Stephen Wolfram: Physicist and Youngest MacArthur Award Winner

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Hello, this is Guy Kawasaki. Welcome to the Remarkable People podcast. I meet with Steve Wolfram every 10 years or so. It takes that long to recover from interactions with him. And because his brain operates at approximately 10 times the speed of mine. He attended Eden and left without completing a degree. He attended Oxford and left without completing a degree. Academic misfit? I don't think so after all, he then went to the California Institute of Technology and got a PhD in particle physics in a year. All this happened by the time he was 20. Wolfram, by the way, is the youngest MacArthur Award winner at the tender age of 21. But did you know that Wolfram tried to revolutionize the game of cricket? Keep listening and you'll hear how if I explained all the things he's accomplished in math and physics, it would be longer than the interview part of this podcast. If you're into math or physics, you may have used this software application called Mathematica. I met him because of this program. I was one of Apple's Macintosh software evangelists and my job was to help Macintosh developers like Wolfram. You are probably using his computational knowledge engine, AKA search engine for us mortals. It's called Wolfram Alpha and it's used by Bing, duckduckgo, Siri, and Alexa. If you've seen the science fiction movie Arrival, Wolfram and his son helped create the alien language for the movie. We met at my house when he was on tour for his latest book, Adventures of a Computational Explorer. I didn't anticipate meeting him at my house, so I had to rush and wash the dishes so he didn't think we lived like slobs. This was going to be the first and probably last time a Macarthur Wardrum would be in our house. I'm Guy Kawasaki. This is the Remarkable People podcast. And now here's Steve Wolfram.

Tell me about growing up in England in the 60s.

Gosh, I was. I was got really interested in space because space was a sort of thing that was happening at that time. And that was a very, actually a very American oriented thing. I mean in England at that time, people, I don't know, the, the US seemed like a sort of pretty far off place. But I think I, I got sort of. It was a. At any given time in history there's sort of a most exciting thing that's going ON in the 1960s that was space. So I was like following 50 years ago now following, you know, the Apollo 11 landing, all this kind of thing. I got interested from that in sort of how does this all work? And that got me interested in physics. So I started kind of reading books about physics and so on. And I discovered this amazing fact that you could just go to a library and find all these books and start learning stuff and there wasn't really any constraint. I mean I went to, I went to good schools, kind of top schools in England.

Eaton, I'd say.

Yeah, that's, that's, it's, it's, it's fame has gone up and down over time. Now it's, now it's back being famous again because all the prime ministers are coming from there and things. Looking back, I did pretty well in school, so to speak in the sense that I was probably, you know, like maybe even the, the top kid sort of in terms of the scholarships and things I got. I was sort of in the, the top kid or top few kids in the country in the end. But I didn't really recognize that at the time. And I just mostly was interested in learning stuff on my own and kind of spent a lot of my time learning about physics and doing things related to physics.

In any sports, any. I mean, were you just a nerd?

I didn't do sports. I was, I actually had elaborate schemes for avoiding doing sports. I mean, for example, cricket was a big thing and I. Few times I kind of ended up playing cricket and I discovered that, you know, cricket has this thing called overs. You know, when, when the whole sort of field is reflected and people sort of change their positions perhaps because they're getting so damn bored standing, standing around in the. But you know, I discovered that there were these, there were these positions that were sort of invariance with respect to those over things. You could just stay in one place and just hang out there. I remember the one time friend of mine got me to be in some sort of trying, I don't know, some, some cricket team type thing. I discovered that, you know, in cricket you're supposed to throw a ball in this overhand, you know, crazy thing where it's very hard to get it to aim correctly. But I, I, you know, if you just roll the ball along the ground and line it up, it, you can actually get it to go more or less in the right direction. And so I, I did that once and, and the person who was batting, you know, the ball just slid underneath their bat and got them out and they were like, you know, that you can't do that. It's like, is it against the rules to throw the ball on the ground? No, no, it's not. But this person said with great, with great sort of emotion, but it's not cricket. So this was the one that's kind of a phrase that's commonly used, you know, it's not cricket. I actually got to hear it, you know, in real life, in a relevant setting just once.

So you were applying physics from the very begin, beginning, even in cricket? Yes, yes. So I, I read a little tidbit that you were having difficulty learning arithmetic. Can that possibly be true?

Oh, yeah. I've been terrible at arithmetic. I, I just found it kind of boring. I, and I, you know, in one of these kind of educational lessons about education, so to speak, you know, when I was like seven or something, there was always this kind of, you know, game of who can do arithmetic? Facts. And I discovered that there's only one fact you knitted know to win that game most of the time, which is that seven times eight is 56. Because that was the one fact nobody else would know. But I never learned the other ones. And I, I actually kind of, I have a decent memory. So I, I remember, you know, roughly when I learned, like six times nine. I think I learned in my 40s.

Come on.

Yeah, come on. I need to know.

Steve Wolfram couldn't memorize math.

I, I never. Algebra, Arithmetic.

Arithmetic.

I, I, I never found it terribly interesting. And I just, you know, it's like, you know, occasionally you'd need to work out what, six times nine. Okay, I can figure it out. It takes, you know, it takes a few seconds to figure it out, but it doesn't, it's not, you know, it wasn't, it was something where I don't really have a great excuse, but it was something that I never found that interesting.

This is obviously before Wolfram Alpha.

Well, yeah, but you see, but there's a causal relationship because I, I, I was not keen on doing math, but yet I was really interested in physics, and to do physics, you have to do a bunch of math. And so I was like, how can I do Physics I want to do without having to do all this boring math calculation that I don't want to do. And so that got me interested in how do I use computers to do this. And you know, it's kind of funny thing now that from me as a kid having sort of thought I don't want to do this, I end up spending decades building tools that kind of have got to the point where kind of for the whole world, it's like you don't really have to do this anymore. I love it. It's kind of nice.

I love it. So do you think that maybe we should teach physics than math? Is that possible?

