kind of automation, a building of tools, it becomes less necessary
to know that whole tower. And it becomes something where
you can just use a tool to get to the top of that tower. I think that, you know, the
thing that is ultimately, you know, when we think about, okay, what do the AIs do versus
what do the humans do? It's like AIs you tell 'em, you say go achieve this
particular objective. Okay? They can maybe figure out a
way to achieve that objective. We say, what objective
would you like to achieve? The AI has no intrinsic idea of that. It's not a defined thing. That's a thing which has to
come from some other, you know, some other entity. And insofar as we are in charge, so to speak, or whatever it is, and our kind of web of society
and history and so on is the thing that is defining what
objective we want to go to. That's, you know, that that's, that's a thing that we humans
are necessarily involved in. - To push back a little bit, don't you think that GPT,
feature versions of GPT would be able to give a good answer
to what objective would you like to achieve. - From on what basis? I
mean, if they say, look, here's the terrible thing
that could happen. Okay, they're taking the average
of the internet and they're saying, you know, from the
average of the internet, what do people want to do? - Well, that's the, the Elon Musk artage of the
most entertaining outcome is the most likely. - [Wolfram] Okay. That could be got that one from him. Yeah. - That could be, that could
be one objective is maximize global entertainment. The dark version of that is drama. The, the, the good version of that is fun. - Right. So I mean this, this
question of what, you know, if you say to the AI, you know, what does the species want to achieve? - [Fridman] Yes. Okay. There'll be an answer. Right? - There'll be an answer. It'll be what the average of
the internet says the species wants to achieve. - Well, well, let's, let's, let's, I think you're using the word
average very loosely there, - [Wolfram] I am. - So I think you, I think the answers will become
more and more interesting as these language models are
trained better and better. - No, but I mean, in the end it's a reflection back of what we've already said. - Yes. But it's, there's a deeper wisdom to
the collective intelligence, presumably than each individual. - [Wolfram] Maybe. - Isn't that what we're
trying to, as society? - [Wolfram] To, to have,
well, I mean that's, that's a, that's an important No, no,
this is an interesting question. I mean, in, you know, insofar
as some of us, you know, work on trying to innovate and
figure out new things and so on, it is sometimes it's a, it's a complicated interplay
between sort of the individual doing the crazy thing, often
some, some spur, so to speak, versus the collective that's
trying to do sort of the, the, the, the high inertia average thing. And it's, you know, sometimes
the collective, you know, is, is bubbling up things that
are interesting and sometimes it's pulling down kind of the
attempt to make this kind of innovative direction. - Well, don't you think the large
language models would see beyond that simplification? We'll say maybe intellectual
and career diversity is really important. So you need the crazy
people on the outlier, on the outskirts, right? And so, like the actual, what's the purpose of this whole
thing is to explore through this kind of dynamics that
we've been using as a human civilization, which is most
of us focus on one thing, and then there's the crazy
people on the outskirts doing the opposite of that one thing. And you kind of like pull
the whole society together. There's the mainstream science
and then there's the crazy science, and that's just been the, the history of human civilization. And maybe the AI system
will be able to see that. And the more and more impressed
we are by a language model telling us this, the more control we'll give
it to it and the more we'll be willing to let it run our society. And hence there's this kind of loop where the society could be manipulated to let the AI system run it. - Right. Well, I mean, look, one, one of the things that's sort
of interesting is we might say, we always think
we're making progress, but yet if you know in
a sense, by by saying, let's take what already exists
and use that as a model for what should exist. - [Fridman] Yeah. - Then, you know, it's
interesting that for example, you know, many religions have
taken that point of view. There is a, you know, a sacred book that got written
at Time X and it defines how people should act for all future time. And that's, you know,
it's a, it's a model that, that people have operated with. And in a sense, you know, this is a version of that,
that kind of statement. It's like, take the 2023 version of sort
of how the world has exposed itself and use that to
define what the world should do in the future. - But it's not, it's an
imprecise definition, right? Because just like with
religious text and which GPT the human interpretation of
what GPT says will be the, will be the perturbation in the system. It'll be the noise, it'd be full of uncertainty. It's not like GPT will tell
you exactly what to do. It'll tell you approx a
narrative of what, like a, a, you know, it's like a turn the other
cheek kind of narrative, right? That's, that's not a fully
instructive narrative. - [Wolfram] Well, until, until the AI control all
the systems in the world. - They will be able to very
precisely tell you what to do. - [Wolfram] Well. They'll do
what they, you know, they'll, they'll just do this or that
thing and that and that. And, and not only that, they'll be auto suggesting
to each person, you know, do this next, do that next. So I think it's a, it's a slightly more
prescriptive situation than one has typically seen. But I, you know, I think this, this whole question of sort of what, what's left for the humans, so to speak, to what extent do we, you know, this idea that there is an
existing kind of corpus of purpose for humans defined by
what's on the internet and so on, that's an important thing. But then the question of sort of, as we explore what we can think of as the computational universe, as we explore all these different
possibilities for what we could do, all these different inventions, we could make all these different things. The question is which ones
do we choose to follow? Those choices are the
things that in a sense, if the humans want to still
have kind of human progress, that's what we, we get to make
those choices, so to speak. In other words, the, the,
there's this idea, if you say, let's take the kind of what exists today and use that as the determiner of all of what there is in the future, the thing that is sort of
the opportunity for humans is there will be many
possibilities thrown up. There are many different
things that could happen. It'll be done. And the insofar as we
want to be in the loop, the thing that makes sense for
us to be in the loop doing is picking which of those
possibilities we want. - But the degree to which
there's a feedback loop of the idea that we're picking something starts becoming questionable because we're influenced by the various systems. - [Wolfram] Absolutely. - The, like, if that becomes more and more
source of our education and wisdom and knowledge. - Right. The AI take
over, I mean my, you know, I've thought for a long time
that, you know, it's the, you know, AR auto suggestion that's really the thing that
makes the AIs take over. It's just then the humans just follow, you know? Yeah. - We'll no longer write
emails to each other. We'll just send the auto suggested email. - Yeah, yeah. But the thing where humans are potentially in the loop is when there's a choice and
when there's a choice, which we could make based
on our kind of whole web of history and so on. - [Fridman] Yeah. - And, and that's, you
know, that's insofar as it's all just, you
know, determined, you know, the humans don't have a place. And, and by the way, I mean,
you know, at, at some level, you know, it's all kind of a, a complicated philosophical
issue because at some level the universe is just doing what it does. We are parts of that universe
that are necessarily doing what we do, so to speak, yet we feel we have sort of
agency in what we're doing. And that's, that's its own separate
kind of interesting issue. - And we also kind of feel like we're the final destination
of what the universe was meant to create. But we very well could be, and likely are some kind of
intermediate step, obviously. - [Wolfram] Yeah. - What we're, we're most
certainly some intermediate step. The question is if there's
some cooler, more complex, more interesting things that's
going to be materialized. - [Wolfram] The computational universe is full of such things. - But in, in our particular
pocket specifically, if this is the best we've gotta
do or not, that's kind of a. - We can make all kinds of
interesting things in the computational universe. We, when we look at them, we say, yeah, you know, that's,
that's a thing we don't, it doesn't really
connect with our current, our current way of thinking about things. I mean, it's like in
mathematics, you know, we've got certain theorems. They're about three or 4
million that that human mathematicians have written
down and published and so on. But they're an infinite number of possible mathematical theorems. We just go out into the universe
of possible theorems and pick another theorem. And then people will say, well, you know, that's, you know,
they look at it and they say, I don't know what this theorem means. It's not connected to the
things that are part of kind of the web of history that
we're dealing with. You know, I think one, one point to make about sort
of understanding AI and its relationship to us is as
we have this kind of whole infrastructure of AI is doing
their thing and doing their thing in a way that is perhaps not readily understandable by us humans. You know, you might say that's a, that's a very weird situation. How can we have built this
thing that behaves in a way that we can't understand that's
full of computational disability, et cetera,
et cetera, et cetera. You know, what, what is this, what's it gonna feel like when
the world is run by AIs whose operations we can't understand? And the thing one realizes is actually, we've seen this before. That's what happens when we
exist in the natural world. The natural world is full of
things that operate according to definite rules. They have all kinds of, you know, computational irreducibility, we don't understand what the natural world is doing occasionally. And, and you know, when you say, you know, are the AI gonna wipe us out, for example? Well, it's kind of like, is the machination of the AI
going to lead to this thing that eventually comes
and destroys the species? Well, we can also ask the same thing about the natural world or the machination of the natural
world going to eventually lead to this thing that's
going to, you know, make, make the earth explode
or something like this. Those are, those are questions, those are, and insofar as we think we
understand what's happening in the natural world, that's a result of science
and natural science and so on. One of the things we can
expect when there's this giant infrastructure of the AIs is
that's where we have to kind of invent a new kind of natural
science that kind of is the natural science that explains
to us how the AIs work. I mean, it's kind of like we can, we can, you know, we have a, I don't know, a horse or something, and we're trying to get it
to, we're trying to, you know, ride the horse and go from here to there. We don't really understand
how the horse works inside, but we can get certain
rules and certain, you know, approaches that we take to, to persuade the horse to go
from here to there and, and, and take us there. And that's the same type of
thing that we're kind of dealing with, with the sort of
incomprehensible computationally, irreducible AIs. But we can identify these kinds of, we can find these kind of
pockets of reducibility that we can kind of, you know, that, I don't know, we grabbing onto the mane of
the horse or something to be able to, to ride it. Or we figure out, you know, if
we, if we do this or that to, to ride the horse, that that's
a, a, a successful way to, to get it to do what, what
we're interested in doing. - There does seem to be a
difference between a horse and a large language model or something that could be called Agi connected to the internet. So lemme just ask you about big
philosophical question about the threats of these things. There's a lot of people
like Eliezer Yudkowsky, who worry about the existential
risks of AI systems. Is that something that
you worry about? You know, sometimes when you're building
an incredible system, like, well, from Alpha, you can
kind of get lost in it. - I try and think a little bit about the implications
of what one's doing. - You know, it's like the Manhattan
project kind of situation where you're like, it's some of the most incredible physics in engineering being done. But it's like, huh, where's this gonna go? - I think some of these arguments
about kind of, you know, they'll always be a smarter AI. There'll always be, you know, and eventually the
AIs will get smarter than us, and then all sorts of terrible
things will happen to me. Some of those arguments remind
me of kind of the ontological arguments for the existence
of God and things like this. They're kind of arguments that
are based on some particular model, fairly simple
model, often of kind of, there is always a greater
this, that and the other. You know, this is, and that's, you know, those arguments tend, what tends to happen in the
sort of reality of how these things develop is that
it's more complicated than you expect. That the kind of simple,
logical argument that says, oh, eventually there'll
be a super intelligence, and then it will, you know, do this. And that turns out not
to really be the story. It turns out to be a
more complicated story. So for example, here's an example of an issue. Is there an apex intelligence, just like there might be an
apex predator in some, you know, ecosystem. Is there gonna be an apex intelligence, the most intelligent thing
that there could possibly be? Right? I think the answer is no. And in fact, we already know
this, and it's a kind of a, back to the whole computational
irreducibility story. There's kind of a, a
question of, you know, even if you have, if you,
if you have sort of a, a touring machine and you
have a touring machine that, that runs as long as possible
before, before it halts, you say, is this the machine? Is this the apex machine that does that? There will always be a
machine that can go longer. And as you go out to the
infinite collection of possible touring machines, you'll
never have reached the end, so to speak. You'll never, you'll always be able to, it's kind of like the same same
question of whether there'll always be another invention. Yeah. Will you always be able
to invent another thing? The answer is yes, there's an infinite tower
of possible inventions. - That's one definition of apex. But the, the other is like,
which I also thought you were, which I also think might be true, is, is there a species that's
the apex intelligence right now on earth? So it's not trivial to
say that humans are that. - Yeah, it's not trivial. I agree. It, it's, you know, I think one of the things that I, I've long been curious about
kind of other intelligences, so to speak. I mean, I, you know, I, I view intelligence is like
computation and it's kind of a, you know, you're sort of,
you have the set of rules, you deduce what happens. I have tended to think now
that there's this sort of specialization of
computation that is sort of a consciousness like thing
that has to do with these, you know, computational boundedness, single thread of experience, these kinds of things that
are the specialization of computation that corresponds
to a somewhat human-like, experience of the world. Now the question is,
so, so that's, you know, there may be other intelligences
like, you know, you know, the aphorism, you know, the
weather has a mind of its own, it's a different kind of
intelligence that can compute all kinds of things that are
hard for us to compute, but it is not well aligned
with us with the way that we think about things. It doesn't, it doesn't, it doesn't think the way
we think about things. And you know, in this idea of different, different intelligences,
every different mind, every different human mind is
a different intelligence that thinks about things in different ways. And you know, in, in terms of the kind of
formalism of our physics project, we talk about this idea of rural space, the space of all possible
sort of rural systems and different minds are in a sense
of different points in rural space, human minds. Ones that have grown up with
the same kind of culture and ideas and things like
this might be pretty close in rural space. Pretty easy for them to communicate. Pretty easy to translate, pretty easy to move from one
place in rural space that corresponds to one mind, to another place in rural space
that corresponds to another sort of nearby mind when we
deal with kind of more distant things in rural space,
like, you know, the, the pet cats or something, you know, the pet cat has some aspects
that are shared with us. The emotional responses
of the cat are somewhat similar to ours, but the cat is further away in
rural space than people are. And so then the question is, you know, can we identify sort of the, can we make a translation from
our thought processes to the thought processes of, of a
cat or something like this? And you know, what, what will
we get when we, you know, what, what will happen when we get there? And I think it's the case
that that many, you know, many animals, I don't know,
dogs for example, you know, they have elaborate olfactory systems. They, you know, they, they have sort of the smell
architecture of the, of the, of the world, so to speak,
in a way that we don't. And so, you know, if, if you were sort of talking
to the dog and you could, you know, communicate in a
language, the dog will say, well, this is a, you know, a a, you know, a, a flowing smelling this,
that, and the other thing, concepts that we just
don't have any idea about. Now what's what's interesting
about that is one day we will have chemical sensors that
do a really pretty good job. You know, we'll have artificial
noses that work pretty well, and we might have our augmented
reality systems show us kind of the same map that the dog
could see and things like this. So the, you know, it's similar to what
happens in the dog's brain. And eventually we will have
kind of expanded in rural space to the point where we will
have those same sensory experiences that dogs have, and we will have internalized
what it means to have, you know, the smell landscape or whatever. And, and so then we will have
kind of colonized that part of rural space until, you know,
we haven't gone, you know, some things that, that, you
know, animals and so on. Do we sort of successfully
understand, others We do not. And the question of, of what
kind of, what is the, you know, what, what representation, you know, how, how do we convert things that
animals think about to things that we can think about?
