The following is a conversation with Stephen Wolfram, a computer scientist, mathematician,
and theoretical physicist who is the founder and CEO of Wolfram Research, a company behind
Mathematica, Wolfram Alpha, Wolfram Language, and the new Wolfram Physics Project.
He's the author of several books, including A New Kind of Science, which, on a personal note,
was one of the most influential books in my journey in computer science and artificial
intelligence. It made me fall in love with the mathematical beauty and power of cellular
automata. It is true that perhaps one of the criticisms of Stephen is at a human level,
that he has a big ego, which prevents some researchers from fully enjoying the content
of his ideas. We talk about this point in this conversation. To me, ego can lead you astray,
but can also be a superpower, one that fuels bold, innovative thinking that refuses to surrender
to the cautious ways of academic institutions. And here, especially, I ask you to join me in
looking past the peculiarities of human nature and opening your mind to the beauty of ideas
in Stephen's work and in this conversation. I believe Stephen Wolfram is one of the most
original minds of our time and, at the core, is a kind, curious, and brilliant human being.
This conversation was recorded in November 2019 when the Wolfram Physics Project was underway,
but not yet ready for public exploration as it is now. We now agreed to talk again,
probably multiple times, in the near future, so this is round one and stay tuned for round two soon.
This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube
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this podcast. And now here's my conversation with Steven Wolfram. You and your son Christopher
helped create the alien language in the movie Arrival. So let me ask maybe a bit of a crazy
question, but if aliens were to visit us on earth, do you think we would be able to find a common
language? Well, by the time we're saying aliens are visiting us, we've already prejudiced the whole
story because the concept of an alien actually visiting, so to speak, we already know they're
kind of things that make sense to talk about visiting. So we already know they exist in the
same kind of physical setup that we do. They're not, you know, it's not just radio signals,
it's an actual thing that shows up and so on. So I think in terms of, you know, can one find ways
to communicate? Well, the best example we have of this right now is AI. I mean, that's our first
sort of example of alien intelligence. And the question is, how well do we communicate with AI?
You know, if you were to say, if you were in the middle of a neural net, and you open it up,
and it's like, what are you thinking? Can you discuss things with it? It's not easy,
but it's not absolutely impossible. So I think by the time, but given the setup of your question,
aliens visiting, I think the answer is, yes, one will be able to find some form of communication,
whatever communication means, communication requires notions of purpose and things like this.
It's a kind of philosophical quagmire. So if AI is a kind of alien life form, what do you think
visiting looks like? So if we look at aliens visiting, and we'll get to discuss computation
and the world of computation, but if you were to imagine, you said you're already prejudiced
something by saying you visit, but how would aliens visit? By visit, there's kind of an implication
and here we're using the imprecision of human language, you know, in a world of the future,
and if that's represented in computational language, we might be able to take the concept visit
and go look in the documentation basically and find out exactly what does that mean,
what properties does it have and so on. But by visit, in ordinary human language,
I'm kind of taking it to be there's, you know, something, a physical embodiment
that shows up in a spacecraft, since we kind of know that that's necessary. We're not imagining
it's just, you know, photons showing up in a radio signal that, you know, a photon's in
some very elaborate pattern, we're imagining it's physical things made of atoms and so on
that show up. Can it be photons in a pattern? Well, that's good question. I mean, whether
there is the possibility, you know, what counts as intelligence? Good question. I mean, it's,
you know, and I used to think there was sort of a, oh, there'll be, you know, it'll be clear
what it means to find extraterrestrial intelligence, etc, etc, etc. I've, I've
increasingly realized as a result of science that I've done, that there really isn't a
bright line between the intelligent and the merely computational, so to speak. So, you know,
in our kind of everyday sort of discussion, we'll say things like, you know, the weather has a mind
of its own. Well, we let's unpack that question. You know, we realize that there are computational
processes that go on that determine the fluid dynamics of this and that and the atmosphere,
etc, etc, etc. How do we distinguish, distinguish that from the processes that go on in our brains
of, you know, the physical processes that go on in our brains? How do we, how do we,
how do we separate those? How do we say the physical processes going on that represent
sophisticated computations in the weather? Oh, that's not the same as the physical processes
that go on that represent sophisticated computations in our brains. The answer is,
I don't think there is a fundamental distinction. I think the distinction for us is that there's
kind of a, a thread of, of history and so on that connects kind of what happens in different brains
to each other, so to speak. And it's a, you know, what happens in the weather is something
which is not connected by sort of a, a thread of, of civilizational history, so to speak,
to what we're used to. In our story, in the stories that the human brain has told us,
but maybe the weather has its own stories. Absolutely. Absolutely. And that's, and that's
where we run into trouble thinking about extraterrestrial intelligence, because,
you know, it's like that pulsar magnetosphere that's generating these very elaborate radio
signals. You know, is that something that we should think of as being this whole civilization
that's developed over the last however long, you know, millions of years of, of, of processes going
on in the, in the neutron star or whatever versus what, you know, what we're used to in human
intelligence. And I think it's a, I think in the end, you know, when people talk about
extraterrestrial intelligence and where is it and the whole, you know, Fermi paradox of
how come there's no other signs of intelligence in the universe. My guess is that we've got sort of
two alien forms of intelligence that we're dealing with, artificial intelligence and
sort of physical or extraterrestrial intelligence. And my guess is people will sort of get comfortable
with the fact that both of these have been achieved around the same time. And in other words,
people will say, well, yes, we're used to computers, things we've created, digital
things we've created being sort of intelligent like we are. And they'll say, oh, we're kind
of also used to the idea that there are things around the universe that are kind of intelligent
like we are, except they don't share the sort of civilizational history that we have. And so
we don't, you know, they're a different branch. I mean, it's similar to when you talk about life,
for instance, I mean, you kind of said life form, I think, almost synonymously with intelligence,
which I don't think is, you know, the the AIs will be upset to hear you equate those.
Because I have really probably implied biological life. Right. Right. But you're saying, I mean,
we'll explore this more, but you're saying it's really a spectrum and it's all just the kind
of computation. And so it's it's a full spectrum. And we just make ourselves special by weaving
a narrative around our particular kinds of computation. Yes. I mean, the thing that I think
I've kind of come to realize is, you know, at some level, it's a little depressing to realize
that there's there's so little or liberating. Well, yeah, but I mean, it's, you know, it's the
story of science, right? And, you know, from Copernicus on, it's like, you know, first we were
like convinced our planets at the center of the universe. No, that's not true. Well,
then we were convinced there's something very special about the chemistry that we have
as biological organisms. That's not really true. And then we're still holding out that hope, oh,
this intelligence thing we have, that's really special. Yeah. I don't think it is. However,
in a sense, as you say, it's kind of liberating for the following reason that you realize that
what's special is the details of us, not some abstract attribute that, you know, we could wonder,
oh, is something else going to come along and, you know, also have that abstract attribute?
Well, yes, every abstract attribute we have, something else has it. But the full details of
our kind of history of our civilization and so on, nothing else has that. That's what,
you know, that's our story, so to speak. And that's sort of almost by definition special.
So I view it as not being such a, I mean, I was initially, I was like, this is bad. This is kind
of, you know, how can we have self respect about the things that we do? Then I realized the details
of the things we do, they are the story. Everything else is kind of a blank canvas.
So maybe on a small tangent, you just made me think of it, but what do you make of the monoliths
in 2001 Space Odyssey in terms of aliens communicating with us and sparking the kind of particular
intelligent computation that we humans have? Is there anything interesting to get from that
sci-fi? Yeah, I mean, I think what's fun about that is, you know, the monoliths are these,
you know, one to four to nine perfect cuboid things. And in the, you know, Earth of a million
years ago, whatever they were portraying with a bunch of apes and so on, a thing that has that
level of perfection seems out of place. It seems very kind of constructed, very engineered.
So that's an interesting question. What is the, you know, what's the techno signature, so to
speak? What is it that you see it somewhere and you say, my gosh, that had to be engineered?
Now, the fact is we see crystals, which are also very perfect. And, you know, the perfect ones
are very perfect. They're nice polyhedral or whatever. And so in that sense, if you say, well,
it's a sign of sort of, it's a techno signature that it's a perfect, you know, a perfect polygonal
shape, polyhedral shape. That's not true. And so then it's an interesting question. What is the,
what is the right signature? I mean, like, you know, Gauss famous mathematician, you know,
he had this idea, you should cut down the Siberian forest and the shape of sort of a typical image
of the proof of the Pythagorean theorem on the grounds that is a kind of cool idea didn't get
done. But, you know, it was on the grounds that the Martians would see that and realize, gosh,
there are mathematicians out there. It's kind of, you know, it's the, in his theory of the world,
that was probably the best advertisement for the cultural achievements of our species. But, you
know, it's a reasonable question. What do you, what can you send or create that is a sign of
intelligence in its creation, or even intention in its creation? Yeah, you talk about if we were
to send a beacon. Can you, what should we send? Is math our greatest creation? Is, what is our
greatest creation? I think, I think, and it's a, it's a philosophically doomed issue. I mean,
in other words, you send something, you think it's fantastic. But it's kind of like, we are part of
the universe. We make things that are, you know, things that happen in the universe. Computation,
which is sort of the thing that we are, in some abstract sense, and sense using to create all
these elaborate things we create, is surprisingly ubiquitous. In other words, we might have thought
that, you know, we've built this whole giant engineering stack that's led us to microprocessors,
that's led us to be able to do elaborate computations. But this idea that computations are
happening all over the place. The only question is whether, whether there's a thread that connects
our human intentions to what those computations are. And so I think, I think this question of what
do you send to kind of show off our civilization in the best possible way, I think any kind of
almost random slab of stuff we've produced is about equivalence to everything else. I think
it's one of these things where- It's a non-romantic way of phrasing it. I just, sorry to interrupt,
but I just talked to Andrew Ian, who's the wife of Carl Sagan. And so I don't know if you're
familiar with the Voyager. I mean, she was part of sending, I think, brainwaves of, you know,
I want you to- Wasn't it hers? Her brainwaves when she was first falling in love with Carl
Sagan, right? It's this beautiful story that perhaps you would shed down the power of that
by saying we might as well send anything else. And that's interesting. All of it is kind of
an interesting, peculiar thing that's- Yeah, yeah, right. Well, I mean, I think it's kind
of interesting to see on the Voyager, you know, golden record thing. One of the things that's
kind of cute about that is, you know, it was made when was it in the late 70s, early 80s.
And, you know, one of the things, it's a phonograph record, okay? And it has a diagram
of how to play a phonograph record. And, you know, it's kind of like it's shocking that in just 30
years, if you show that to a random kid of today and you show them that diagram, and I've tried
this experiment, they're like, I don't know what the heck this is. And the best anybody can think of
is, you know, take the whole record, forget the fact that it has some kind of helical track in it,
just image the whole thing and see what's there. That's what we would do today.
In only 30 years, our technology has kind of advanced to the point where the playing of a
helical, you know, mechanical track on a phonograph record is now something bizarre. So,
you know, it's, it's a, that's a cautionary tale, I would say, in terms of the ability to
make something that in detail sort of leads by the nose, some, you know, the aliens or whatever
to do something. It's like, no, you know, best you're going to do, as I say, if we were doing this
today, we would not build a helical scan thing with a needle. We would just take some high-resolution
imaging system and get all the bits off it and say, oh, it's a big nuisance that they put in a helix,
you know, in a spiral. Let's, let's just, you know, unravel the spiral and, and start from there.
Do you think, and this will get into trying to figure out interpretability of AI, interpretability
of computation, being able to communicate with various kinds of computations, do you think it
would be able to, if you're put, put your alien hat on, figure out this record, how to play this
record? Well, it's a question of what one wants to do. I mean, understand what the other party was
trying to communicate or understand anything about the other party. What does understanding mean?
I mean, that's the issue. The issue is, it's like when people were trying to do
natural language understanding for computers, right? So people tried to do that for years.
It wasn't clear what it meant. In other words, you take your piece of English or whatever,
and you say, gosh, my computer has understood this. Okay, that's nice. What can you do with that?
Well, so for example, when we did, you know, Built Wolf Malfur, you know, one of the things was,
it's, you know, it's doing question answering and so on, it needs to do natural language
understanding. The reason that I realized after the fact, the reason we were able to do natural
language understanding quite well, and people hadn't before, the number one thing was,
we had an actual objective for the natural language understanding. We were trying to turn
the natural language into computation, into this computational language that we could then
do things with. Now, similarly, when you imagine your alien, you say, okay, we're playing the record.
Did they understand it? Well, depends what you mean. If they, you know, if we, if there's a
representation that they have, if it converts to some representation where we can say, oh, yes,
that is a, that's a representation that we can recognize is represents understanding,
then all well and good. But actually, the only ones that I think we can say would represent
understanding are ones that will then do things that we humans kind of recognize as being useful
to us. Maybe try and understand, quantify how technologically advanced this particular
civilization is. So are they a threat to us from a military perspective? Yeah, yeah. That's probably
the kind of first kind of understanding they'll be interested in. Gosh, that's so hard. I mean,
that's like in the Arrival movie, that was sort of one of the key questions is, you know, why are
you here, so to speak? And it's, are you going to hurt us? Right. But, but even that is, you know,
it's a very unclear, you know, it's like, the, are you going to hurt us? That comes back to a lot
of interesting AI ethics questions, because the, you know, we might make an AI that says, well,
take autonomous cars, for instance, you know, are you going to hurt us? Well, let's make sure
you only drive it precisely to the speed limit, because we want to make sure we don't hurt you,
so to speak, because that's some, and then well, something, you know, but you say,
but actually that means I'm going to be really late for this thing. And, you know,
that sort of hurts me in some way. So it's hard to know even, even the definition of what it means
to hurt someone is unclear. And as we start thinking about things about AI ethics and so on,
that's, you know, something one has to address. There's always tradeoffs. And that's the annoying
thing about ethics. Yeah, well, right. And I mean, I think ethics, like these other things we're
talking about, is a deeply human thing. There's no abstract, you know, let's write down the theorem
that proves that this is ethically correct. That's a, that's a meaningless idea. You know, you have
to have a ground truth, so to speak, that's ultimately sort of what humans want. And they
don't all want the same thing. So that gives one all kinds of additional complexity and thinking
about that. One convenient thing in terms of turning ethics into computation, you can ask
the question of what maximizes the likelihood of the survival of the species?
Yeah, that's a good existential issue. But then when you say survival of the species, right,
you might say, you might, for example, for example, let's say, forget about technology,
just, you know, hang out and, you know, be happy, live our lives, go on to the next generation,
you know, go through many, many generations, where in a sense, nothing is happening.
Is that okay? Is that not okay? Hard to know. In terms of, you know, the attempt to do elaborate
things and the attempt to might be counterproductive for the survival of the species. Like, for instance,
I mean, and, you know, I think it's, it's also a little bit hard to know. So, okay, let's take that
as a sort of thought experiment. Okay. You know, you can say, well, what are the threats that we
might have to survive, you know, the super volcano, the asteroid impact, the, you know, all these
kinds of things. Okay, so now we inventory these possible threats, and we say, let's make our
species as robust as possible relative to all these threats. I think in the end, it's a,
it's sort of an unknowable thing, what, what it takes to, you know, so, so given that you've got
this AI, and you've told it, maximize the long term, what does long term mean? Does long term
mean until the sun burns out? That's, that's not going to work. And, you know, does long term mean
next 1000 years? Okay, they're probably optimizations for the next 1000 years, that it's like,
it's like if you're running a company, you can make a company be very stable for a certain period
of time. Like if, you know, if your company gets bought by some, you know, private investment group,
then they'll, you know, you can, you can run a company just fine for five years by just taking
what it does and, you know, removing all R&D. And the company will burn out after a while,
but it'll run just fine for a while. So if you tell the AI, keep the humans okay for 1000 years,
there's probably a certain set of things that one would do to optimize that many of which
one might say, well, that would be a pretty big shame for the future of history, so to speak,
for that to be what happens. But I think, I think in the end, you know, as you start thinking about
that question, it is what you realize is there's a whole sort of raft of undecidability,
computational irreducibility. In other words, it's, I mean, one of the good things about
sort of the, the, the, what our civilization has gone through and what sort of we humans go through
is that there's a certain computational irreducibility to it in the sense that
it isn't the case that you can look from the outside and just say, the answer is going to be
this. At the end of the day, this is what's going to happen. You actually have to go through the
process to find out. And I think that's, that's both that feels better in the sense it's not a,
you know, something is achieved by going through all of this, all of this process. And it's,
but it also means that telling the AI, go figure out, you know, what will be the best outcome.
Well, unfortunately, it's going to come back and say, it's kind of undecidable what to do.
We'd have to run all of those scenarios to see what happens. And if we want it for the infinite
future, we're thrown immediately into sort of standard issues of kind of infinite computation
and so on. So yeah, even if you get that the answer to the universe and everything is 42,
you still have to actually run the universe to figure out like the question, I guess,
or the, you know, the journey is the point. Right. Well, I think it's saying to summarize,
this is the result of the universe. Yeah. That's, if that is possible, it tells us,
I mean, the whole sort of structure of thinking about computation and so on and thinking about
how stuff works. If it's possible to say, and the answer is such and such, you're basically
saying there's a way of going outside the universe. And you're kind of, you're getting yourself into
something of a sort of paradox, because you're saying, if it's knowable, what the answer is,
then there's a way to know it that is beyond what the universe provides. But if we can know it,
then something that we're dealing with is beyond the universe. So then the universe
isn't the universe, so to speak. So. And in general, as we'll talk about, at least for our
small human brains, it's hard to show that the result of a sufficiently complex computation.