I don't think physics is the thing I think, I think computation is the thing that is really kind of, it's the paradigm of today's world. I mean, just as, you know, a few hundred years ago kind of was a big deal when people realized you could use math to figure out stuff about the world. And that's what you know to modern physics, modern engineering, things like this. You know, the big thing in today's world is we can use computation to figure stuff out. And turns out you can teach computation. The ideas of computation, which is different from programming, we can talk about that later. But these sort of ideas of computation you can teach. And it's very, it's very nice because it's very kind of self learnable and it doesn't have the feature that math has where you know, somebody says oh, you know what's seven times eight? And you say oh, 50, 55. Oh, no, that's not right. Okay, we have, you know, it's 56. Okay, you know, how do you, you kind of just have to be told what's right. Whereas if you're doing things computationally and you're using a computer, you know, you tell the computer do this, the computer does something totally crazy. You can see yourself that something happened that was wrong. It isn't some teacher telling you that it's something that you can, you know, see for yourself and kind of have the interaction yourself. But, but you know, in. No, I think that the teaching computation is a great way into teaching kind of sort of systematic thinking and so on. And in fact, if you learn a certain amount about computation, a bunch of the things that people say, oh, that's so abstract, that's so hard to understand about math become really quite easy to understand because you have a sort of concrete foundation for actually thinking about the things and exploring the things and so on.

I bet people listening to this and I have To I am, too. How do you define computation then? Just to make sure we're on the same page.

Okay, So, I mean, it's. What is, you know, computational thinking as far as I'm concerned? It's organizing your thoughts clearly enough that you can explain them to a sufficiently smart computer. Okay, so that means if we're saying I don't know, well, like, here's a type of problem that's sort of a computational thinking problem. Okay, so let's say you're. You're given a point on the Earth, given its latitude and longitude, and you're asked the question, you know, you're going to make a map and you're going to make a default zoom level at that. Okay, so if the thing lands in the middle of the Pacific Ocean, default zoom level of a mile is pretty stupid. It's just a bunch of blue ocean there. If it lands in the middle of Manhattan, you know, a mile might be a pretty good. Or maybe less than a mile is a good default zoom. So the question is, how do you figure that out? What's kind of, you might say, well, let's look at the density of people around that place, or, let's look at how many features there are on the map around that place. These are things that you can kind of think about computationally, and then you kind of are defining, well, what do I actually want to know? Do I want to know the density of features? Okay, how do I define the density of features? Well, I can say that's, I don't know, something like the number of geometric primitives that occur in that region of a map or the compression of the map that you can get, or something like this. And this is kind of what, you know, this is. That's computational thinking, is figuring that stuff out. Turns out interesting thing because I. It's a sort of little hobby. I. I end up interacting a bunch with kids, talking about these kinds of things. And kids are pretty good at this stuff. You have to, you know, teach them the kind of language to communicate that to an actual computer to get it to do it. But this kind of, you know, seems like common sense, trying to organize one's thoughts in a way that could be explained to a sufficiently smart computer. That's something people find. You know, different people do it in different ways, but it's something people are sort of intrinsically able to do. Now, you know, one of the problems with sort of traditional math in the abstract form is it's very kind of cold. It's very unclear what's Going on. It's, you know, you're just told it works this way, you know, x plus one is equal to one plus X. Okay, maybe somebody can prove that that's true. But it's, it doesn't feel connected to anything that one can normally think about. And I think that this, and this whole area of, of computation, it's, you know, for math. One of the things I find talking to kids is they'll say, you know, I'll say you've learned a bunch of math. Say, where did you use that math in your sort of general life and times? They'll think, say, well, actually I've only used it in the math classes.

Yeah.

And that's kind of a bad.

Yeah.

Thing, Right? Yeah. But then you start talking about, you know, well, how can you use computation? And there's sort of a, for every kind of area, there's kind of a computational X that you can talk about. So it might be, you know, computational art history or it might be, I don't know, computational magic or something, or it might be computational marketing, whatever. These are all things that one can use the kind of paradigm of computation on. And they're things that people, they sort of engage much more with the things that people think about.

But what if you're totally dependent on computers? You can't do 7 times 8 without 50 getting 56 without a computer. But that's what a skeptic would say, right?

Yeah. And, and, you know, we're totally dependent on lots of stuff in the modern world. I mean, it's, you know, fair enough. You know, the story of, you know, the kind of, the one thing that's really advanced in human history is kind of, you know, technology and the amount of automation that we have. And there are plenty of things that, you know, your typical kid these days couldn't drive a stick shift car probably in the U.S. right. I know my four kids, I think one of them can, can drive a stick shift car. But, you know, and I think these are things where it's sort of an interesting thing in education because, you know, more and more is known in the world. And so you might say, how can we possibly educate people? Because there's so much more known. I mean, you know, how can it only take, you know, 12 years or something to educate people? But, but the reason is because we are abstracting more and more. You don't need to know all the details of every possible case of this or that because there's a sort of general principle that you can learn about that. And so it's the same thing with all these different. Math is an example of that. I mean, the concepts of math worth learning as a matter of how to think about the world. Math is also, as a field, it is probably the single most developed kind of intellectual area which has had the most kind of layers of work done on it. So the kind of, you know, modern math is built on hundreds of years of kind of intellectual development in a way that's. That's more of a kind of tower than pretty much any other field. And it's kind of. It's kind of an impressive achievement of our civilization. And it's not what people learn about in elementary math, but there are kind of. It's. It's, you know, if you want to learn sort of intellectual history, it's a. It's a kind of an important area to understand, but that's not what typically is taught.

This is completely going down a rabbit hole. But do you know the story of Joshua Bell playing the violin in the Metro in Washington, D.C. i do not. Okay, so Joshua Bell, the violinist, they sort of dress him up as a homeless person, put them in the Washington metro, and he's playing, you know, obviously, Joshua Bell is Joshua Bell, and they watch what happens. You know, did people stop? Did they listen? Did they give him money? Nobody. And the lesson is, you know, if he were in a concert hall, everybody would be standing in line paying hundreds and thousands of dollars to listen to him. But in the metro, no one can put two and two together. They can't judge his music without the context. The reason why, I tell you is because what you should do is disguise yourself and go apply for a job at Google. And then when Google gives you one of these computational kind of questions, you can just smoke the answer because you're Steve Wolfram. I mean, what a great answer that would be.

I'm not a big believer. You know, I have to say. Yeah. In the assessing of people, because I've, you know, spent. I've now been running my company for 33 years. Yes. More than half my life.

Yes.

My wife often reminds me of that. After you've been a CEO for more than half your life, it has. Has all kinds of terrible effects. But, you know, I think the, you know, one of the things I've managed to accumulate wonderfully talented people. And I have to say that sort of the, you know, test, you know, can you write this algorithm on a whiteboard or something?