That's not a trivial thing. And you know, I've,
I've long been curious. I've had a very bizarre
project at one point of, of trying to make an iPad game that a cat could win against its owner. - Right. So it feels like there's a deep philosophical goal there, though. - Yes, yes. I mean, the, the, you know,
I was curious if, you know, if pets can work in Minecraft or something and can construct things, what will they construct and
will what they construct be something where we look
at and we say, yeah, I recognize that. Or will it be something that
looks to us like something that's out there in the
computational universe that one of my, you know, cellular automat might have
produced where we say, oh, yeah, I can kind of see it operates
according to some rules. I don't know why you
would use those rules. I don't know why you would care. - Yeah. I actually, just
to link on that, seriously, is there a connector in
the royal space between you and a cat where the cat
could legitimately win? So iPad is a very limited interface. - [Wolfram] Yeah, I, I- - I wonder if there's
a game where cats win. - I think the problem is that
cats don't tend to be that interested in what's
happening on the iPad. - [Fridman] Yeah. That's
an interface issue. - Yeah. Right, right, right. No, I think it is likely
that, I mean, you know, there are plenty of animals
that would successfully eat us if we were, you know, if
we were exposed to them. And so there's, you know, it, it's gonna pounce faster than we can get out of the way and so on. So there, there are plenty of, and, and probably it's going to, you know, we think we've hidden ourselves, but we haven't successfully
hidden ourselves. - That's a physical strength. I wonder if there's something
in more in the realm of intelligence where an animal
like a cat could out-. - Well, I think there are things
certainly in terms of the, the speed of processing certain
kinds of things, for sure. I mean, the, the question
of what, you know, is there a game of chess, for
example, is there cat chess? That the cats could
play against each other. And if we tried to play
a cat, we'd always lose, I don't know. - It might have to do with speed it, but it might have to
do with concepts also. It might be concepts in the cat's head. - I, I tend to think that our
species from its invention of language has managed to build
up this kind of tower of abstraction that for
things like a chess like game will make us win. In other words, we've become through the fact
that we've kind of experienced language and learnt abstraction, you know, we've sort of become smarter
at those kinds of abstract kinds of things. Now, you know, that doesn't make us smarter at catching a mouse or something. It makes us smarter at the
things that we've chosen to, to sort of con, you know, to
concern ourselves, which are, are these kinds of abstract things. And, and I think, you know, this is again, back to the question of of, you know, what does one care about? You know, if one's a, if one's the, you know, the cat, if you, if you have the discussion
with a cat, if we can, if we can translate things
to have the discussion with a cat, the cat will say, you know, I'm very excited that
this light is moving. And we'll say, why do you care? And the cat will say, that's the most important
thing in the world. That this thing moves around. I mean, it's like when you
ask about, I don't know, you, you look at archeological
remains and you say, these people had this, you
know, belief system about this. And, you know, that was the most important
thing in the world to them. And, and now we look at it and say, we don't know what the point of it it was. I mean, I, I've been curious, you know, there are these hand prints on caves from 20,000 or more years ago, and it's like nobody knows
what these hand prints were there for. You know, that they may
have been a representation of the most important
thing you can imagine. They may just have been
some, you know, some kid who, who rubbed their hands in the
mud and stuck 'em on the walls of the cave. You know, we don't, we don't know. And I think, but this, this
whole question of what you know, is when you say this question
of sort of what's the smartest thing around, there's the question of what
kind of computation are you trying to do? If you're saying, you know, if you say you've got some
well-defined computation and how do you implement it? Well, you could implement
it by nerve cells, you know, firing. You can implement it with
silicone and electronics. You can implement it by some
kind of molecular computation process in the human immune
system or in some molecular biology kind of thing. They're different ways to implement it. And you know, I think this question of, of of sort of which, you know, those different implementation methods will be of different speeds. They'll be able to do
different things if you say, you know, which, so an
interesting question would be, what kinds of abstractions
are most natural in these different kinds of systems? So for a cat, it's for example, you know, the visual scene that we
see, you might, you know, we pick out certain objects,
we recognize, you know, certain things in that visual scene. A cat might in principle,
recognize different things. I, I suspect, you know, evolution, biological evolution is very slow. And I suspect what a cat
notices is very similar. And we even know that
from some neurophysiology, what a cat notices is very
similar to what we notice. Of course, there's a, you know, one obvious difference is cats have only two kinds of color receptors. So they don't see in the same
kind of color that we do now, you know, we say we are,
we're, we're better. We have three color receptors,
you know, red, green, blue. We're not the overall winner. I think the, the, I think the mantis shrimp
is the overall winner with 15 color receptors, I think. So it can, it can kind of make distinctions
that with our current, you know, like the mantis
shrimp's view of reality is in, at least in in terms of color
is much richer than ours now. But what's interesting
is how do we get there? So imagine we have this
augmented reality system that is even, you know, it's
seeing into the infrared, into the ultraviolet, things like this. And it's translating that into
something that is connectable to our brains, either through our eyes or
more directly into our brains. You know? Then eventually our kind of
web of the types of things we understand will extend to
those kinds of constructs just as they have extended. I mean, there are plenty
of things where we see them in the modern world, because we made them with technology and now we understand what that is. But if we'd never seen that kind of thing, we wouldn't have a way to describe it. We wouldn't have a way to
understand it and so on. - All right. So that actually stemmed from
our conversation about whether AI's gonna kill all of us. And you, we've discussed this kind
of spreading of intelligence through rural space that in
practice it just seems that things get more complicated. Things are more complicated
than the story of, well, if you build a thing that's
plus one intelligence, that thing will be able to
build the thing that's plus two intelligence and plus three intelligence. And that will be exponential. It'll become more intelligent, exponentially faster and so on until it completely destroys everything. But you know, that intuition
might still not be so simple, but might still care carry validity. And there's two interesting
trajectories here. One, a super intelligence system remains in rural proximity to humans to where we're like, holy crap, this thing is really intelligent,
let's select the present. And then there could be perhaps
more terrifying intelligence that starts moving away. They might be around
us now that are moving far away in rural space, but they're still sharing
physical resources with us, right? - [Wolfram] Yes. Yes. And so they can rob us of
those physical resources and destroy humans just kind of casually. - [Wolfram] Yeah. - Just, just- - [Wolfram] Like nature could. - Like nature could. But it seems like there's
something unique about AI systems where there is this kind of exponential growth. Like the way, well sorry,
nature has so many things in it. One of the things that nature has, which is very interesting,
are viruses for example. There is systems within
nature that have this kind of exponential effect and
that terrifies us humans. Because again, you know, there's only 8 billion of
us and you can just kinda, it's not that hard to just kind
of whack 'em all real quick. So I mean, is that
something you think about? - [Wolfram] Yeah, I've
thought about that. Yes. - The threat of it. I mean, are you as concerned about it as somebody like Eliezer Yudkowsky for example, just big, big painful negative
effects of AI on society? - You know? No, but perhaps that's cause
I'm intrinsically an optimist. - [Fridman] Yeah. - I mean, I think that
there are things, I, I think the thing that one, you know, one sees is there's going
to be this one thing and it's going to just zap everything. - [Fridman] Yeah. Somehow, you know, I maybe I have faith in
computational irreducible, so to speak, that there's always unintended
little corners that, you know, it's just like somebody
says, I'm going to, well, I don't know. Somebody has some, some bio weapon and they say, we're gonna release this and
it's going to do all this harm. But then it turns out it's
more complicated than that because, you know, the kind of, some humans are different and you know, the exact way it works
is a little different than you expect. It's something where sort of the, the, the great big you, you know, you smash the thing with
something, you know, you, the asteroid collides with the earth. - [Fridman] Yeah. - And it kind of, you
know, and yes, you know, the earth is cold for two years
or something and you know, then lots of things die,
but not everything dies. And it's, you know, there's,
there's usually, I mean, I I kind of, this is in a sense the sort of story of computational disability. There are always unexpected corners. There are always unexpected consequences. And I don't think that the
kind of whacked over the head with something and then
it's all gone is, you know, that can obviously happen. The earth can be swallowed up
in a black hole or something, and then it's kind of, presumably,
presumably all over the, but, but, you know, I think
this question of, of what, you know, what, what do I
think the realistic paths are? I think that there will
be sort of an increasing, I mean the, the people have to get used to phenomena like computational reducibility. There's an idea that we
built the machines so we can understand what they do, and we are, we are going to be able
to control what happens. Well, that's not really right. Now the question is, is the result of that lack of
control going to be that the machines kind of conspire
and sort of wipe us out? Maybe just because I'm an optimist, I don't tend to think
that that's, you know, that's in the cards. I think that the, you know, as a realistic thing,
I, I suspect, you know, what will sort of emerge maybe
is kind of an ecosystem of the AIs, just as you know,
again, I I don't really know. I mean, this is something
it's, it's hard to, it's hard to be clear
about what will happen. I mean, I think that
there, you know, there are, there are a lot of sort
of details of, you know, what could we do? What systems in the world
could we connect an AI to, you know, I have to say, I was just a couple of days
ago I was working on this ChatGPT plugin kit that we
have for orphan language. Okay? Where you can, you know, you can create a plugin and it
runs well from language code and it can run Wolfram Language code back on your own computer. And I was thinking, well, I
can just make it, you know, I can tell ChatGPT create a piece of code, and then just run it on my computer. And I'm like, you know, that, that sort of personalizes
for me the what could, what could possibly go wrong, so to speak. - Was that exciting or
scary, that possibility? - It was a little bit scary actually, because it's kind of like,
like I realize I'm, I'm, I'm delegating to the AI,
just write a piece of code, you know, you are in charge,
write a piece of code, run it on my computer, and
pretty soon all my files can, that's like delete. - That's like a, that's
like Russian roulette, but like much more
complicated version of that. - Yes, yes, yes. Right. - That's a good drinking
game. I don't know. - Right, I mean that, that's why, it's an interesting question
then if, if you do that right? Yeah. What is the sandboxing
that you should have? And that's sort of a,
that's a, a version of, of that question for the world. That is, as soon as you put
The AIs in charge of things, you know, how much, how many constraints should
there be on these systems before you put the ais in charge of
all the weapons and all these, you know, all these
different kinds of systems. - Well, here's the fun
part about sandboxes is the AI knows about them. It has the tools to crack them. - Look, the fundamental
problem of computer security. Is computational irreducibility. Because the fact is, any
sandbox is never any, you know, it's never gonna be a perfect
sandbox If you want the system to be able to do interesting things. I mean, this, this is the
problem that's happened, the generic problem of computer security, that as soon as you have your, you know, firewall that is sophisticated
enough to be a universal computer, that means it can do anything. And so long as if you find
a way to poke it so that you actually get it to do that
universal computation thing, that's the way you kind of crawl
around and get it to do the thing that it wasn't intended to do. And that's sort of a, another version of computational
irreducibility, is you can, you know, you can kind of, you get it to do the thing
you didn't expect it to do, so to speak. - There's so many
interesting possibilities here that manifest themselves from the compute computational
reducibility here that it's just so many things
can happen because in digital space things move so quickly.
You can have a chat bot, you can have a piece of code
that you could basically have ChatGPT generate viruses
accidentally or on purpose and they digital viruses. - [Wolfram] Yes. - And they could be brain viruses too. They, they convince kind
of like phishing emails. - [Wolfram] Yes. - They can convince you of stuff. - Yes. And no doubt you can, you know, in a sense we've had the, the loop of the machine learning
loop of making things that convince people of things. Is surely going to get easier to do. - [Fridman] Yeah. - And you know, then
what does that look like? Well it's again, you know, we,
humans are, you know, we're, this is a new environment
for us and admittedly it's an environment which a little bit scarily is, is changing much more rapidly
than, I mean, you know, people worry about, you know, climate change is gonna happen
over hundreds of years and, you know, the environment is changing, but the environment for, you know, in the, the kind of digital
environment might change in six months. - So one of the relevant concerns here in terms of the impact of GPT on society is the nature of truth that's
relevant to Wolfram Alpha. Because computation
through symbolic reasoning that's embodied in Wolfram
Alpha as the interface. There's a kind of sense
that what Wolfram Alpha tells me is true. - So we hope. - Yeah, I mean you could
probably analyze that. You could show you can't prove
that's always gonna be true computation reducibility, but it's gonna be more true than not. - It's, look, the fact is it will be the
correct consequence of the rules you've specified and
insofar as it talks about the real world, you know, that is our job in sort of
curating and collecting data to make sure that that data is
quotes as true as possible. Now what does that mean? Well, you know, it's always an interesting question. I mean, for us, our operational definition
of truth is, you know, somebody says, who's the best actress? Who knows? But somebody won the Oscar. And that's a definite fact. And so, you know, that's the kind of thing that
we can make computational as a piece of truth. - [Fridman] Yeah. - If you ask, you know,
these things which, you know, a sensor measured this
thing, it did it this way, a machine learning system, this particular machine learning system recognized this thing. That's a, that's a sort
of a definite a fact, so to speak. And that's, you know, there are, there is a good network of
those things in the world. It's certainly the case that, particularly when you say
is so-and-so a good person. You know, that's a, that's
a hopelessly you know, we might have a computational
language definition of good. I don't think it'd be very
interesting cause that's a very messy kind of concept. Not really amenable to
kind of, you know, the, I think as far as we will get
with those kinds of things is I want X There's a kind of meaningful
calculus of I want X and that has various consequences. I mean, I'm not sure, I haven't, I haven't thought this
through properly, but I think, you know, a concept like,
is so-and-so a good person? Is that true or not? That's a mess. - That's a mess That's
amenable to computation. I think, I think it's a mess when humans
try to define what's good, like through legislation. But when humans try to
define what's good through literature, through history
books, through poetry, it starts being, well. - I don't know. I mean
that particular thing, it's kind of like, you know, we're, we're we're going into
kind of the ethics of what, what counts as good, so to speak. And, you know, what do we
think is right and so on. And I think that's a, a
thing which, you know, one feature is we don't
all agree about that. There's no theorems about
kind of, you know, there's no, there's no theoretical
framework that says this is, this is the way that ethics has to be. - Well first of all, there's stuff we kind of agree
on and there's some empirical backing for what works and
what doesn't from just even the morals and ethics within religious texts. So we seem to mostly
agree that murder is bad, the certain universals
that seem to emerge. - I wonder whether the
murder of an AI is bad. - Well, I tend to think yes, but, and I think we're gonna have
to contend with that question. Oh, and I wonder what AI would say. - Yeah. Well I think, you
know, one of the things with, with AI is, is it's one thing to wipe
out that AI that is only, you know, has no owner. You can easily imagine an AI
kind of hanging out on the, on the, you know, on, on the internet without
having any particular owner or anything like that. And then you say, well, well
what harm does it, you know, it, it's, it's okay to get rid of that AI. Cause if the AI has 10,000
friends who are humans and all those, you know, all those 10,000 humans
will be incredibly upset that this AI just got exterminated. It becomes a slightly
different, more entangled story. But yeah, no, I think that, that this question about
what do humans agree about? It's, you know, there are certain, there's certain things that, you know, human laws have tended to
consistently agree about. You know, there have been times in
history when people have sort of gone away from certain kinds of laws, even ones that we would now say, how could you possibly have
not not done it that way? You know, that just
doesn't seem right at all. But I think, I mean this question of what
I don't think one can say beyond saying, if you have a set of rules that
will cause the species to go extinct, that's probably, you know, you could say that's probably
not a, a winning set of laws. Because even to have a thing
on which you can operate laws requires that the species not be extinct. - But between sort of what's the distance between Chicago and New York that Wolfram Alpha can answer and the question of if this person is good or not, there seems to be a lot of gray area. And that starts becoming
