Stephen Wolfram shares remarkable lessons from his life and how he build the things he wanted to use.
Welcome to Remarkable People.
Stephen Wolfram is “physicist” but that’s like saying LeBron James is a “basketball player” and Sir Donald Bradman is a “cricketer.”
Have you ever wondered what it’s like to get a Ph.D. in Physics at twenty and win a MacArthur Award at 21? For that matter, have you pondered the math of rhinoceros horns? These are just two subjects that are covered in episode.
If you’re a math or physics geek, you’ve probably used a software application that he created called Mathematica. He’s also built a knowledge engine called Wolfram Alpha that you have used in Siri, Alexa, Bing, or DuckDuckGo.
Warning: your head might explode by listening to this episode though Wolfram describes what he does as simply “figuring stuff out.”
It has been proven that the universe is computationally equivalent to my ego. Stephen Wolfram
Click To Tweet
This week’s question is:
Question: If you could, what would you create to make your life easier?
Use the #remarkablepeople hashtag to join the conversation!
Follow Remarkable People Host, Guy Kawasaki
Other topics of interest:
Guy Kawasaki: Hello. This is Guy Kawasaki. Welcome to the Remarkable People Podcast.
Guy Kawasaki: I meet with Stephen Wolfram every ten years or so. It takes that long to recover from interactions with him because his brain operates at approximately ten times the speed of mine. He attended Eton 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 Ph.D. 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.
Guy Kawasaki: 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 his 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’re probably using his computational knowledge engine, aka a 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.
Guy Kawasaki: 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 he lived like slobs. This was going to be the first and probably last time a MacArthur Award winner would be in our house.
Guy Kawasaki: I’m Guy Kawasaki. This is the Remarkable People Podcast. Now, here’s Stephen Wolfram.
Guy Kawasaki: Tell me about growing up in England in the ’60s. What was it like? Your memories…
Stephen Wolfram: I don’t know. I thought that I was a typical growing-up-in-the-’60s kid in England. I got really interested in space because space was a thing that was happening at that time, and that was a very American-oriented thing. In England at that time, the US seemed like a pretty far-off place. I think at any given time in history there’s a most exciting thing that’s going on. In the 1960s, that was space, so 50 years ago, I was following the Apollo 11 landing and all this kind of thing. I got interested from that in how does this all work, and that got me interested in physics, so I started 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.
Stephen Wolfram: I went to good schools: top schools in England.
Guy Kawasaki: Eton?
Stephen Wolfram: Yeah. Its fame has gone up and down over time. Now it’s back to being famous again because all the prime ministers are coming from there and things.
Stephen Wolfram: Looking back, I did pretty well in school in the sense that was probably even the top kid in terms of scholarships and things I got. I was in 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 was just mostly interested in learning stuff on my own and spent a lot of my time learning about physics and doing things related to physics.
Guy Kawasaki: Any sports? Were you just a nerd?
Stephen Wolfram: I didn’t do sports. I actually had elaborate schemes for avoiding doing sports. For example, cricket was a big thing, and the few times I ended up playing cricket I discovered that cricket has this thing called overs: when the whole field is reflected, and people change their positions, perhaps because they’re getting so damn bored standing around. I discovered that there were these positions that were invariant with respect to those over things, so you could just stay in one place and hang out there.
Stephen Wolfram: I remember the one time a friend of mine got me to be in some sort of cricket team-type thing, I discovered that… In cricket, you’re supposed to throw a ball in this overhand crazy thing where it’s very hard to get it to aim correctly, but if you just roll the ball along the ground and line it up, you can actually get it to go more-or-less in the right direction. I did that once, and the person who has batting, the ball just slid underneath their bat and got them out.
They were like, “You can’t do that.” “Is it against the rules to throw the ball along the ground?” “No, it’s not, but…” this person said with great emotion… “it’s not cricket.” That’s a phrase that’s commonly used: “That’s not cricket.” I actually got to hear it in real life in a relevant setting, just once.
Guy Kawasaki: So you were applying physics from the very beginning, even in cricket.
Stephen Wolfram: Yes.
Guy Kawasaki: I read a little tidbit that you were having difficulty learning arithmetic. Can that possibly be true?
Stephen Wolfram: Oh yeah. I was terrible at arithmetic. I found it boring. In one of these educational lessons about education, when I was seven or something, there was always this game of who can do arithmetic facts, and I discovered that there’s only one fact that you needed to know to win that game most of the time, which is that seven times eight is 56. That was the one fact nobody else would know. But I never learned the other ones. I have a decent memory, so I remember roughly when I learned six times nine. I think I learned it in my 40s.
Guy Kawasaki: Come on.
Stephen Wolfram: Yeah. I didn’t know that.
Guy Kawasaki: Stephen Wolfram couldn’t memorize math? Well, not math: algebra.
Stephen Wolfram: I never found it interesting.
Guy Kawasaki: Arithmetic?
Stephen Wolfram: Arithmetic. I never found it terribly interesting. Occasionally you’d need to work out six times nine. Okay, I can figure it out. It takes a few seconds to figure it out, but it was something where… I don’t really have a great excuse. It was something that I never found that interesting.
Guy Kawasaki: This is obviously before Wolfram Alpha.