No.

Bad idea.

What's a good idea?

You just, you talk to people about what they know About. And you know, my, my principle is, you know, if I'm talking to somebody, if I spend, you know, time doing some interview and at the end of it I can't answer the question, you know, can this person, you know, what will this person do in the job that I'm imagining they would do? Yes, If I'm still mystified, if I still don't really understand the, then I won't hire that person. You know, it's like if I think I understand them and I can see how they'll actually, you know, work in what, what I want them to do, then that's a good sign for me.

Fair enough. So a few more questions about your youth. So, you know, you're, you're at Eton, you're, you know, the top of everything. Who are your heroes at the time?

That is an interesting question. I wasn't really a very hero oriented character. I mean, I think I was some. No, I didn't really have, I mean, it's probably, you know, I, I wanted to be a physicist at that time. So I knew, you know, about a bunch of the famous physicists of then and the famous physicists of before then, so to speak. I think by the time, I mean, I, I started meeting those people, but by the time I was 14, 15 years old, and I think, you know, as soon as you start meeting people, the whole sort of, you know, concept of a, of a, you know, a distant hero, you know, kind of, kind of disappears. I was just like, these are people, they're, you know, they, some of them, you know, they seem to know what they're talking about. They seem smart. Some of them didn't seem so smart, you know, it was, it was, yeah, I don't think I ever really had a. But, yeah, it's bad, it's something, I suppose it's one of those deficits. But I think, no, I didn't really have particularly, I also didn't really have kind of a role model. I just wanted to sort of generically be a physicist. And that was kind of a generic role model, so to speak.

So at 20, you get a PhD in physics, at 21, you win a MacArthur Award. I can't even wrap my mind around that. So what, what is that like? I mean, at 21 you've accomplished what?

Yeah, so, I mean, it was kind of fun, you know, getting my, I, I, in retrospect, I should have made the minor effort that it would have taken to get my PhD while I was still a teenager. So I could keep saying for the rest of my life. I had a PhD when I was a teenager, but you know, it was, it was kind of, it, you know, for me at that time I was very focused on just, I want to do science, I want to figure stuff out. So it was like, let me get to the point where I can just do that. I don't want to be messing around, you know, taking classes, which I never really did, or, you know, or those kinds of things. I just want to go and, you know, do science because that's what I'm interested in. And I think some people on the outside were kind of like, oh gosh, you know, what a, what an awkward situation to be in, you know, getting all these awards and things, you're young, etc. Etc. Etc. But I wasn't taking these awards terribly seriously. I mean, it wasn't like I was thinking, oh gosh, that. So one of the, one of the traps I think for people is, you know, they do well early on and then they kind of, they think, oh, they're these huge expectations, they got to do this and that and the other. I, I think my own expectations and perhaps even I would say my own opinion of myself was always higher than I think the outside then, you know, I attributed to the outside world. No, I, I mean, I just, just, I didn't really, you know, it was, it was one of these things where as I say, the, I mean, perhaps the thing that at that time, you know, I got my PhD, and then I'm like, okay, I'd had this goal from when I was like 10 years old or something to be a physicist. Okay, I'm 20 years old now, I'm a physicist. Great. So then it's a question of sort of the what next? And so I kind of started thinking, okay, I'm going to make this, you know, longer term plan. What do I want to do be doing? And so the first thing that I realized is I need sort of better tools. They were computational computer tools to do the things I wanted. So I'm like, okay, you know, how am I going to get these tools? Well, I was interacting with the various groups that have built sort of experimental versions of these things. But eventually I decided I just have to build the stuff myself. So I got into the. Within a couple of weeks of doing my PhD, I kind of got into designing this big computer system that was a forerunner of things that I've done more recently. And you know, and then I had to like, okay, I've got to sort of officially learn computer science and so on. Which was a lot easier in those days because there was a lot less known. You know, I just keep going, doing things. I mean, I think that was the. I wasn't really thinking, oh, you know, it's so cool that I'm in this place at this time. I mean, okay, I did things like, you know, I got, you know, doctor on my credit card, which is an early thing, which I still have. Back in those days, it was much rarer. And so people, you know, you go and check in for a flight or something, and the person would say, you know, I've got this ailment. Can you tell me, you know, can you help me with this? It's like, sorry, wrong kind of doctor.

But did you call up American Express or Visa and say, put my title.

How does that work? I think you just fill it out in some form. I'm sure that's what I did, and I haven't thought about it since, so to speak. It's at this point now, if I'm in some particularly business setting, the really, the kiss of death is if somebody refers to me as professor, then I know they're absolutely not taking me seriously.

Why is that?

Because it's like, you know, I've spent some large part of my life actually building stuff in the software industry and so on.

It's like, as opposed to just studying it or teaching it.

If I'm in some, you know, random business meeting with some, you know, CEO of some company and they refer to me as professor, it's like, this is. This is dead. Because, you know, it's. They think I'm some crazy, you know, intellectual academic nerd, not somebody who actually builds software that people use type thing.

And makes money.

Oh, yeah.

And makes money. Yes. So can we fast forward a little to Mathematica? Would you say that. Is it a product? Is it a theory? Is it a philosophy? What is it?