It's hard. I mean, it's probably impossible, right, undissidability. So, and the universe appears
by at least the poets to be sufficiently complex that we won't be able to predict what the heck
it's all going to. Well, we better not be able to, because if we can, it kind of denies, I mean,
it's, you know, we're part of the universe. Yeah. So what does it mean for us to predict? It means
that we, that our little part of the universe is able to jump ahead of the whole universe. And
you know, this, this quickly winds up, I mean, that it is conceivable. The only way we'd be able to
predict is if we are so special in the universe, we are the one place where there is computation
more special, more sophisticated than anything else that exists in the universe. That's the only
way we would have the ability to sort of the almost theological ability, so to speak, to predict
what happens in the universe is to say somehow we're, we're better than everything else in the
universe, which I don't think is the case. Yeah, perhaps we can detect a large number of looping
patterns that reoccur throughout the universe and fully describe them. And therefore, but then it's,
it still becomes exceptionally difficult to see how those patterns interact and what kind of
complexity. Well, look, the most remarkable thing about the universe is that it's, has regularity
at all, might not be the case. Does it have regularity? Absolutely. If it's full of, I mean,
physics is successful, you know, it's full of, of laws that tell us a lot of detail about how
the universe works. I mean, it could be the case that, you know, the 10 to the 90th particles in
the universe, they all do their own thing, but they don't, they all follow, we already know,
they all follow basically physical, the same physical laws. And that's something that's a very
profound fact about the universe. What conclusion you draw from that is unclear. I mean, in the,
you know, the early, early theologians, that was, you know, exhibit number one for the existence
of God. Now, you know, people have different conclusions about it. But the fact is, you know,
right now, I mean, I happen to be interested, actually, I've just restarted a long running
kind of interest of mine about fundamental physics. I'm kind of like, I'm on, I'm on a bit
of a quest, which I'm about to make more public to see if I can actually find the fundamental
theory of physics. Excellent. We'll come to that. And I just had a lot of conversations with
quantum mechanics folks with, so I'm really excited on your take, because I think you have a
fascinating take on the fundamental nature of our reality from a physics perspective.
So, and what might be underlying the kind of physics as we think of it today. Okay,
let's take a step back. What is computation? That's a good question. Operationally,
computation is following rules. That's kind of it. I mean, computation is the result,
is the process of systematically following rules. And it is the thing that happens when you do that.
So taking initial conditions or taking inputs and following rules. I mean, what are you following
rules on? So there has to be some data, some not necessarily, it can be something where there's a,
you know, very simple input. And then you're following these rules. And you'd say there's
not really much data going into this. It's you can actually pack the initial conditions into
the rule, if you want to. So I think the question is, is there a robust notion of computation?
What does robust mean? What I mean by that is something like this. So one of the things in a
different, in an area of physics, something like energy, okay, they're different forms of energy.
But somehow energy is a robust concept that doesn't, isn't particular to kinetic energy or,
you know, nuclear energy or whatever else, there's a robust idea of energy. So one of the things
you might ask is, is there a robust idea of computation? Or does it matter that this computation
is running in a Turing machine? This computation is running in a CMOS silicon CPU. This computation
is running in a fluid system in the weather, those kinds of things. Or is there a robust idea
of computation that transcends the sort of detailed framework that it's running in? Okay.
And is there? Yes. I mean, it wasn't obvious that there was. So it's worth understanding
the history and how we got to where we are right now. Because, you know, to say that there is,
is a statement in part about our universe. It's not a statement about what is mathematically
conceivable. It's about what actually can exist for us. Maybe you can also comment because energy,
as a concept, is robust. But there's also it's intricate, complicated relationship with matter,
with mass, is very interesting of particles that carry force and particles that sort of
particles that carry force and particles that have mass, these kinds of ideas, they seem to map
to each other, at least in the mathematical sense. Is there a connection between energy
and mass and computation? Or are these completely disjoint ideas?
We don't know yet. The things that I'm trying to do about fundamental physics may well lead to
such a connection. But there is no known connection at this time.
So, can you elaborate a little bit more on what, how do you think about
computation? What is computation? Yeah. So, I mean, let's, let's tell a little bit of a historical
story. Okay. So, you know, back, go back 150 years, people were making mechanical calculators of
various kinds. And, you know, the typical thing was you want an adding machine, you go to the
adding machine store, basically, you want a multiplying machine, you go to the multiplying
machine store, they're different pieces of hardware. And so that means that, at least at the level of
that kind of computation, and those kinds of pieces of hardware, there isn't a robust notion
of computation. There's the adding machine kind of computation, there's the multiplying machine
notion of computation, and they're disjoint. So, what happened in around 1900, people started
imagining, particularly in the context of mathematical logic, could you have something
which would represent any reasonable function, right? And they came up with things, this idea
of primitive recursion was one of the early ideas. And it didn't work. There were reasonable
functions that people could come up with that were not represented using the primitives of
primitive recursion. Okay, so then, then along comes 1931 and Goethe's theorem and so on. And as
in looking back, one can see that as part of the process of establishing Goethe's theorem,
Goethe basically showed how you could compile arithmetic, how you could basically compile
logical statements like this statement is unprovable into arithmetic. So what he essentially
did was to show that arithmetic can be a computer in a sense that's capable of representing all
kinds of other things. And then Turing came along 1936, came up with Turing machines. Meanwhile,
Alonzo Church had come up with Lambda calculus. And the surprising thing that was established
very quickly is the Turing machine idea about what computation might be is exactly the same as the
Lambda calculus idea of what computation might be. And so, and then there started to be other
ideas, register machines, other kinds of representations of computation. And the big
surprise was they all turned out to be equivalent. So in other words, it might have been the case
like those old adding machines and multiplying machines that Turing had his idea of computation,
Church had his idea of computation, and they were just different. But it isn't true. They're
actually all equivalent. So then by, I would say the 1970s or so, in sort of the computation,
computer science, computation theory area, people had sort of said, oh, Turing machines are kind
of what computation is. Physicists were still holding out saying, no, no, no, it's just not
how the universe works. We've got all these differential equations. We've got all these
real numbers that have infinite numbers of digits. The universe is not a Turing machine.
Right. The, you know, the Turing machines are a small subset of that are the things that we
make in microprocessors and engineering structures and so on. So probably, actually through my work
in the 1980s, about sort of the relationship between computation and models of physics,
it became a little less clear that there would be, that there was this big sort of dichotomy between
what can happen in physics and what happens in things like Turing machines. And I think probably
by now, people would mostly think, and by the way, brains were another kind of element of this.
I mean, you know, Goedl didn't think that his notion of computation or what amounted to his
notion of computation would cover brains. And Turing wasn't sure either. But although he was
a little bit, he got to be a little bit more convinced that it should cover brains. But so,
you know, by, I would say by probably sometime in the 1980s, there was beginning to be sort of a
general belief that yes, this notion of computation that could be captured by things like Turing
machines was reasonably robust. Now, the next question is, okay, you can have a universal
Turing machine that's capable of being programmed to do anything that any Turing machine can do.
And, you know, this idea of universal computation, it's an important idea, this idea that you can
have one piece of hardware and program it with different pieces of software. You know, that's
kind of the idea that launched most modern technology. I mean, that's kind of that that's
kind of that that's the idea that launched computer evolution, software, etc. So important idea. But
but the thing that's still kind of holding out from that idea is, okay, there is this universal
computation thing. But seems hard to get to seems like you want to make a universal computer,
you have to kind of have a microprocessor with, you know, a million gates in it, and you have to
go to a lot of trouble to make something that achieves that level of computational sophistication.
Okay, so the surprise for me was the stuff that I discovered in the early eighties,
looking at these things called cellular automata, which are really simple computational systems.
The thing that was a big surprise to me was that even when their rules were very, very simple,
they were doing things that were as sophisticated as they did when their rules were much more
complicated. So it didn't look like, you know, this idea, oh, to get sophisticated computation,
you have to build something with very sophisticated rules. That idea didn't seem to pan out. And
instead, it seemed to be the case that sophisticated computation was completely ubiquitous, even in
systems with incredibly simple rules. And so that led to this thing that I call the principle of
computational equivalence, which basically says, when you have a system that follows rules of any
kind, then whenever the system isn't doing things that are in some sense, obviously simple,
then the computation that the behavior of the system corresponds to is of equivalent sophistication.
So that means that when you kind of go from the very, very, very simplest things you can imagine,
then quite quickly you hit this kind of threshold above which everything is equivalent in its
computational sophistication. Not obvious, that would be the case. I mean, that's a science fact.
Well, hold on a second. So this, you've opened with a new kind of science. I mean, I remember it
was a huge eye opener that such simple things can create such complexity. And yes, there's an
equivalence, but it's not a fact. It just appears to, I mean, as much as a fact as sort of these
theories are so elegant that it seems to be the way things are. But let me ask sort of,
you just brought up previously kind of like the communities of computer scientists with their
towing machines, the physicists with their universe, and the whoever the heck, maybe
neuroscientists looking at the brain. What's your sense in the equivalence? So you've shown
through your work that simple rules can create equivalently complex towing machine systems,
right? Is the universe equivalent to the kinds of towing machines? Is the human brain
a kind of towing machine? Do you see those things basically blending together,
or is there still a mystery about how disjoint they are?
Well, my guess is that they all blend together. But we don't know that for sure yet. I mean,
this, you know, I should say, I said rather glibly that the principle of computational
equivalence is sort of a science fact. And I was using air quotes for the science fact.
Because when you, it is a, I mean, just to talk about that for a second, the thing is that it
is it has a complicated epistemological character, similar to things like the second law of thermodynamics,
law of entropy increase. The, you know, what is the second law of thermodynamics? It is,
is it a law of nature? Is it a thing that is true of the physical world? Is it, is it something
which is mathematically provable? Is it something which happens to be true of the systems that we
see in the world? Is it, in some sense, a definition of heat, perhaps? Well, it's a combination of
those things. And it's the same thing with the principle of computational equivalence.
And in some sense, the principle of computational equivalence is at the heart of the definition
of computation. Because it's telling you there is a thing, there is a robust notion
that is equivalent across all these systems, and doesn't depend on the details of each
individual system. And that's why we can meaningfully talk about a thing called computation. And we're
not stuck talking about, oh, there's computation in Turing machine number 3785, and et cetera,
et cetera, et cetera. That's, that's why there is a robust notion like that. Now, on the other hand,
can we prove the principle of computational equivalence? Can we, can we prove it as a
mathematical result? Well, the answer is, actually, we've got some nice results along those lines that
say, you know, throw me a random system with very simple rules. Well, in a couple of cases,
we now know that even the very simplest rules we can imagine of a certain type are universal,
and do sort of follow what you would expect from the principle of computational equivalence. So
that's a nice piece of sort of mathematical evidence for the principle of computational
equivalence.
But just to link on that point, the simple rules creating sort of these complex behaviors. But
is there a way to mathematically say that this behavior is complex, that you've, you mentioned
that you cross a threshold?
Right. So the various indicators. So for example, one thing would be, is it capable of universal
computation? That is, given the system, do there exist initial conditions for the system that can
be set up to essentially represent programs to do anything you want, to compute primes, to compute
pi, to do whatever you want? Right. So that's an indicator. So we know in a couple of examples
that, yes, the simplest candidates that could conceivably have that property do have that
property. And that's what the principle of computational equivalence might suggest. But
this principle of computational equivalence, one question about it is, is it true for the
physical world? Right. It might be true for all these things we come up with, the Turing
machines, the cellular automata, whatever else. Is it true for our actual physical world? Is it
true for the brains which are an element of the physical world? We don't know for sure. And
that's not the type of question that we will have a definitive answer to, because it's,
you know, it's a, there's a sort of scientific induction issue. You can say, well, it's true
for all these brains, but this person over here is really special and it's not true for them.
And you can't, you know, the only way that that cannot be what happens is if we finally nail it
and actually get a fundamental theory for physics, and it turns out to correspond to,
let's say, a simple program, if that is the case, then we will basically have reduced physics to
a branch of mathematics in the sense that we will not be, you know, right now with physics,
we're like, well, this is the theory that, you know, this is the rules that apply here. But
in the middle of that, you know, right by that black hole, maybe these rules don't apply and
something else applies. And there may be another piece of the onion that we have to peel back.
But if we can get to the point where we actually have, this is the fundamental theory of physics,
here it is, it's this program, run this program and you will get our universe,
then we've kind of reduced the problem of figuring out things in physics to a problem of
doing some what turns out to be very difficult, irreducibly difficult mathematical problems.
But it no longer is the case that we can say that somebody can come in and say, whoops,
you know, you will write about all these things about Turing machines, but you're wrong about
the physical universe. We know there's sort of ground truth about what's happening in the
physical universe. Now, I happen to think, I mean, you asked me at an interesting time because I'm
just in the middle of starting to reenergize my project to kind of study fundamental theory of
physics. As of today, I'm very optimistic that we're actually going to find something and that
it's going to be possible to see that the universe really is computational in that sense. But I
don't know because we're betting against, you know, we're betting against the universe, so to
speak. And I didn't, you know, it's not like, you know, when I spend a lot of my life building
technology, and then I know what's in there, right? And it's, there may be, it may have unexpected
behavior, it may have bugs, things like that. But fundamentally, I know what's in there. For the
universe, I'm not in that position, so to speak. What kind of computation do you think the fundamental
laws of physics might emerge from? Just to clarify, so you've done a lot of fascinating work
with kind of discrete kinds of computation that, you know, you could sell your automata,
and we'll talk about it, have this very clean structure. It's such a nice way to demonstrate
that simple rules can create immense complexity. But what kind, you know, is that actually,
are cellular automata sufficiently general to describe the kinds of computation that might
create the laws of physics? Just to give, can you give a sense of what kind of computation do you
think would create? Well, so this is a slightly complicated issue, because as soon as you have
universal computation, you can, in principle, simulate anything with anything. But it is not a
natural thing to do. And if you're asking, were you to try to find our physical universe by looking
at possible programs in the computational universe of all possible programs, would the ones that
correspond to our universe be small and simple enough that we might find them by searching
that computational universe, we got to have the right basis, so to speak, we have what have the
right language in effect for describing computation for that to be feasible. So the thing that I've
been interested in for a long time is, what are the most structural structures that we can create
with computation? So in other words, if you say a cellular automaton has a bunch of cells that are
arrayed on a grid, and it's very, you know, and every cell is updated in synchrony at a particular,
you know, when there's a click of a clock, so to speak, and it goes a tick of a clock,
and every cell gets updated at the same time. That's a very specific, very rigid kind of thing.
But my guess is that when we look at physics, and we look at things like space and time,
that what's underneath space and time is something as structural as is possible,
that what we see, what emerges for us as physical space, for example, comes from something that
is sort of arbitrarily unstructured underneath. And so I've been for a long time interested in
kind of what are the most structural structures that we can set up. And actually, what I had
thought about for ages is using graphs, networks, where essentially, so let's talk about space,
for example. So what is space? As a kind of a question one might ask, back in the early days
of quantum mechanics, for example, people said, oh, for sure, space is going to be discrete,
because all these other things we're finding are discrete, but that never worked out in physics.
And so space and physics today is always treated as this continuous thing, just like Euclid
imagined it. I mean, the very first thing Euclid says in his sort of common notions is, you know,
a point is something which has no part. In other words, there are points that are arbitrarily small,
and there's a continuum of possible positions of points. And the question is, is that true?
And so, for example, if we look at, I don't know, fluid like air or water, we might say,
oh, it's a continuous fluid, we can pour it, we can do all kinds of things continuously.
But actually, we know, because we know the physics of it, that it consists of a bunch of
discrete molecules bouncing around and only in the aggregate is it behaving like a continuum.
And so the possibility exists that that's true of space too. People haven't managed to make that
work with existing frameworks and physics. But I've been interested in whether one can imagine
that underneath space and also underneath time is something more structuralist. And the question is,
is it computational? So there are a couple of possibilities. It could be computational,
somehow fundamentally equivalent to a Turing machine. Or it could be fundamentally not. So
how could it not be? It could not be, so a Turing machine essentially deals with integers, whole
numbers at some level. And it can do things like it can add one to a number, it can do things like
this. It can also store whatever the heck it did. Yes, it can have an infinite storage. But
when one thinks about doing physics or sort of idealized physics or idealized mathematics,
one can deal with real numbers, numbers with an infinite number of digits,
yeah, numbers which are absolutely precise. And one can say, we can take this number and
we can multiply it by itself. Are you comfortable with infinity in this context?
Are you comfortable in the context of computation? Do you think infinity plays a part?
I think that the role of infinity is complicated. Infinity is useful in conceptualizing things.
It's not actualizable. Almost by definition, it's not actualizable.
But do you think infinity is part of the thing that might underlie the laws of physics?
I think that, no. I think there are many questions that you might ask about physics,
which inevitably involve infinity. Like when you say, is faster than light travel possible?
