Stephen Wolfram is 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 is 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. Support this podcast by signing up with these sponsors: - ExpressVPN at https://www.expressvpn.com/lexpod - Cash App - use code "LexPodcast" and download: - Cash App (App Store): https://apple.co/2sPrUHe - Cash App (Google Play): https://bit.ly/2MlvP5w EPISODE LINKS: Stephen's Twitter: https://twitter.com/stephen_wolfram Stephen's Website: https://www.stephenwolfram.com/ Wolfram Research Twitter: https://twitter.com/WolframResearch Wolfram Research YouTube: https://www.youtube.com/user/WolframResearch Wolfram Research Website: https://www.wolfram.com/ Wolfram Alpha: https://www.wolframalpha.com/ A New Kind of Science (book): https://amzn.to/34JruB2 This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon. Here's the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time. OUTLINE: 00:00 - Introduction 04:16 - Communicating with an alien intelligence 12:11 - Monolith in 2001: A Space Odyssey 29:06 - What is computation? 44:54 - Physics emerging from computation 1:14:10 - Simulation 1:19:23 - Fundamental theory of physics 1:28:01 - Richard Feynman 1:39:57 - Role of ego in science 1:47:21 - Cellular automata 2:15:08 - Wolfram language 2:55:14 - What is intelligence? 2:57:47 - Consciousness 3:02:36 - Mortality 3:05:47 - Meaning of life
Chapter 1: What is Stephen Wolfram's background and significance?
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 five 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.
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Chapter 2: How does ego influence scientific research?
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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?
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, 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? If you were to say, if you were in the middle of a neural net and you open it up and it's like, what are you thinking?
Can you discuss things with it? It's not easy, but it's not absolutely impossible. So I think by the time, but given the setup of your question, aliens visiting, I think the answer is yes, one will be able to find some form of communication, whatever communication means, communication requires notions of purpose and things like this. It's a kind of philosophical quagmire.
So if AI is a kind of alien life form, what do you think visiting looks like? So if we look at aliens visiting, And we'll get to discuss computation and the world of computation. But if you were to imagine, you said you already prejudiced something by saying you visit.
But how would aliens visit?
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Chapter 3: What is the connection between AI and alien communication?
By visit, there's kind of an implication. And here we're using the imprecision of human language. In a world of the future, 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 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 photons showing up in a radio signal, photons 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 a good question. I mean, whether there is the possibility, you know, what counts as intelligence? Good question. I mean, it's, you know, and I used to think there was sort of a, oh, there'll be, you know, it'll be clear what it means to find extraterrestrial intelligence, etc., etc., etc.
I've 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. Right. So in our kind of everyday sort of discussion, we'll say things like, the weather has a mind of its own. Well, let's unpack that question.
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 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 has told us, but maybe the weather has its own stories. that it tells itself.
Absolutely. And that's where we run into trouble thinking about extraterrestrial intelligence because it's like that pulsar magnetosphere that's generating these very elaborate radio signals.
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Chapter 4: How do cellular automata relate to complex systems?
Do you think we'd be able to, if you 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.
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. 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 Malfa, 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 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 there's a representation that they have, if it converts to some representation where we can say, oh yes, that's a representation that we can recognize 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 trying to understand understanding
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Chapter 5: What is the role of ego in science and mathematics?
The computer program is kind of a brain.
Right. Well, it's a telescope or it's a tool. It lets you see stuff.
But there's something fundamentally different between a computer and a telescope. I mean, I'm hoping not to romanticize the notion, but it's more general.
The computer is more general than the telescope. And it's, I think, I mean, this point about... People say, oh, such and such a thing was almost discovered at such and such a time. The distance between 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 I think I've been fortunate in my life that I've spent a lot of my time building computational language, and that's an activity that, in a sense, works by sort of having to kind of create another level of abstraction and kind of be open to different kinds of structures.