Well, so. So Mathematica is very much a product. Okay. I think the thing that Mathematica is based on, this thing called Wolfram language, is it's more of a thing, so to speak, which gets deployed in different products. So it's. I mean, Wolfram language is what I've basically been working on for, well, at least a third of a century. And kind of the goal is to sort of encapsulate as much kind of computational intelligence and as much kind of computational knowledge about the world as possible into this language that we humans can use to express ourselves and that we can explain to computers what to do with, so to speak. And so it's that, that's kind of the, I view it as being sort of in the, in the, in the long view of history, the kind of this computational language that I've spent all this time developing, it's kind of like this is sort of an attempt to have a definite notation for, for talking about computation. So back a long time ago, talk about math again, a long time ago, 400 years ago or something, long before we were around the, you know, if you were doing math, you were writing it out in words. There wasn't a notation. There were no plus signs, there were no equal signs, things like this. And people had a, you know, it was not easy to kind of make, systematically communicate about math, math in those days. And then mathematical notation got invented and that led to algebra and calculus and all of these kind of mathematical sciences that exist today. And that the, the, the analog of that today, I think, is the things I've been trying to do with computational language. Can we have a kind of notation for computation that people can understand? In this case, didn't happen with math, but now machines can also understand and that we can use to kind of crispen up our thinking and our communication about computational kinds of things. So that, that's, you know, that's the, that in a sense is a, that's the kind of intellectual, abstract version of, you know, what I've been trying to do for a long time. And it has, it has many sort of consequences and connections to things which, yes, are kind of philosophy. But, you know, one of the things that, that I always find fun is when one goes from these very sort of fancy, abstract, intellectual ideas, and then it's actual code, and then it's an actual product that people use all the time. And I think that's really neat. And, you know, what we see a lot is things which used to be sort of pure philosophy turn into code and products and so on. And so, for example, one thing I've been interested in more recently is something which people last tried to look at about 400 years ago, 350 years ago maybe, which was these things called philosophical languages, which is kind of how do you express things about the world in a, in an abstract way without using a specific human language? And so a place where this comes up is things in, in modern times is things like legal contracts. So people, you know, when you write a legal contract, it's more or less in English if you're in the US or something, but it's sort of in legalese because you don't want it to be quite in something as vague as English. You want it to be something a little bit tighter. But the question is, can you make a kind of an abstract symbolic language that can express what you want to express in a contract, you know, a computational contract? And, yeah, we're getting close to being able to do this. And that's, you know, it's something you can, you know, expect these things to get executed autonomously with blockchains and all kinds of other things. But basically, it's part of this sort of going from sort of the philosophical idea that there's a representation of meaning that isn't just words and languages, but is something deeper that turns into this very practical thing about computational contracts and all that kind of thing.

Okay, I. I have to ask you to tell us a story about Steve Jobs and helping or telling you to name Mathematica. Mathematica.

Well, you know, it's funny because we now have this product called Wolfram Alpha.

Yes.

And the original name for Mathematica was Omega. So over the course of many years, we went from Omega to Alpha. But. But Steve was, you know, we were. I started interacting with him actually pretty soon after. We had very early versions of Mathematica because he was going to bring out this NEXT computer, and it was sort of oriented towards education and he wanted. And so we kind of made this deal pretty early on to bundle what would be called Mathematica on the next computer. So everybody who got a Next could. Could use Mathematica. And turned out that was. I think that was a pretty good deal on both sides. It's pretty smart of Steve to figure out that that was a good idea. Yes. A bunch of people bought NEXT because of that. A bunch of people used our stuff because of that.

You were the killer app of next.

Yeah, thanks. I mean, I think, you know, there were some good footnotes to history. Like, you know, there were these computers that were bought at cern, the particle physics center in Geneva, Switzerland, that were bought by the theory group there because they thought it was a cheap way to get Mathematica, so to speak, was to buy the whole computer. And then those computers. The person who was responsible for that system was a person called Tim Berners.

Lee, I've heard that name before.

Right. Who ended up using those computers to build his first web setup. So it's kind of like that was kind of an amusing footnote to history that came out of that. But in terms of naming of products, I had thought of the name Mathematica, but I thought it was Too long, too ponderous, et cetera. And I had this whole list of other names. What's kind of funny, and I put that list on the web some number of years ago. What's kind of funny is all these names, including really horrifyingly awful ones, have actually been used as product names in the intervening years. But Steve was like, he had a kind of theory of naming at that point, which was take the generic word for something. And I think he said romanticism it. So, you know, he was very, he was the example of Trinitron, which was a now long lost brand, probably from Sony, which was a television, you know, brand and represented the three cathode ray tube guns or something in a color television, which younger people who are listening to this have probably never heard of. Right. Makes me feel old. But in any case, the, you know, so at that time, kind of the, the sort of. The killer app of the thing we were building was math for Mathematic Computation. I mean, in the end, the, the bigger picture is sort of all about computation in general, but at that time it was kind of math was the, the first killer app for that type of approach. So, so Steve was, you got to call it Mathematica because it's kind of like math, but it's sort of romanticizing that word. And so I was like, did he.

Do this in a civil manner or did he just tell you that, you.

Know, no, it was actually perfectly civilized. It's just like, I think you should do this. It wasn't, it wasn't kind of a petulant, you know, you've do this. It was, I think you should do this. No, I, I always had very, very, very civilized interactions with Steve actually. And I, you know, it was, it was funny because I also liked the fact that, you know, he would, sometimes I would tell him something and he would say, I don't care. And then sometime later he would say, actually, I do care. And you know, that happened actually with Wolf Malpha. You know, I first, I like showed it to him before we released it and so on. He says, I don't know why I care about this. Okay. So then a little while goes by and there's this little company called Siri that had licensed our stuff and, you know, put a wrapper around it and provided, you know, made it sort of a thing that could be thought of as an intelligent assistant rather than, you know, what we had, you know, the use case that we were primarily dealing with, which was, you know, ask questions on the web and so on. And then he looks at this and he says, okay, now I get it type thing. I remember. But I mean, there are a bunch of things that when we were working on early first version of Mathematica and interacting a lot with next, there were all kinds of just be more ambitious type pieces of input from Steve that were nice.

Did he at any point try to explain math or physics to you?

No, I don't think so. It turns out I know somebody who knew Steve in high school. Okay. And the person I know knew Steve in high school is now, well, a physicist actually. Saw him just recently at University of Washington. But he was like, yeah, Steve was always kind of a weird person in high school. But, you know, he was one of the kids who would go to some other nearby high school, which my friend also did, and go learn calculus and things. So I kind of knew that little bit about Steve that even though he was like, I don't know anything about this math stuff, et cetera, et cetera, et cetera, he actually had learned calculus when he was in high school. So that was a, you know, one of those kinds of out of band pieces of information that I happen to have.

I fully expected you to tell me a story of Steve trying to explain physics to you.

No, no, no, no. Actually, I mean, I, I'm trying to remember. I mean, he was. You know, a lot of times in the tech industry there are people who are really quite, you know, I would say, intellectually interested in a lot of these kinds of science things.

It's been a while since I conducted.

This interview and I've been thinking about this passage. Here's what I've come up. Steve Jobs considered Steve Wolfram an intellectual equal, maybe even superior. So he did not try to explain physics to Steve Wolfram. It's a historical moment. Please take note.