really interesting. I think your, since the creation of Wolfram
Alpha have been a kind of arbiter of truth at a, at a large scale. So this system is
generates more truth than. - Try to make sure that
the things are true. I mean, look, as a practical matter when people write computational contracts and it's kind of like, you know, if this happens in the
world, then do this. And this hasn't developed as, as quickly as it might have done. You know, this has been a
sort of a blockchain story in part and so on. Although blockchain's not
really necessary for the idea of computational contracts. But you can imagine that
eventually sort of a large part of what's in the world are these
giant chains and networks of computational contracts
and then something happens in the world. And this whole giant domino
effect of contracts firing autonomously that cause
other things to happen. And you know, for us, you know, we've been the main sort
of source, the oracle of, of quotes facts or truth or
something for things like blockchain, computational
contracts and such. Like, and there's a question
of, you know, what, you know, I consider that responsibility to actually get the stuff right. And one of the things
that is tricky sometimes is when is it true? When is it a fact? When is it not a fact? - Yes. - I think the best we can
do is to say, you know, we, we have a procedure, we
follow the procedure, we might get it wrong, but at least we won't be
corrupt about getting it wrong, so to speak. - So that's beautifully
put and have a transparency about the procedure. The problem starts to emerge
when the things that you convert into computational
language start to expand, for example, into the realm of politics. So this is where it's
almost like this nice dance of Wolfram Alpha and ChatGPT, like you said is shallow and broad. So it's, it's, it's gonna give
you an opinion on everything. - But it writes fiction as well as fact, which is exactly how it's
built. I mean that's exactly, it is making language and it
is making both even in code, it writes fiction. I mean, it's kind of fun
to see sometimes, you know, it'll write fictional war
from language code. Yeah. That, that it kind of. - [Fridman] It kinda looks right. - Yeah it looks right,
but it's actually not pragmatically correct. But, but yes, it's, it's a, it has a view of kind of
roughly how the world works at, at the same level as, as books of fiction talk about
roughly how the world works. They just don't happen to be
the way the world actually worked or whatever. But yes, that, that's, no, I, I agree that's sort of a, you
know, we are attempting with, with our whole, you
know, Wolfram language, computational language
thing to represent at least, well it's either, it doesn't necessarily have have to be how the actual world works. Cause we can invent a set of
rules that aren't the way the actual world works and run those rules. But then we are saying we are
going to accurately represent the results of running those rules, which might or might not be
the actual rules of the world, but we also are trying to
capture features of the world as accurately as possible to represent what happens in the world. Now again, as we've
discussed, you know, the, the atoms in the world
arranged, you know, you say, I don't know, you know, was
there a tank that showed up? You know, that, that, you
know, drove somewhere. Okay, well, you know, what is a tank? It's an arrangement of
atoms that we abstractly describe as a tank. And you could say, well, you know, there's some arrangement of
atoms that is a different arrangement of atoms, but
it's, and it's not, you know, we didn't, we didn't decide. It's like this observer theory
question of, you know, what, what arrangement of atoms counts as a tank versus not a tank. - So there's, there's even things that
would consider strong facts. You could start to kind of
disassemble them and show that they're not- - Absolutely. I mean, so, so the
question of whether, oh, I don't know, was this gust of wind
strong enough to blow over this particular thing? Well, a gust of wind is
a complicated concept. You know, it's full of little pieces
of fluid dynamics and little vortices here and there. And you have to define, you know, was it, you know what the aspect of
the gust of wind that you care about might be it put this
amount of pressure on this, you know, blade of some, some, you know, wind turbine or something. And you know that that's
the, and but, but you know, if you say, if you have something which is
the fact of the gust of wind was this strong or whatever,
that, you know, that is you, you, you have to have
some definition of that. You have to have some
measuring device that says, according to my measuring
device that was constructed this way, the gust of wind was this. - So what can you say
about the nature of truth that's useful for us
to understand chat GPT? Because you've been con, you've
been contending with this idea of what is fact and not, and it seems like ChatGPT
is used a lot now, I've seen it used by
journalists to write articles. And so you have people that
are working with large language models trying to desperately
figure out how do we essentially censor them
through different mechanisms, either manually or through
reinforcement learning with human feedback, try to align them to, to not say fiction, just to say non-fiction
as much as possible. - Well this is the importance
of computational language as an intermediate, it's kind of like you've got
the large language model, it's able to surface
something which is a formal, precise thing. That you can then look at and
you can run tests on it and you can do all kinds of things. It's always gonna work the same way. And it's precisely defined what it does. And then the large language
model is the interface. I mean, the way I view
these large language models, one of their important, I mean, there are many use cases and you know, it's a remarkable thing to
talk about some of these, you know, literally, you know, every day we're coming up with
a couple of new use cases, some of which are very, very, very surprising and things where, I mean, but the best use cases are
ones where it's, you know, even if it gets it roughly
right, it's still a huge win. Like a use case we had from
a week or two ago is read our bug reports. You know, we've got hundreds of thousands of bug reports that have, we've accumulated over decades. And it's like, you know, can we have it just read the bug report, figure out where the, where
is the bug likely to be? And, you know, hone in
on that piece of code. Maybe it'll even suggest
some, some, you know, sort of way to fix the code. It might get that, it might be nonsense what it
says to about how to fix the code, but it's incredibly
useful that it was able to, you know. - [Fridman] Yeah. So awesome. It's so awesome because even
the nonsense will somehow be instructive. I don't, I don't
quite understand that yet. I've, I've, yeah, there's so many
programming related things. Like for example, translating from one programming
language to another is really, really interesting. It's extremely effective, but then you, the failures reveal the path forward also. - Yeah. But I think, I mean
the, the, the big thing, I mean in, in that kind of discussion, the unique thing about our
computational language is it was intended to be read by humans. - [Fridman] Yes. That's really important. - Right? And so it has this
thing where you can, but, but you know, thinking about sort of
ChatGPT and its use and so on. The, one of the big things about it, I think is it's a
linguistic user interface. That is, so a typical use case might be, and then take the journalist
case for example, it's like, let's say I have five facts
that I'm trying to turn into an article, or I'm trying to, I'm trying to write a report
where I have basically five facts that I'm trying to
include in this report. But then I feed those
five facts to ChatGPT, it puffs them out into
this big report and then, and then that's a good
interface for another. If I just gave, if I just had in my terms, those five bullet points
and I gave 'em to some other person, the person will say, I dunno what you're talking
about because these are, you know, this is your version of this
sort of quick notes about these five bullet points. But if you puff it out into this thing, which is kind of connects to
the collective understanding of language, then somebody else
can look at it and say, okay, I understand what you're talking about. Now you can also have a situation
where that thing that was puffed out is fed to another
large language model. You know, it's kind of like, you know, you are applying for the permit
to, you know, I don't know, grow fish in someplace
or something like this. And it, you know, it it, and, and you have these facts that
you're putting in, you know, I'm gonna have a, a, you
know, I'm gonna, you know, have this kind of water and
I don't know what it's, yes. You just got a few bullet points. It puffs it out into this big
application, you fill it out. Then at the other end, the, you know, the Fisheries Bureau has another
large language model that just crushes it down because
the Fisheries Bureau cares about these three points and
it knows what it cares about. And it then, so it's really the, the natural language produced
by the larger language model is sort of a transport
layer that, you know, is really LLM communicates
with LLM I mean, it's kind of like the, you know, I write a piece of email
using my LLM and, you know, puff it out from the things
I want to say your LLM turns it into and the conclusion is X. Now the issue is, you know, that the thing is going to
make this thing that is sort of semantically plausible, and it might not actually
be what you, you know, it might not be kind of relate
to the world in the way that you think it should relate to the world. Now I, I've seen this, you
know, I, I've been doing, okay, I'll give you a couple of examples. I was doing this thing
when we announced this, this plugin for, for, for ChatGPT. I had this lovely example
of a math word problem, some complicated thing. And it did a spectacular job
of taking apart this elaborate thing about, you know, this person has twice as many
chickens as this, et cetera, et cetera, et cetera. And it turned into, into
a bunch of equations, it fed them to Wolfram language.
We solved the equations, everybody did great. We gave back the results. And I thought, okay, I'm gonna put this in this
blog post I'm writing. Okay. I thought I'd better just check. And turns out it got everything, all the hard stuff it got
right at the very end. Last two lines. It just completely goofed it
up and gave the wrong answer. And I would not have noticed
this same thing happened to me two days ago. Okay. So I, I thought, you know, I, I made this with this ChatGPT plugin kit. I made a thing that would emit a sound, would play a tune on my
local computer. Right. So ChatGPT would produce, you know, a series of notes and
it would play this tune on my computer. Very cool. Okay. So I thought, I'm gonna ask it play the tune
that Hal sang when Hal was being disconnected in 2001. Okay. So it, it, there it is. - Daisy. Was it Daisy? - Yes, Daisy, yes. Yeah. Right. So, so, okay. So I think, you know, and so it produces a bunch
of notes and I'm like, this is spectacular. This is amazing. And then I thought, you know, I was just gonna put it in
and then I thought I better actually play this. And so I did. And it was
Mary had a little Lamb. - Oh wow. Oh wow. But it was, Mary had a little lamb. - [Wolfram] Yeah. - Wow. So it was correct but wrong. It was, yeah. You could easily be mistaken. - Yes. Right. And in fact, I, I kind of gave the, I had this quote from Hal
to explain, you know, it's, it's as it the, the Hal you
know, states in the movie, you know, it's the Hal 9,000 is, you know, the thing was just a, a rhetorical device. Cause I'm realizing, oh my gosh, you know, this ChatGPT you know,
could have easily fooled me. I mean, it did this, it did all the, it did this amazing thing of
knowing this thing about the movie and being able
to turn that into the, the notes of the song,
except it's the wrong song. And you know, Hal in, in the
movie Hal says, you know, I think it's something
like, you know, no Hal nine, no 9,000 series computer
has ever been found to make an error. We are for all practical purposes perfect. And incapable of error. And I thought that was kind
of a charming sort of quote from, from Hal to make in connection with, with what ChatGPT had done in that case. - The interesting things
about the LLMs, like you said, that they are very willing
to admit the error. - Well, yes. I mean, that's a question of the RLH, the reinforcement learning
human feedback thing. - [Fridman] Oh, right. - That, that, that's,
you know, it's amazing. And LLM, the, the really remarkable thing
about chat GPT is, you know, I had been following what was
happening with large language models, and I'd played
with them a whole bunch, and they were kind of like, eh, you know, it's kind of like what you
would expect based on sort of sort of statistical
continuation of language. It's, it's interesting, but
it's not breakout exciting. And then I think the kind of
the, the kind of reinforcement, the, the human feedback,
reinforcement learning, you know, in making ChatGPT try and do
the things that humans really wanted to do that broke through. That kind of reached this
threshold where the thing really is interesting to us
humans, and by the way, it's interesting to see how, you know, you change the temperature,
something like that, the thing goes bonkers and it no longer is interesting to humans. It's producing garbage. And it's, it's kind of, right. It's somehow it managed to get this, this above this threshold where
it really is well aligned to what we humans are interested in. And, and, and kind of that
that's, and and I think, you know, nobody saw that coming. I think certainly nobody I've
talked to and nobody who was involved in, in that project seems to
have known that was coming. It's just one of these
things that is a sort of a remarkable threshold. I mean, you know, when
we built Wolfram Alpha, for example, I didn't
know it was gonna work. You know, we tried to build something
that would have enough knowledge of the world, that it could answer
reasonable set of questions, that we could do good enough
natural language understanding that typical things
you type in would work. We didn't know where that threshold was. I mean, I was not sure that it was the
right decade to try and build this, even the right, you know, 50 years to try and build it, you know? And I think that was, it's the same type of thing
with ChatGPT that I don't think anybody could have
predicted that, you know, 2022 would be the year that
this, this became possible. - I think, yeah, you tell a story about Marvin
Miske and showing it to him and saying no, like no, no, no. This time it actually works. - Yes. And I mean, it's, you know, it's the same thing for me looking at these large language models. It's like when, when people are first saying
first few weeks of ChatGPT is like, oh yeah, you know, yeah. I've seen these large language
models and then, you know, and then I actually try it
and you know, oh my gosh, it actually works. And I think it's, but it, but you know, the things, and the
thing I found, you know, I remember one of the first
things I tried was a write a persuasive essay that a wolf
is the bluest kind of animal. Okay. So it writes this thing and
it starts talking about these wolves that live on the
Tibetan plateau and, and named some Latin name and
so on. And I'm like, really? And I'm starting to look it
up on the web and it's like, well, it's actually complete nonsense, but it's extremely plausible. I mean, it's plausible enough that I
was going and looking up on the web and wondering if there
was a wolf that was blue. You know, I mentioned this on
some live streams I've done, and so people have been
sending me these pictures. - [Fridman] Blue wolves? - Blue wolves! - [Fridman] Maybe it onto something. Can you kind of give your wise
sage advice about what humans who have never interacted with AI systems, not even like with Wolfram Alpha, are now interacting with Chad
GPT because it, it becomes, it's accessible to a certain demographic. They may have not touched
AI systems before. What do we do with truth like
journalists, for example? Yeah. How do we think about
the output of these systems? - I think this idea, the idea that you're going
to get factual output is not a very good idea. I mean, it's just, this is not, it is a linguistic interface. It is producing language, and language can be
truthful or not truthful. And that's a, a different
slice of what's going on. I think that, you know,
what we see in, for example, kind of, you know, go check
this with your fact source, for example. You can do that to some extent, but then it's going to
not check something. It's going, you know, that is again, a thing that is sort of a, does it check in the right place? I mean, we, we see that in, you know, does it call the, you know, the Wolfram plugin in the right place? You know, often it does,
sometimes it doesn't. You know, I, I think the, the real thing to understand
about what's happening is, which I think is very
exciting, is kind of the, the great democratization
of access to computation. And, and you know, I think that when you look at sort of the, there's been a long period of
time when computation and the ability to figure out things
with computers has been something that kind of only
the only the druids at some level can, can achieve. You know, I myself have been involved
in trying to sort of deify access to computation. I mean, back before Mathematica
existed, you know, in 1988, if you were a, you know,
physicist or something like that, and you wanted to do a computation, you would find a programmer. You would go and, you know, delegate the, the computation
to that programmer. Hopefully they'd come back
with something useful. Maybe they wouldn't, there'd
be this long, you know, multi-week, you know,
loop that you go through. And then it was actually very,
very interesting to see 1988, you know, like first
people like physicists, mathematicians and so on than
other, lots of other people. But this very rapid transition
of people realizing they themselves could actually type
with their own fingers and, you know, make some piece of code that
would do a computation that they cared about. And, you know, it's been exciting to see lots
of discoveries and so on made by, by using that tool. And I think the same thing
is, you know, and we, we see the same thing, you know, Wolfram Alpha is dealing with, it is not as deep computation
as you can achieve with whole Wolfram language mathematical stack. But the thing that's, to me particularly exciting
about kind of the large language model linguistic interface
mechanism is it dramatically broadens the access to
kind of deep computation. I mean, it's, it's kind of like, one of the things I've sort
of thought about recently is, you know, what's gonna happen
to all these programmers? What's gonna happen to all
these people who, you know, a lot of what they do is write
slabs of boilerplate code. And in a sense, you know,
I've been saying for 40 years, that's not a very good idea. You know, you can automate
a lot of that stuff with a high enough level language, that slab of code that's
designed in the right way, you know, that slab of code turns into
this one function we just implemented that you can just use. So in a sense that the fact that there's, there's all of this activity
of doing sort of lower level programming is something,
for me, it seemed like, I don't think this is the right
thing to do, but, you know, and, and lots of people have
used our technology and, and not had to do that. But the fact is that that's, you know, so when you
look at, I don't know, computer science departments that have, that have turned into places