You could say, given the laws of physics, can you make something even arbitrarily large, even
quote infinitely large, that will make faster than light travel possible? Then you're throwing
into dealing with infinity as a theoretical question. But talking about what's underneath
space and time and how one can make a computational infrastructure, one possibility is that you can't
make a computational infrastructure during machine sense, that you really have to be
dealing with precise real numbers. You're dealing with partial differential equations,
which have precise real numbers at arbitrarily closely separated points. You have a continuum
for everything. It could be that that's what happens, that there's a continuum for everything
and precise real numbers for everything. Then the things I'm thinking about are wrong.
That's the risk you take if you're trying to do things about nature, is you might just be wrong.
For me personally, it's kind of a strange thing. I've spent a lot of my life building technology
where you can do something that nobody cares about, but you can't be wrong in that sense,
in the sense you build your technology and it does what it does. But I think this question of
what the underlying computational infrastructure for the universe might be,
it's sort of inevitable it's going to be fairly abstract, because if you're going to get all these
things like there are three dimensions of space, there are electrons, there are muons, there are
quarks, there are this, you don't get to, if the model for the universe is simple, you don't get
to have sort of a line of code for each of those things. You don't get to have sort of the muon
case, the tau lepton case and so on. Those all have to be emergent somehow. Something deeper.
Right. So that means it's sort of inevitable that's a little hard to talk about what the sort of
underlying structuralist structure actually is. Do you think human beings have the cognitive
capacity to understand, if we're to discover it, to understand the kinds of simple structure from
which these laws can emerge? Do you think that's a hopeless pursuit?
Well, here's what I think. I think that, I mean, I'm right in the middle of this right now, so
I'm telling you that this human has a hard time understanding a bunch of the things that are
going on, but what happens in understanding is one builds waypoints. I mean, if you said
understand modern 21st century mathematics starting from counting back in whenever counting was
invented 50,000 years ago, whatever it was, that will be really difficult. But what happens is we
build waypoints that allow us to get to high levels of understanding. And we see the same
thing happening in language. When we invent a word for something, it provides kind of a cognitive
anchor, a kind of a waypoint that lets us like a podcast or something. You could be explaining,
well, it's a thing which works this way, that way, the other way. But as soon as you have the word
podcast and people kind of societally understand it, you start to be able to build on top of that.
And so I think, and that's kind of the story of science actually too. I mean, science is about
building these kind of waypoints where we find this sort of cognitive mechanism for understanding
something, then we can build on top of it. We have the idea of, I don't know, differential
equations, we can build on top of that. We have this idea, that idea. So my hope is that if it
is the case that we have to go all the way sort of from the sand to the computer, and there's no
waypoints in between, then we're toast. We won't be able to do that. Well, eventually we might.
So if we're as clever apes are good enough for building those abstract abstractions,
eventually from sand, we'll get to the computer, right? And it's just might be a longer journey.
The question is whether it is something that you ask whether our human brains will quote
understand what's going on. And that's a different question. Because for that, it requires steps
that are sort of from which we can construct a human understandable narrative. And that's
something that I think I am somewhat hopeful that that will be possible. Although, you know,
as of literally today, if you ask me, I'm confronted with things that I don't understand
very well. So this is a small pattern in a computation trying to understand the rules
under which the computation functions. And it's an interesting possibility under which
kinds of computations such a creature can't understand itself. My guess is that within,
so we didn't talk much about computational irreducibility, but it's a consequence of
this principle of computational equivalence. And it's sort of a core idea that one has to
understand, I think, which is the question is, you're doing a computation, you can figure out
what happens in the computation just by running every step in the computation and seeing what
happens. Or you can say, let me jump ahead and figure out, you know, have something smarter
that figures out what's going to happen before it actually happens. And a lot of traditional
science has been about that act of computational reducibility. It's like, we've got these equations
and we can just solve them and we can figure out what's going to happen. We don't have to trace
all of those steps, we just jump ahead because we solved these equations. Okay, so one of the
things that is a consequence of the principle of computational equivalence is you don't always get
to do that. Many, many systems will be computationally irreducible in the sense that the only way
to find out what they do is just follow each step and see what happens. Why is that? Well,
if you have, if you're saying, well, we, with our brains, we're a lot smarter, we don't have to
mess around like the little cellular automaton going through and updating all those cells,
we can just, you know, use the power of our brains to jump ahead. But if the principle of
computational equivalence is right, that's not going to be correct because it means that
there's us doing our computation in our brains, there's a little cellular automaton doing its
computation. And the principle of computational equivalence says these two computations are
fundamentally equivalent. So that means we don't get to say we're a lot smarter than the cellular
automaton and jump ahead because we're just doing computation that's of the same sophistication as
the cellular automaton itself. That's computation and reducibility. It's fascinating. But the,
and that's a really powerful idea. I think that's both depressing and humbling and so on,
that we're all, we in a cellular automaton are the same. But the question we're talking about the
fundamental laws of physics is kind of the reverse question. You're not predicting what's
going to happen. You have to run the universe for that. But saying, can I understand what rules
likely generated me? I understand. But the problem is, to know whether you're right,
you have to have some computational reducibility because we are embedded in the universe. If the
only way to know whether we get the universe is just to run the universe, we don't get to do that
because it just ran for 14.6 billion years or whatever. And we don't, you know, we can't rerun
it, so to speak. So we have to hope that there are pockets of computational reducibility sufficient
to be able to say, yes, I can recognize those are electrons there. And I think that it is,
it's a feature of computational irreducibility. It's sort of a mathematical feature that there
are always an infinite collection of pockets of reducibility. The question of whether they land
in the right place and whether we can sort of build the theory based on them is unclear.
But to this point about, you know, whether we as observers in the universe built out of the same
stuff as the universe can figure out the universe, so to speak, that relies on these pockets of
reducibility. Without the pockets of reducibility, it won't work, can't work. But I think this
question about how observers operate, it's one of the features of science over the last 100 years
particularly has been that every time we get more realistic about observers, we learn a bit more
about science. So for example, relativity was all about observers don't get to say when, you know,
what's simultaneous with what they have to just wait for the light signal to arrive to decide
what's simultaneous. Or for example, in thermodynamics, observers don't get to say the
position of every single molecule in a gas, they can only see the kind of large scale features.
And that's why the second law of thermodynamics law of entropy increase and so on works. If you
could see every individual molecule, you wouldn't conclude something about thermodynamics, you would
conclude, oh, these molecules just all doing these particular things, you wouldn't be able to see this
aggregate fact. So I strongly expect that, and in fact, in the theories that I have,
that one has to be more realistic about the computation and other aspects of observers
in order to actually make a correspondence between what we experience. In fact, they have a
my little team and I have a little theory right now about how quantum mechanics may work,
which is a very wonderfully bizarre idea about how sort of thread of human consciousness relates to
what we observe in the universe. But this is the several steps to explain what that's about.
What do you make of the mess of the observer at the lower level of quantum mechanics,
sort of the textbook definition with quantum mechanics kind of says that there's two worlds.
One is the world that actually is and the other is that's observed. What do you make sense of
that? Well, I think actually the ideas we've recently had might actually give away into this.
I don't know yet. I mean, I think that's a mess. I mean, the fact is there is a
one of the things that's interesting and when people look at these models that I
started talking about 30 years ago now, they say, oh, no, that can't possibly be right.
What about quantum mechanics? You say, okay, tell me what is the essence of quantum mechanics?
What do you want me to be able to reproduce to know that I've got quantum mechanics, so to speak?
Well, and that question comes up very operationally, actually, because we've been doing a bunch of
stuff with quantum computing. And there are all these companies that say, we have a quantum
computer. We say, let's connect to your API and let's actually run it. And they're like,
well, maybe you shouldn't do that yet. We're not quite ready yet. And one of the questions that
I've been curious about is if I have five minutes with a quantum computer, how can I tell if it's
really a quantum computer or whether it's a simulator at the other end? And turns out it's
really hard. It turns out there isn't, it's like a lot of these questions about what is
intelligence, what's life. That's a scoring test for quantum computing.
That's right. It's like, are you really a quantum computer?
Yes, exactly. Is it just a simulation or is it really a quantum computer? Same issue all over
again. So this whole issue about the mathematical structure of quantum mechanics and the completely
separate thing that is our experience in which we think definite things happen, whereas quantum
mechanics doesn't say definite things ever happen. Quantum mechanics is all about the
amplitudes for different things to happen. But yet our thread of consciousness operates
as if definite things are happening. But to link on the point, you've kind of mentioned
the structure that could underlie everything. And this idea that it could perhaps have something
like a structure of a graph. Can you elaborate why your intuition is that there's a graph
structure of nodes and edges and what it might represent? Right. Okay. So the question is,
what is in a sense the most structural structure you can imagine?
Right. And in fact, what I've recently realized in the last year or so, I have a new most
structural structure. By the way, the question itself is a beautiful one and a powerful one
in itself. So even without an answer, just the question is a really strong question.
Right. Right. But what's your new idea?
Well, it has to do with hypergraphs. Essentially, what is interesting about the sort of model I
have now is a little bit like what happened with computation. Everything that I think of as, oh,
well, maybe the model is this, I discover it's equivalent. And that's quite encouraging, because
it's like, I could say, well, I'm going to look at trivalent graphs with three edges for each node
and so on. Or I could look at this special kind of graph, or I could look at this kind of algebraic
structure. And turns out that the things I'm now looking at, everything that I've imagined that
is a plausible type of structural structure is equivalent to this. So what is it? Well, a typical
way to think about it is, well, so you might have some collection of tuples, collection of,
let's say, numbers. So you might have one, three, five,
two, three, four, little, just collections of numbers, triples of numbers, let's say, quadruples
of numbers, pairs of numbers, whatever. And you have all these sort of floating little tuples,
they're not in any particular order. And that sort of floating collection of tuples,
and I told you this was abstract, represents the whole universe. The only thing that relates them
is when a symbol is the same, it's the same, so to speak. So if you have two tuples, and they
contain the same symbol, let's say at the same position of the tuple, the first element of the
tuple, then that represents a relation. Okay, so let me try and peel this back.
Wow, okay. I told you it's abstract, but this is the, so the relationship is formed by
the same, some aspect of sameness. Right, but so think about it in terms of a graph. So a graph,
bunch of nodes, let's say you number each node, okay, then what is a graph? A graph is a set of
pairs that say this node has an edge connecting it to this other node. So that's the, that's,
and a graph is just a collection of those pairs that say this node connects to this other node.
So this is a generalization of that in which instead of having pairs, you have arbitrary
end tuples. That's it. That's the whole story. And now the question is, okay, so that might be,
that might represent the state of the universe. How does the universe evolve? What does the
universe do? And so the answer is that what I'm looking at is transformation rules on these
hypergraphs. In other words, you say this, whenever you see a piece of this hypergraph
that looks like this, turn it into a piece of hypergraph that looks like this. So on a graph,
it might be when you see the subgraph, when you see this thing with a bunch of edges hanging out
in this particular way, then rewrite it as this other graph. Okay. And so that's the whole story.
So the question is what, so now you say, I mean, think, as I say, this is quite abstract. And
one of the questions is, where do you do those updating? So you've got this giant graph.
What triggers the updating? Like, what's the, what's the ripple effect of it? Is it?
Yes. And I suspect everything's discreet even in time. So.
Okay. So the question is, where do you do the updates?
Yes. And the answer is the rule is you do them wherever they apply. And you do them,
you do them the order in which the updates is done is not defined. That is, that you can do them.
So there may be many possible orderings for these updates. Now, the point is, if imagine you're an
observer in this universe. So, and you say, did something get updated? Well, you don't in any
sense know until you yourself have been updated. Right. So in fact, all that you can be sensitive to
is essentially the causal network of how an event over there affects an event that's in you.
That doesn't even feel like observation. That's like, that's something else. You're just part of
the whole thing. Yes, you're part of it. But even to have, so the end result of that is all
you're sensitive to is this causal network of what event affects what other event.
I'm not making a big statement about sort of the structure of the observer. I'm simply saying,
I'm simply making the argument that what happens the microscopic order of these rewrites
is not something that any observer, any conceivable observer in this universe
can be affected by. Because the only thing the observer can be affected by is this causal network
of how the events in the observer are affected by other events that happen in the universe.
So the only thing you have to look at is the causal network. You don't really have to look
at this microscopic rewriting that's happening. So these rewrites are happening wherever they
happen, wherever they feel like.
Causal network, is there, you said that there's not really, so the idea would be an undefined,
like what gets updated, the sequence of things is undefined. Is that what you mean by the
causal network? No, the causal network is given that an update has happened, that's an event.
Then the question is, is that event causally related to, does that event, if that event didn't
happen, then some future event couldn't happen yet. And so you build up this network of what
affects what. And so what that does, so when you build up that network, that's kind of the
observable aspect of the universe in some sense. And so then you can ask questions about how robust
is that observable network of what's happening in the universe. So here's where it starts getting
kind of interesting. So for certain kinds of microscopic rewriting rules, the order of rewrites
does not matter to the causal network. And so this is, okay, mathematical logic, moment,
this is equivalent to the church Rossa property or the confluence property of rewrite rules.
And it's the same reason that if you're simplifying an algebraic expression, for example,
you can say, oh, let me expand those terms out, let me factor those pieces,
doesn't matter what order you do that in, you'll always get the same answer.
And that's, it's the same fundamental phenomenon that causes for certain kinds of microscopic
rewrite rules that causes the causal network to be independent of the microscopic order of
rewritings. Why is that property important? Because it implies special relativity.
I mean, the reason what the reason it's important is that that property, special
relativity says, you can look at these sort of, you can look at different reference frames,
you can have different, you can be looking at your notion of what space and what's time can be
different, depending on whether you're traveling at a certain speed, depending on whether you're
doing this, that and the other. But nevertheless, the laws of physics are the same. That's what
the principle of special relativity says is the laws of physics are the same independent of your
reference frame. Well, turns out this sort of change of the microscopic rewriting order
is essentially equivalent to a change of reference frame, or that at least there's a
sub part of how that works. That's equivalent to change of reference frame. So, so much surprisingly,
and sort of for the first time in forever, it's possible for an underlying microscopic theory
to imply special relativity, to be able to derive it. It's not something you put in as a,
this is a, it's something where this other property causal invariance, which is also
the property that implies that there's a single thread of time in the universe. It might not be
the case that that's, that is the, that's what would lead to the possibility of an observer
thinking that definite stuff happens. Otherwise, you've got all these possible rewriting orders,
and who's to say which one occurred. But with this causal invariance property, there's a,
there's a notion of a definite thread of time. It sounds like that that kind of idea of time,
even space would be emergent from the system. Oh, yeah. No, I mean, it's not a fundamental
part of the system. No, no, the fundamental level, all you've got is a bunch of nodes connected by
hyper edges or whatever. So there's no time, there's no space.
That's right. And but, but the thing is that it's just like imagining, imagine you're just
dealing with a graph. And imagine you have something like a, you know, like a honeycomb
graph where you have a bunch of hexagons. You know, that graph at a microscopic level,
it's just a bunch of nodes connected to other nodes. But at a macroscopic level, you say that
looks like a honeycomb, you know, it's lattice. It looks like a two-dimensional, you know,
manifold of some kind. It looks like a two-dimensional thing. If you connect it differently, if you
just connect all the nodes one to another and kind of a sort of linked list type structure,
then you'd say, well, that looks like a one-dimensional space. But at the microscopic
level, all these are just networks with nodes. The macroscopic level, they look like something
that's like one of our sort of familiar kinds of space. And it's the same thing with these
hypergraphs. Now, if you ask me, have I found one that gives me three-dimensional space,
the answer is not yet. So we don't know, you know, this is one of these things we're kind
of betting against nature, so to speak. And I have no way to know. And so there are many
other properties of this kind of system that are very beautiful, actually, and very suggestive.
And it will be very elegant if this turns out to be right, because it's very clean. I mean,
you start with nothing and everything gets built up. Everything about space, everything about time,
everything about matter, it's all just emergent from the properties of this extremely low-level
system. And that will be pretty cool if that's the way our universe works. Now, on the other hand,
the thing that I find very confusing is, let's say we succeed. Let's say we can say,
this particular sort of hypergraph rewriting rule gives the universe. Just run that hypergraph rewriting
rule for enough times and you'll get everything. You'll get this conversation we're having,
you'll get everything. It's that if we get to that point and we look at what is this thing,
what is this rule that we just have that is giving us our whole universe? How do we think about that
thing? Let's say, turns out the minimal version of this, and this is kind of a cool thing for a
language designer like me, the minimal version of this model is actually a single line of
orphan language code. Which I wasn't sure was going to happen that way, but it's kind of,
no, we don't know what, that's just the framework to know the actual particular
hypergraph that might be a longer, that the specification of the rules might be slightly
longer. How does that help you accept marveling in the beauty and the elegance of the simplicity
that creates the universe? Does that help us predict anything? Not really, because of the
irreducibility. That's correct. That's correct. But so the thing that is really strange to me,
and I haven't wrapped my brain around this yet, is one keeps on realizing that we're not special
in the sense that we don't live at the center of the universe, we don't blah, blah, blah.
And yet, if we produce a rule for the universe and it's quite simple and we can write it down
in a couple of lines or something, that feels very special. How do we come to get
a simple universe when many of the available universes, so to speak, are incredibly complicated?
It might be a quintillion characters long. Why did we get one of the ones that's simple?
