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 on 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,

review it with 5 stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter

at Lex Friedman, spelled F R I D M A N. As usual, I'll do a few minutes of ads now,

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to get a discount and to support 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 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 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,

et cetera, et cetera, et cetera. 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, 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, et cetera, et cetera, et cetera. How do we 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 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 thread 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 thread of civilizational history, so to speak, to what we're used to.

In our story, in the stories that the human brain told us, but maybe the weather has its own

stories that it tells itself. Absolutely. Absolutely. 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 processes going on 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 AIs will

be upset to hear you equate those two things. Because I have really probably implied biological

life. Right. 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 a kind of computation. And so 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 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 over this intelligence thing we have. That's really special. 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, 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 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?

It was hers. Her brainwaves when she was first falling in love with Carl Sagan, right? So 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.

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, one was it in the late 70s, early 80s. And, you know, one of the things it's a

phonograph record. 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, 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

some, 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 them 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, that's probably the kind of first kind of understanding

that will 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, 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.

That okay? Is that not okay? Hard to know. In terms of, you know, the, the attempt to do

elaborate things and the attempt to might be counterproductive for the survival of the species.

Like for instance, I mean, in, you know, I think it's, it's also a little bit hard to know. So,

okay, let's take that as a, 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, there are probably optimizations

for the next 1000 years that are, 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 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. Yes. To figure out. Yes. 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 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? Undissidibility. So, and the universe appears by at least the

poets to be sufficiently complex, they 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. 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 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 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 measures. Well, look, the most remarkable thing about the universe is that

it's has regularity at all. Might not be the case. If you would have regularity, do you absolutely

that 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 will 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, the, 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? It'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

that 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?

That is, what does it mean? What I mean by that is something like this. So,

so one of the things that are different in an area of physics, something like energy.

Okay, they're different forms of energy. There's, 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, you know, 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 comp 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 be 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

Goedl's theorem and so on. And as in looking back, one can see that as part of the process of

establishing Goedl's theorem, Goedl 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 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 Turing machines are a small subset of 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 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 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.

It 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 80s, I'm 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,

no, no, no, 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 Turing 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 Turing machine systems,

right? Is the universe equivalent to the kinds of Turing machines? Is the human brain

a kind of Turing 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, then we'll,

the thing is that it is, it has a complicated epistemological character, similar to things

like the second law of thermodynamics, the law of entropy increase. The, you know,

what is the second law of thermodynamics? 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 three, seven, eight, five,

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.

Just to look 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 brain 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 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 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 right now with

physics. We're like, well, this is the theory that this is the rules that apply here. But in the

middle of that, 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 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 the 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 the universe, so to speak. And it's not like when

I spend a lot of my life building technology, and then I know what's in there. And 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?

So just to clarify, 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've got to have the right basis, so to speak. We have

got to 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, it has a bunch of cells that are arrayed on a grid, and every cell is updated

in synchrony at a particular, 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 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 discreet, because all these

other things we're finding are discreet. 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, 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, some level. And, you know, 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 what, 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, 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, you know, 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 thrown into dealing with infinity as a kind of theoretical question.

But I mean, 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 just have precise real numbers at arbitrarily

closely separated points. You have a continuum for everything. Could be that that's what happens,

that there's sort of a continuum for everything and precise real numbers for everything. And then

the things I'm thinking about are wrong. And that's the risk you take if you're trying to

sort of do things about nature is you might just be wrong. It's not, for me personally,

it's kind of a strange thing because I've spent a lot of my life building technology

where you can do something that nobody cares about, but you can't be sort of 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 sort of 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. All of those things have to be emergent somehow.

Right. 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 our 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 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 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'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 or 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 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 a 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,

and that's, I don't know yet. I mean, it's, I think that's, it's a mess. I mean, the fact is,

there is a, one of the things that's interesting, and when, you know, people look at these models

that I started talking about 30 years ago now, they say, oh, no, that can't possibly be right.

You know, what about quantum mechanics? Right? 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, 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, and 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?

Right? And turns out it's really hard. It turns out there isn't, it's, it's, it's like a lot of

these questions about sort of what is intelligence, what's life. That's a scoring test for quantum

computer. That's right. That's right. It's like, are you really a quantum computer? And I think

the simulation, the, yes, exactly. Is it just a simulation? Or is it really a quantum computer?