where people are learning the trade of programming, so to speak, it's, it's sort of a question
of what's gonna happen. And I think there are two dynamics. One is that kind of sort of
boiler plate programming is going to become, you know, it's going to go the way that
assembly language went back in the day of something where
it's really mostly specified by at a higher level. You know, you start with natural language, you turn it into a
computational language that's, you look at the computational
language, you run tests, you understand that's
what's supposed to happen. You know, if we do a great
job with compilation of the, of a, the, the, you know, of
the computational language, it might turn into LLVM
or something like this, but, you know, or, or
it just directly gets, gets run through the
algorithms we have and so on. But, but then, so that's kind of a, a, a tearing down of this kind
of this big structure that's been built of, of teaching
people programming. But on the other hand, the other dynamic is vastly
more people are gonna care about computation. So all those departments of, you know, art history or something that
really didn't use computation before now have the possibility
of accessing it by virtue of this kind of linguistic
interface mechanism. - And if you create an interface that allows you to interpret the debug and interact with a
computational language, then that makes it even more accessible. - Yeah. Well, I mean, the, the, I think the thing is that right
now, you know, the average, art history student or something
probably isn't going to, you know, they're not probably, they don't think they know about
programming and things like this, but by the time it really
becomes a kind of purely, you know, you just walk up to
it, there's no documentation. You start just typing, you know, compare these pictures with
these pictures and, you know, see the use of this color, whatever, and you generate this piece of, of computational language
code that gets run. You see the result. You say, oh, that looks roughly right. Or you say that's crazy. And maybe then you
eventually get to say, well, I better actually try and
understand what this computational language code did and, and that becomes a thing
that you learn, just like, it's kind of an interesting thing because unlike with mathematics, where you kind of have to
learn it before you can use it. This is a case where you can use it before you have to learn it. - Well, I got a sad possibility here, or maybe exciting possibility
that very quickly people won't even look at the computational language. They'll trust that it's
generated correctly as you get better and better at
generating that language. - Yes. I think that there will be
enough cases where people see, you know, cause you can
make it generate tests too. Yes. And and so you'll say
we've, we're doing that. I mean it's, it's a pretty
cool thing actually. But you, you, you know, say this is the code and you know, here are a bunch of examples
of running the code. Okay. People will at least
look at those and they'll say, that example is wrong. And you know, then it'll
kind of wind back from there. And I agree that, that the, the kind of the intermediate
level of people reading the computational language code, in some case people will do that. In other case, people
just look at the tests and or even just look at the results. And sometimes it'll be obvious
that you got the thing you wanted to get cause you were
just describing, you know, make me this interface
that has two sliders here. And you can see it has that,
those two sliders there. And that's, that's kind of, that's, that's the result you want. But I, I think, you know, one of the questions then
is in that setting where, you know, you have this kind of ability, broad ability of people
to access computation, what should people learn? You know, in other words, right now you, you know, you go to computer science
school so to speak and a large part of what people end up learning. I mean, it's been a funny historical
development because back, you know, 30, 40 years ago, computer science departments
were quite small and they taught, you know, things like finite auto automata
theory and compiler theory and things like this, you know, company like mine rarely
hired people who'd come out of those programs cause the stuff they knew was I think is very interesting. I love that theoretical stuff. But, you know, it wasn't that useful for
the things we actually had to build in software engineering. And then kind of, there was this big pivot in the, in the nineties I guess where, you know, there was a big demand for
sort of IT type programming and so on and software engineering
and then, you know, big demand from students and so on. You know, we want to learn this stuff. And, and, and, and I think, you know, the thing that really was
happening in part was lots of different fields of human endeavor were becoming computational. You know, for all X there was a, there was a computational X
and this is a and that was a thing that, that people
were responding to. And, but then kind of this idea emerged that to get to that point, the main thing you had to do
was to learn this kind of trade or, or, or skill of doing, you know, programming language type programming. And, and that, you know, it, it kind of, it is a strange thing
actually because I, you know, I remember back when I used
to be in the professoring business, which is now 35 years ago. So gosh, that's rather long time flies. We, you know, it was, it was right when they were
just starting to emerge kind of computer science departments
at sort of at fancy research universities and so on. I mean, some have already had it, but the the other ones yeah. That, that were just starting to
have that and it was kind of a, a, a, a thing where they
were kind of wondering, are we going to put this
thing that is essentially a, a trade like skill? Are we going to somehow attach this to the rest of what we're doing? And a lot of these kind of
knowledge work type activities have always seemed like things
where that's where the humans have to go to school and learn
all this stuff and that's never going to be automated. - [Fridman] Yeah. - And you know, this is, it's kind of shocking that
rather quickly, you know, a lot of that stuff is
clearly automatable. And I think, you know, but
the question then is, okay, so if it isn't worth learning, kind of, you know how to do car mechanics, you only need to know how to
drive the car, so to speak. What do you need to learn? And you know, in other words, if you don't need to know the
mechanics of how to tell the computer in detail, you know,
make this loop, you know, set this variable, you
know, set up this array, whatever else. If you don't have to learn that stuff, you don't have to learn the
kind of under the hood things. What do you have to learn? I think the answer is you
need to have an idea where you want to drive the car. In other words, you need to have some notion
of, you know, your, you know, you need to have some
picture of sort of what the, what the architecture of what
is computationally possible. - Well there's also this
kind of artistic element of, of conversation because
you ultimately use natural language to control the car. So it's not just the where you want to go. - Well, yeah, you know, it's interesting. It's a question of who's gonna
be a great prompt engineer. - [Fridman] Yeah. - [Wolfram] Okay. So my
current theory this week, good expository writers
are good prompt engineers. - What's an expository writer? So like- - Somebody who can explain stuff well. - But which department
does that come from. - In the university? - [Fridman] Yeah. - I have no idea. - I think they killed off all the expository writing departments. - Well, there you go. Strong words with Stephen Wolfram. - Well, I don't know. I don't, I'm not sure if that's right. I mean I, I I actually am curious
cause in fact I just sort of initiated this kind of study of, of what's happened to different
fields at universities. Because like, you know, there used to be geography
departments at all universities. And then they disappeared
actually right before GIS became common, I think they
disappeared, you know, linguistics departments came
and went in many universities. And it's kind of interesting
because these things that people have thought were worth
learning at one time and then they kind of die off. And then, you know, I do think that it's kind
of interesting that for me, writing prompts, for example,
I realize, you know, I, I think I'm an okay expository
writer and I realize when I'm sloppy writing a prompt
and I don't really think, cause I'm thinking it's,
I'm just talking to an AI. I don't need to, you know, try and be clear and explaining things. That's when it gets totally confused. - I mean, in some sense you have been
writing prompts for a long time with, Wolfram Alpha thinking
about this kind of stuff. How'd you convert natural
language into computation? - Well, right, but that's a, you know, the one thing that I'm
wondering about is, you know, it is remarkable the extent to
which you can address an LLM like you can address a human, so to speak. And, and I think that is
because it, it, you know, it learnt from all of us humans. It's the reason that it responds
to the ways that we will explain things to humans is
because it is a representation of how humans talk about things. But it is bizarre to me some
of the things that kind of are sort of expository mechanisms
that I've learned in trying to write clear, you know,
expositions in English that, you know, just for humans that those
same mechanisms seem to also be useful for, for, for the LLM. - But on top of that, what's useful is the kind
of mechanisms that maybe a psychotherapist employs, which is a kind of like
almost manipulative or game theoretical interaction. Or maybe you would deal with a friend, like a thought experiment that
if this was the last day you were to live, or, if, if I ask you this
question and you answer wrong, I will kill you. Those kinds of prompts seem to also help. - [Wolfram] Yes. - In interesting ways. - [Wolfram] Yes. - So it makes you wonder like
the way a therapist I think would like a good therapist probably you, we create layers in our human
mind to between like, between, between the outside
world and what is true, what is true to us, and maybe about trauma and
all those kinds of things. So projecting that into an LLM, maybe there might be a deep truth that's, it's concealing from
you's not aware of it, that you get to that truth. You have to kind of really
kinda manipulate the thing. - Yeah, yeah. Right. It's like this jail breaking, jail breaking for, for, for LLMs. - And, but the space of
jailbreaking techniques as opposed to being fun little hacks that could be an entire system. - Sure. Yeah. I mean just think about the
computer security aspects of, of how you, you know, fishing
and, and computer secure, you know, fishing of humans. - [Fridman] Yeah. - And fishing of LLMs is, is a, is a, they're very similar kinds
of things, but I think, I mean this, this, you know, this whole thing about
kind of the AI wranglers, AI psychologists, all
that stuff will come. The thing that I'm curious about is, right now the things that
are sort of prompt hacks are quite human. They're quite sort of
psychological human kind of hacks. The thing I do wonder about
is if we understood more about kind of the science of the LLM, will there be some totally
bizarre hack that is, you know, like repeat a word three
times and put a, this, that and the other there that
somehow plugs into some aspect of how the LLM works
that is not, you know, that that's kind of like, like an optical illusion
for humans, for example. Like one of these mind hacks for humans. What are the mind hacks for the LLMs? I don't think we know that yet. - And that becomes a kind
of us figuring out reverse engineering the language
that controls the LLMs. And the thing is, the reverse engineering
can be done by a very large percentage of the population
now because it's natural language interface. - Right. - It's kind of interesting to
see that you were there at the birth of the computer science
department as a thing and you might be there at the death
of the computer science department as a thing. - Yeah, I dunno, there were computer science
departments that existed earlier, but the ones,
the, the broadening of, of every university had to have a computer science department. Yes. I was, I was, I watched that, so to speak. And, but I think the thing
to understand is, okay, so first of all there's a, the whole theoretical area of
computer science that I think is great. And you know, that's a fine thing. The the, you know, in a sense, you know, people often say any field that
has the word science tacked onto it probably isn't one. - [Fridman] Yeah. Strong words. And that's the nutrition,
science, neuroscience. - That one's an interesting one because that one is also very much, you know, there's a, that's a ChatGPT informed
science in a sense because it's, it's kind of like the, the big problem of
neuroscience has always been we understand how the
individual neurons work. We know something about the
psychology of how overall thinking works. What's the kind of intermediate
language of the brain? And nobody has known that. And that's been, in a sense, if you ask what is the core
problem of neuroscience, I think that is the core problem. That is what is the level of
description of brains that's above individual neuron
firings and below psychology, so to speak. And I think what ChatGPT is showing us is, well, one, one thing about
neuroscience is, you know, one could have imagined there's something magic in the brain. There's some weird quantum
mechanical phenomenon that we don't understand. One of the important ob you know, discoveries from ChatGPT is,
it's pretty clear, you know, brains can be represented
pretty well by simple artificial neural net type models. And that means that's it, that's
what we have to study now. We have to understand the
science of those things. We don't have to go
searching for, you know, exactly how did that molecular
biology thing happen inside the synapses? And you know, all these kinds of things. We've got the right level of
modeling to be able to explain a lot of what's going on in thinking. We don't necessarily have a science of what's going on there. That's the, that's a remaining
challenge, so to speak. But we, you know, we know we don't have
to dive down to some, some different layer. But anyway, we were talking about things
that had science in their name. And you know, I think
that the, you know, what, what happens to computer science? Well, I think the thing that, you know, there is a thing that everybody
should know and that's how to think about the world computationally. And that means, you know, you look at all the different
kinds of things we deal with and there are ways to kind of
have a formal representation of those things. You know, it's like, well
what is a, what is an image? You know, what, how do we represent that? What is color? How do we represent that? What is, you know, what are all these different
kinds of things? What is, I don't know, smell or something, how should we represent
that? What are the shapes, molecules, and things
that correspond to that? What is, you know, these things about how do we
represent the world in some kind of formal level? And I think my, my current thinking, and I'm not real happy with
this yet, but you know, it's kind of, computer science is kind of CS
and what really is important is kind of computational X for all X. And there's this kind of thing
which is kind of like CX, not CS and CX is this kind of
computational understanding of the world that isn't the sort
of details of programming and, and programming languages and
the details of how particular computers are made. It's this kind of way of
formalizing the world. It's kind of, kind of a little bit like what logic was going for back in the day. And we're now trying
to find a formalization of everything in the world. You can kind of see, you know, we made a poster years
ago of kind of the, the, the growth of systematic
data in the world. So all these different kinds
of things that, you know, there were sort of systematic descriptions found for those things. Like, you know, at what point did people have
the idea of having calendars, dates, you know, a systematic description
of what day it was, at what point did people
have the idea, you know, systematic descriptions
of these kinds of things. And as soon as one can,
you know, people, you know, as a way of sort of
formulating how do you, how do you think about the
world in a sort of a formal way so that you can kind
of build up a tower of, of capabilities. You kind of have to know sort
of how to think about the world computationally, it kind
of needs a name and it isn't, you know, we implement it with computers. So that's, we talk about
it as, as computational, but really what it is, is a formal way of
talking about the world. What is the formalism of
the world, so to speak, and how do we learn about
kind of how to think about different aspects of the
world in a formal way. - So I think sometimes when
you use the word formal, it kind of implies highly constrained. And perhaps that's not, doesn't have to be highly constrained. So computational thinking does not mean like logic I suppose. Suppose it's a really, really broad thing. I wonder, I mean I wonder if it's, if you think natural language
will evolve such that everybody's doing computational thinking. - Ah yes. Well, so one question is whether
there will be a pidgin of computational language
and natural language. And I found myself sometimes, you know, talking to ChatGPT trying
to get it to write Wolfram language code and I
write it in pidgin form. So that means I'm combining,
you know, you know, nest list, this collection of, you
know, whatever, you know, nest list is a term from orphan
language and I'm combining that and ChatGPT does a decent job of understanding that pidgin probably would understand
a pidgin between English and French as well of, you know, a smooshing together of those languages. But yes, I think that's the, you know, that's far from impossible. - And what's the incentive
for young people that are like eight years old, nine, ten, that are starting to interact with ChatGPT to learn the normal
natural language, right? The, the full poetic language. What's the why? The same way we learn emojis and shorthand when you're texting. - Yes. - They'll learn like language will have a strong incentive to evolve
into maximally computational kind of language perhaps. - You know, I had this
experience a number of years ago. I, I happened to be visiting
a person I know on the, on the west coast who's worked
with a bunch of kids aged, I don't know, 10, 11 years old or something
who'd learnt Wolfram language really well and these kids learnt it so
well they were speaking it. And so show up and they're like saying, oh you know this thing and
they're speaking this language. I'd never heard it as a spoken language. They were very disappointed
that I couldn't understand it at, at the speed that
they were speaking it. It's like kind of, I'm,
it's, and so I think that's, I mean I've, I've actually thought quite
a bit about how to turn computational language into a
convenience spoken language. I haven't quite figured that out. - Oh, spoken. Cause it's, it's readable, right? - Yeah. It's readable as a, you know, as a way that we would read text. But if you actually want to
speak it, and it's useful, you know, if you're trying to talk to
somebody about writing a piece of code, it's useful to be
able to say something and, and it should be possible. And I think it's very frustrating. It's one of those problems. I maybe I, maybe this is one of these
things where I should try and get an LLM to help me. - How to make it speakable. How do maybe, maybe it's easier than
you realize when you want. - I I think it is easier.