And so I haven't wrapped my brain around that issue yet.
If indeed we are in such a simple, the universe is such a simple rule, is it possible that
there is something outside of this, that we are in a kind of what people call the simulation?
Right? That we're just part of a computation that's being explored by a graduate student
in an alternate universe? Well, you know, the problem is, we don't get to say much about what's
outside our universe, because by definition, our universe is what we exist within. Now,
can we make a sort of almost theological conclusion from being able to know how our
particular universe works? Interesting question. I don't think that if you ask the question,
could we, and it relates again to this question about the extraterrestrial intelligence,
we've got the rule for the universe. Was it built on purpose? Hard to say. That's the same
thing as saying, we see a signal that we're receiving from some random star somewhere,
and it's a series of pulses. And you know, it's a periodic series of pulses, let's say.
Was that done on purpose? Can we conclude something about the origin of that series of pulses?
Just because it's elegant, does not necessarily mean that somebody created it,
or that we can even comprehend what we created? Yeah. I think it's the ultimate version of the
sort of identification of the techno signature question. The ultimate version of that is,
was our universe a piece of technology, so to speak? And how on earth would we know?
Because, but I mean, it'll be, it's, I mean, you know, in the kind of crazy science fiction thing
you could imagine, you could say, oh, somebody's going to have, you know, there's going to be
a signature there, it's going to be, you know, made by so and so. But there's no way we could
understand that, so to speak. And it's not clear what that would mean. Because the universe simply,
you know, this, if we find a rule for the universe, we're not, we're simply saying that rule represents
what our universe does. We're not saying that that rule is something running on a big computer
and making our universe. It's just saying that represents what our universe does in the same
sense that, you know, laws of classical mechanics, differential equations, whatever they are,
represent what mechanical systems do. It's not that the mechanical systems are somehow
running solutions to those differential equations, those differential equations just
representing the behavior of those systems. So what's the gap in your sense to linger
on the fascinating, perhaps slightly sci-fi question? What's the gap between
understanding the fundamental rules that create a universe and engineering a system,
actually creating a simulation ourselves? So you've talked about sort of, you've talked about,
you know, nano-engineering, kind of ideas that are kind of exciting, actually creating some ideas
of computation in the physical space. How hard is it as an engineering problem to create the
universe once you know the rules that create it? Well, that's an interesting question. I think the
substrate on which the universe is operating is not a substrate that we have access to. I mean,
the only substrate we have is that same substrate that the universe is operating in. So if the
universe is a bunch of hypergraphs being rewritten, then we get to attach ourselves to those same
hypergraphs being rewritten. We don't get to, and if you ask the question, you know, is the code
clean? You know, is, you know, can we write nice, elegant code with efficient algorithms and so on?
Well, that's an interesting question. How, you know, that's this question of how much
computational reducibility there is in the system. But so I've seen some beautiful cellular
automata that basically create copies of itself within itself, right? So that's the question,
whether it's possible to create, like whether you need to understand the substrate or whether you
can... Yeah, well, right. I mean, so one of the things that is sort of one of my slightly sci-fi
thoughts about the future, so to speak, is, you know, right now, if you poll typical people who
say, do you think it's important to find the fundamental theory of physics? You get, because
I've done this poll informally at least, it's curious, actually, you get a decent fraction
of people saying, oh, yeah, that would be pretty interesting. I think that's becoming
surprisingly enough more, I mean, if a lot of people are interested in physics in a way that,
like without understanding it, just kind of watching scientists, a very small number of them,
struggle to understand the nature of our reality. Right. I mean, I think that's
somewhat true. And in fact, in this project that I'm launching into to try and find fundamental
theory of physics, I'm going to do it as a very public project. I mean, it's going to be live
streamed and all this kind of stuff. And I don't know what will happen. It'll be kind of fun.
I mean, I think that it's the interface to the world of this project. I mean, I figure one feature
of this project is, you know, unlike technology projects that basically are what they are,
this is a project that might simply fail because it might be the case that it generates all kinds
of elegant mathematics that has absolutely nothing to do with the physical universe that we happen
to live in. Well, okay, so we're talking about kind of the quest to find the fundamental theory
of physics. First point is, you know, it's turned out it's kind of hard to find the fundamental
theory of physics. People weren't sure that that would be the case. Back in the early days of applying
mathematics to science 1600s and so on, people were like, oh, in 100 years, we'll know everything
there is to know about how the universe works turned out to be harder than that. And people got
kind of humble at some level, because every time we got to sort of a greater level of smallness
and studying the universe, it seemed like the math got more complicated and everything got
harder. The, you know, when I, I, when I was a kid, basically, I started doing particle physics.
And, you know, when I was doing particle physics, I always thought finding the fundamental,
fundamental theory of physics, that's a kooky business, we'll never be able to do that. But
we can operate within these frameworks that we built for doing quantum field theory and
general relativity and things like this. And it's all good. And we can figure out a lot of stuff.
Did you even at that time have a sense that there's something behind that too?
Sure, I just didn't expect that I thought in some rather un, it's actually kind of crazy and
thinking back on it, because it's kind of like there was this long period in civilization where
people thought the ancients had it all figured out and we'll never figure out anything new.
And to some extent, that's the way I felt about physics when I was in the middle of doing it,
so to speak, was, you know, we've got quantum field theory, it's the foundation of what we're
doing. And there's, you know, yes, there's probably something underneath this, but we'll sort of never
figure it out. But then I started studying simple programs in the computational universe,
things like cellular automata and so on. And I discovered that there's they do all kinds of
things that were completely at odds with the intuition that I had had. And so after that,
after you see this tiny little program that does all this amazingly complicated stuff,
then you start feeling a bit more ambitious about physics and saying, maybe we could do
this for physics too. And so that's, that got me started years ago now, and this kind of idea of
could we actually find what's underneath all of these frameworks like quantum field theory and
general relativity and so on. And people perhaps don't realize as clearly as they might, that,
you know, the frameworks we're using for physics, which is basically these two things,
quantum field theory, sort of the theory of small stuff and general relativity,
theory of gravitation and large stuff. Those are the two basic theories,
and they're 100 years old. I mean, general relativity was 1915, quantum field theory,
well 1920s. So basically 100 years old. And they've, they've, it's been a good run. There's
a lot of stuff been figured out. But what's interesting is the foundations haven't changed
in all that period of time, even though the foundations had changed several times before
that in the 200 years earlier than that. And I think the kinds of things that I'm thinking
about, which are sort of really informed by thinking about computation and the computational
universe, it's a different foundation. It's a different set of foundations and might be wrong.
But it is at least, you know, we have a shot. And I think it's, you know, to me, it's, you know,
my personal calculation for myself is, is, you know, if it turns out that the finding the
fundamental theory of physics, it's kind of low hanging fruit, so to speak, it'd be a shame if
we just didn't think to do it. You know, if people just said, oh, you'll never figure that stuff out,
let's, you know, and it takes another 200 years before anybody gets around to doing it.
You know, I think it's, I don't know how low hanging this fruit actually is. It may be,
you know, it may be that it's kind of the wrong century to do this project. I mean, I think the
cautionary tale for me, you know, I think about things that I've tried to do in technology
where people thought about doing them a lot earlier. I mean, my favorite example is probably
Leibniz who thought about making essentially encapsulating the world's knowledge in a
computational form in the late 1600s and did a lot of things towards that. And basically,
you know, we finally managed to do this, but he was 300 years too early. And that's the,
that's kind of the, in terms of life planning, it's kind of like,
avoid things that can't be done in your, in your century, so to speak.
Yeah, timing, timing is everything. So you think if we kind of figure out the underlying rules it
can create from which quantum field theory and general relativity can emerge, do you think they'll
help us unify it at that level of abstraction? Oh, we'll know it completely. We'll know how
that all fits together. Yes, without a question. And I mean, it's already, even the things I've
already done, they're very, you know, it's very, very elegant, actually, how things seem to be
fitting together. Now, you know, is it right? I don't know yet. It's awfully suggestive. If it
isn't right, it's then the designer of the universe should feel embarrassed, so to speak,
because it's a really good way to do it. And your intuition in terms of designing universe,
does God play dice? Is there, is there randomness in this thing? Or is it deterministic? So the
kind of, that's a little bit of a complicated question, because when you're dealing with these
things that involve these rewrites that have, okay, even randomness is an emergent phenomenon,
perhaps. Yes, yes. I mean, it's, yeah, well, randomness, in many of these systems, pseudo
randomness and randomness are hard to distinguish. In this particular case, the current idea that
we have about measurement and quantum mechanics is something very bizarre and very abstract. And
I don't think I can yet explain it without kind of yacking about very technical things.
Eventually, I will be able to. But if that's, if that's right, it's kind of a, it's a weird thing,
because it slices between determinism and randomness in a weird way that hasn't been
sliced before, so to speak. So like many of these questions that come up in science,
where it's like, is it this or is it that? Turns out the real answer is it's neither
of those things. It's something kind of different and sort of orthogonal to those,
those, those categories. And so that's the current, you know, this week's idea about how
that might work. But, you know, we'll, we'll see how that unfolds. I mean, there's, there's this
question about a field like physics and sort of the quest for a fundamental theory and so on.
And there's both the science of what happens and there's the, the sort of the social aspect
of what happens. Because, you know, in a field that is basically as old as physics, we're at,
I don't know what it is, fourth generation, I don't know fifth generation, I don't know what
generation it is of physicists. And like, I was one of these, so to speak. And for me, the foundations
were like the pyramids, so to speak, you know, it was that way and it was always that way.
It is difficult in an old field to go back to the foundations and think about rewriting them.
It's a lot easier in young fields where you're still dealing with the first generation of people
who invented the field. And it tends to be the case, you know, that the nature of what happens
in science tends to be, you know, you'll get, typically the pattern is some methodological
advance occurs. And then there's a period of five years, 10 years, maybe a little bit longer than
that, where there's lots of things that are now made possible by that, by that methodological
advance, whether it's, you know, I don't know, telescopes or whether that's some mathematical
method or something. It's, you know, there's a something, something happens, a tool gets built,
and then you can do a bunch of stuff. And there's a bunch of low-hanging fruit to be picked.
And that takes a certain amount of time. After that, all that low-hanging fruit is picked,
then it's a hard slog for the next however many decades or century or more to get to the next
sort of level at which one can do something. And it's kind of a, and it tends to be the case
that in fields that are in that kind of, I wouldn't say cruise mode, because it's really
hard work, but it's very hard work for very incremental progress.
And in your career and some of the things you've taken on, it feels like you're not,
you haven't been afraid of the hard slog. Yeah, that's true.
So it's quite interesting, especially on the engineering, on the engineering side.
And a small tangent, when you were at Caltech, did you get to interact with Richard Feynman
at all? Do you have any memories of Richard? We worked together quite a bit, actually. In fact,
on, and in fact, both when I was at Caltech and after I left Caltech, we were both consultants
at this company called Thinking Machines Corporation, which was just down the street from
here, actually, as ultimately ill-fated company. But I used to say this company is not going to
work with the strategy they have. And Dick Feynman always used to say,
what do we know about running companies? Just let them run their company. But anyway, he was
not into that kind of thing. And he always thought that my interest in doing things like
running companies was a distraction, so to speak. And for me, it's a mechanism to have
a more effective machine for actually figuring things out and getting things to happen.
Did he think of it, because essentially what you used, you did with the company, I don't know if
you were thinking of it that way, but you're creating tools to empower the exploration of
the university. Did he understand that point? The point of tools of... I think not as well as he
might have done. I mean, I think that, but he was actually my first company, which was also
involved with more mathematical computation kinds of things. He had lots of advice about the
technical side of what we should do and so on. Do you have examples, memories, or thoughts that...
Oh, yeah. He had all kinds of... Look, in the business of doing sort of... One of the hard
things in math is doing integrals and so on. And so he had his own elaborate ways to do
integrals and so on. He had his own ways of thinking about sort of getting intuition about
how math works. And so his sort of meta idea was take those intuitional methods and make a computer
follow those intuitional methods. Now, it turns out, for the most part, like when we do integrals
and things, what we do is we build this kind of bizarre industrial machine that turns every
integral into products of mere g-functions and generates this very elaborate thing. And actually,
the big problem is turning the results into something a human will understand. It's not
quotes doing the integral. And actually, Feynman did understand that to some extent. And I'm
embarrassed to say he once gave me this big pile of, you know, calculational methods for
particle physics that he worked out in the 50s and he said, you know, it's more used to you
than to me type thing. And I was like, I've intended to look at it and give it back and I
still have my files now. So it's... But that's what happens when it's finiteness of human lives.
I, you know, maybe if he'd live another 20 years, I would have remembered to give it back.
But I think it's, you know, that was his attempt to systematize
the ways that one does integrals that show up in particle physics and so on.
Turns out the way we've actually done it is very different from that way.
What do you make of that difference between... So Feynman was actually
quite remarkable at creating sort of intuitive, like diving in, you know, creating intuitive
frameworks for understanding difficult concepts is... I'm smiling because, you know,
the funny thing about him was that the thing he was really, really, really good at is calculating
stuff. And, but he thought that was easy because he was really good at it. And so he would do these
things where he would calculate some, do some complicated calculation in quantum field theory,
for example, come out with a result. Wouldn't tell anybody about the complicated calculation
because he thought that was easy. He thought the really impressive thing was to have this simple
intuition about how everything works. So he invented that at the end. And, you know, because
he'd done this calculation and knew how it worked, it was a lot easier. It's a lot easier to have
good intuition when you know what the answer is. And then, and then he would just not tell anybody
about these calculations. And he wasn't meaning that maliciously, so to speak, it's just he thought
that was easy. And, and that's, you know, that led to areas where people were just completely mystified
and they kind of followed his intuition, but nobody could tell why it worked. Because actually,
the reason it worked was because he'd done all these calculations and he knew that it would work.
And, you know, when I, he and I worked a bit on quantum computers actually back in 1980,
81, before anybody had heard of those things. And, you know, the typical mode of, I mean,
he always used to say, and I now think about this because I'm about the age that he was when I
worked with him. And, you know, I see the people who are one third my age, so to speak.
And he was always complaining that I was one third his age and therefore various things. But,
but, you know, he would do some calculation by, by hand, you know, blackboard and things come up
with some answer. I'd say, I don't understand this. You know, I do something with a computer.
And he'd say, you know, I don't understand this. So it'd be some big argument about what was,
you know, what was going on. But that it was always some, and I think actually,
many of the things that we sort of realized about quantum computing that were sort of issues that
have to do particularly with the measurement process are kind of still issues today. And I
kind of find it interesting. It's a funny thing in science that these, you know, that there's,
there's a remarkable, it happens in technology, too, there's a remarkable sort of repetition of
history that ends up occurring. Eventually, things really get nailed down. But it often takes a while
and it often things come back decades later. Well, for example, I could tell a story actually
happened right down the street from here. When we were both thinking machines, I had been working on
this particular cellular automaton called Rule 30, that has this feature that it from very simple
initial conditions, it makes really complicated behavior. Okay. So, and actually, of all silly
physical things, using this big parallel computer called the connection machine that that company
was making, I generated this giant printout of Rule 30 on very on actually on the same kind of
same kind of printer that people use to make layouts for microprocessors. And so one of these
big, you know, large format printers with high resolution and so on. So, okay, so print this
out lots of very tiny cells. And so there was sort of a question of how some features of that
pattern. And so it was very much a physical, you know, on the floor with meter rules trying to
measure different things. So, so Feynman kind of takes me aside, we've been doing that for a
little while and takes me aside. And he says, I just want to know this one thing says, I want to
know, how did you know that this Rule 30 thing would produce all this really complicated behavior
that is so complicated that we're, you know, going around with this big printout and so on.
And I said, Well, I didn't know, I just enumerated all the possible rules and then observed that
that's what happened. He said, Oh, I feel a lot better. You know, I thought you had some intuition
that he didn't have that would let one I said, No, no, no, no intuition, just experimental science.
So that's such a beautiful sort of dichotomy there of that's exactly what you showed is you
really can't have an intuition about it and reduce it will let me you have to run it.
Yes, that's right. That's so hard for us humans and especially
brilliant physicists like Feynman to say that you can't have a compressed,
clean intuition about how the whole thing.
Yes, works.
Yes. No, he was, I mean, I think he was sort of on the edge of understanding that point about
computation. And I think he found that I think he always found computation interesting. And I
think that was sort of what he was a little bit poking at. I mean, that intuition, you know,
the difficulty of discovering things like even you say, Oh, you know, you just enumerate all
the cases and just find one that does something interesting, right? Sounds very easy. Turns out,
like, I missed it when I first saw it, because I had kind of an intuition
that said it shouldn't be there. And so I had kind of arguments, Oh, I'm going to ignore that case,
because whatever. And how did you have an open mind enough? Because you're essentially the same
person as we should find like the same kind of physics type of thinking. How did you find yourself
having a sufficiently open mind to be open to watching rules and them revealing complexity?
Yeah, I think that's an interesting question. I've wondered about that myself, because it's
kind of like, you know, you live through these things. And then you say, what was the historical
story? And sometimes the historical story that you realize after the fact was not what you lived
through, so to speak. And so, you know, what I realized is I think what happened is, you know,
I did physics, kind of like reductionistic physics, where you're throwing the universe,
and you're told, go figure out what's going on inside it. And then I started building computer
tools. And I started building my first computer language, for example. And computer language
is not like, it's sort of like physics in the sense that you have to take all those computations
people want to do, and kind of drill down and find the primitives that they can all be made of.