Same issue all over again. But that, so, you know, this, this whole issue about

the sort of 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? So, 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, what is interesting about the sort of

a model I have now is, 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, you

know, 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, some collection of tuples, collection of, 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's represents a relation.

Okay, so let me, let me try and peel this back. Wow. Okay. So it's, it's, it's, I told you it's

I told you it's abstract, but this is, this is the, this is so the relationship is formed by the same,

some aspect of sameness. Right. But, but so think about it in terms of a graph. Yeah. 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 an

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, 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 sub graph, 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,

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. It 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, 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. It's a,

that's what you mean by the causal network. But then 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?

Okay, 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 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,

somewhat 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, a 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, 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, this 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, 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. So there

are many other properties of this, this kind of system that have are very beautiful, actually, and

very suggestive. And it will be very elegant if this turns out to be right, because it's very,

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, that will be pretty cool if that's the way our

universe works. Now, do I, on the other hand, the thing that, 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 cool thing for a language designer like

me, the minimal version of this model is actually a single line of orphan language code. So that's,

which I wasn't sure was going to happen that way. But it's, it's a, that's, it's kind of,

it's kind of, no, we don't know what, we don't know what, that's, that's just the framework to

know the actual particular hypergraph that might be a longer, 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? That 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, my brain around this yet, is, you know, one is, one keeps on

realizing that we're not special in the sense that, you know, 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 and 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, you know, 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 as

you 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 extraterrestrial intelligence, you know,

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 from, you know, that we're, you know, receiving from some, you know, 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 are 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 just... 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, 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 in studying the universe,

it seemed like the math got more complicated and everything got got harder. The, you know,

when 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 is 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, 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,

the, 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, my favorite example is

probably Leibniz who, 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 space 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, there are very, you know, it's,

it's very, very elegant, actually, how, 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 the 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,

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

getting things, 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 your,

to empower the exploration of the university. Do you think he,

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, you know, he was actually my first company, which was also

involved with, well, was involved with more mathematical computation kinds of things. You know,

he was quite, he had lots of advice about the technical side of what we should do and so on.

Do you have examples of memories or thoughts that?

Oh, yeah, yeah, he had all kinds of, look, in the business of doing sort of, you know,

one of the hard things in math is doing integrals and so on, right? 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, is we build this kind of bizarre industrial machine

that turns every integral into, you know, 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

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. It's, um, I, you know, maybe if he'd live another 20 years, I would have, I would

have remembered to give it back. But I think, um, uh, it's, you know, that, that was his attempt to

systematize, um, 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 to chain? 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, uh, do some complicated calculation in quantum field theory, for example, come out with

the results. 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 what, 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, 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.

Yeah. 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, but it was always

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 a 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. 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. He 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 when I said no, 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 and reduce it.

I mean, 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 works. 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 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

thrown 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 is really different, because you're just, 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, this is kind of a brain. Right. And it's, well, it's, or a telescope,

or, you know, it's a tool, it's, it lets you see stuff. But there's something fundamentally

different between a computer and a telescope. I mean, it just, I'm hoping not to romanticize

the notion, but it's more general, the computer is more general. And 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 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 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. 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? It's complicated and necessary. I mean, you know,

I've had, look, I've spent more than half my life CEO in 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 all tied up with leadership

and with how one develops an organization and all these kinds of things. So, you know,

it may be that if I'd been an academic, for example, I could have sort of, you know,

checked the ego, put it on, put it 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 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'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 feature too. He never really believed it. He actually believed in experts

much less than I believe in experts. Wow. So, that's a fundamentally powerful

property of ego and saying like, not that I am wrong, but that the world is wrong and tell 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 result of

I'm pretty sure I'm right and turns out I'm not. I mean, that's the... But the thing is that

the idea that one tries to do things that... So, for example, 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 100 years, how am I going to be able to do this? And 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 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 kind of gives

one a false sense of achievement because it's kind of easy to discover stuff that's going to survive

if you happen to be picking the low hanging fruit of a rapidly expanding field.

And 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. 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. One 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, that 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, yeah, it's supposed to just

dropping a thing that says here, here's the full, the full thing. 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 him, 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,

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's name wasn't on, 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. It's, 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, I think Steve had it had a bad deal there.