But, you know, it's 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 title is something like a new kind of science. You've accomplished a huge amount. And in fact, somebody said that Newton didn't have an ego and I looked into it and he had a huge ego. But from an outsider's perspective, some have said that you have a bit of an ego as well.
Do you see it that way? Does ego get in the way? Is it empowering? Is it both sort of?
It's complicated and necessary. I mean, you know, I've had, look, I've spent more than half my life CEOing a tech company. Right. Okay. And, you know, that is a, I think it's actually very, it means that one's ego is not a distant thing.
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Chapter 6: How does Stephen Wolfram view the concept of intelligence?
And so that developed a certain amount of sort of intellectual confidence that I don't think I otherwise would have had. And, you know, in a sense, I mean, I was fortunate that I was working in a field, particle physics, during its sort of golden age of rapid progress.
And that 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, you know, picking the low-hanging fruit of a rapidly expanding field.
I mean, the reason 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. Yeah, that's right.
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, 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, there are 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. I said, no, it isn't. It's something different. Yeah.
And you could have done sort of, I'm really glad you did what you did, but you could have done sort of academically just publish, keep publishing small papers here and there, and then you would just keep getting this kind of resistance, right? You would get like, it's supposed to just dropping a thing that says, here it is, here's the full thing.
Right. No, I mean, that was my calculation is that basically, you know, you could introduce little pieces. It's like, you know, one possibility is like, it's the secret weapon, so to speak. It's this, you know, I keep on discovering these things in all these different areas. Where'd they come from? Nobody knows.
But I decided that in the interests of one only has one life to lead, and writing that book took me a decade anyway. There's not a lot of wiggle room, so to speak. One can't be wrong by a factor of three, so to speak, in how long it's going to take. I thought the best thing to do, the thing that most people
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Chapter 7: What is the significance of Wolfram Language?
Yeah, so 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 1,200 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'd 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 and 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... So wait, what are cellular automata? Fair enough.
So typical case of a cellular automaton, it's an array of cells that 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 if the cell and its right neighbor are not the same and or the cell on the left is black or something, then make it black on the next step. And if not, make it white. Typical rule.
That rule, I'm not sure I said it exactly right, but a rule very much like what I just said has the feature that if you started off from just one black cell at the top, it makes this extremely complicated pattern. So some rules... you get a very simple pattern. Some rules, you have the rule is simple, you start them off from a sort of simple seed, you just get this very simple pattern.
But other rules, and this was the big surprise when I started actually just doing the simple computer experiments to find out what happens, is that they produce very complicated patterns of behavior. So for example, this rule 30 rule has the feature you started off from just one black cell at the top, makes this very random pattern.
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Chapter 8: How does Wolfram Alpha relate to human knowledge?
You just start off from that, and then you're going down the page. And 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've got to get... There's got to be some trace of that simplicity here. Okay, we'll run it, let's say, for 400 steps. That's what it does. It's kind of aliasing a bit on the screen there.
But you can see there's a little bit of regularity over on the left. But there's a lot of stuff here that just looks very complicated, very random. And that's a big sort of shock to, was a big shock to my intuition, at least, that that's possible. The mind immediately starts, is there a pattern? There must be a repetitive pattern. There must be, that's where the mind goes.
So indeed, that's what I thought at first. And 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, you know, I did all kinds of analysis of using mathematics, statistics, cryptography, 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 is Whereas looking at the natural world and seeing all this complexity that exists in the natural world, the question is, 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, you know, you took a walk and something profoundly hit you, or was this a gradual thing? a lobster 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, you know, how did people come to figure out this or that thing?
And there's always a long kind of sort of preparatory, you know, there's a need to be prepared in a mindset in which it's possible to see something. I mean, in the case of Rule 30, I was around June 1st, 1984, was kind of a silly story in some ways. I finally had a high resolution laser printer. So I thought, I'm going to generate a bunch of pictures of the cellular automata.
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