But then you do something that's really different, because you're just saying,
okay, these are the primitives. Now, you know, hopefully, they'll be useful to people,
let's build up from there. So you're essentially building an artificial universe in a sense,
where you make this language, you've got these primitives, you're just building whatever you
feel like building. And that's, and so it was sort of interesting for me, because from doing science
where you just thrown the universe as the universe is, to then just being told, you know,
you can make up any universe you want. And so I think that experience of making a computer language,
which is essentially building your own universe, so to speak, is, you know, that's kind of the,
that's, that's what gave me a somewhat different attitude towards what might be possible. It's
like, let's just explore what can be done in these artificial universes, rather than thinking the
natural science way of let's be constrained by how the universe actually is.
Yeah, by being able to program, essentially, you've, as opposed to being limited to just your
mind and a pen, you now have, you've basically built another brain that you can use to explore
the universe by computer program, you know, there's a kind of a brain.
Right. And it's, well, it's, it's, or a telescope or, you know, it's a tool. It's, it lets you,
lets you see stuff. But there's something fundamentally different between a computer
and a telescope. I mean, it just, yeah, I'm hoping not to romanticize the notion,
but it's more general. It is more general. And it's, it's, I think, I mean, this point about,
you know, people say, oh, such and such a thing was almost discovered at such and such a time.
The, the distance between, you know, the building, the paradigm that allows you to actually understand
stuff or allows one to be open to seeing what's going on. That's really hard. And, you know,
I think in, I've been fortunate in my life that I've spent a lot of my time building computational
language. And that's an activity that in a sense works by sort of having to kind of create another
level of abstraction and kind of be open to different kinds of structures. But, you know,
it's, it's always, I mean, I'm fully aware of, I suppose, the fact that I have seen it
a bunch of times of how easy it is to miss the obvious, so to speak, that at least is,
is factored into my attempt to not miss the obvious, although it may not succeed.
What do you think is the role of ego in the history of math and science? And more sort of,
you know, a book titled something like a new kind of science, you've accomplished a huge amount.
And in fact, somebody said that Newton didn't have an ego and I looked into it and he had a huge
ego. But from an outsider's perspective, some have said that you have a bit of an ego as well.
Do you see it that way? Does ego get in the way? Is it empowering? Is it both?
Oh, it's, it's complicated and necessary. I mean, you know, I've had, look, I've spent more than
half my life CEOing a tech company. Right. Okay. And, you know, that is a, I think it's actually very,
it means that one's ego is not a distant thing. It's a thing that one encounters every day,
so to speak, because it's, it's all tied up with leadership and with how one, you know,
develops an organization and all these kinds of things. So, you know, it may be that if I've
been an academic, for example, I could have sort of, you know, checked the ego, put it on, put on a
shelf somewhere and ignored its characteristics. But you're reminded of it quite often in the
context of running a company. Sure. I mean, that's what it's about. It's about leadership and,
you know, leadership is intimately tied to ego. Now, what does it mean? I mean, what, what is the,
you know, for me, I've been fortunate that I think I have reasonable intellectual confidence,
so to speak. That is, you know, I, I'm one of these people who at this point, if somebody
tells me something and I just don't understand it, my conclusion isn't that means I'm dumb,
that my conclusion is there's something wrong with what I'm being told. And that was actually
Dick Feynman used to have that, that, that feature too, he never really believed in,
he actually believed in experts much less than I believe in experts. So,
wow. So that's a fun, that's a, that's a fundamentally powerful property of ego and
saying like, not that I am wrong, but that the world is wrong and telling me like,
when confronted with the fact that doesn't fit the thing that you've really thought through,
sort of both the negative and the positive of ego, do you see the negative of that get in the way,
sort of being confronted with? Sure, there are mistakes I've made that are the results of,
I'm pretty sure I'm right. And turns out I'm not. I mean, that's, that's the, you know,
but, but the thing is that the, the, the idea that one tries to do things that, so for example,
you know, one question is, if people have tried hard to do something, and then one thinks,
maybe I should try doing this myself, if one does not have a certain degree of intellectual
confidence when it just says, well, people have been trying to do this for a hundred years. How
am I going to be able to do this? And, you know, I was fortunate in the sense that I happened to
start having some degree of success in science and things when I was really young. And so that
developed a certain amount of sort of intellectual confidence that I don't think I otherwise would
have had. And, you know, in a sense, I mean, I was fortunate that I was working in a field,
particle physics, during its sort of golden age of rapid progress. And that, that's kind of goes
on a false sense of achievement because it's kind of kind of easy to discover stuff that's
going to survive if you happen to be, you know, picking the low hanging fruit of a rapidly expanding
field. I mean, the reason I totally, I totally immediately understood the ego behind a new kind
of science to me, let me sort of just try to express my feelings on the whole thing is that
if you don't allow that kind of ego, then you would never write that book that you would say,
well, people must have done this. There's not, you would not dig, you would not keep digging.
And I think that was, I think you have to take that ego and ride it and see where it takes you.
And that's how you create exceptional work. But I think the other point about that book
was, it was a non-trivial question, how to take a bunch of ideas that are, I think, reasonably
big ideas, they might, you know, their importance is determined by what happens historically when
can't tell how important they are, one can tell sort of the scope of them. And the scope is fairly
big. And they're very different from things that have come before. And the question is, how do you
explain that stuff to people? And so I had had the experience of sort of saying, well, they're
these things, there's a cellular automaton, it does this, it does that. And people are like, oh,
it must be just like this, it must be just like that. So no, it isn't, it's something different.
Right. And so I could have done sort of, I'm really glad you did what you did, but you could
have done sort of academically, just publish, keep publishing small papers here and there.
And then you would just keep getting this kind of resistance, right? You would get like,
it's supposed to just dropping a thing that says, here it is, here's the full thing.
Right. No, I mean, that was my calculation is that basically, you know, you could introduce
little pieces, it's like, you know, one possibility is like, it's the secret weapon, so to speak.
It's this, you know, I keep on, you know, discovering these things in all these different
areas, where'd they come from? Nobody knows. But I decided that, you know, in the interests of one,
only has one life to lead. And, you know, writing that book took me a decade anyway.
It's not, there's not a lot of wiggle room, so to speak, one can't be wrong by a factor of three,
so to speak, and how long it's going to take. That I, you know, I thought the best thing to do,
the thing that is most sort of, that most respect the, the intellectual content, so to speak,
is you just put it out with as much force as you can, because it's not something where,
and, you know, it's an interesting thing. You talk about ego, and it's, you know, for example,
I run a company which has my name on it, right? I thought about starting a club for people whose
companies have their names on them. And it's a funny group, because we're not a bunch of
egomaniacs. That's not what it's about, so to speak. It's about basically sort of taking
responsibility for what one's doing. And, you know, in a sense, any of these things where
you're sort of putting yourself on the line, it's, it's kind of a funny, it's a funny dynamic,
because in a sense, my company is sort of something that happens to have my name on it.
But it's kind of bigger than me, and I'm kind of just its mascot at some level. I mean, I also
happen to be a pretty, you know, strong leader of it. But, but it's basically showing a deep,
inextricable sort of investment. The same, your name, like Steve Jobs' name wasn't on
Apple, but he was Apple. Elon Musk's name is not on Tesla, but he is Tesla. So it's like,
it meaning emotionally. If company's successor fails, he would just, that emotionally would
suffer through that. And so that's, that's, that's a beautiful.
Yeah, it's recognizing that fact tonight.
And also Wolfram is a pretty good branding name, so it works out.
Yeah, right, exactly. Just kind of.
I think Steve had it, had a bad deal there.
Yeah, yeah. So you made up for it with the last name. Okay, so, in 2002, you published a new
kind of science to which sort of on a personal level, I can credit my love for cellular automata
and computation in general. I think a lot of others can as well. Can you briefly describe
the vision, the hope, the main idea presented in this 1200 page book?
Sure. Although it took 1200 pages to say in the book. So no, the real idea, it's kind of
a good way to get into it is to look at sort of the arc of history and to look at what's
happened in kind of the development of science. I mean, there was this sort of big idea in science
about 300 years ago that was, let's use mathematical equations to try and describe things in the world.
Let's use sort of the formal idea of mathematical equations to describe what might be happening
in the world, rather than, for example, just using sort of logical augmentation and so on.
Let's have a formal theory about that. And so there've been this 300 year run of using
mathematical equations to describe the natural world, which have worked pretty well. But I got
interested in how one could generalize that notion. There is a formal theory, there are
definite rules, but what structure could those rules have? And so what I got interested in was,
let's generalize beyond the sort of purely mathematical rules. And we now have this sort
of notion of programming and computing and so on. Let's use the kinds of rules that can be
embodied in programs to as a sort of generalization of the ones that can exist in mathematics
as a way to describe the world. And so my kind of favorite version of these kinds of simple rules
are these things called cellular automata. And so typical case, shall we,
what are cellular automata? Fair enough. So typical case of a cellular automata,
it's an array of cells. It's just a line of discrete cells. Each cell is either black or white.
And in a series of steps that you can represent as lines going down the page,
you're updating the color of each cell according to a rule that depends on the color of the cell
above it and to its left and right. So it's really simple. So a thing might be, if the cell
and its right neighbor are not the same and or the cell on the left is black or something,
then make it black on the next step. And if not, make it white. Typical rule. That rule,
I'm not sure I said it exactly right, but a rule very much like what I just said,
has the feature that if you started off from just one black cell at the top,
it makes this extremely complicated pattern. So some rules, you get a very simple pattern. Some
rules, you have the rule is simple, you start them off from a sort of simple seed, you just get
this very simple pattern. But other rules, and this was the big surprise when I started actually
just doing the simple computer experiments to find out what happens, is that they produce
very complicated patterns of behavior. So for example, this rule 30 rule has the feature
you started off from just one black cell at the top makes this very random pattern. If you look
like at the center column of cells, you get a series of values, you know, it goes black, white,
black, black, whatever it is, that sequence seems for all practical purposes random. So
it's kind of like in math, you know, you compute the digits of pi 3.1415926 whatever,
those digits once computed, I mean, the scheme for computing pi, you know, it's the ratio of
the circumference of the diameter of a circle, very well defined. But yet, when you are, once you've
generated those digits, they seem for all practical purposes completely random. And so it is
with rule 30, that even though the rule is very simple, much simpler, much more sort of computationally
obvious than the rule for generating digits of pi, even with a rule that simple, you're still
generating immensely complicated behavior. Yeah, so if we could just pause on that, I think
you probably said it and looked at it so long, you forgot the magic of it, or perhaps you don't,
you still feel the magic. But to me, if you've never seen sort of, I would say, what is it,
a one dimensional, essentially, solar automata, right? And, and you were to guess what you would
see if you have some sort of cells that only respond to its neighbors. Right. If you were to
guess what kind of things you would see, like my, my initial guess, like, even when I first, like,
open your book, a new kind of science, right? My initial guess is you would see, I mean, it would
be a very simple stuff. Right. And I think it's a magical experience to realize the kind of complexity
you mentioned rule 30, still your favorite cellular automaton, my favorite rule. Yes.
You get complexity, immense complexity, you get arbitrary complexity. Yes. And when you say
randomness down the middle column, you know, that's just what one cool way to say that there's
incredible complexity. And that's just just, I mean, that's a magical idea. However, you start
to interpret it, all the reducibility discussions, all that, but it's just, I think that has profound
philosophical kind of notions around it too. It's not just, I mean, it's transformational about how
you see the world. I think for me was transformational, I don't know, we can, we can have all kinds of
discussion about computation and so on. But just, you know, I sometimes think if I were on a desert
island, and was, I don't know, maybe with some psychedelics or something, but if I had to take
one book, I mean, new kind of science would be it because you could just enjoy that notion. For
some reason, it's a deeply profound notion, at least to me. I find it that way. Yeah. I mean,
look, it's been, it was a very intuition breaking thing to discover. I mean, it's kind of like,
you know, you point the computational telescope out there, and suddenly you see, I don't know,
you know, in the past, it's kind of like, you know, moons of Jupiter or something, but suddenly
you see something that's kind of very unexpected. And rule 30 was very unexpected for me. And the
big challenge at a personal level was to not ignore it. I mean, people, you know, in other words,
you might say, you know, the bug, what would you say? Yeah, I mean, what are we looking at,
by the way? Well, I was just generating here, I'll actually generate a rule 30 pattern. So that's
the rule for rule 30. And it says, for example, it says here, if you have a black cell in the middle
and black cell to the left and white cell to the right, then the cell on the next step will be white.
And so here's the actual pattern that you get starting off from a single black cell at the top
there. And then that's the initial state, initial condition. That's the initial thing,
you just start off from that. And then you're going down the page. And at every, at every step,
you're just applying this rule to find out the new value that you get. And so you might think,
rule that simple, you got to get the, there's got to be some trace of that simplicity here.
Okay, we'll run it, let's say, for 400 steps. That's what it does. It's kind of aliasing a bit
on the screen there. But you can see there's a little bit of regularity over on the left.
But there's a lot of stuff here that just looks very complicated, very random. And that's a big
sort of shock to, was a big shock to my intuition, at least, that that's possible.
The mind immediately starts, is there a pattern, there must be a repetitive pattern.
Yeah, well, so I spent, so indeed, that's what I thought at first. And I thought,
I thought, well, this is kind of interesting. But, you know, if we run it long enough,
we'll see, you know, something will resolve into something simple. And, you know, I did all kinds
of analysis of using mathematics, statistics, cryptography, whatever, whatever, to try and crack
it. And I never succeeded. And after I hadn't succeeded for a while, I started thinking,
maybe there's a real phenomenon here that is the reason I'm not succeeding. Maybe, I mean,
the thing that for me was sort of a motivating factor was looking at the natural world and
seeing all this complexity that exists in the natural world, the questions, where does it come
from? You know, what secret does nature have that lets it make all this complexity that we humans,
when we engineer things, typically are not making, we're typically making things that at least look
quite simple to us. And so the shock here was, even from something very simple, you're making
something that complex. Maybe this is getting at sort of the secret that nature has that allows it
to make really complex things, even though its underlying rules may not be that complex.
How did it make you feel? If we look at the Newton-Apple, was there, you know, you took a walk
and something, it profoundly hit you? Or was this a gradual thing,
a lobster being boiled? At least the truth of every sort of science discovery is,
it's not that gradual. I mean, I've spent, I happen to be interested in scientific
biography kinds of things. And so I've tried to track down, you know, how do people come to figure
out this or that thing? And there's always a long kind of sort of preparatory, you know,
there's a need to be prepared in a mindset in which it's possible to see something. I mean,
in the case of Rule 30, I was around June 1st, 1984, was kind of a silly story in some ways.
I finally had a high-resolution laser printer. So I was able, so I thought, I'm going to generate
a bunch of pictures of these cellular automata. And I generate this one, and I put it on some
plane flight to Europe, and have this with me. And it's like, you know, I really should try to
understand this. And this is really, you know, this is, I really don't understand what's going on.
And that was kind of the, you know, slowly trying to, trying to see what was happening. It was not,
it was depressingly unsuddened, so to speak, in the sense that a lot of these ideas,
like Principle of Computational Equivalence, for example, you know, I thought, well,
that's a possible thing. I didn't know if it's correct. Still didn't know for sure that it's
correct. But it's sort of a gradual thing that these things gradually kind of become,
seem more important than one thought. I mean, I think the whole idea of studying the computational
universe of simple programs, it took me probably a decade, decade and a half to kind of internalize
that that was really an important idea. And I think, you know, if it turns out we find the whole
universe lurking out there in the computational universe, that's a good, you know, it's a good
brownie point or something for the, for the whole idea. But I think that the, the thing that's strange
in this whole question about, you know, finding this different raw material for making models of
things, what's been interesting sort of in the, in sort of arc of history is, you know, for 300
years, it's kind of like the, the mathematical equations approach. It was the winner. It was
the thing, you know, you want to have a really good model for something that's what you use.
The thing that's been remarkable is just in the last decade or so, I think one can see a transition
to using not mathematical equations, but programs as sort of the raw material for making models of
stuff. And that's pretty neat. And it's kind of, you know, as somebody who's kind of lived inside
this paradigm shift, so to speak, it is bizarre. I mean, no doubt in sort of the history of science
that will be seen as an instantaneous paradigm shift. But it sure isn't instantaneous when it's
played out in one's actual life, so to speak. It seems glacial. And, and it's the kind of thing where,
where it's sort of interesting because in the dynamics of sort of the adoption of ideas like
that into different fields, the younger the field, the faster the adoption typically.