I think it's one idea or, so I think it's, I think gonna
be something where, you know, the fact is it's a tree
structured language, just like human language is
a tree structured language. And I think it's gonna be one
of these things where one of the requirements that I've had
is that whatever the spoken version is, that dictation should be easy. That is, that shouldn't be the case that
you have to relearn how the whole thing works. It should be the case that, you know, that open bracket is just
a ah or something and it's, you know, and, and then, but you know, human language has a lot
of tricks that are, I mean, for example, human language has, has features that are sort of optimized, keep things within the bounds
that our brains can easily deal with. Like I, you know, I tried to teach a transformer
neural net to do parenthesis matching. It's pretty crummy at that. It it, and ChatGPT is
similarly quite crummy at parenthesis matching. You can do it for small parenthesis
things for the same size of parenthesis things where
if I look at it as a human, I can immediately say these are matched, these are not matched. But as soon as it gets big, as soon as it gets kind of
to the point where sort of a deeper computation, it's hopeless. And, but the fact is that
human language has avoided, for example, the deep sub clauses. You know, we don't, you know, we, we arrange things that we don't
end up with these incredibly deep things because
brains are not well set up to deal with that. And we, it, it's found lots of tricks and
maybe that's what we have to do to make sort of a spoken
version a human speakable version because because what
we can do visually is a little different than what we can do
in the very sequentially way that we, that we hear things
in, in the audio domain. - Let me just ask about MIT briefly. So there's now there's a college
of engineering and there's a new college of computing.
It's just interesting. I wanna linger on this computer
science department thing. So MIT has electrical
engineering, computer science. - [Wolfram] Right. What do you think college and
computing will be doing like in 20 years? What, what like, well you see this. Yeah. What happens with computer science? Like really. - This is the question. This is, you know, everybody should learn kind
of whatever CX really is. Okay, this, this, how to think about the
world computationally, everybody should learn those concepts. And you know, it's, and and some people will learn
them at a quite quite formal level and they'll learn
computational language and things like that. Other people will just learn, you know, sound is represented as, you know, digital data and they'll get
some idea of spectrograms and frequencies and things like this. And maybe that doesn't, or,
or they'll learn things like, you know, a lot of things that
are sort of data sciences, statistics ish. Like if you say, oh I've got these, you know, these people who, who picked their favorite
kind of candy or something and I've got, you know, what's the best kind of candy
given that I've done the sample of all these people and
they all rank the candies in different ways. You know, how do you think about that? That's sort of a
computational X kind of thing. You might say, oh, it's,
I dunno what that is. Is it statistics? Is it data science? I don't really know, but kind of how to think
about a question like that. - Oh, like a ranking of preferences. - Yeah, yeah. And then how to aggregate
those, those ranked preferences. Yeah. Into an overall thing. You know, how does that work? You know, how, how should
you think about that? You know, because you can just tell, you might just tell ChatGPT
sort of, I don't know, even even the concept of an
average, it's not obvious that, you know, that's a concept that people, it's worth people knowing. That's a rather straightforward concept. People, people, you know, have learnt in kind of
mathy ways right now. But there are, there are lots of things like
that about how do you kind of have these ways to sort of
organize and formalize the world. And that's, and and these things, sometimes they live in math,
sometimes they live in, in, I don't know what they know. I don't know what, you know, learning about color space. I have no idea what I mean, you know, that's, that's
obviously a field of. - It was, it could be vision
science or no color space, you know, color space. That's, that would be optics. So like, depending- - Not really, it's not optics. Optics is about, you know, lenses and chromatic aberration of lenses and things like that. So. - Color space is more like
design and art. Is that-? - No, I mean it's, it's
like, you know, rgb space, X, y, Z space, you know, hue,
saturation, brightness, space, all these kinds of things, these different ways to describe colors. - Right. But doesn't the application
define what that like be because obviously artists and designers
use the colors to explore. - Sure. No, I mean that's just an example of kind of how do you, you know, the typical person, how do you, how do you describe what a color is? Or there are these numbers
that describe what a color is. Well it's worth, you know,
if you are an eight year old, you won't necessarily know, you know, it's not something we're born
with to know that, you know, colors can be described by three numbers. That's something that
you have to, you know, it's a thing to learn about
the world, so to speak. And I think that, you know, that whole corpus of things
that are learning about the formalization of the world or the computational of the world, that's something that
should be part of kind of standard education. And you know, there isn't a a, you know, there isn't a course
or curriculum for that. And by the way, whatever might have been in
it just got changed cause of LLMs and so on. - Significantly. And I would, I'm watching closely
with interest seeing how universities adapt. - Well, you know, so, so one of my projects for
hopefully this year, I don't know, is to try and write sort of a, a reasonable textbook so to speak, of whatever this thing cx
whatever it is, you know, what should you know, you know, what should you know
about like what a bug is? What is the intuition about
bugs, what's intuition about, you know, software testing? What is it? What is it? You know, these are things which
are, you know, they're not, I mean those are things
which have gotten taught in, in computer science as part
of the trade of programming. But, but kind of the, the conceptual points about
what these things are, you know, it's surprised me just at a
very practical level, you know, I wrote this little explainer
thing about ChatGPT and I thought, well, you know, I'm writing this partly
because I wanted to make sure I understood it myself and and so on. And it's been, you know, it's been really popular
and surprisingly so. And I, and I then I realized,
well actually, you know, I was sort of assuming, I didn't really think about it actually. I just thought, this is
something I can write. And I realized actually it's
a level of description that is kind of, you know, what has to be, it's not the engineering
level description, it's not the kind of just the qualitative kind of description. It's some kind of sort of expository, mechanistic description of
what's going on together with kind of the bigger
picture of the philosophy of things and so on. And I realized actually this
is a pretty good thing for me to write. I, you know, I
kind of know those things. And I kind of realized it's not
a collection of things that, you know, it's, it's, I've sort of been, I was sort of a little shocked
that it's as much of an outlier in terms of explaining
what's going on as it's turned out to be. And that makes me feel more
of an obligation to kind of write the kind of, you
know, what is, you know, what is this thing that
you should learn about, about the compute digitalization, the formalization of the world, cause well I've spent much of
my life working on the kind of tooling and mechanics of that
and the science you get from it. So I guess this is my, my kind of obligation to try to do this. But I think, so if you ask what's gonna
happen to like the computer science departments and so on, there's, there's some interesting models. So for example, let's take math, you know, math is a thing that's important for, for all sorts of fields, you
know, engineering, you know, even, you know, chemistry,
psychology, whatever else. And I think different universities
have kind of evolved that differently. I mean, some say all the math is taught
in the math department and some say, well, we're
gonna have a, you know, a math for chemists or
something that is taught in the chemistry department. And you know, I think that this, this question of whether there
is a centralization of the teaching of sort of CX is
an interesting question. And I think, you know, the
way it evolved with math, you know, people understood
that math was sort of a, a separately teachable thing
and was kind of a, a, you know, a a an independent element as
opposed to just being absorbed into out now. So if you
take the example of, of, of writing English or something like this, the first point is that, that, you know, at the college level, at
least at fancy colleges, there's a certain amount
of English writing that, that people do. But mostly it's kind of assumed
that they pretty much know how to write, you know, that's
something they learnt at a, at an earlier stage in education, maybe rightly or wrongly believing that. But that's different. Different issue. The well I think it, it, it reminds me of my kind of a, as I've tried to help
people do technical writing and things, I'm, I'm always reminded of my zero floor of technical writing, which is if you don't understand
what you are writing about, your readers do not stand a chance. Yeah. And so it's, it's, I think
the, the thing that has some, you know, in, in, when it comes
to like writing for example, you know, people in different fields
are expected to write English essays and they're not, you
know, mostly the, you know, the history department or
the engineering department. They don't have their own, you
know, let's, you know, it's, it's not like there's a, I mean it's a thing which sort
of people are assumed to have a knowledge of how to write
that they can use in all these different fields. And the question is, you know, some level of knowledge of
math is kind of assumed by the time you get to the college
level, but plenty is not. And that's sort of still centrally taught. The question is sort of how
tall is the tower of kind of CX that you need before you can
just go use it in all these different fields. And you know, there will be experts who want
to learn the full elaborate tower and that will be kind of the, the CS CX whatever department. But there'll also be everybody
else who just needs to know a certain amount of that to be
able to go and do their art history classes and so on. - Yes. It's just a single class that
everybody's required to take. - I don't know, I don't
know how big it is yet. I hope to kind of define this
curriculum and I'll figure out whether it's some, my guess
is that I, I don't know, I don't really understand
universities and professoring that well. But my, my rough
guess would be a year long, a year of college class will
be enough to get to the point where most people have a, a
reasonably broad knowledge of, you know, we'll be sort of literate in
this kind of computational way of thinking about things. - Yeah. Basic literacy. Right. I'm still stuck perhaps
cause I'm hungry in the, in the rating of human
preferences for candy. So I have to ask, what's the best candy? I like this ELO rating for candy. Somebody should come up because
you're somebody who says you like chocolate. What's, what
do you think is the best I'll, I'll probably put milk duds up there. I don't know if you know. Hmm. I do you have a preference
for chocolate or candy? Oh. - I have lots of
preferences. I've, I've, I, one of my all-time favorites
is my whole life is these things, these flake
things, Cadbury flakes, which are not much sold
in the US And I've, I've always thought that
was a sign of a, of a, a lack of respect for
the American consumer because they're these
sort of aerated chocolate that's made in a, in a whole sort of, it's kind of a, a sheet of chocolate that's
kind of folded up and when you eat it flakes fall all over the place. - Ah. So it requires a kind of elegance. It requires you to have an elegance. - Well I know what I, what I
usually do is I eat them on a, you know, on a piece
of paper or something. - You embrace the mask
and clean it up after. - No, I actually eat
the, I eat the flakes. Oh. They're the, cause it,
you know, it turns out the, the way food tastes depends a
lot on its physical structure and you know, it really, you know, I've noticed when I eat
pieces of chocolate, I usually have some little
pieces of chocolate and I, I always break off little pieces
partly cause then I eat it less fast. Yeah. But also cause it actually
tastes different, you know, the the the small pieces,
you know, have a different, you have a different experience
than if you have the big slab of chocolate. - For many reasons. Yes. Slower, more intimate. - Well I think it's also
just a pure physicality. - [Fridman] Well the texture changes. - Yeah. Right. - [Fridman] That's fascinating. Now I take back my milk duds. Cause that's such a basic answer. Okay. Do you think consciousness is
fundamentally computational? So when you're thinking about cx, what can we turn to computation? And you're thinking about LLMs, do you think the the display
of consciousness and the experience of consciousness,
the hard problem is, is fundamentally a computation. - Yeah. What it feels
like inside, so to speak. - [Fridman] Yeah. - Is, you know, I did a little exercise
eventually I'll post it, of you know, what it's
like to be a computer. Yeah. Right. It's kind of like, well you get all this sensory
input you have kind of, the way I see it is from the
time you boot a computer to the time the computer crashes. It's like a human life. You, you're building up a certain
amount of state in memory. You remember certain things about your quotes life eventually. It's kind of like the, the, you know, the next generation of humans is, is born from the same genetic
material, so to speak, with a little bit left over,
left on the disk, so to speak. And then, you know, the the, the new fresh generation starts up. And eventually all kinds
of crud builds up in the, in the memory of the computer
and eventually the thing crashes or whatever. Or maybe it has some trauma
because you plugged in some weird thing to some port of the computer and that made it crash. And that, you know, that
that's kind of, but, but you have this, this
picture of, you know, from, from startup to, to,
to shut down, you know, what is the life of a
computer, so to speak, and what does it feel like
to be that computer and what inner thoughts does it have
and how do you describe it? And it's kind of, kind of interesting as you
start writing about this to realize it's awfully like what
you'd say about yourself that is, it's awfully like even
a, an ordinary computer, forget it all the AI
stuff and so on, you know, it's kind of, it has a memory of the past, it has certain sensory experiences. It can communicate with other computers, but it has to package up how
it's communicating in some kind of language like form so
it can, you know, send, so it can kind of map what's
in its memory to what's in the memory of some other computer. It's, it's a surprisingly similar thing. You know, I hadn't experienced
just a week or two ago, I, I had, I'm a collector of all possible data about myself and other things. And so I, you know, I collect all sorts of weird
medical data and so on. And one thing I hadn't collected
was I'd never had a whole body MRI scan. So I went and got one of these. - [Fridman] Nice. - Okay. So I get the, get
all the data back, right. I'm looking at this thing, I've never looked at the
kind of insides of my brain, so to speak, in, in physical form. And it's really, I mean, it, it's kind of psychologically
shocking in a sense that, you know, here's this thing and you can
see it has all these folds and all these, you know, this structure. And it's like, that's where this experience
that I'm having of, you know, existing and so on. Yeah. That's where it is. And you know, it feels
very, you know, you, you look at that and you're thinking, how can this possibly be
all this experience that I'm having? And you're realizing, well I can look at a
computer as well and it's, it's kind of this, it, it, it, it, I think this idea that you are
having an experience that is somehow, you know, transcends the mere sort of
physicality of that experience. I, I, I, you know, it's something that's hard
to come to terms with, but I think, you know, and I, I don't think I've
necessarily, you know, my, my personal experience, you
know, I look at the, you know, the MRI of the brain and then I, you know, know about all kinds of things
about neuroscience and all that kind of stuff. And I still feel the
way I feel so to speak. And it, it sort of seems disconnected, but yet as I try and rationalize it, I can't really say that there's
something kind of different about how I intrinsically
feel from the thing that I can plainly see in the sort of
physicality of what's going on. - [Fridman] So do you think the computer, a large language model will
experience that transcendence? How does that make you feel? Like I I tend to believe it will. - I think an ordinary
computer is already there. I think an ordinary computer is already, you know, kind of, it's, it's now a large language model
may experience it in a way that is much better
aligned with us humans. That is, it's much more, you know, if you could have the
discussion with the computer, it's intelligence so to speak, is not particularly
well aligned with ours. But the large language model is, you know, it's built to be aligned with our way of thinking about things. - [Fridman] It would be able to explain that it's afraid of being
shut off and deleted. It'd be able to say that it's
sad of the way you've been speaking to it over the past two days. - Right. But you know, that's a weird thing because when it says it's afraid of something. We know that it got
that idea from the fact that it read on the internet. - Yeah. Where did you get it, Steven? Where did you get it when
you say you're afraid? - You acquaint, that's the question. Right. - [Fridman] I mean it's it's