What's different about science and technology is in technology you're just building stuff and you can build whatever you want. In a sense, people may care about it, people may not care about it. In science, for better or worse, there's an actual world out there and, you know, you can't just make stuff up. You know, it has to be. It has to actually be. You know, if you want the science to really mean something, it has to correspond to how our universe happens to work. And so, you know, I think sometimes the mentality of the sort of the, the people who are used to the tech world, it's a little different now. You know, having said that, I have to say that some of the science that I've done and that actually I'm about to start doing again is very much of the, you know, can we sort of a tech informed approach to science that perhaps is methodologically a bit different from the kind of, oh, we've got the big universe out there, let's just pick away at it and try and find what's happening in pieces of it. My approach has tended to be there's a whole kind of giant sort of universe of universes. And can we explore this whole much broader space of things which include things that aren't our particular universe, and then is our particular universe an example of these? And that's kind of a more a somewhat different approach.

Is this what you refer to as the new kind of science?

It's related to that. It's kind of an outgrowth of that. I mean, the new kind of science is all about, kind of started from this. We're back to talking about math again. But you know, in, you know, the tradition for the last 300 years, basically, if you're doing exact science, is write down a mathematical equation that represents something about the world. And that was what Newton and Galileo, all these people, that's what kind of made them famous, was doing that stuff. It's been kind of a successful thing for 300 years. What I got interested in, long time ago now is, okay, so there are things we can't explain using things like mathematical equations. There are things even in physics, but also in biology and other places. It's like, how can we generalize what we do and to something more general than just mathematical equations and still be making precise theories of things. And so what I realized is, well, you can use pros programs instead of equations to represent how things work in some particular system. So you say this, you know, this, this system, it's, there's a program that says, you know, operates according to these rules. This is how the system is going to work. Those rules may not correspond to the kinds of things you write down with, you know, algebraic operations and standard mathematical kinds of things. And so that, that was kind of the, the, the starting point. And then the question was, okay, so if we're using programs to talk about the world, what kinds of program things in the world? And so that got me into the question of, okay, so we just look at the universal possible programs. What's it like? And so, you know, usually we write a program, we go to lots of trouble. We're going to write a piece of a word processor or we're going to write some program where we know what it's for. And it's a big complicated program. The question is, if you just think about programs kind of in the wild sort of programs that are natural programs, programs that are just. If you just started enumerating programs at random, little tiny programs that. It's just like we get to this program and what does it do? So I had assumed that if you had a very simple program, it would just do very simple things, but it isn't true. And this was the big thing that I discovered in the early 1980s was if you just look at the sort of universal possible simple programs, you very quickly find ones that are really complicated in their behavior. The program itself is tiny.

Can you give an example of this?

My favorite example is this thing called Rule 13, which is A. A thing that just operates on a row of black and white cells, and at every step it just says, make the color of a cell. Be some fixed rule based on the color of that cell on step before and the color of its two neighbors. So it's a very simple thing. You can write it down. And yeah, it's my favorite science discovery. So it's on my business card. So it's just a, you know, little, tiny, tiny doctor. Yes, right, right. No, actually, the doctor is not on my business card. The only credit card parts. But the. It's. See, in business, Dr. Is A. Is a. Is a term of disrespect in some ways.

Okay, but.

But any case, the. The. The Rule 30 is so, you know, very simple rule. You can state it kind of, you know, you can say it in a sentence, although it's kind of a boring sentence that involves ands and ors and things like that. But then you started off from just one black cell, and you see what it does, and it makes this incredibly complicated pattern. Pattern. And that for me is kind of the. Was a sort of major kind of aha moment discovering that. I mean, I kind of. It's sort of. You look in this computational universe of programs and you're finding a phenomenon that you absolutely didn't expect. I mean, for, you know, people would think simple program, simple behavior. You know, we're used to. When we build things with engineering, if we want to make something complicated, it takes us a lot of effort. We have to have, you know, complicated plans. It's got a of. Lot. Lot of little complicated components and so on. But in the computational universe of all possible programs, that's not the way it works. There are lots of programs that are really simple to construct but do really complicated things. And, you know, why does one care about that? Well, because that's basically what nature is doing, and that's how nature makes complicated things. Because it's not under the same constraint that we're under when we do engineering. We have traditionally been under the. The constraint that we kind of have to be able to foresee what our engineering system is going to do. We're not just saying, put this together and it'll do something. Nature ends up with things where it's just put this together and it'll do something. It doesn't. It's not under a constraint of being understandable, you know, from the outset. And so that's, you know, in this computational universe, the sort of big discovery is that there's a lot of complexity that's easy to get, and it seems to be the same sort of essential idea that nature uses to make complicated things. It's also something we can use for technology. You know, when, when you have this very simple program and it does this very complicated thing, sometimes that complicated thing will be something that we humans find useful. Like Even my Rule 30 cellular automaton thing is we used it as a random number generator for a long time because it's, it's very simple process, but generates something which for all practical purposes seems random. So you would never have, if you were saying, I'm going to invent a random number generator, you would never have come up with this. But once you find it, you can say, let's mine it from the computational universe and use it for that. It's kind of like, you know, in, in, in ordinary physical technology, you know, we find liquid crystals and then we say, gosh, this is a cool, cool scientific physics phenomenon or something. Oh, well, actually, we realize we can use that to make displays, for instance. And it's the same kind of thing in the computational universe. You find these programs and they do remarkable things, and then sometimes they do things which, you know, we humans find useful for, for things that we want to do.

What is computational paradigm, or is this the same thing?

Well, it's kind of, it's kind of thinking about things in computational terms. So thinking about, you know, given a question, sort of trying to formulate it in with the kinds of thinking that you could kind of talk to a smart computer about. So I don't know. I'm looking at, you have this giant display on a wall of a rhinoceros. So I'm thinking about, you know, how do I make that rhinoceros computational? Okay, so I'll give you an example. It's Got a couple of horns at the front, right? So, and, you know, one question would be, what is the. What's the diversity of rhinoceros horns? So how do I think about, you know, the space of shapes of rhinoceros horns? And that's something which we can then sort of start engaging with computationally. If that's the signature of a rhinoceros is the shape of its horn or something, then, you know, we can say that the space of shapes, and maybe they're actually sub breeds of rhinoceros that are sort of separated from other ones because their horns are different, you know, different shape and so on. And this is kind of how you start sort of engaging with some question about the world kind of computationally.

Okay, does all of this mean we're a simulation? I mean, are we. Are we kidding ourselves here?