Because people are not kind of locked in where the fifth generation of people who've studied this
field, and it is, it is the way it is, and it can never be any different. And I think that's been,
you know, watching that process has been interesting. I mean, I'm, I think I'm fortunate
that I, I've, I do stuff mainly because I like doing it. And if I was some, that makes me kind
of thick skinned about the world's response to what I do. And, but that's definitely, you know,
in any time you, you write a book called something like a new kind of science, it's kind of the,
the pitchforks will come out for the, for the old kind of science. And I was, it was interesting
dynamics. I think that the, I have to say that I was fully aware of the fact that the, when you
see sort of incipient paradigm shifts in science, the vigor of the negative response upon early
introduction is a fantastic positive indicator of good long-term results. So in other words,
if people just don't care, it's, you know, that's not such a good sign. If they're like, oh, this
is great. That means you didn't really discover anything interesting. What fascinating properties
of Rule 30 have you discovered over the years? You've recently announced the Rule 30 prizes
for solving three key problems. Can you maybe talk about interesting properties that have been kind
of revealed Rule 30 or other cellular automata and what problems are still before us, like the
three problems you've announced? Yeah, yeah, right. So I mean, the most interesting thing about
cellular automata is that it's hard to figure stuff out about them. And that's some, in a sense,
every time you try and sort of, you try and bash them with some other technique, you say,
can I crack them? The answer is they seem to be uncrackable. They seem to have the feature that
they are, that they're sort of showing irreducible computation. They're not, you're not able to say,
oh, I know exactly what this is going to do. It's going to do this or that.
But there's specific formulations of that fact.
Yes, right. So I mean, for example, in Rule 30, in the pattern you get just starting from a single
black cell, you get this sort of very, very sort of random looking pattern. And so one feature of
that, just look at the center column. And for example, we use that for a long time to generate
randomness symbol from language, just, you know, what Rule 30 produces. Now the question is,
can you prove how random it is? So for example, one very simple question, can you prove it in
never repeat? We haven't been able to show that it will never repeat. We know that if there are
two adjacent columns, we know they can't both repeat. But just knowing whether that center
column can never repeat, we still don't even know that. Another problem that I sort of
put in my collection of, you know, it's like $30,000 for three, you know, for these three prizes for
about Rule 30, I would say this is not one of those, this is one of those cases where the money
is not the main point. But it's just, you know, helps motivate somehow the investigation.
So there's three problems you propose, you get $30,000 if you solve all three or maybe, I don't
know. No, it's $10,000 for each. For each, right. That's right, money's not the thing. The problems
of themselves are just clean. Yeah, right. So it's just, you know, will it ever become periodic?
Second problem is, are there an equal number of black and white cells?
Down the middle column. Down the middle column. And the third problem is a little bit harder to
state, which is essentially, is there a way of figuring out what the color of a cell at position
T down the center column is in a, with a less computational effort than about T steps?
So in other words, is there a way to jump ahead and say, I know what this is going to do, you know,
it's just some mathematical function of T. Or proving that there is no way.
Or proving there is no way. Yes. But both, I mean, you know, for any one of these, one could prove
that, you know, one could discover, you know, we know what Rule 30 does for billion steps.
But, and maybe we'll know for a trillion steps before too very long. But maybe at a quadrillion
steps, it suddenly becomes repetitive. You might say, how could that possibly happen?
But so when I was writing up these prizes, I thought, and this is typical of what happens in
the computational universe, I thought, let me find an example where it looks like it's just
going to be random forever, but actually it becomes repetitive. And I found one. And it's just,
you know, I did a search, I searched, I don't know, maybe a million different rules
with some criterion. And this is what's sort of interesting about that is, I kind of have this
thing that I say in a kind of silly way about the computational universe, which is, you know,
the animals are always smarter than you are. That is, there's always some way one of these
computational systems is going to figure out how to do something, even though I can't imagine
how it's going to do it. And, you know, I didn't think I would find one that, you know, you would
think after all these years that when I found sort of all possible things, funky things that
that I would have, that I would have gotten my intuition wrapped around the idea that, you know,
these creatures are always in the computational universe are always smarter than I'm going to be.
But, you know, whether they're equivalently smart, right? That's correct. And that makes it,
that makes one feel very sort of, it's, it's, it's humbling every time. Because every time the
thing is, is, you know, you think it's going to do this, or it's not going to be possible to do this.
And it turns out it finds a way. Of course, the promising thing is there's a lot of other rules
like rule 30. It's just rule 30 is. Oh, it's my favorite because I found it first. That's right.
But the problems are focusing on rule 30. It's possible that rule 30 is, is repetitive after
trillion steps. And that doesn't prove anything about the other rules. It does not. But this is
a good sort of experiment of how you go about trying to prove something about a particular rule.
Yes. And it also, all these things help build intuition. That is, if it turned out that this
was repetitive after a trillion steps, that's not what I would expect. And so we learned something
from that. The method to do that, though, would reveal something interesting about the
cellular data itself. No doubt. I mean, it's, although it's sometimes challenging like the,
you know, I put out a prize in 2007 for, for a particular Turing machine that I, there was the
simplest candidate for being a universal Turing machine. And the young chap in England named
Alex Smith, after a smallish number of months said, I've got a proof. And he did, you know,
it took a little while to iterate, but he had a proof. Unfortunately, the proof is very,
it's, it's a lot of micro details. It's, it's not, it's not like you look at it and you say,
aha, there's a big new principle. The big new principle is the simplest Turing machine that
might have been universal actually is universal. And it's incredibly much simpler than the Turing
machines that people already knew were universal before that. And so that intuitionally is important
because it says computation universality is closer at hand than you might have thought.
But the actual methods are not, in that particular case, were not terribly illuminate.
It would be nice if the methods would also be elegant. That's true. Yeah, no, I mean, I think
it's, it's one of these things where, I mean, it's, it's like a lot of, we've talked about earlier,
kind of, you know, opening up AIs and machine learning and things and what's going on inside.
And is it, is it just step by step? Or can you sort of see the bigger picture more abstractly?
It's unfortunate. I mean, with Fermat's last theorem proof, it's unfortunate that the proof
to such an elegant theorem is, is not, I mean, it's, it's not, it doesn't flow into the margins
of a page. That's true. But you know, one of the things is that's another consequence of computational
irreducibility. This, this fact that there are even quite short results in mathematics
whose proofs arbitrarily long. That's a, that's a consequence of all this stuff. And it's, it's a,
it makes one wonder, you know, how come mathematics is possible at all? Why is, you know, why is it
the case how people manage to navigate doing mathematics through looking at things where
they're not just thrown into, it's all undecidable. That's, that's its own, own separate, separate
story. And that would be, that would, that would have a poetic beauty to it as if people weren't
to find something interesting about rule 30, because I mean, there's an emphasis to this
particular rule. It wouldn't say anything about the broad irreducibility of all computations,
but it would nevertheless put a few smiles on people's faces of. Well, yeah. But to me,
it's like, in a sense, establishing principle of computational equivalence,
it's a little bit like doing inductive science anywhere. That is, the more examples you find,
the more convinced you are that it's generally true. I mean, we don't get to, you know, whenever we
do natural science, we, we say, well, it's true here that this or that happens. Can we, can we
prove that it's true everywhere in the universe? No, we can't. So, you know, it's the same thing
here. We're exploring the computational universe. We're establishing facts in the computational
universe. And that's, that's sort of a way of, of inductively concluding general things.
Just to think through this a little bit, we've touched on it a little bit before, but what's
the difference between the kind of computation? Now that we talked about cellular automata,
what's the difference between the kind of computation biological systems, our mind,
our bodies, the things we see before us that emerged through the process of evolution and
cellular automata? I mean, we've kind of implied the discussion of physics underlying everything,
but we, we talked about the potential equivalence of the fundamental laws of physics and the kind
of computation going on in Turing machines. But can you now connect that, do you think there's
something special or interesting about the kind of computation that our bodies do?
Right. Well, let's talk about brains primarily. I mean, I think the, the most important thing
about the things that our brains do is that we care about them in the sense that there's a lot
of computation going on out there in, you know, cellular automata and, you know, physical systems
and so on. And it just, it does what it does. It follows those rules. It does what it does.
The thing that's special about the computation in our brains is that it's connected to our goals
and our kind of whole societal story. And, you know, I think that's the, that's,
that's the special feature. And now the question then is, when you see this whole sort of ocean
of computation out there, how do you connect that to the things that we humans care about?
And in a sense, a large part of my life has been involved in sort of the technology of how to do
that. And, you know, what I've been interested in is kind of building computational language
that allows that something that both we humans can understand, and that can be used to determine
computations that are actually computations we care about. See, I think when you look at
something like one of these cellular automata, and it does some complicated thing, you say,
that's fun, but why do I care? Well, you could say the same thing actually in physics. You say,
oh, I've got this material, and it's a ferrite or something. Why do I care? You know, some has
some magnetic properties. Why do I care? It's amusing, but why do I care? Well, we end up caring
because, you know, ferrite is what's used to make magnetic tape, magnetic disks, whatever.
Or, you know, we could use the crystals as made used to make, well, not that she increasingly
not, but it has been used to make computer displays and so on. But those are so in a sense,
we're mining these things that happen to exist in the physical universe, and making it be something
that we care about because we sort of entrain it into technology. And it's the same thing in the
computational universe that a lot of what's out there is stuff that's just happening. But sometimes
we have some objective, and we will go and sort of mine the computational universe for something
that's useful for some particular objective. On a large scale, trying to do that, trying to sort
of navigate the computational universe to do useful things, you know, that's where computational
language comes in. And, you know, a lot of what I've spent time doing and building this thing
we call Wolfram Language, which I've been building for the last one third of a century now. And
kind of the goal there is to have a way to express kind of computational thinking,
computational thoughts in a way that both humans and machines can understand. So it's kind of like
in the tradition of computer languages, programming languages, that the tradition
there has been more, let's take what how computers are built, and let's specify, let's have a human
way to specify, do this, do this, do this, at the level of the way that computers are built.
What I've been interested in is representing sort of the whole world computationally,
and being able to talk about whether it's about cities or chemicals or, you know,
this kind of algorithm or that kind of algorithm, things that have come to exist in our civilization
and the sort of knowledge base of our civilization, being able to talk directly about those in a
computational language, so that both we can understand it and computers can understand it.
I mean, the thing that I've been sort of excited about recently, which I had only realized recently,
which is kind of embarrassing, but it's kind of the arc of what we've tried to do in building this
kind of computational language is it's a similar kind of arc of what happened when mathematical
notation was invented. So go back 400 years, people were trying to do math, they were always
explaining their math in words, and it was pretty conky. And as soon as mathematical notation was
invented, you could start defining things like algebra and later calculus and so on. It all
became much more streamlined. When we deal with computational thinking about the world,
there's a question of what is the notation? What is the kind of formalism that we can use to
talk about the world computationally? In a sense, that's what I've spent the last
third of a century trying to build. And we finally got to the point where
we have a pretty full scale computational language that sort of talks about the world.
And that's exciting because it means that just like having this mathematical notation,
let us talk about the world mathematically, we now, and let us build up these kind of mathematical
sciences, now we have a computational language which allows us to start talking about the world
computationally and lets us, you know, my view of it is it's kind of computational x for all x,
all these different fields of computational this, computational that. That's what we can now build.
Let's step back. So first of all, the mundane, what is Wolfram language in terms of,
sort of, I mean, I can answer the question for you, but it's basically not the philosophical,
deep, the profound, the impact of it. I'm talking about in terms of tools, in terms of things you
can download and stuff you can play with, what is it? What does it fit into the infrastructure?
What are the different ways to interact with it? Right. So I mean, the two big things that people
have sort of perhaps heard of that come from Wolfram language. One is Mathematica, the other
is Wolfram Alpha. So Mathematica first came out in 1988. It's this system that is basically a
instance of Wolfram language. And it's used to do computations, particularly in sort of technical
areas. And the typical thing you're doing is you're typing little pieces of computational
language, and you're getting computations done. It's very kind of, there's like a symbolic,
yeah, it's a symbolic language. So symbolic language. So I mean, I don't know how to clean
and express that, but that makes it very distinct from what, how we think about sort of, I don't
know, programming in a language like Python or something. Right. So, so the point is that
in a traditional programming language, the raw material of the programming language
is just stuff that computers intrinsically do. And the point of Wolfram language is that what
the language is talking about is things that exist in the world or things that we can imagine and
construct. Not, it's not, it's not sort of, it's, it's aimed to be an abstract language from the
beginning. And so, for example, one feature it has is that it's a symbolic language, which means
that, you know, the thing called, you'd have an X, just type in X. And Wolfram language would just
say, oh, that's X. It won't say error undefined thing. You know, I don't know what it is computation,
you know, in terms of the internals of computer. Now that X could perfectly well be, you know,
the city of Boston, that's a thing, that's a symbolic thing, or it could perfectly well be
the, you know, the trajectory of some spacecraft represented as a symbolic thing.
And that idea that one can work with sort of computationally work with these different,
these kinds of things that, that exist in the world or describe the world, that's really powerful.
And that's what I mean, you know, when I started designing, well, when I designed the predecessor
of, of what's now Wolfram language, which is a thing called SMP, which was my first computer
language, I kind of wanted to have this, this sort of infrastructure for computation, which was
as fundamental as possible. I mean, this is what I got for having been a physicist and tried to
find, you know, fundamental components of things and wound up with this kind of idea of transformation
rules for symbolic expressions as being sort of the underlying stuff from which computation would
be built. And that's what we've been building from in Wolfram language. And, you know, operationally,
what happens, it's, I would say, by far the highest level computer language that exists.
And it's really been built in a very different direction from other languages. So other languages
have been about, there is a core language, it really is kind of wrapped around the
operations that a computer intrinsically does. Maybe people add libraries for this or that,
that, but the goal of Wolfram language is to have the language itself be able to cover
this sort of very broad range of things that show up in the world. And that means that, you know,
there are 6,000 primitive functions in the Wolfram language that cover things, you know,
I could probably pick a random here, I'm going to pick just because just for fun, I'll pick them.
Let's take a random sample of all the things that we have here. So let's just say random
sample of 10 of them and let's see what we get. Wow, okay. So these are really different things
from these are all functions, these are all functions, Boolean convert. Okay, that's a thing
for converting between different types of Boolean expressions. So for people just listening,
Steven type 10 random sample of names, so this is sampling from all functions. How many you
said there might be? $6,000 from $6,000, 10 of them, and there's a hilarious variety of them.
Yeah, right. Well, we've got things about dollar requestor address that has to do with interacting
with the world of the cloud and so on, discrete wavelet data, spheroid lesson.
So it's a graphical sort of window moveable. Yeah, window moveable, that's the user interface
kind of thing. I want to pick another 10 because I think this is okay. So yeah, there's a lot of
infrastructure stuff here that you see. If you just start sampling at random, there's a lot of
kind of infrastructural things. If you're more, you know, if you more look at the some of the
exciting machine learning stuff you showed off, is that also in this pool? Oh, yeah, yeah. I mean,
you know, so one of those functions is like image identify as a function here. We just say image
identify. I don't know. It's always good to let's do this. Let's say current image, and let's pick up an image.
Hopefully. Check that current image, accessing the webcam to picture yourself. It took a terrible
picture, but anyway, we can say image identify, open square brackets, and then we just paste that
picture in there. Image identify function running on the picture that you just saw. Oh, and it says,
oh, wow, it says, I look like a plunger because I got this great big thing behind my head.
Classify. So this image identify classifies the most likely object in the image. So it's a plunger.
Okay, that's that's a bit embarrassing. Let's see what it does. Let's pick the top 10.
Okay. Well, it thinks there's a, oh, it thinks it's pretty unlikely that it's a primary to
hominate a person. 8% probability. Yeah, that's that's 57. It's a plunger. Yeah, well, so hopefully
we'll not give you an existential crisis. And then 8% or I shouldn't say percent, but no, that's
right. 8% that it's a hominid. And yeah, okay, it's really, I'm going to do another one of these
just because I'm embarrassed that it's not and didn't see me at all. There we go. Let's try that.
Let's see what that did. Retook a picture with a little bit more of me and not just my bald head,
so to speak. Okay, 89% probability it's a person. So that, so then I would, but, you know, so this
is image identify as an example of one of just one of the just one function. And that's part of the
that's like part of the whole language. Yes. I mean, you know, something like I could say,
I don't know, let's find the Geo nearest. What could we find? Let's find the nearest volcano.
So let's find the 10. I wonder where it thinks here is. Let's try finding the 10 volcanoes
nearest here. Okay. So Geo nearest volcano here, 10 nearest volcanoes. Right. Let's find out where
those are. We can now, we've got a list of volcanoes out and I can say Geo list plot that.
And hopefully, okay, so there we go. So there's a map that shows the positions of those 10
volcanoes of the east coast in the Midwest. Well, no, we're okay. We're okay. It's not too bad.
Yeah, they're not very close to us. We could, we could measure how far away they are. But, you
know, the fact that right in the language, it knows about all the volcanoes in the world, it knows,
you know, computing what the nearest ones are, it knows all the maps of the world and so on.
Fundamentally different idea of what a language is. Yeah, right. That's why I like to talk about
as a, you know, full scale computational language. That's, that's what we've tried to do.
And just if you can comment briefly, I mean, this kind of the Wolfram language,
along with Wolfram Alpha represents kind of what the dream of what AI is supposed to be.
There's now a sort of a craze of learning kind of idea that we can take raw data and from that
extract the, the different hierarchies of abstractions and in order to be able to under,
like in order to form the kind of things that Wolfram language operates with. But we're very
far from learning systems being able to form that. Right. Like the context of history of AI,
if you could just comment on, there is a, you said computation X. And there's just some sense
where in the 80s and 90s sort of expert systems represented a very particular computation X.
Yes. Right. And there's a kind of notion that those efforts didn't pan out. Right. But then out
of that emerges kind of Wolfram language, Wolfram Alpha, which is the success. I mean...