Yeah. So you, you made up for it with the last name. Okay, so, 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, you know, 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 automaton, 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 a 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, you know, 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 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 to 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 have 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,

cellular automata, right? 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 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 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. So 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 one cool way to say that there's

incredible complexity. And that's 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, it was transformational. I don't know, we can have

all kinds of discussions about computation and so on. But just, you know, I sometimes think,

if I were on a desert island, and with, I don't know, maybe with some psychedelics or something,

but if I had to take one book, I mean, you 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 point the computational telescope out there,

and suddenly you see, I don't know, in the past, it's kind of like 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, in other words, you might say, you know,

it's a bug. What would you say? Yeah, what would you say? 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 that there's got to be some trace of that simplicity here.

Okay, we'll run it, let's say for 400 steps. It'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, there must be. So I spent so indeed, that's what I thought at first. And I thought,

I thought, well, this is kind of interesting. But if we run it long enough, we'll see something

will resolve into something simple. And 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? 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 a, you took a walk and

something, it profoundly hit you? Or was this a gradual thing,

a lapse of being boiled? 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, how did people come to figure out this or that thing? And there's always

a long sort of preparatory, 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, I was on some plane flight to Europe, you 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. I still don'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 with 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, 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, 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 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 that it will 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 ever repeat, we still don't even know that. Another problem that

I sort of put in my collection of, it's like $30,000 for three, for these three prizes for

about rule 30, I would say this is not one of those cases where the money is not the main point.

But it's just 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. That's right, money is not the thing. The problems

of themselves are just clean formulations. So it's just, 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,

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, for any one of these, one could prove

that one could discover, we know what rule 30 does for a billion steps. 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, 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 kind of silly way

about the computational universe, which is, 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 I didn't think I

would find one that you would think after all these years that when I found sort of all possible

things, funky things, that I would have gotten my intuition wrapped around the idea that these

creatures are always in the computational universe are always smarter than I'm going to be.

But whether they're equivalently smart, right? That's correct. And that makes it,

that makes one feel very sort of, 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 problem is the 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. And that's right. But the problems are focusing

on rule 30. It's possible that rule 30 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 stuff. No doubt. I mean, it's, although it's sometimes challenging, like the, you know,

I put out a prize in 2007 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 a lot of micro details.

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, but 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 of 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, 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. Yes. That's a, that's a consequence of all the stuff. And it's,

it's a, it makes one wonder, you know, how come mathematics is possible at all? Right. 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

were 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've 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 should

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 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, 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, you know,

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 play with, what is it? What, 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 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 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, 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 some dollar request or address that has to do with

interacting with the world of the cloud and so on, discrete wavelet data, spheroid.

Social graphical sort of window. Yeah, window movable. That's a user interface kind of thing.

I want to pick another 10 because I think this is some. 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 that's an 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. Oh, and it says, oh,

wow, it says, I look like a plunger because I got this great big thing behind my.

Classify. So this image identify classifies the most likely object in the image. So it was a plunger.

Okay, 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

hominid a person 8% probability. Yeah, that's that's 57. It's a plunger. Yeah, well, hopefully

will 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. We took a picture with a little bit, 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 them, just one

function out of this 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. 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 years volcanoes. Right,

let's find out where those are. We can now we've got a list of volcanoes out and I can say geolist

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 and the Midwest and well, no, we're okay. We're okay.

There's no, 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. It's a fundamentally different idea of what a language

is. Yeah, right. And that's, 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 be 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.

Right. 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 we're just

going to do the blocks world over here, so to speak, you don't really, it's, it's, 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'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 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 Wolfram language have 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. Yeah, yeah. 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, out little algorithmic methods, human curation methods and so on,

just when people try to enter a query, and then converting to the process of converting,

NLU defined beautifully as converting their query into computational, 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 and 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. You have Burt in there,

I think. Oh, yeah. So it's, of course, you have, you have integrating all the models. I mean,

this is the hybrid thing that people have always dreamed about or talking about.

I'm actually just surprised, frankly, that Wolfram language is not more popular 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 what we're talking about,

there's egos and personalities 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, wow, that's a really, you know, impressive smart person,