Well, I think that this whole simulation argument thing, it's kind of charming how some of these kind of sort of theological religious ideas and so on sort of get reiterated in these very different, bizarre, you know, different wrappings, so to speak. And it's kind of like if we look at our universe, so one. One thing about our universe, this is a very almost theological fact about our universe, which is that it has definite laws. Might not be the case. Might be the case that, you know, there are 10 to the 90th particles in the universe. They might all do their own thing. There might be no order to the universe. And so it was, you know, the first thing that is surprising in a sense, and that sort of early theologians made a lot of. Made a lot of hay out of, was the universe is an orderly place, doesn't need to be an orderly place. We don't know why it should be an orderly place, but it is. But, you know, when it comes to thinking about, you know, what would it mean if the universe was a simulation? You know, this is a. It's a philosophically wrong idea. But it takes a few steps to explain why. Why that that's the case. I mean, basically, if the universe has definite rules, then then the universe, it's just running. The universe is just doing what it does and running according to those rules. Now, if you ask, where do those rules come from? Well, the universe has the rules the universe has. Now you can say, why does it have those rules? For example, why doesn't have much more complicated rules? Why does it have. We don't actually know how simple the rules for the universe are. We don't know, you know, if we could write a program that would reproduce the universe, we don't know if that program is a million lines long, a quintillion. Quintillion. Quintillion lines long, or three lines long. You know, I think there's a possibility that it's three lines long. And so that's why actually I'm just about to launch a serious effort. I've been interested in this for decades, but I'm finally ready. Plus, I'm getting so old, so to speak, that I got to do this now or never to actually make a serious sort of assault on. Can we find the fundamental theory of physics? Can we find the fundamental theory of our universe? And, you know, might be the wrong century to try this, but if it's, if it turns out the sort of rule for the universe is simple and be pretty embarrassing if we have the technology now to find it, so to speak, be pretty embarrassing if we just hadn't bothered to look for it, so to speak, that might not be simple enough to find. It might be that there are things about validating whether it's correct that things we can't do yet with the current state of our, our science, so to speak. But, but, but anyway. So imagine that we have the rules for the universe. Imagine we succeeded. We've got the rules. We can write them down. You know, I could, you know, tell you them in a few sentences, for example, and you say, okay, that's the universe. Now you say, well, what do we conclude from that? Is the, you know, what would it mean for the universe then to be a simulation? It's like you say, well, you know, you. How. What are those rules running on? Well, they're not running on anything. They're just rules that describe how a universe works. It's not like you have to take those rules and put them on a computer and run those rules. These are just rules that describe how the universe works. I told you, this is philosophically a little bit complicated. So I'm, I'm. But, but essentially the, the, the other side of it is to say, when we look at these rules, do these rules somehow. Do they feel like they're an artifact? Do they feel like they were sort of produced by some intelligence on purpose making these rules? Or are they just rules our universe happens to have? And so this then goes into the question of how can you tell if a thing was made for a purpose? And that's another huge can of worms. So, you know, even when we look at something like, I don't know, Stonehenge, for instance, we say, what was Stonehenge for? Okay, well, that's not culturally that far away from modern times, but it's still really hard for us to tell what it was for. If you're presented with some, you know, if we, if we found some extraterrestrial radio signal and we say, is this, this for a purpose? Is it an intended thing or is it just some natural process that's producing this thing? Really hard to tell in the abstract. And that's kind of why. That's kind of one reason. It's. There isn't even a. A meaning to saying this. You know, the rules for the universe, were they made on purpose? Were they made sort of as a. As a thing by some other entity that was then, when. Then we exist as a. As a simulation with respect to that entity? It doesn't really quite make sense. Sorry. That was. It's. It's a, It's a difficult. It's a complicated area. I mean, this is kind of.

Do I dare ask what's God's role in this?

Or, you know, if there is a God. Well, that's a, that's a, that's a. It's a funny question because in a sense, the, you know, as I was mentioning, in early theology, the very fact that the universe is orderly was seen as evidence for the existence of God.

Yes.

Okay. Now, I don't think. But then, in a sense, if the universe is just. You have these rules and you run them, that, in a sense, doesn't leave any place for a God, so to speak, because it's just like it's running a program. There's no God needed to run this program. It's just a program and it's running. Right. And there are no miracles that come from the outside, so to speak.

Maybe God was the programmer.

Well, right. So then that comes back to the question of why this universe and not another one? One. Right. And that is something that I can't. You know, first we have to find what the rules for our universe are, which we haven't succeeded in doing. And as I say, maybe, you know, maybe wrong century to try and find them. If we find them, then that really is a kind of in your face question. Why these rules? Why not other ones? What can we conclude from this? You know, is that, you know, evidence for something sort of beyond our universe? Is it just what we happen to get? You know, we happen to get this universe, not universe? I don't know. And, and that's a, you know, it's, it's a, It's a curious question. I mean, when, when people. Oh, I don't know. Like Isaac Newton back in the 1680s was talking about, you know, figured out a bunch of stuff about the planets and the motion of planets. And he was at that time, he was talking about, you know, well, once the planets are set in motion, then it's kind of just a matter of mathematics to decide what will happen happen. But you know, how the planets started off that he said, you know, that's the hand of God, so to speak, that determines that. Now, today, it always used to be the case. People would say the fact that we had nine planets, well, now it's eight, but used to be nine. You know, is one of these just random facts about the world, not something you could ever derive from mathematics or something like this? It is now interesting that now that we know about zillions of exoplanets, we actually know that it is a derivable fact that our solar system will have about that number of planets and the distribution of sizes will be about what it is and so on. So in Newton's time, he had no choice but to say, you know, the way it started, it's just God, so to speak. And, you know, a few hundred years later, we can see there's a more general category of thing, namely all these different solar systems. And we can come back and look at ours and say, actually, actually, we kind of understand this one. I don't know, if we find a fundamental theory of physics, I'm sure there will be interpretations on many sides about what that actually means in these terms.

What is your reaction to the refutation of science these days, particularly at the highest levels of our government? You know, it seems like truth and science and facts.

It seems to be.

It's whatever you agree with is true. So how do you.