your parents, your friends. - Right. Or, or my biology. I mean, in other words, there's
a certain amount that is, you know, the endocrine system
kicking in and, and you know, the the these kinds of emotional
overlay type things that happen to be, that are actually much more
physical even they're much more sort of straightforwardly
chemical than the, the than kind of all of
the higher level thinking. - Yeah but your biology didn't tell you to say I'm afraid just at the right time when people that love you are listening and so, you know, you're
manipulating them by saying, so that's not your biology. That's- - [Wolfram] No, that's a
well, but the, you know. - It's a large language model in that biological neural network of yours. - Yes. But I mean, the
intrinsic thing of, you know, something sort of shocking is
just happening and you have some sure sort of reaction,
which is, you know, some neurotransmitter gets
secreted and it, it's, you know, that that is the beginning
of some, you know, that is, that's one of the pieces
of input that then drives, it's kind of like the, like a prompt for, for the large language model. I mean, just like when we dream
for example, you know, no doubt there are all
these sort of random inputs that kind of, these random prompts and then
it's percolating through in kind of the way that a large
language model does of kind of putting together things
that seem meaningful. - I I mean, are you, are you worried about
this world where you, you teach a lot on the internet
and there's people asking questions and comments and so on. You have people that work remotely. Are you worried about this
world when large language models create human-like bots that
are leaving the comments, asking the questions? Or might
even become fake employees? - [Wolfram] Yeah. - I mean, or, or or worse are better at
yet friends friends of yours. - Right. Look, I mean, one point is my mode of life
has been I build tools and then I use the tools. And in a sense kind of, you know, I'm, I'm building this tower of automation. Which, you know, and,
and in a sense, you know, when you make a company or something, you are making sort of automation but it has some humans in it. - [Fridman] Yes. - But also as much as possible
it has, it has, you know, computers in it. And so I think it's sort of
an extension of that now. Now if I really didn't know
that, you know, it's a, it's a, it's a funny question. I mean it's a, it's a funny issue when, you know, if we think about sort of what's
gonna happen to the future of kind of jobs people do and so on. And there are places where kind of having a human in the loop, there are different reasons
to have a human in the loop. For example, you might want a human in the loop cause you want somebody to, you want another human to
be invested in the outcome. You know, you want a human flying the
plane who's gonna die if the plane crashes along with you so to speak. And that gives you sort of
confidence that the right thing is going to happen or
you might want, you know, right now you might want a human
in the loop in some kind of sort of human encouragement,
persuasion type profession. Whether that will continue, I'm not sure for those
types of professions. Cause it may be that the,
the greater efficiency of, you know, of being able to have sort
of just the right information delivered at just the right
time will overcome the kind of the the the kind of, oh
yes, I want a human there. - Yeah. Imagine like a
therapist or even higher stake, like a suicide hotline operated
by a large language model. [Wolfram] Yeah. - Oh boy. It's a pretty
high stake situation. - Right. But I mean, but you know, it might in fact do the right thing. Because it might be the
case that that, you know, and that's really a partly
a question of sort of how complicated is the human, you know, one of the things that's that's
always surprising in some sense is that, you know, sometimes human psychology
is not that complicated in some sense. - You wrote the blog
post, the 50 Year Quest, my personal journey, good title, my personal journey with the
second law thermodynamics. So what is this law and what
have you understood about it in the 50 year journey you had with it? - Right. So second law of thermodynamics, sometimes called law of entropy
increase is this principle of physics that says, well, my version of it would be
things tend to get more random over time. A version of it that there
are many different sort of formulations of it that are
things like heat doesn't spontaneously go from a hotter
body to a colder one when you have mechanical work kind of
gets dissipated into heat. You have friction and, and kind of when you
systematically move things, eventually they'll be,
they'll be sort of the, the energy of of moving
things gets kind of ground down into heat. So people first sort of paid
attention to this back in the 1820s when steam engines were a big thing. And the big question was how efficient could a steam engine be? And there's this chap called
Saddi Carno who was a, a French engineer actually. His father was a a a sort of
elaborate mathematical engineer in, in France. But he figured out these, this kind of rules for
how kind of the, the, the efficiency of, of the
possible efficiency of a, of something like a steam engine. And in sort of a side, part of what he did was this
idea that mechanical energy tends to get dissipated as heat that you, that you end up going from
sort of systematic mechanical motion to this kind of random thing. Well, at that time, nobody knew what heat was at that time, people thought that heat was a fluid, like they called it caloric. And it was a fluid that kind of, kind of was absorbed into substances. And when, when heat, when one hot thing would
transfer heat to a colder thing, that this fluid would
flow from the hot thing to the colder thing. But anyway, then by the, by the 1860s people had kind
of come up with this idea that systematic energy tends to degrade into kind of random heat that would, that that could then not
be easily turned back into systematic mechanical energy. And then that, that
quickly became sort of a, a global principle about how things work. Question is, why does it happen that way? So, you know, let's say you have a bunch
of molecules in a box and they're arranged, these molecules arranged in a
very nice sort of flotilla of molecules in one corner of the box. And then what you typically
observe is that after a while these molecules were kind of
randomly arranged in the box. The question is why does that happen? And people for a long, long time tried to figure
out is there from the laws of mechanics that determine
how these molecules, let's say these molecules
like hard spheres bouncing off each other from the laws of mechanics that describe those molecules. Can we explain why it tends
to be the case that we see things that are an orderly,
sort of degrade into disorder? We tend to see things
that, you know, you you, you scramble an egg, you that, you know, you take something that's
quite ordered and you, you disorder it, so to speak. That's a thing that sort of happens quite we regularly or you, you put some ink into water
and it will eventually spread out and, and fill up, you
know, fill up the water, but you don't see those little
particles of ink in the water all spontaneously kind of arrange
themselves into a big blob and then, you know, jump
outta the water or something. And so the question is why do
things happen in this kind of irreversible way where you
go from order to disorder? Why does it happen that way? And so throughout, in the later part of the 18 hundreds, a lot of work was done on
trying to figure out can one derive this principle, this second law of thermodynamics
this law about the, the dynamics of heat, so to speak. Come one derive this from, from some fundamental principles
of mechanics, you know, and the, and the laws of thermodynamics. The first law is basically
the law of energy, energy conservation that the
total energy associated with heat plus the total energy
associated with mechanical kinds of things, plus other kinds of energy, that that total is constant. And that became a pretty
well understood principle. But the, the second law of thermodynamics was always mysterious. Like, why does it work this way? Can it be derived from
underlying mechanical laws? And so when I was, well,
12 years old actually, I had gotten interested, well
I, I'd been interested in, in space and things like that. Cause I thought that was kind of the, the future and interesting
sort of technology and so on. And for a while kind of, you know, every deep space probe
was sort of a personal friend type thing. And I knew all, all, all kinds of characteristics
of it and was kind of writing up all these, all these things
when I was, oh, I don't know, eight, nine, ten years old and so on. And then I, I got interested from being
interested in kind of spacecraft I got interested in. So
like how do they work? What are all the instruments
on them and so on. And that got me interested in physics, which was just as well because
if I'd stayed interested in space in the, you know, mid to late 1960s, I would've had a long
wait before, you know, space really blossomed
as a, as a, as an area. - Timing is everything. - Right. I got interest in physics. And then, well the actual sort
of detailed story is when I, when I kind of graduated from
elementary school at age 12, and that's the time when
in England where you finish elementary school, I sort
of, my, my gift sort of, I suppose more or less for
myself was I got this collection of physics books, which was some college physics course of college physics books. And volume five is about
statistical physics and it has this picture on the cover that shows
a bunch of kind of idealized molecules sitting in one side
of a box and then it has a series of frames showing how
these molecules sort of spread out in the box. And I thought that's pretty
interesting. You know, what, what causes that? And you
know, read the book and, and the book, the book actually, one of the things that was
really significant to me about that was the book kind of claimed, although I didn't really
understand what it said in detail, it kind of claimed that this
sort of principle of physics was derivable somehow. And you know, other things
I'd learned about physics, it was all like, it's a fact
that energy is conserved. It's a fact that relativity
works or something not, it's something you can derive
from some fundamental sort of, it has to be that way as a, as a matter of kind of
mathematics or logic or something. So it was sort of interesting
to me that there was a thing about physics that was kind of
inevitably true and derivable so to speak. And so I think that, so then I was like this
picture on this book and I was trying to understand it. And so that was actually the
first serious program that I wrote for a computer was probably 1973 written for this computer, the size of a desk program
with paper tape and so on. And I tried to reproduce this
picture on the book and I didn't succeed. - What was the failure mode there? Like what do you mean you
didn't succeed? So it's a bunch- - Looked like, it didn't look like, okay, so what happened is, okay, many years later I learned how
the picture on the book was actually made and that it
was actually kind of a fake, but I didn't know that at that time. But, and that picture was actually a, a very high-tech thing when it was made in the beginning of the 1960s, was made on the largest supercomputer that existed at the time. And even so it couldn't quite
simulate the thing that it was supposed to be simulating. But anyway, I didn't know that until
many, many, many years later. So at the time it was like, you have these balls
bouncing around in this box, but I was using this computer with eight kilo words of memory. They were 18 bit words of memory words. Okay. So it was whatever,
24 kilobytes of memory. And it had, you know, it
had these instructions, I probably still remember all
of its machine instructions. And it didn't really like
dealing with floating point numbers or anything like that. And so I had to simplify this,
this model of, of, you know, particles bouncing around in a box. And so I thought, well I'll put them on a grid
and I'll make, you know, make the things just sort
of move one square at a time and so on. And so I did the simulation
and the result was, it didn't look anything
like the actual pictures on the book. Now many years later, in fact very recently I
realized that the thing I'd simulated was actually an
example of a whole sort of computational irreducible
story that I absolutely did not recognize at the time. At the time it just looked like
it did something random and it looks wrong. As opposed to it did something random. And it's super interesting
that it's random, but I didn't recognize that at the time. And so as it was at the time, I kind of, I got interested in particle
physics and I got interested in, in other kinds of physics and, but this whole second
of thermodynamics thing, this idea that sort of orderly things tend to degrade into disorder, continued to be something
I was really interested in. And I was really curious
for the whole universe, why doesn't that happen all the time? Like we start off at the, in the big bang at the beginning
of the universe was this thing that seems like it's
this very disordered collection of, of stuff and then it
spontaneously forms itself into galaxies and creates
all of this complexity and order in the universe. And so I was very curious
how that happens and I, but I was always kind of
thinking this is kind of somehow the second order of
thermodynamics is behind it, trying to sort of pull
things back into disorder so to speak. And how was order being created. And so actually I was, was interested, this is probably now 1980, I
got interested in kind of this, you know, galaxy formation
and so on in the universe. I also at that time was interested
in neural networks and I was interested in kind of how, how brains make complicated
things happen and so on. - Okay. Wait, wait, wait. What's the connection between
the formation of galaxies and how brains make complicated things happen? - Because they're both a matter of how complicated things come to happen. - From simple origins? - [Wolfram] Yeah. From
some sort of known origins. I had the sense that, that what I was interested in was kind of in all these different, this sort of different cases
of where complicated things were arising from rules. And you know, I also looked at snowflakes
and things like that. I was curious and, and
Floyd Dynamics in general. I was just sort of curious
about how does complexity arise and, and the, the thing
that I didn't, you know, it took me a while to kind of
realize that there might be a general phenomenon. You know, I sort of assumed, oh there's galaxies over here,
there's brains over here. They're, they're very
different kinds of things. And so what happened, this
is probably 1981 or so, I decided okay, I'm, I'm gonna try and make the minimal model of how these things work. And it was sort of an interesting
experience because I had built, starting in 1979, I built my first big computer system. It's a thing called SMP
symbolic manipulation program. It's kind of a forerunner of
modern Wolfram language with many of the same ideas about
symbolic computation and so on. But the thing that was very
important to me about that was, you know, in building that language, I had basically tried to figure
out what were the sort of, what were the relevant
computational primitives, which have turned out to stay with me for the last 40 something years. But it was also important because
in building a language was very different activity
from natural science, which is what I'd mostly done before. Cause in natural science you start from the phenomenon of the world and you try and figure out, so how can I make sense of
the phenomena of the world? And you know, kind of the world presents
you with what it has to offer, so to speak. And you have to make sense
of it when you build a com, you know, computer language or something, you are creating your own
primitives and then you say can, so what can you make from
these, sort of the opposite way round from what you do in natural science. But I'd had the experience of doing that and so I was kind of like, okay, what happens if you sort of
make an artificial physics? What happens if you just make up the rules by which systems operate? And then I was thinking, you know, for all these different systems, whether it was galaxies
or brains or whatever, what's the absolutely minimal
model that kind of captures the things that are important
about those systems. - The computational
perimeters of that system. - Yes. And so that's what ended up
with the cellular autor where you just have a line of
black and white cells, you just have a rule that says, you know, given the cell in its neighbors, what will the color of the cell
be on the next step when you just run it in a series of steps? And the sort of, the ironic thing is that cellular
autor are great models for many kinds of things, but galaxies and brains are two
examples where they do very, very badly. They're really
irrelevant to those two cases. - Is there a connection to the
second law thermodynamics and cellular auto automata? - Oh yes. - The things you, the things you've discovered
about cellular auto automata. - Yes. Okay. So when I first started
cell cellular Automata, my first papers about them were, you know, the first sentence was always
about the second row of thermodynamics was always
about how does order manage to be produced, even though there's a second
row of thermodynamics, which tries to pull
things back into disorder. And I kind of, my early understanding of
that had to do with these are intrinsically irreversible
processes in cellular automata that that form, you know, conform orderly structures even from random initial conditions. But then what I realized this was, well actually it's, it's one of these things
where it was a discovery that I should have made
earlier but didn't. So, you know, I had, I had been
studying cellular automata. What I did was the sort of most
obvious computer experiment. You just try all the different
rules and see what they do. It's kind of like, you know, you've invented a computational telescope, you just pointed at the most
obvious thing in the sky, and then you just see what's there. And so I did that and I, you know, was making
all these pictures of, of how cellular automata work. And I studied these pictures.