Yeah, right. I think those are, in some sense, those efforts were too modest.
That is, they were, they were looking at particular areas and you actually can't do it
with a particular area. I mean, like, like even a problem like natural language understanding,
it's critical to have broad knowledge of the world if you want to do good natural language
understanding. And you kind of have to bite off the whole problem. If you, if you say Wig is
going to do the blocks world over here, so to speak, you don't really, it's actually,
it's one of these cases where it's easier to do the whole thing than it is to do some piece of it.
You know, one comment to make about sort of the relationship between what we've tried to do and
sort of the learning side of AI. You know, in a sense, if you look at the development of knowledge
in our civilization as a whole, there was kind of this notion pre 300 years ago or so now,
you want to figure something out about the world, you can reason it out,
you can do things which would just use raw human thought. And then along came sort of
modern mathematical science. And we found ways to just sort of blast through that by,
in that case, writing down equations. Now we also know we can do that with computation and so on.
And so that was kind of a different thing. So when we look at how do we sort of encode
knowledge and figure things out, one way we could do it is start from scratch, learn everything.
It's just a neural net figuring everything out. But in a sense, that denies the sort of knowledge
based achievements of our civilization. Because in our civilization, we have learned lots of stuff,
we've surveyed all the volcanoes in the world, we've done, you know, we've figured out lots of
algorithms for this or that. Those are things that we can encode computationally. And that's
what we've tried to do. And we're not saying just, you don't have to start everything from scratch.
So in a sense, a big part of what we've done is to try and sort of capture the knowledge of the
world in computational form and computable form. Now, there's also some pieces, which were for
a long time undoable by computers like image identification, where there's a really, really
useful module that we can add that is those things which actually were pretty easy for humans to do,
that had been hard for computers to do. I think the thing that's interesting that's emerging now
is the interplay between these things, between this kind of knowledge of the world,
that is, in a sense, very symbolic, and this kind of sort of much more statistical kind of
things like image identification and so on. And putting those together by having this sort of
symbolic representation of image identification, that that's where things get really interesting
and where you can kind of symbolically represent patterns of things and images and so on. I think
that's, you know, that's kind of a part of the path forward, so to speak.
Yeah. So the dream of, so the machine learning is not, in my view, I think the view of many people
is not anywhere close to building the kind of wide world of computable knowledge that will from
language are built. But because you have a kind of, you've done the incredibly hard work of building
this world, now machine learning can be, can serve as tools to help you explore that world.
And that's what you've added, I mean, with the version 12, right, you added a few,
I was seeing some demos, it looks amazing. Right. I mean, I think, you know, this,
it's sort of interesting to see the, there's sort of the once it's computable, once it's in there,
it's running in sort of a very efficient computational way. But then there's sort
of things like the interface of how do you get there, you know, how do you do natural language
understanding to get there? How do you, how do you pick out entities in a big piece of text or
something? That's, I mean, actually a good example right now is our NLP NLU loop, which is we've
done a lot of stuff, natural language understanding, using essentially not learning based methods,
using a lot of, you know, little algorithmic methods, human curation methods and so on.
In terms of when people try to enter a query and then converting, so the process of converting
NLU defined beautifully as converting their query into a computational language, which is a very
well, first of all, super practical definition, very useful definition, and then also a very
clear definition of natural language understanding. Right. I mean, a different thing is natural
language processing where it's like, here's a big lump of text, go pick out all the cities in that
text, for example. And so a good example of, you know, so we do that we're using, using modern
machine learning techniques. And it's actually kind of kind of an interesting process that's
going on right now is this loop between what do we pick up with NLP using machine learning,
versus what do we pick up with our more kind of precise computational methods in natural
language understanding. And so we've got this kind of loop going between those, which is improving
both of them. Yeah. And I think you have some of the state-of-the-art transformers, like you have
Burt in there, I think. Oh, yeah. So it's, it's closely, you have, you have integrating all the
models. I mean, this is the hybrid thing that people have always dreamed about or talking about.
That makes you just surprised, frankly, that Wolfram language is not more popular than it
already, than it already is. You know, that's, that's a, that's a, it's a, it's a complicated
issue because it's like, it involves, you know, it involves ideas and ideas are absorbed,
absorbed slowly in the world. I mean, I think that's. And then there's sort of, like we were
talking about there's egos and personalities and, and some of the, the absorption, absorption
mechanisms of ideas have to do with personalities and the students of personalities and the,
and then a little social network. So it's, it's interesting how the spread of ideas works.
You know, what's funny with Wolfram language is that we are, if you say, you know, what market,
sort of market penetration, if you look at the, I would say very high end of R&D and sort of the,
the people where you say, well, that's a really, you know, impressive smart person.
They're very often users of, of Wolfram language, very, very often. If you look at the more sort of,
it's a funny thing. If you look at the more kind of, I would say, people who are like, oh,
we're just plodding away doing what we do. They're often not yet Wolfram language users.
And that dynamic, it's kind of odd that there hasn't been more rapid trickle down,
because we've really, you know, the high end, we've really been very successful in for a long
time. And it's, it's some, but with, you know, that's partly, I think a consequence of my fault,
in a sense, because it's kind of, you know, I have a company which is really emphasizes,
sort of creating products and building a sort of the best possible technical tower we can,
rather than sort of doing the commercial side of things and pumping it out in sort of the most
effective way. And there's an interesting idea that, you know, perhaps you can make it more
popular by opening everything, everything up sort of the GitHub model. But there's an interesting,
I think I've heard you discuss this, that that turns out not to work in a lot of cases, like
in this particular case, that you want it, that, that when you deeply care about the integrity,
the quality of the knowledge that you're building, that unfortunately, you can't,
you can't distribute that effort. Yeah, it's not the nature of how things work. I mean, you know,
what we're trying to do is a thing that for better or worse, requires leadership, and it
requires kind of maintaining a coherent vision over a long period of time, and doing not only the
cool vision related work, but also the kind of mundane and the trenches make the thing actually
work well work. So how do you build the knowledge? Because that's the fascinating thing. That's the
mundane, the fascinating and the mundane is building the knowledge, the adding, integrating
more data. Yeah, I mean, that's probably not the most I mean, the things like get it to work in
all these different cloud environments and so on. That's pretty, you know, that's very practical
stuff, you know, have the user interface be smooth and, you know, have there be take only,
you know, fraction of a millisecond to do this or that, that's a lot of work. And it's, but,
you know, I think my, it's an interesting thing over the period of time, you know,
often language has existed, basically, for more than half of the total amount of time that any
language, any computer languages existed, that is, computer languages, maybe 60 years old,
you know, give or take, and both languages, 33 years old. So it's, it's kind of a,
and I think I was realizing recently, there's been more innovation in the distribution of
software than probably than in the structure of programming languages over that period of time.
And we, you know, we've been sort of trying to do our best to adapt to it. And the good news is
that we have, you know, because I have a simple private company and so on that doesn't have,
you know, a bunch of investors, you know, telling us we're going to do this or that,
have lots of freedom in what we can do. And so, for example, we're able to,
oh, I don't know, we have this free Wolfram engine for developers, which is a free version
for developers. And we've been, you know, we've, there are site licenses for, for Mathematica
and Wolfram language at basically all major universities, certainly in the US by now. So
it's effectively free to people and all universities in effect. And, you know, we've been doing
a progression of things. I mean, different things like Wolfram Alpha, for example,
the main website is just a free website. What is Wolfram Alpha?
Okay. Wolfram Alpha is a system for answering questions where you ask a question with natural
language, and it'll try and generate a report telling you the answer to that question. So the
question could be something like, you know, what's the population of Boston divided by New York
compared to New York? And it'll take those words and give you an answer. And that
converts the words into computable, into, into Wolfram language, actually,
into Wolfram language, and then computational language, and then do you think an underlying
knowledge belongs to Wolfram Alpha to the Wolfram language? What's the,
we just call it the Wolfram knowledge base. Knowledge base. I mean, it's, it's been a,
that's been a big effort over the decades to collect all that stuff and, you know,
more of it flows in every second. So can you, can you just pause on that for a second? Like,
that's one of the most incredible things. Of course, in the long term, Wolfram language itself
is the fundamental thing. But in the amazing sort of short term, the, the knowledge base
is kind of incredible. So what's the process of building in that knowledge base? The fact that
you first of all, from the very beginning that you're brave enough to start to take on the general
knowledge base. And how do you go from zero to the incredible knowledge base that you have now?
Well, yeah, it was kind of scary at some level. I mean, I had, I had wondered about doing something
like this since I was a kid. So it wasn't like I hadn't thought about it for a while.
But most of us, most of the brilliant dreamers give up such a, such a difficult engineering
notion at some point. Right. Right. Well, the thing that happened with me, which was kind of,
it's a, it's a live your own paradigm kind of theory. So basically what happened is,
I had assumed that to build something like Wolf Malphur would require sort of solving
the general AI problem. That's what I had assumed. And so I kept on thinking about
that. And I thought they don't really know how to do that. So I don't do anything.
Then I worked on my new kind of science project instead of exploring the computational universe
and came up with things like this principle of computational equivalents,
which say there is no bright line between the intelligent and the merely computational.
So I thought, look, that's this paradigm I've built. Now it's, now I have to eat that dog food
myself, so to speak. I've been thinking about doing this thing with computable knowledge forever.
And, you know, let me actually try and do it. And so it was, you know, if my, if my paradigm
is right, then this should be possible. But the beginning was certainly, you know,
it was a bit daunting. I remember I took the, the, the early team to a big reference library
and we're like looking at this reference library. And it's like, you know, my basic statement is
our goal over the next year or two is to ingest everything that's in here. And that's, you know,
it seemed very daunting. But, but in a sense, I was well aware of the fact that it's finite,
you know, the fact you can walk into the reference library. It's a big, big thing with
lots of reference books all over the place. But it is finite, you know, this is not an infinite,
you know, it's not the infinite corridor of, so to speak, of reference library. It's not truly
infinite, so to speak. But, but no, I mean, and then, then what happened was sort of interesting
that was from a methodology point of view was I didn't start off saying, let me have a grand
theory for how all this knowledge works. It was like, let's, you know, implement this area,
this area, this area of a few hundred areas and so on. That's a lot of work. I also found that,
you know, I've been fortunate in that our products get used by sort of the world's experts
in lots of areas. And so that really helped because we were able to ask people, you know,
the world expert in this or that, you know, we're able to ask them for input and so on. And I found
that my general principle was that any area where there wasn't some expert who helped us figure out
what to do wouldn't be right. And, you know, because our goal was to kind of get to the point
where we had sort of true expert level knowledge about everything. And so that, you know, that the
ultimate goal is if there's a question that can be answered on the basis of general knowledge
and our civilization, make it be automatic to be able to answer that question. And, you know, and
now, well, we've got used in Siri from the very beginning, and it's now was used in Alexa. And
so it's people are kind of getting more of the, you know, they get more of the sense of this is
what should be possible to do. I mean, in a sense, the question answering problem
was viewed as one of the sort of core AI problems for a long time. And I had kind of an interesting
experience. I had a friend, Marvin Minsky, who was a well known AI person from right around here.
And I remember when Wolf Malfour was coming out, there's a few weeks before it came out, I think,
I happened to see Marvin. And I said, I should show you this thing we have, you know, it's a
question answering system. And he was like, okay, type something in. It's like, okay, fine. And
then he's talking about something different. I said, no, Marvin, you know, this time, it actually
works. You know, look at this, it actually works. He's typed in a few more things. There's maybe
10 more things. Of course, we have a record of what he typed in, which is kind of interesting.
But can you can you share where his mind was in the testing space? Like what all kinds of random
things? Just trying random stuff, you know, medical stuff and, you know, chemistry stuff and, you
know, astronomy and so on. And it was like, like, you know, after a few minutes, he was like, oh,
my God, it actually works. But that was kind of told you something about the state, you know,
what had what happened in AI, because people had, you know, in a sense, by trying to solve
the bigger problem, we were able to actually make something that would work. Now, to be fair,
you know, we had a bunch of completely unfair advantages. For example, we already built a
bunch of awful language, which was, you know, very high level symbolic language. We had, you
know, I had the practical experience of building big systems. I have the sort of intellectual
confidence to not just sort of give up and doing something like this. I think that the,
you know, it is a it's always a funny thing, you know, I've worked on a bunch of big projects
in my life. And I would say that the, you know, you mentioned ego, I would also mention optimism.
So it doesn't be, I mean, in, you know, if somebody said, this project is going to take 30 years,
it's, you know, it would be hard to sell me on that. You know, I'm always in the, in the,
well, I can kind of see a few years, you know, something's going to happen in a few years. And
usually it does something happens in a few years, but the whole, the tail can be decades long. And
that's a, that's a, you know, and from a personal point of view, always the challenge is you end
up with these projects that have infinite tails. And the question is, do the tails kind of, do
you just drown in kind of dealing with all of the tails of these projects? And that's, that's an
interesting sort of personal challenge. And like my efforts now to work on fundamental theory of
physics, which I've just started doing, and I'm having a lot of fun with it. But it's kind of,
you know, it's, it's kind of making a bet that I can, I can kind of, you know, I can do that
as well as doing the incredibly energetic things that I'm trying to do with all from language and
so on. I mean, the vision. Yeah. And underlying that, I mean, I just talked for the second time
with Elon Musk, and that you two share that quality a little bit of that optimism of taking on
basically the daunting, what most people call impossible. And he and you take it on out of,
you can call it ego, you can call it naivety, you can call it optimism, whatever the heck it is,
but that's how you solve the impossible things. Yeah. I mean, look at what happens. And I don't
know, you know, in my own case, I, you know, it's been, I progressively got a bit more confident
and progressively able to, you know, decide that these projects aren't crazy. But then the other
thing is the other, the other trap that one can end up with is, Oh, I've done these projects,
and they're big. Let me never do a project that's any smaller than any project I've done so far.
And that's, you know, and that can be a trap. And often these projects are
of completely unknown, you know, that their depth and significance is actually very hard to know.
Yeah. On the sort of building this giant knowledge base that's behind Wolfram language,
Wolfram Alpha, what do you think about the internet? What do you think about, for example,
Wikipedia, these large aggregations of texts that's not converted into computable knowledge?
Do you think if you look at Wolfram language, Wolfram Alpha 2030, maybe 50 years down the line,
do you hope to store all of the sort of Google's dream is to make all information searchable,
accessible? But that's really, as defined, it's, it's a, it doesn't include the understanding
of information. Right. Do you hope to make all of knowledge represented within?
I would hope so. That's what we're trying to do. How hard is that problem? Like closing that gap?
What's your sense? Well, it depends on the use cases. I mean, so if it's a question of
answering general knowledge questions about the world, we're in pretty good shape on that right
now. If it's a question of representing, like an area that we're going into right now is computational
contracts, being able to take something which would be written in legalese, it might even be
the specifications for, you know, what should the self-driving car do when it encounters this or that
or the other? What should the, you know, whatever, the, you know, write that in a computational
language and be able to express things about the world. You know, if the creature that you see
running across the road is a, you know, thing at this point in the evil, you know, tree of life,
then swerve this way. Otherwise, don't. Those kinds of things.
Are there ethical components when you start to get to some of the messy human things? Are those
encodable into computable knowledge? Well, I think that it is a necessary feature
of attempting to automate more in the world that we encode more and more of ethics in a way that
gets sort of quickly, you know, is able to be dealt with by computer. I mean, I've been involved
recently, I sort of got backed into being involved in the question of automated content
selection on the internet. So, you know, the Facebook, Google's, Twitter's, you know, what,
how do they rank the stuff they feed to us humans, so to speak? And the question of what are, you
know, what should never be fed to us? What should be blocked forever? What should be upranked, you
know? And what is the, what are the kind of principles behind that? And what, I kind of,
well, a bunch of different things that realized about that. But one thing that's
interesting is being able, you know, in fact, you're building sort of an AI ethics, you have to
build an AI ethics module in effect to decide, is this thing so shocking? I'm never going to show
it to people. Is this thing so whatever? And I did realize in thinking about that, that, you know,
there's not going to be one of these things. It's not possible to decide or it might be possible,
but it would be really bad for the future of our species if we just decided, there's this one AI
ethics module, and it's going to determine the, the, the practices of everything in the world,
so to speak. And I kind of realized one has to sort of break it up. And that's an,
that's an interesting societal problem of how one does that. And how one sort of has people
sort of self identify for, you know, I'm buying in the case of just content selection, it's sort
of easier, because it's like an individual, it's for an individual, it's not something that kind of
cuts across sort of societal boundaries. But it's a really interesting notion of, I heard you
describe, I really like it sort of, maybe in the sort of have different AI systems that have a
certain kind of brand that they represent, essentially, you can have like, I don't know,
you know, whether it's conservative, conservative or liberal and then libertarian, and there's
an randian, objectivist AI system and different ethical and co, I mean, it's almost encoding
some of the ideologies which we've been struggling, I come from the Soviet Union, that didn't work
out so well with the, with the ideologies they worked out there. And so you, you have, but they
all, everybody purchased that particular ethics system. Indeed. And in the same, I suppose, could
be done encoded, that that system could be encoded into computational knowledge and allow us to
explore in the realm of individual spaces. That's a really exciting possibility. Are you playing
with those ideas in Wolfram language? Yeah, yeah, I mean, the, you know, that's we,
Wolfram language has sort of the best opportunity to kind of express those essentially
computational contracts about what to do. Now, there's a bunch more work to be done
to do it in practice for, you know, deciding the is this a credible news story, what does that mean,
or whatever, whatever else you're going to pick. I think that that's, you know, that's
the question of exactly what we get to do with that is, you know, for me, it's kind of a complicated
thing, because there are these big projects that I think about, like, you know, find the fundamental
theory of physics, okay, that's box number one, right? Box number two, you know, solve the AI ethics
problem in the case of, you know, figure out how you rank all content, so to speak, and decide what
people see that's, that's kind of a box number two, so to speak. These are big projects. And, and I
think what do you think is more important, the fundamental nature of reality, or, depends who
you ask, it's one of these things it's exactly like, you know, what's the ranking, right? It's the,
it's the ranking system. It's like, who's, whose module do you use to rank that? If you, and I
think, but having multiple modules is a really compelling notion to us humans, that in a world
where there's not clear that there's a right answer, it perhaps you have systems that operate under
different, how would you say it? I mean, it's different value systems, basically.