You know, I think one of the things. Science is in some ways its own worst enemy in that regard, because what's happened is there are things that science has done a really good job of establishing. There are things where there is science that can be said about them. But it's kind of overreached in some way or another. And people then get suspicious because they say, you know, people would say, for example, you know, evolution, okay, evolution is the whole story of biology. People would say, well, it's not, you know, there's. There's other things going on. And some of them even I've figured out in the sort of science of, like, how complicated forms arise in biology, which is something where people say it must just be evolution because evolution is all there is. Well, actually, evolution on Its own can't explain why there are complicated forms. That's a consequence of. Actually it's a sort of computational phenomenon that is similar to this Rule 30 phenomenon where the rules can be simple, the behavior is, and the forms that are produced aren't simple. So it's a place where when people just say it's evolution, that's all there is. If you say there's anything else to biology other than evolution, you're wrong. You know, that's an overreach, so to speak, in the sense that actually there is something else going on which is non trivial science. And it's not that. It's not that the other thing that's going on is not science. It's absolutely science. It's actually probably cleaner science than evolution in many ways. But it's something where people sort of say we've got one piece of science and so let's carry that all the way, so to speak. And you know, I think the other thing that happens, there's an important phenomenon not yet well understood, although I did, I happened to testify for a Senate subcommittee a couple of months ago and now this, this term is now in the Congressional Record. So that's, I don't know what that means about it, but the term is computational irreducibility. And what does that mean? So you say, well, I know the rules for how some system behaves. So then you'd say, well, then I can figure out everything about what the system does. Well, actually that's not really true because you might run the system for a billion steps and you have to go through all those billion steps. The question is, can you figure out what's going to happen in the system more efficiently than just running those billion steps. Steps or you just have to run all those steps and see what happens. And that's important when it comes to, oh, I don't know, predicting the climate or something. Right. You have to. The question is, can you just say, oh, this is the answer or do you have to kind of. Is there this computational irreducibility phenomenon that means it's sort of. There's an irreducible amount of computational work that you have to do to figure out what's going to happen and where. It's very hard to make some sort of simple, oh, it's just going to do this type, type claims. And I think one of the things that that happens is people will say, oh, we have this, you know, this science, we know something about this scientifically. So then we will just sort of take that particular idea in science and take it to its end conclusion. And because it's science, that must be the whole story. But actually it's not. You just got one particular piece of the science and you forgot about other parts, particularly this phenomenon of computational irreducibility. That means even though, you know the equations for things about, you know, fluid in the atmosphere and so on, even though you know, that doesn't mean it's easy to tell what's going to happen. And I think that what has tended to happen is that people have said, you know, science is in many ways the new religion, so to speak, speak in the sense that that's. People say we believe in science, but in some ways what they're, they believe in a version of science that actually is not well informed by things like computational irreducibility. And they believe in a version of science that is, is sort of has simple cut and dried answers to things. And other people say, look, common sense tells me this can't be right. And they're right, it's not right. You know, it's a, it's a piece of the story. And there are certain places where the, you know, that story comes through for science spectacularly. But there are other ones, including some of these most controversial ones, whether it's in medical area or in, you know, climate, these kinds of things where it's not so obvious. It's not, you know, it's not something where I think it's a, you know, a cut and dried story. Now people can say awfully silly things about, you know, people can come up with crazy conclusions. I mean, I'm not arguing, arguing that, that all the things that are said that sort of on the other side from the, the cut and dried science make sense, but it isn't true that the cut and dried science is really the whole story. So I, I have a lot, you know, I feel that actually it's, it's a, it's a thing where science has done itself a considerable disservice by trying to make things seem cut and dried and simple when they're actually not. And people, people are rightly, you know, some people are rightly suspicious and say it can't be the whole story, you know, and, and then, then there's a whole attack on sort of science as a whole, which is also unfair. But it's, it's, I think, a more complicated picture. And I think that there's some things where it's, I always kind of, kind of wince when I hear these, these sort of science has proved that Blank, blank, blank. When it's like, I know how the science works. You can't possibly have proved that from, from, you know, this is, it's a much more complicated story and there are things you can say, but there are also a lot of kind of footnotes and caveats and so on.

But how about the other extreme where, you know, I know the Earth is flat.

Yeah. Right. So I'm, I'm saying there's, there's, there are, you know what you have to do. You, it's like you have to be a more sophisticated consumer of science. Okay. It's no good to say, you know, it's the same thing has happened in past years, probably with religion. You could be an unsophisticated consumer of religion and just say, okay, The Earth is 6,000 years old, and that's, you know, it says that in the Bible and therefore it must be true. Right, right. You know, there's, there's more to religion than that statement, so to speak. And, you know, you can, you have to take from it what you, you what actually makes sense and not just take the, you know, the obvious, the most obvious things. And, and I think this is a, a place where actually in this whole sort of computational thinking thing, you know, I think people will, as that finally gets, you know, well absorbed, some of these ideas, some of these pieces of intuition like computational irreducibility will become quite commonplace. I mean, it's just like, like we talk about, you know, you talk in, I don't know, probably in marketing and things like that, about force and momentum and acceleration, things like this. These are all terms which were basically invented by people like Newton and Galileo to describe early, you know, to describe physics. They're terms that come from science that have found their way into our kind of general way of thinking about the world. There are similar kinds of ideas that come from the sort of computational way of thinking about the world, like computational irreducibility. And as we really start to absorb these, we get to have a, you know, a more nuanced way of thinking about these kinds of scientific questions.

Okay, so the first time we met 20, 30 years ago, I came out of that meeting and I said to my wife, that is the smartest person I have ever met in my life. You know, he. I just could barely keep up with you. And so my question I have so some, you know, sort of off the wall questions is, number one.

You'Re kind.

Of off the scale on intelligence. And, and do you ever have a moment, you say, you know, God, I Just, I wish I was just kind of a regular person. I'm not thinking about all this kind of stuff. I'm not thinking about irreducibility. I'm not thinking about whether it's a simulation. I'm just drinking beer, watching football. I mean, do you ever have moments like that?

Well, I don't like either beer or football. Cricket? No, I'm not into cricket either. But, you know, look, I don't think of myself. I guess for me, you know, people tell me, oh, you're so smart about this or that thing. But to me, that's not what it feels like to me, you know, I'm always trying to figure out things that are difficult for me to figure out. Now maybe some of those things are really, really difficult, difficult for some other people to figure out. But for me, I'm always kind of, you know, I'm always struggling to figure stuff out. So it doesn't, you know, it, the, the, the kind of, the internal perception is not one of, you know, kind of, I mean, you know, the fact that I'm, I'm always trying to figure stuff out.