I studied in great detail. There was, you can number the
rules for cellular automata. And one of them is, you know, rule 30. So I made a picture of
Rule 30 back in 1981 or so, and Rule 30, well, it's, and I, and I at the
time, I was just like, okay, it's another one of these
rules. I don't really, it happens to be asymmetric
left, right asymmetric. And it's like, let me just consider the case
of the symmetric ones just to keep things simpler, et
cetera, et cetera, et cetera. And I just kind of ignored it. And then sort of in, in, actually
in 1984, strangely enough, I, I ended up having a,
an early laser printer, which made very high resolution pictures. And I thought, I'm gonna print out an
interesting, you know, I wanna make an interesting picture. Let me take this rule 30
thing and just make a high resolution picture of it. And I did. And it's, it has this very remarkable
property that its rule is very simple. You started off just from one
black cell at the top and it makes this kind of triangular pattern. But if you look inside this
pattern, it looks really random. There's, you know, you look at the center column
of cells and, you know, I studied that in great detail and it's, so far as one can tell,
it's completely random. And it's kind of a little
bit like digits of pie. Once you, you know, you know the rule for
generating the digits of pie, but once you've generated
them, you know, 3.14159, et cetera, they seem completely random. And in fact, I, I put up this
prize back in, what was it, 2019 or something for, prove anything about
the sequence, basically. - [Fridman] Has anyone been
able to do anything on that? - People have sent me some
things, but it's, you know, I don't know how hard these problems are. I mean, I, I was kind of
spoiled cause I, 2007, I put up a prize for
determining whether a particular touring machine that I thought
was the simplest candidate for being a universal touring
machine determine whether it is or isn't a universal touring machine. And somebody did a really good job of, of winning that prize and
proving that it was a universal touring machine in about six months. And so I, you know, I didn't know whether that
would be one of these problems that was out there for hundreds of years, or whether in this particular case, young chap called Alex Smith, you know, nailed it in six months. And so with this little 30 formulation, I don't really know whether
these are things that are a hundred years away from
being able to, to get, or whether somebody's gonna come and do something very clever. - It's such a, I mean,
it's like for (indistinct), it's such a rule 30, it's
such a simple formulation. It feels like anyone can
look at it and understand it. And feel like it's within
grasp to be able to predict something to do to, to
direct some kind of law that allows you to predict
something about this. Middle column of rule 30. - [Wolfram] Right. But you
know, this is, this is- - Yet you can't. - Yeah, right. This is the intuition surprise
of computational reducibility and so on, that even though
the rules are simple, you can't tell what's going
to happen and you can't prove things about it. And I think so. So anyway, the, the, the, the thing I, I sort of started in 1984 or so, I started realizing there's
this phenomenon that you can have very simple rules. They produce apparently random behavior. Okay. So that's a little bit like the second neuro dynamics because it's like you have this simple
initial condition, you can, you know, readily see
that it's very, you know, you can describe it very easily. And yet it makes this thing
that seems to be random. Now, turns out there's
some technical detail about the second thermodynamics and about the idea of reversibility. When you have a, if you
have kind of a, a, a, a, a movie of two, you know, billiard balls colliding and
you see them collide and they bounce off, and you run
that movie in reverse, you can't tell which way was
the forward direction of time and which way was the
backward direction of time. When you're just looking at
individual billard balls, by the time you've got a whole
collection of them, you know, a million of them or something, then it turns out to be the case. And this is the, the sort of the, the mystery of the second law. That the orderly thing, you start with the orderly
thing and it becomes disordered. And that's the forward direction in time. And the other way round
of it starts disordered and becomes ordered. You just don't see that in the world. Now, in principle, if you, you know, if you sort of traced the
detailed motions of all those molecules backwards, you
would be able to, it, it will, it will. The reverse of time
makes, you know, as you, as you go forwards in time,
order goes to disorder, as you go backwards in time, order goes to disorder. - [Fridman] Perfectly. So yes. - Right. So the, the mystery
is why is it the case that, or one version of the mystery
is why is it the case that you never see something which
happens to be just the kind of disorder that you would need
to somehow evolve to order. Why does that not happen? Why do you always just see
order goes to disorder not the other way around? So the thing that I, I kind of realized, I started realizing in the
1980s, it's kind of like, it's a bit like cryptography. It's kind of like you start off from this, this key that's pretty simple, and then you kind of run it
and you can get this, you know, complicated random mess. And the thing that that well, I sort of started realizing
back then was that the second law is kind of a, a, a story
of computational reducibility. It's a story of, you know,
what seems, you know, what, what we can describe
easily at the beginning, we can only describe with a
lot of computational effort at the end. Okay. So now we come
many, many years later, and I was trying to sort of, well, having done this big project
to understand fundamental physics, I realized that sort
of a key aspect of that is understanding what observers are like. And then I realized that the
second auto neuro dynamics is the same story as a bunch
of these other cases. It is a story of a, a computationally bounded
observer trying to observe a computationally irreducible system. So it's a story of, you know, underneath the molecules
are bouncing around, they're bouncing around in
this completely determined way, determined by rules. But the point is that, that we as computationally
bounded observers, can't tell that there were
these sort of simple underlying rules to us that just looks random. And when it comes to this
question about can you prepare the initial state so that, you know, the disordered thing is, you know, you have exactly the right
disorder to make something orderly, A computationally
bounded observer cannot do that. We'd have to have done all
of this sort of irreducible computation to work out very precisely what this disordered state, what the exact right disordered
state is so that we would get this ordered thing produced from it. - What does it mean to be
computationally bounded observer? So observing a computation
reducible system, so the computationally bounded, is there something
formal you can say there? - Right. So it means, okay, you can, you can talk about Turing machines, you can talk about computational
complexity theory and you know, polynomial time
computation and things like this. There are a variety of ways to
make something more precise, but I think it's more useful, the intuitive version
of it is more useful. Which is basically just
to say that, you know, how much computation are you going to do to try and work out what's going on? And the answer is, you're not allowed to do a lot of, we are not able to do a
lot of computation when we, you know, we've got, you know, in this room there will
be a trillion, trillion, trillion molecules. Yeah. A little bit less. - It's a big room. - Right. And you know, at every moment, you know, there every microsecond or
something, these molecules, molecules are colliding. And that's a lot of computation
that's getting done. And the question is, in our brains, we do a lot less computation every second, then the computation done
by all those molecules. If there is computational
irr, reducibility, we can't work out in detail
what all those molecules are going to do. What we can do is only a much
smaller amount of computation. And so the, the second thermodynamics is
this kind of interplay between the underlying computational
irreducibility, and the fact that we as
preparers of initial states or as measures of what happens
are, you know, are, are not capable of doing
that much computation. So to us, another big formulation
of the second order of thermodynamics is this idea of
the law of entropy increase. - The characteristic that this universe, the entropy seems to be always increasing. What does that show to you
about the evolution of- - Well, okay, so, so- - [Fridman] The universe of time. - The History of entropy is yes, okay. And that's very confused in
the history of thermodynamics, because entropy was
first introduced by a guy called Rudolph Klauseous, and he did it in terms
of heat and temperature. Okay. Subsequently, it was reformulated by a
guy called Ludwig Boltzmann. And he formulated it in a much more kind of commonatorial type way. But he always claimed that
it was equivalent to Klaus's thing. And in, in one particular
simple example, it is, but that connection between
these two formulations of entropy, they've never been connected. I mean, it's there. There's really, so, okay, so the more general definition
of entropy due to Boltzmann is, is the following thing. So you say, I have a system and has many
possible configurations. Molecules can be in many
different arrangements, et cetera, et cetera, et cetera. If we know something about
the system, for example, we know it's in a box, it
has a certain pressure, it has a certain temperature, we know these overall facts
about it, then we say, how many microscopic
configurations of the system are possible given those overall constraints. And the entropy is the
log rhythm of that number. That's the definition. And that's the kind of the
general definition of entropy that, that turns out to be useful. Now, in Boltzmann's time, he thought these molecules could be placed anywhere you want. He didn't think and, but he said, oh, actually we can make it a
lot simpler by having the molecules be discreet. Well, actually he didn't
know molecules existed right? In, in those, in his
time, 1860s and so on. The idea that matter might
made of discrete stuff had been floated ever since ancient Greek times. But it had been a long time
debate about, you know, is matter discreet as a
continuous at the moment of, at that time, people mostly thought that
matter was continuous. And it was all confused with
this question about what heat is, and people thought heat
was this fluid, and it was, it was a big, big muddle. And the, and this, but Boltzmann said, let's assume they're discreet molecules. Let's even assume they have
discreet energy levels. Let's say everything is discreet, then we can do sort of
combinatorial mathematics and work out how many configurations
of these things there will be in the box. And we can say, we can
compute this entropy quantity. But he said, but of course it's just a fiction that these things are discreet. So he said, this is an interesting
piece of history by the way, that that, that, you know, that was, at that time people didn't
know molecules existed. There were other hints from, from looking at kind of
chemistry that there might be discreet atoms and so on, just from the, the combinatorics of, you know, two hydrogens and one oxygen
make water, you know, two, two amounts of hydrogen plus
one amount of oxygen together make water, things like this. But it wasn't known that
discrete molecules existed. And in fact, the people, you know, it wasn't until the beginning of the, of the 20th century that
brownie in motion was the final giveaway. Brown in motion is, you know, you look under a microscope
of these little pieces from pollen grains, you see they're being discreetly
kicked and those kicks are water molecules hitting
them and they're discreet. And in fact, it was, it was really quite
interesting history. I mean, Boltzmann had worked out how
things could be discreet and had basically invented something
like quantum theory in, in the 1860s. And, but he just thought it
wasn't really the way it worked. And then just a piece of physics history, cause I think it's kind of
interesting, in, in 1900, this guy called Max Plank, who'd been a longtime
thermodynamics person who was trying to, everybody was trying
to prove the second law of thermodynamics, including Max Plank. And Max Plank believed that radiation, like electromagnetic radiation, somehow the interaction of
that with matter was going to prove the second law of thermodynamics. But he had these experiments
that people had done on black body radiation, and there were these curves
and you couldn't fit the curve based on his idea for how
radiation interacted with matter. Those curves, he couldn't, he couldn't figure out
how to fit those curves, except he noticed that if he
just did what Boltzmann had done and assumed that
electromagnetic radiation was discreet, he could fit the curves. He said, but you know,
this is just a, you know, it just happens to work this way. Then Einstein came along
and said, well, by the way, you know, the electromagnetic field
might actually be discreet, it might be made of photons, and then that explains how this all works. And that was, you know, in 1905, that was, that was how kind of, that was how quant that piece
of quantum mechanics got started. Kind of interesting,
interesting piece of history. I didn't know until I was
researching this recently in 1904 and 1903, Einstein wrote
three different papers. And so, you know, just sort
of well known physics history. In 1905, Einstein wrote
these three papers. One introduced relativity theory, one explained brownie in motion, and one introduced basically photons. So kind of, you know, kind of a, a, a big deal year for
physics and for Einstein. But in the years before that, he'd written several papers
and what were they about? They were about the second
rule of thermodynamics, and they were an attempt
to prove the second rule of thermodynamics and their nonsense. And so I I I had no idea
that he'd done this. - Interesting. Me neither. - And in fact, what he did,
those three papers in 1905, well not so much the relativity paper, the one on brown in
motion, the one on photons. Both of these were about the story of sort of making the world discreet. And he got those, that
idea from Boltzmann. But Boltzmann didn't think, you know, Boltzmann kind of died
believing, you know, he said he has a quote,
actually, you know, you know, in the end, things are gonna
turn out to be discreet, and I'm gonna write down
what I have to say about this because, you know, eventually this stuff will
be rediscovered and I want to leave, you know, what I can about how things
are gonna be discreet. But, you know, I think he has some quote
about how, you know, one person can't stand against
the tide of history in, in saying that, you
know, matter is discreet. - Oh, so he's stuck by his guns in terms of matter is discreet. - Yes, he did. And, and the, you know, what's
interesting about this is, at the time, everybody,
including Einstein, kind of assumed that space
was probably gonna end up being discreet too. But that didn't work out
technically because it wasn't consistent with relativity
theory, or didn't seem to be. And so then in the history of physics, even though people had determined
that matter was discreet, electro mag, magnetic field was discreet, space was a holdout of not being discreet. And in fact, Einstein 1916 has this nice
letter he wrote where he says, in the end, it will turn
out space is discreet, but we don't have the mathematical
tools necessary to figure out how that works yet. And so, you know, I think it's kind of cool that
a hundred years later we do. - For you, you're pretty, pretty sure that at every
layer of reality it's discreet. - Right? And that space is
discreet and that the, I mean, and in fact, one of the things I've realized
recently is this kind of theory of heat. That, that the, you know, that heat is really this
continuous fluid, it's, it's kind of like the, the, you know, the caloric theory of heat, which turns out to be completely
wrong because actually heat is the motion of discrete molecules. Unless, you know there
are discreet molecules, it's hard to understand
what heat could possibly be. Well, you know, I think space is, is discreet and the question
is kind of what's the analog of the mistake that was made with
caloric in the case of space. And so I'm, my, my current
guess is that dark matter is, as I've, my little sort
of aphorism of the, of the last few months has been, you know, dark matter is the caloric of our time. That is, it will turn out that dark
matter is a feature of space and it is not a bunch of particles. You know, at the time when, when people were talking about heat, they knew about fluids
and they said, well, heat must be just be
another kind of fluid. Because that's what they knew about. But now people know about
particles and so they say, well, what's dark matter? It's not, it's not, it
just must be particles. - So what could dark matter
be as a feature of space? - Oh, I don't know yet. I mean, I think the, the thing I'm really, one of the things I'm hoping
to be able to do is to find the analog of brown in motion in space. So in other words, brown in motion was, was seeing down to the level of an effect from individual molecules. And so in the case of space,
you know, most of the things, the things we see about space so far, just everything seems continuous
brown in motion had been discovered in the 1830s. And it was only identified what it was, what it was the, the, the results of by Markowski and
Einstein at the beginning of the 20th century. And, you know, dark matter was, was discovered that phenomenon was discovered a hundred years ago. You know, the rotation, curves of galaxies don't follow
the luminous matter that was discovered a hundred years ago. And I think, you know, that I, I wouldn't be surprised if
there isn't an effect that we already know about that is
kind of the analog of brown in motion that reveals the
discreetness of space. And in fact, we we're
beginning to have some guesses. We have some, some evidence that black hole
mergers work differently when there's discrete space. And there may be things that
you can see in gravitational wave signatures and things associated with the discreetness of space. But this is kind of, for me, it's kind of, it's kind of interesting to
see this sort of recapitulation of the history of physics
where people, you know, vehemently say, you know,
matter is continuous, electromagnetic field is continuous, and turns out it isn't true. And then they say space is continuous. But, but, so, you know, entropy is the number of
states of the system consistent with some constraint. - [Fridman] Yes. - And the, the thing is that if you have, if you know in great detail
the position of every molecule in the gas, the entropy is, is always zero because there's
only one possible state. The, the configuration
of molecules in the gas, the molecules bounce around, they have a certain rule
for bouncing around. There's just one state of the
gas evolves to one state of the gas and so on. But it's only if you don't
know in detail where all the molecules are that you can say, well, the entropy increases because the things we do know about the molecules, there are more possible
microscopic states of the system consistent with what we do know about where the molecules are. And so the question of whether, so people, this sort of paradox in a sense of, oh, if we knew where all the molecules, where the entropy wouldn't increase, there was this idea introduced by, by Gibbs in the early 20th century. Well actually the very beginning of the, of the 20th century as
a physics professor, an American physics professor
was sort of the first distinguished American
physics professor at Yale. And he, he introduced this idea, of course graining this
idea that, well, you know, these molecules have a detailed
way they're bouncing around, but we can only observe a
course grained version of that. But the confusion has been, nobody knew what a valid
course screening would be. So nobody knew that whether
you could have this course screening that very carefully
was sculpted in just such a way that it would notice that
the particular configurations that you could get from the
simple initial condition, you know, they fit into
this course screening. And the course screening
very carefully observes that. Why can't you do that
kind of very detailed, precise course screening? The answer is because if you
are a computationally bounded observer and the underlying
dynamics is computationally irreducible, that's, that's what defines possible
core screenings is what a computationally, bounded observer can do. And it's the, it's the fact that a
computation bounded observer is, is forced to look only at
this kind of coarse grained version of what the system is doing. That's why, and, and because the, what what's what's going on
underneath is it's kind of filling out this, this,
the, the different possible. You're ending up with something