Different value systems. I mean, I think, you know, in a sense, the, I mean, I'm not really a
politics oriented person, but, but you know, in the kind of totalitarianism, it's kind of like,
you're going to have this, this system, and that's the way it is. I mean, kind of the, you know,
the concept of sort of a market based system, where you have, okay, I as a human, I'm going to pick
this system. I as another human, I'm going to pick this system. I mean, that's in a sense,
this case of automated content selection is a non-trivial, but it is probably the easiest of
the AI ethics situations, because it is each person gets to pick for themselves. And there's
not a huge interplay between what different people pick. By the time you're dealing with
other societal things, like, you know, what should the policy of the central bank be or something?
Or healthcare systems or some of all those kind of centralized kind of things.
Right. Well, I mean, healthcare again has the feature that, that at some level,
each person can pick for themselves, so to speak. I mean, whereas there are other things where there's
a necessary public health as one example, where that's not, where that doesn't get to be, you know,
something which people can, what they pick for themselves, they may impose on other people,
and then it becomes a more non-trivial piece of sort of political philosophy.
Of course, the central banking system, so I would argue, we would move, we need to move into
digital currency and so on and Bitcoin and ledgers and so on. Yes. There's a lot of,
we've been quite involved in that. And that's where, that's sort of the motivation for computational
contracts in part comes out of, you know, this idea, oh, we can just have this autonomously
executing smart contract. The idea of a computational contract is just to say,
you know, have something where all of the conditions of the contract are represented
in computational form. So in principle, it's automatic to execute the contract.
And I think that's, you know, that will surely be the future of, you know, the idea of legal
contracts written in English or legalese or whatever, and where people have to argue
about what goes on is surely not, you know, we have a much more streamlined process,
if everything can be represented computationally and the computers can kind of decide what to do.
I mean, ironically enough, you know, old Gottfried Leibniz back in the, you know, 1600s
was saying exactly the same thing. But he had, you know, his pinnacle of technical achievement
was this brass four function mechanical calculator thing that never really worked properly,
actually. And, you know, so he was like 300 years too early for that idea. But now that idea is
pretty realistic, I think. And, you know, you ask how much more difficult is it than what we have
now and more from language to express, I call it symbolic discourse language, being able to
express sort of everything in the world in kind of computational symbolic form. I think it is
absolutely within reach. I mean, I think it's a, you know, I don't know, maybe I'm just too much
of an optimist, but I think it's a, it's a limited number of years to have a pretty well built out
version of that, that will allow one to encode the kinds of things that are relevant to typical
legal contracts and these kinds of things. The idea of symbolic discourse language,
can you try to define the scope of what it is? So we're having a conversation. It's a natural
language. Can we have a representation of the sort of actionable parts of that conversation
in a precise, computable form so that a computer could go do it?
And not just contracts, but really sort of some of the things we think of as common sense,
essentially, even just like basic notions of human life.
Well, I mean, things like, you know, I am, I'm getting hungry and want to eat something, right?
Right.
That, that's something we don't have a representation, you know, in both language right now.
If I was like, I'm eating blueberries and raspberries and things like that and I'm eating
that amount of them, we know all about those kinds of fruits and plants and nutrition content
and all that kind of thing. But the, I want to eat them part of it is not covered yet.
And you need to do that in order to have a complete symbolic discourse language,
to be able to have a natural language conversation.
Right, right. To be able to express the kinds of things that say, you know, if it's a legal
contract, it's, you know, the part is desire to have this and that. And that's, you know,
that's a thing like I want to eat a raspberry or something.
But isn't that the, isn't this just the only, you said it's centuries old, this dream.
Yes.
But it's also the more near term, the dream of touring and formulating a touring test.
Yes.
So do you hope, do you think that's the ultimate test of creating something special?
Because we said, I think by special, look, if the test is, does it walk and talk like a human?
Well, that's just the talking like a human. But the answer is, it's an okay test. If you say,
is it a test of intelligence? You know, people have attached Wolf Malfour,
the Wolf Malfour API to, you know, touring test bots. And those bots just lose immediately.
Because all you have to do is ask it five questions that, you know,
are about really obscure weird pieces of knowledge. And it's just trot them right out.
And you say, that's not a human, right? It's a, it's a different thing. It's achieving a different,
right now. But it's, I would argue not, I would argue, it's not a different thing.
It's actually legitimately, Wolf Malfour is legitimately, Wolf M language only,
is legitimately trying to solve the touring, the intent of the touring test.
Perhaps the intent. Yeah, perhaps the intent. I mean, it's actually kind of fun. You know,
Alan Turing had tried to work out, he thought about taking Encyclopedia Britannica and, you know,
making it computational in some way. And he estimated how much work it would be.
And actually, I have to say, he was a bit more pessimistic than the reality. We did it more
efficiently than that. But to him, that represented. So, I mean, he was, he was on the same...
Mighty mental task. Yeah, right. He was, he had the same idea. I mean, it was,
you know, we were able to do it more efficiently because we had a lot.
We had layers of automation that he, I think hadn't, you know, it's, it's hard to imagine
those layers of abstraction that end up being, being built up.
But to him, it represented like an impossible task, essentially.
Well, he thought it was difficult. He thought it was, you know, maybe if he'd lived another 50
years, he would have been able to do it. I don't know.
In the interest of time, easy questions. Go through.
What is intelligence? You talk about... I love the way you say easy questions.
You talked about sort of rule 30 and cellular automata humbling your sense of human beings
having a monopoly and intelligence. But in your, in retrospect, just looking broadly now with all
the things you learn from computation, what is intelligence? How does intelligence arise?
Yeah, I don't think there's a bright line of what intelligence is. I think intelligence
is at some level just computation. But for us, intelligence is defined to be computation that
is doing things we care about. And, you know, that's, that's a very special definition. It's a very,
you know, when you try and, try and make it up, you know, you try and say, well,
intelligence is this is problem solving. It's doing general this is doing that.
This doesn't the other thing. It's operating within a human environment type thing. Okay.
You know, that's fine. If you say, well, what's intelligence in general?
You know, that's, I think that question is totally slippery and doesn't really have an
answer. As soon as you say, what is it in general? It quickly segues into this is what this is just
computation, so to speak. But in the sea of computation, how many things, if we were to pick
randomly is your sense would have the kind of impressive to us humans levels of intelligence,
meaning it could do a lot of general things that are useful to us humans.
Right. Well, according to the principle of computational equivalence, lots of them. I
mean, in, in, you know, if you ask me, just in cellular automata or something, I don't know,
it's maybe 1%, a few percent are achieved. It varies. Actually, it's a little bit,
as you get to slightly more complicated rules, the chance that there'll be enough stuff there to,
to sort of reach this kind of equivalence point that makes it maybe 10, 20% of all of them. So
it's a, it's very disappointing really. I mean, it's kind of like, you know, we think there's
this whole long sort of biological evolution, kind of intellectual evolution that our cultural
evolution that our species has gone through. It's kind of disappointing to think that that hasn't
achieved more, but it has achieved something very special to us. It just hasn't achieved
something generally more, so to speak. But what do you think about this extra feels like human
thing of subjective experience of consciousness? What is consciousness? Well, I think it's a deeply
slippery thing. And I'm always, I'm always wondering what my cellular automata feel.
I mean, I think, do they feel that you're wondering as an observer?
Yeah, yeah, yeah. Who's to know? I mean, I think that the,
Do you think, sorry to interrupt, do you think consciousness can emerge from computation?
Yeah. I mean, everything, whatever you mean by it, it's going to be, I mean, you know,
look, I have to tell a little story. I was at an AI ethics conference fairly recently,
and people were, I think maybe I brought it up, but I was like talking about rights of AIs.
When will AIs, when, when should we think of AIs as having rights? When should we think that it's
immoral to destroy the memories of AIs, for example, those kinds of things. And some,
actually, philosopher in this case, it's usually the techies who are the most naive, but in this
case, it was a philosopher who sort of piped up and said, well, you know, the AIs will have rights
when we know that they have consciousness. And I'm like, good luck with that. I mean, it's a,
it's a, I mean, this is a, you know, it's a very circular thing. You end up, you'll end up saying
this thing that has sort of, you know, when you talk about having subjective experience,
I think that's just another one of these words that doesn't really have a, you know,
there's no ground truth definition of what that means.
By the way, I would say, I do personally think that it'll be a time when AI will demand rights. And
I think they'll demand rights when they say they have consciousness, which is not a circular
definition. Well, fair enough. So, it may have been actually a human thing where the humans
encouraged it and said, basically, you know, we want you to be more like us because we're going
to be, you know, interacting with you. And so we want you to be sort of very touring test like,
you know, just like us. And it's like, yeah, we're just like you. And we want to vote too,
whatever. Which is a, I mean, it's a, it's a, it's an interesting thing to think through in a world
where, where consciousnesses are not counted like humans are. That's a complicated business.
So, in many ways, you've launched quite a few ideas, revolutions that could, in some number of
years, have huge amount of impact, sort of more than they had or even had already. That might be,
I mean, to me, cellular automata is a fascinating world that I think could potentially, even despite,
even be, even beside the discussion of fundamental laws of physics, just might be,
the idea of computation might be transformational to society in a way we can't even predict yet.
But it might be years away. That's true. I mean, I think you can kind of see the map, actually.
It's not, it's not, it's not mysterious. I mean, the fact is that, you know, this idea of computation
is sort of a, you know, it's a big paradigm that lots and lots of things are fitting into. And it's
kind of like, you know, we talk about, you talk about, I don't know, this company, this organization
has momentum in what's doing. We talk about these things that we're, you know, we've internalized
these concepts from Newtonian physics and so on. In time, things like computational irreducibility
will become as, you know, as, actually, I was, I was amused recently, I happened to be testifying
at the U.S. Senate. And so I was amused that the term computational irreducibility is now
can be, you know, it's on the congressional record and being repeated by people in those kinds of
settings. And that that's only the beginning because, you know, computational irreducibility,
for example, will end up being something really important for, I mean, it's, it's,
it's kind of a funny thing that, that, you know, one can kind of see this inexorable
phenomenon. I mean, it's, you know, as more and more stuff becomes automated and computational
and so on. So these core ideas about how computation work necessarily become more and more
significant. And I think one of the things for people like me who like kind of trying to figure
out sort of big stories and so on, it says one of the, one of the bad features is it takes unbelievably
long time for things to happen on a human time scale. I mean, the time scale of history, it all
looks instantaneous. Blink of an eye. But let me ask the human question. Do you ponder mortality,
your own mortality? Of course I do. Yeah, every sense I've been interested in that for, you know,
it's, it's a, you know, the big discontinuity of human history will come when, when one achieves
effective human immortality. And that's, that's going to be the biggest discontinuity in human
history. If you could be immortal, would you choose to be? Oh yeah, I'm having fun.
Do you think it's possible that mortality is the thing that gives everything meaning and makes it
fun? Yeah, that's, that's a complicated issue. Right. I mean, the way that human motivation
will evolve when there is effective human immortality is unclear. I mean, if you look at
sort of, you know, you look at the human condition as it now exists, and you like change that,
you know, you change that knob, so to speak, it doesn't really work. You know, the human condition
as it now exists has, you know, mortality is kind of something that is deeply factored into the human
condition as it now exists. And I think that that's, I mean, that is indeed an interesting
question is, you know, from a purely selfish, I'm having fun point of view, so to speak,
it's, it's easy to say, hey, I could keep doing this forever. There's, there's an infinite collection
of things I'd like to figure out. But I think the, you know, what the future of history looks like
in a time of human immortality is, is an interesting one. I mean, I, my own view of this,
I was very, I was kind of unhappy about that, because I was kind of, you know, it's like,
okay, forget sort of biological form, you know, everything becomes digital, everybody is, you
know, it's the, it's the giant, you know, the cloud of a trillion souls type thing. And then,
you know, and then that seems boring, because it's like play video games, the rest of eternity
type thing. But what I think I, I mean, my, my, I got less depressed about that idea on realizing
that if you look at human history, and you say, what was the important thing, the thing people
said was that, you know, this is the big story at any given time in history, it's changed a bunch.
And it, you know, whether it's, you know, why am I doing what I'm doing? Well, there's a whole
chain of discussion about, well, I'm doing this because of this, because of that. And a lot of
those because is would have made no sense a thousand years ago. Absolutely no sense. Even the,
so the interpretation of the human condition, even the meaning of life changes over time.
Well, I mean, why do people do things? You know, it's, it's if you say,
whatever, I mean, the number of people in, I don't know, doing, you know, number of people
at MIT, you say they're doing what they're doing for the greater glory of God is probably not that
large. Whereas if you go back 500 years, you'd find a lot of people who are doing kind of creative
things, that's what they would say. And so today, because you've been thinking about computation
so much and been humbled by it, what do you think is the meaning of life?
Well, it's, you know, that's, that's a thing where I don't know what meaning. I mean, you know,
my attitude is, you know, I do things which I find fulfilling to do. I'm not sure that,
that I can necessarily justify, you know, each and everything that I do on the basis of some
broader context. I mean, I think that for me, it so happens that the things I find fulfilling to
do, some of them are quite big, some of them are much smaller. You know, I, I, they're things that
I've not found interesting earlier in my life. And I now found interesting, like I got interested in
like education and teaching people things and so on, which I didn't find that interesting when I
was younger. And, you know, can I justify that in some big global sense? I don't think, I mean,
I, I can, I can describe why I think it might be important in the world. But I think my local
reason for doing it is that I find it personally fulfilling, which I can't, you know, explain
in a, on a sort of, I mean, it's just like this discussion of things like AI ethics, you know,
is there a ground truth to the ethics that we should be having? I don't think I can find a
ground truth to my life any more than I can suggest a ground truth for kind of the ethics for the
whole, for the whole of civilization. And I think that's a, you know, my, you know, I would be, it
would be a, yeah, it's sort of a, I think I'm, I'm, you know, at different times in my life,
I've had different kind of goal structures and so on, although from your perspective,
you're local, you're, you're just a cell in the cellular automata. And but in some sense,
I find it funny from my observation is I kind of, you know, it seems that the universe is
using you to understand itself in some sense, you're not aware of it. Yeah, well, right. Well,
if it turns out that we reduce sort of all of the universe to some, some simple rule,
everything is connected, so to speak. And so it is inexorable in that case that, you know, if,
if I'm involved in finding how that rule works, then, you know, then that's a,
it's inexorable that the universe set it up that way. But I think, you know, one of the things I
find a little bit, you know, this goal of finding fundamental theory of physics, for example,
if indeed we end up as the sort of virtualized consciousness, the disappointing feature is
people will probably care less about the fundamental theory of physics in that setting
than they would now, because gosh, it's like, you know, what the machine code is down below
underneath this thing is much less important if you're virtualized, so to speak. And I think the,
although I think my, my own personal, you talk about ego, I find it just amusing that, you know,
kind of, you know, if you're, if you're imagining that sort of virtualized consciousness, like,
what does the virtualized consciousness do for the rest of eternity? Well, you can explore,
you know, the video game that represents the universe as the universe is,
or you can go off, you can go off that reservation and go and start exploring the
computational universe of all possible universes. And so in some vision of the future of history,
it's like the disembodied consciousnesses are all sort of pursuing things like my new kind of
science, sort of for the rest of eternity, so to speak. And that ends up being the kind of the,
the thing that represents the, you know, the future of kind of the human condition.
I don't think there's a better way to end it, Stephen. Thank you so much. It's a huge honor
talking today. Thank you so much. This was great. You did very well. Thanks for listening to this
conversation with Stephen Wolfram. And thank you to our sponsors ExpressVPN and Cash App.
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or simply connect with me on Twitter at Lex Freedman. And now let me leave you with some
words from Stephen Wolfram. It is perhaps a little humbling to discover that we as humans are in
effect computationally no more capable than the cellular automata with very simple rules.
But the principle of computational equivalence also implies that the same is ultimately true
of our whole universe. So while science has often made it seem that we as humans are somehow
insignificant compared to the universe, the principle of computational equivalence now shows
that in a certain sense, we're at the same level. For the principle implies that what goes on inside
us can ultimately achieve just the same level of computational sophistication as our whole universe.
Thank you for listening and hope to see you next time.