Do you feel it's a burden?

No, absolutely not. I mean, I, I, you know, I like what I do. I like figuring stuff out. I, I think that the one thing I've been actually in recent years, I've been really interested in the question of sort of how does one find talent in the world? And I've been kind of, I've, you know, because I've, you know, spent a lot of time building, you know, a great collection of people at the company and things. But I've also been kind of, you know, I, I, I like interacting with talented people and I've, I, so I suppose one thing, thing that I've noticed is that, you know, like, I, I interact with kids a bunch and we have various programs for, for high school kids and things like this. One of the things that I have noticed is that it's surprisingly difficult to sort of break out of the elite bubble that one tends to live in. And it's like there probably are kids out there who would really benefit from exposure to all this stuff. And it's like I don't have connection to any of these, these. And it's also the case that, that I sometimes worry that, you know, I have a particular way of thinking about stuff. And that may not, you know, it may not be the way that, you know, kid in Place X wants to think about things and, you know, am I, am I serving as kind of a missionary for, for my ways of thinking about things and is that the right thing to do? And so on. But so, you know, that's one place where, where I suppose it is the fact that I've sort of lived in this kind of elite bubble I sometimes find a little bit frustrating because I'm curious. That means I'm sort of curious about what, what the rest of the world is like, so to speak.

Well, let's suppose that you go surfing, or let's suppose that you go to a hockey game. Are you sitting there thinking about the math and the physics of hockey and surfing? And you know, just, see, these are.

Things I, I don't do because I don't find them, you know, well, I'm surfing, I'd just be useless at. But, but, you know, I, I, for example, I'm not into sports at all because I'm not into playing it, I'm not into watching it. Why? I don't know, because. Perhaps because I don't know enough about it to know why I care, so to speak. But for me, you know, I like figuring stuff out. I like building stuff, stuff. And those activities don't satisfy those particular, for whatever reason, the drive that I have to do those particular kinds of things.

Then, then two last questions. Okay, Question number one is, who's the smartest person you ever met?

You know, I don't rank people by, by smartness because, you know, you got to realize this, the question of if it was the case that everything that came up somebody else could figure out, and then that everything you think about somebody else is there in front of you, so to speak, able to figure it out, then in a sense you'd say, okay, that's a smarter person than whatever. And I have to say, in my own life, I, you know, know, ever since I was in kindergarten, I happened to go to a kindergarten in Oxford, England, where there are a bunch of smart kids. And so I, I've always had this thought, you know, eventually I'm going to find, go to this place where I'm going to just find all these people who are fundamentally smarter than me. And I, you know, I went through different kinds of places. The fancy universities, Silicon Valley, the this, that, whatever. And you know, I never, I never had that moment where I said, gosh, I finally found the place where everybody is smarter than me. And that was a little bit the high quality problem. Yeah, right. It's, it's a, it's a. Actually it's a little bit disorienting to realize, gosh, there's no place, you know, there's no sort of, you, you asked about sort of heroes. There's no place where it's like, you know, where it's, it's kind of like, so I got to figure this stuff out because it's not like there are other people out there who are going to figure all this stuff out. You know, if they're things that I think about where they're kind of difficult for me, it's like if, if our species is going to figure them out at this time, I'm kind of, I'm kind of it in terms of, of doing that. But no, I think, I think, I don't like to. You know, the thing I've noticed, and I noticed from, from leading people a lot over the course of years that this kind of who's smarter than who is really not the point because there's so many different, different ways in which people can be thinking about things. And you know, somebody can be super good at analytical figuring out of stuff and super useless at actually, you know, kind of conceptualizing what to do. For example, they're great, you know, if you give them a specific problem, they do a great job. If there's something goes wrong with that problem and they have to, like, take a, you know, a turn somewhere, they completely can't do that. They just don't have the, you know, the initiative and the creativity to do that.

Okay, and my last question. What do you want your legacy to be?

Huh? Oh, I don't know. I, you know, I, I think it's, it's an interesting question. Now that I'm getting old, I've got to, I'm supposed to think about questions like that. I think that the. There are things that I've done, particularly understanding the computational universe, building this computational language. These are things that if nothing sort of dreadfully derails, I think I can confidently say that both of these things will end up being of long term importance. I think it's sort of a good question for, for, for me. What? There are things, for example, on the side of science and thinking about the computational universe, there are things that inexorably will happen and that I can, you know, jump up and down and tell people how important it is and so on, and maybe that will make it happen, you know, some number of years earlier. But these are things which inevitably, inexorably, this is the direction that, that science and so on will go in. And I've already seen that over the last couple of decades. Decades. And so I suppose that those are. And, you know, it's. It's hard to know what the. I. I have to sort of deconstruct what the. What the concept of a legacy really is. So that's. That's terrible. You know, that's not what one's supposed to say. It's supposed to say. And it's. It's kind of like, you know, there's the. There's the kind of genetic legacy. You know, I've got four kids. Hopefully they'll do interesting things. And, you know, then there's the kind of the. The. The intellectual legacy of things I figured out that might not have been figured out for a lot longer in, in our history, although they might eventually have been figured out. And then there are things where I created things where they were sort of created the way they were created because I happen to do them, so to speak. I mean, you know, when you do science, in some sense, you will never be. There's never anything you can uniquely contribute. All you can do is perhaps accelerate the process because the world is the way the world is, is, and eventually it's going to be found out. You know, when you do things like, I don't know, writing or. Or creating a computational language, actually, they're things which are more creative acts where there's sort of an infinite number of possibilities. And the one that you happen to choose if it ends up being something that survives, that's something that's more a kind of personal imprint on the world than something which in a sense, inevitably gets discovered at some point.

This is the longest episode of Remarkable People so far. But tell me, what would you have cut in another 10 or 20 years? Maybe I'll interview Wolfram again. Until then, you can read Wolfram's latest book, Adventures of a Computational Explorer. This is a collection of Wolfram's essays. He doesn't cover the topic of cricket, however. I'm Guy Kawasaki, and this was remarkable piece, people. Thanks to Jeff C. For his awesome sound design and Peg Fitzpatrick for her awesome promotion. In the next episode, I'm interviewing Margaret Atwood, author of the Handmaid's Tale and 59 other books. This is remarkable, people.