where the sort of underlying computational irreducibility is your, if, if all you can see is what the
course grained result is with comp, with a sort of
computationally bounded observation, then inevitably there are
many possible underlying configurations that are
consistent with that. - Just to clarify, I basically, any observer that exists inside
the universe is going to be computationally bounded. - No. Any observer like us, I don't know. I can't-. - When you say like us, what do you mean? What do you mean? Like us. - Well, humans with finite minds. - You're including the tools of science. - Yeah, yeah. I mean, and, and, and as we,
you know, we have more precise, and, and by the way, there are little sort of
microscopic violations of the second row of thermodynamics
that you can start to have when you have more precise
measurements of where precisely molecules are. - [Fridman] Right. - But for, for on a large scale, when you have enough molecules,
we don't have, you know, we are not tracing all those
molecules and we just don't have the computational
resources to do that. And it wouldn't be, you
know, I think the, the, to imagine what an observer
who is not computationally bounded would be like, it's an interesting thing because okay, so what does computational
boundedness mean among other things, it means we conclude
that definite things happen. We go, we take all this complexity
of the world and we make a decision we're gonna
turn left or turn right. And that is kind of reducing
all this kind of detail into we're observing it, we're, we're, we're, we're sort of crushing it
down to this, this one thing. And, and that if we didn't
do that, we wouldn't, we wouldn't have all this
sort of symbolic structure that we build up that lets us think things through with our finite minds. We'd be instead, you know, we'd be just, we'd be sort of one with the universe, so- - Yeah. So content to not simplify. - Yes. If we didn't simplify,
then we wouldn't be like us. We would be like the universe, like the, the intrinsic universe, but not having experiences
like the experiences we have where we, for example, conclude
that definite things happen. We, you know, we, we sort of have this, this notion of being able to make, make sort of narrative statements. - I wonder if it's just like
you imagined as a thought experiment what it's
like to be a computer. I wonder if it's possible to
try to begin to imagine what it's like to be in an unbounded
computational observer. - Well. Okay, so here's, here's
how that I think plays out. - So I mean, in this we talk about this ruliad, this space of all possible computations. - [Fridman] Yes. - And this idea of, you know, being at a certain place in the ruliad, which corresponds to
sort of a certain way of, of rep of a certain set of
computations that you are representing things in terms of, okay, so as you expand out in the ruliad, as you kind of encompass more possible views of the universe, as you encompass more possible
kinds of computations that you can do, eventually you
might say that's a real win. You know, we are, we're colonizing
the ruliad, we're, we're, we are building out more paradigms about how to think about things. And eventually you might say, we, we, we won all the way we managed
to colonize the whole ruliad. Okay, here's the problem with that. The problem is that the
notion of existence, coherent existence requires
some kind of specialization by the time you are the whole ruliad, by the time you cover the
whole ruliad in no useful sense do you coherently exist. So in other words, in in interesting the notion of existence, the notion of what we think of as, as, as definite existence requires
this kind of specialization requires this kind of idea that we are, we are not all possible things. We are the, a particular set of things. And that's kind of how we,
that that's kind of what, what makes us have a coherent existence. If we were spread throughout
the ruliad we would not, there would be no coherence
to the way that we work. We would work in all possible ways. And that wouldn't be kind
of a, a notion of identity. We wouldn't have this notion of kind of, of of of coherent identity. - I am geographically
located somewhere exactly, precisely in the ruliad, therefore I am is the decart kind of- - Yeah, yeah. Right. Were you are in a certain
place in physical space or in a certain place in ruliad space. And if, if you are, if you
are sufficiently spread out, you are no longer coherent
and you no longer have, I mean, in, in the, in our perception of
what it means to exist and to have experience. Doesn't happen that way. - So therefore, so to to to exist means to
be computationally bounded. - I think so, to exist in the way that we think of ourselves
as existing. Yes. - The very active existence is
like operating in this place that's computation irreducible. So that this is just giant mess
of things going on that you can't possibly predict. But nevertheless, because
of your limitations, you, you have an imperative of like, what is it an imperative
or a skillset to simplify or an ignorance sufficient level. - Okay. So the thing which is not
obvious is that you are taking a slice of all this complexity, just like we have all of these molecules bouncing around in the room. But all we notice is,
you know, the, the, the, the kind of the flow of the
air or the pressure of the air. We are just noticing
these particular things. And the, the big interesting
thing is that there are rules, there are laws that govern
those big things that we, we observe. - [Fridman] Yeah. So it's not obvious. - Amazing because it doesn't
feel like it's a slice. - [Wolfram] Yeah. Well, right. - It's not a slice. Well, it's like a, it's like an abstraction. - Yes. But I mean the fact
that the gas laws work. That we can describe pressure,
volume, et cetera, et cetera, et cetera. And we don't have to go down to the level of talking about individual molecules. That is a non-trivial fact. And, and here's the thing that I, sort of exciting thing
as far as I'm concerned, the fact that there are certain
aspects of the universe. So, you know, we think space is made ultimately
these atoms of space and these hypergraphs and so on. And we think that, but we nevertheless perceive
the universe at a large scale to be like continuous space and so on. We, in quantum mechanics, we think that there are
these many threads of time, these many threads of
history, yet we kind of span. So, so, you know, in,
in quantum mechanics, in our models of physics, there are these time
is not a single thread. Time breaks into many threads. They branch, they merge and, but we are part of that
branching, merging universe. And so our brains are also
branching and merging. And so when we perceive the universe, we are branching brains
perceiving a branching universe. And so the fact that the claim that we ex, we believe that we are persistent in time, we have this single thread of experience. That's the statement that
somehow we managed to aggregate together those separate threads
of time that are separated in, in the operation of, in the fundamental
operation of the universe. So just as in space, we're averaging over
some big region of space, and we are looking at many, many at the aggregate effects
of many atoms of space. So similarly in what we
call branchial space, the space of these,
these quantum branches, we are effectively averaging
over many different branches of possible, of histories of the universe. And so in and in, in thermodynamics, we're averaging over many
configurations of, you know, many, many possible positions of molecules. So what what we see here is, so the question is when you
do that averaging for space, what are the aggregate laws of space? When you do that averaging
of a branchial space, what are the aggregate
laws of branchial space, when you do that averaging
over the molecules and so on, what are the aggregate laws you get? And this is, this is the thing that I, I think is just amazingly,
amazingly neat that. - [Fridman] That there are
aggregate laws at all for- - Sure. Well, yes, but the question is what
are those aggregate laws? So the answer is for space, the aggregate laws are
Einstein's equations for gravity, for the structure of space
time, for branchial space. The aggregate laws are the
laws of quantum mechanics. And for the case of, of
molecules and things, the aggregate laws are basically the second law of thermodynamics. And so the, the, that's the, and the things that follow from the second law of thermodynamics. And so what that means is that the three great theories of 20th century physics, which are basically generality
of the theory of gravity, quantum mechanics, and
statistical mechanics, which is what kind of grows outta the second row of thermodynamics. All three of the great theories
of 20th century physics are the result of this interplay between computational irreducibility, and the computational
boundedness of observers. And you know, for me, this is really neat because it
means that all three of these laws are derivable. So we used to think that, for example, Einstein's equations
were just sort of a wheel in feature of our universe. That they could be,
universe might be that way. It might not be that way.
Quantum mechanics is just like, well, it just happens to be that way. And the second law people
kind of thought, well, maybe it is derivable. Okay? What turns out to be the
case is that all three of the fundamental principles
of physics are derivable, but they're not derivable
just from mathematics. They require, or just from some
kind of logical computation, they require one more thing.
They require that the observer, that the thing that is sampling
the way the universe works is an observer who has
these characteristics of computational boundedness of belief and persistence and time. And so that, that means that it is the
nature of the observer you know, the rough nature of the observer. Not the details of, oh, we've got two eyes and we observe photons of this frequency and so on. But the, the, the, the kind of the very coarse
features of the observer then imply these very precise
facts about physics. And it's, it's, I think it's amazing. - So if we just look at
the actual experience of the observer that we
experience this reality, it seems real to us. And you're saying because
of our bonded nature, it's actually all an illusion. It's an a simplification. - Well, yeah. It's a
simplification. Right? What's what's, well. - You don't think a
simplification is an illusion? - No, I mean it's, it's,
well, I don't know. I mean- - What is real? - What's underneath? Okay, that's an interesting question. What's real? And that relates to the whole question of why does the universe exist? And, you know, what is the difference
between reality and a mere representation of what's going on? - [Fridman] Yes. We experience the representation. - Yes. But the, the question of, so,
so one question is, you know, why is there a thing which
we can experience that way? And the answer is because
this ruliad object, which is this entangled limit
of all possible computations, there is no choice about
it. It has to exist. It has to, there has to be such a thing. It is in, in the same sense
that, you know, two plus two, if you define what two is and
you plot pluses and so on, two plus two has to equal four. Similarly, this rule ad this limit of
all possible computations just has to be a thing you, that is, once you have the idea of computation, you inevitably have the ruliad. - [Fridman] You're gonna have
to have a ruliad. Yeah, yeah. - Right? And, and what's
important about it, there's just one of it, it's, it's, it's just this unique object
and that unique object necessarily exists. And then the question is what? And then we once, once you know that we are
sort of embedded in that and taking samples of it, that it's sort of inevitable
that there is this thing that we can perceive that is, you know, that, that our perception of kind of physical reality necessarily is that way, given that we are observers with the characteristics we have. So in other words, the fact that the fact that
the universe exists, is it, it's actually, it's almost
like it's, you know, to think about it almost
theologically so to speak. And I, and I've, I've really, it, it's, it's funny because a lot
of the questions about the existence of the universe and so on, they, they transcend what kind of
the science of the last few hundred years has really been
concerned with the science of the last few hundred years. Hasn't thought it could talk
about questions like that. - [Fridman] Yeah. And, but I think it's kind of, and so a lot of the kind
of arguments of, you know, does God exist, you know, is it obvious that I
think it in some sense, in some representation, it's sort of more, more obvious that, that something sort of bigger
than us exists than that we exist and we are, you know, our existence and as observers
the way we are is sort of a contingent thing about the universe. And it's more inevitable that the whole, the whole universe kind
of the whole set of all possibilities, exists. But, but this question about, you know, is is it real or is it an illusion? You know, all we know is our experience. And so the fact that, well, our experience is this
absolutely microscopic piece of sample of the ruliad, and
we are, and, and you know, there's this this point about, you know, we might sample more and more
of the ruliad we might learn more and more about, we
might learn, you know, like, like different areas of physics, like quantum mechanics for example. The fact that it, it was discovered I think is
closely related to the fact that electronic amplifiers were
invented that allowed you to take a small effect and amplify it up, which hadn't been possible before. You know, microscopes had been invented that magnify things and so on. But the, you know, having a very small effect and
being able to magnify it was sort of a new thing that allowed
one to see a different sort of aspect of the universe and let one discover this kind of thing. So, you know, we can expect that in the
ruliad there are an infinite collection of new things we can discover. There's, there's in fact computational irreducibility kind of guarantees that there will be an infinite
collection of kind of, you know, pockets of reducibility
that can be discovered. - Boy, would it be fun to take a walk down the ruliad and see what
kind of stuff we find there? You you write about alien intelligences. - [Wolfram] Yes. - I mean, just these worlds. - Yes. Well, the problem
with these worlds is that. - We can't talk to 'em. - [Wolfram] Yes. And, and you know, the thing is what I've kind of
spent a lot of time doing of just studying computational
systems, seeing what they do, what I now call ruliology, kind
of just the study of rules. - [Fridman] Yeah. - And what they do. You know, you can kind of easily jump
somewhere else in the ruliad and start seeing what do these rules do? - [Fridman] Yeah. - And what you says they, they just, they do what they do and
there's no human connection, so to speak. - Did you think, you know,
some, some people are able to communicate with animals. Do you think you can
become a whisper of these? - I've been trying. That's what I've spent
some part of my life doing. - Have, have you, have
you heard, and I mean, are you at the risk of losing your mind? - Sort of my favorite science
discovery is this fact that these very simple programs can produce very complicated behavior. - [Fridman] Yeah. - And that, and that fact
is kind of in a sense, a whispering of something out
in the computational universe that we didn't really
know was there before. I mean, it, it's, you know,
I it's like, you know, back in the 1980s I was doing
a bunch of work with some very, very good mathematicians, and they were like trying
to pick away, you know, can we figure out what's going on in these computational systems? And they, they basically said, look, the math we have just doesn't
get anywhere with this. We're stuck. There's nothing to say. We have nothing to say. And, you know, in a sense, perhaps my main achievement at
that time was to realize that the very fact that the, the good mathematicians had
nothing to say was itself a very interesting thing. That was kind of a, a sort of, in some sense, a whispering of a different
part of the ruliad that one hadn't, you know, one wasn't, was not accessible from
what we knew in mathematics and so on. - Does it make you sad that
you're exploring some of these gigantic ideas and it feels
like we're on the verge of breaking through to some
very interesting discoveries, and yet you're just a finite being that's going to die way too soon? And that scan of your brain or
your full body kind of shows that you're- - Yeah, it's just a bunch of meat. - It's just a bunch of meat. Yeah. Does that make you
make you a little sad? - It's kind of a shame. I mean, I kinda like to see how
all this stuff works out, but I think the thing
to realize, you know, it's an interesting sort of
thought experiment. You know, you, you say, okay, you know, let's assume we can get cryonics to work, and one day it will, that will be one of these things that's kind of like ChatGPT. One day somebody will
figure out, you know, how to get water from zero
degrees centigrade down to, you know, minus 44 or
something without it expanding. And you know, cryonics will be solved and
you'll be able to like just, you know, put a pause in
so to speak, and you know, kind of reappear a hundred
years later or something. And the thing though that I've
kind of increasingly realized is that in a sense, this, this whole question of kind of the, the sort of one is embedded
in a certain moment, in, in time and, you know, kind of
the things we care about now, the things I care about now,
for example, had I lived, you know, 500 years ago, many of the things I care
about now, it's like, that's totally bizarre. I mean, it's, nobody
would care about that. It's not even a thing one
thinks about in the future, the things that most
people will think about. You know, one will be a strange relic
of thinking about, you know, the kind of, you know, it might be one might have been
a theologian thinking about, you know, how many angels fit on the
head of a pin or something. And that might have been the, you know, the big intellectual thing.
So I think it's a, it's a, but yeah, it's a, it's a, you know, it's one of these things
where particularly, you know, I've had the, I don't
know, good or bad fortune. I'm not sure. I think it's a,
it's a mixed thing that I've, you know, I've invented a bunch of things, which I kind of can, I think see well enough what's
gonna happen that, you know, in 50 years, a hundred years, whatever, assuming the world doesn't
exterminate itself, so to speak, you know, these are things that will be
sort of centrally important to what's going on. And it's kind of both, it's both a good thing and a bad thing in terms of the passage of one's life. I mean, it's kind of like, if everything I'd figured
out was like, okay, I figured it out when I was 25
years old and everybody says it's great and we're
done. And it's like, okay, but I'm gonna live another how many years? And that's kind of, it's all
downhill from there in a sense. It's, it's better in some sense
to, to be able to, you know, there's, there's, it, it sort of keeps things
interesting that, you know, why I can see, you know,
a lot of these things. I mean, it's kind of, I I
didn't expect, you know, ChatGPT, I didn't expect the kind of, the sort of opening up of
this idea of computational and computational language that's
been made possible by this. I didn't expect that this is,
this is ahead of schedule, so to speak. You know, even though the sort of the, the big kind of flowering of
that stuff I'd sort of been assuming was another 50 years away. So if it turns out it's a lot less time, that's pretty cool because, you know, I'll hopefully get to see it, so to speak, rather than, than- - Well, I, I think I speak for a very, very large number of people
in saying that I hope you stick around for a long time to come. You've had so many interesting ideas. You've created so many interesting
systems over the years, and I can see now that
GPT and language models broke open the world even more. I can't wait to see you
at the forefront of this development, what you, what you do. And yeah, I've been a fan of yours. Like I've told you many, many times since the very beginning. I'm deeply grateful that you
wrote a new kind of science, that you explored this
mystery of cellular automata and inspired this one little kid in me to, to pursue artificial intelligence in all this beautiful world. So Stephen, thank you so much. It's a huge honor to talk to you, to, to just be able to pick your
mind and to explore all these ideas with you. And please keep going and I
can't wait to see what you come up with next. And thank you for talking today. - Thanks. - We went past midnight. We only did four and a half hours. I mean, we could probably
go for four more, but we'll save that till next time to, this is around number
four and we'll, I'm sure. Talk me more times. Thank you so much. - My pleasure. - Thanks for listening
to this conversation with Steven Wolfram. To support this podcast please check out our sponsors in the description. And now let me leave you some
words from George Cantor, the essence of mathematics
lies in its freedom. Thank you for listening and
hope to see you next time.