Stephen Wolfram: Well yeah, but there’s a causal relationship because 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. I was like, “How can I do the physics I want to do without having to do all this boring math calculation that I don’t want to do?” That got me interested in, “How do I use computers to do this?”
Stephen Wolfram: It’s a funny thing now that from me as a kid having thought, “I don’t want to do this,” I end up spending decades building tools that have got to the point where… for the whole world… you don’t really have to do this anymore. It’s kind of nice.
Guy Kawasaki: I love it.
Guy Kawasaki: Do you think that we should teach physics then math? Is that possible?
Stephen Wolfram: I don’t think physics is the thing. I think computation is the thing that is the paradigm of today’s world, just as a few hundred years ago it was a big deal when people realized you could use math to figure out stuff about the world. That’s what led to modern physics, modern engineering, things like this. The big thing in today’s world is that we can use computation to figure things out, and it turns out you can teach the ideas of computation, which is different from programming. We can talk about that later.
Stephen Wolfram: The ideas of computation you can teach, and it’s very nice because it’s very self-learnable, and it doesn’t have the feature that math has where if somebody says, “What’s seven times eight?” and you say, “It’s 55.” “No, that’s not right. It’s 56.” 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 tell the computer, “Do this.” The computer does something totally crazy. You can see for yourself that something happened that was wrong, and there isn’t some teacher telling you that. It’s something that you can see for yourself and have the interaction yourself.
Stephen Wolfram: I think teaching computation is a great way into teaching systematic thinking and so on. In fact, if you learn a certain amount about computation, a bunch of the things that people say, “That’s so abstract. That’s so hard to understand about math,” become really quite easy to understand because you have a concrete foundation for thinking about the things and exploring the things and so on.
Guy Kawasaki: I bet people listening to this… I have to admit that I am too… How do you define computation, then? Just to make sure we’re on the same page.
Stephen Wolfram: What is 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. That means if we’re saying… Here’s a type a problem that’s a computational thinking problem. Let’s say you’re given a point on the Earth… given its latitude and longitude… and you’re asked the question… you’re going to make a map and you’re going to make a default zoom level of that. 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, a mile might be pretty good… or maybe less than a mile is a good default zoom. The question is, how do you figure that out?
Stephen Wolfram: You might say, “Let’s look at the density of people around that place. Let’s look at how many features there are on the map around that place.” These are things that you can think about computationally, and then you define, “What do I actually want to know? Do I want to know the density of features? How do I define the density of features?” I can say that’s something like the number of geometric primitives that occur in that region of the map or the compression of the map that you can get or something like this. That’s computational thinking is figuring that stuff out.
Stephen Wolfram: The interesting thing… because of this a little hobby. 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 teach them the language to communicate that to a computer to get it to do it, but this 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… Different people do it in different ways, but it’s something people are intrinsically able to do.
Stephen Wolfram: Now, one of the problems with traditional math in the abstract form is that it’s very cold. It’s very unclear what’s going on. You’re just told it works this way: X+1 is equal to 1+X. Maybe somebody can prove that’s true, but it doesn’t feel connected to anything that one can normally think about. In this whole area of computation, for math, one of the things that I find talking to kids is they’ll say, “We learned a bunch of math.” I’ll say, “Where did you use that math in your general life and times?” They’ll think, say, “Well, actually, I’ve only used it in the math classes.” That’s kind of a bad thing.
Stephen Wolfram: Then you start talking about how can you use computation? For every area, there’s a computational X that you can talk about. It might computational art history. It might be computational magic. Or it might be computational marketing. These are all things that one can use the paradigm of computation on, and they’re things that people… they engage much more the things that people think about.
Guy Kawasaki: But what if you’re totally dependent on computers? You can’t do seven times eight and getting 56 without a computer? That’s what a skeptic would say.
Stephen Wolfram: Yeah. We’re totally dependent on lots of stuff in the modern world. The one thing that’s really advanced in human history is technology and the amount of automation that we have. There are plenty of things that… Your typical kid these days couldn’t drive a stick shift car probably in the US, right? I know my four kids… I think one of them can drive a stick shift car.
Stephen Wolfram: It’s sort of an interesting thing in education because more and more is known in the world. You might say, “How can we possibly educate people because there’s so much more known? How can it only take 12 years or something to educate people?” 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 sort of a general principle that you can learn about that. It’s the same thing with all these different… Math is an example of that.
Stephen Wolfram: The concepts of math are worth learning as a matter of how to think about the world. Math as a field is also the single most-developed kind of intellectual area which has had the most layers of work done on it. Modern math is built on hundreds of years of intellectual development in a way that’s more of a tower than pretty much any other field. It’s kind of an impressive achievement of our civilization. It’s not what people learn about in elementary math, but there are… If you want to learn intellectual history, it’s an important area to understand. But that’s not what typically is taught.
Guy Kawasaki: This is completely going down the rabbit hole, but do you know the story of Joshua Bell playing the violin in the metro in Washington DC?
Stephen Wolfram: I do not.
Guy Kawasaki: Joshua Bell, the violinist. They dress him up as a homeless person. Put him in the Washington metro. He’s playing. Obviously, Joshua Bell is Joshua Bell. They watch what happens. Did people stop? Did they listen? Did they give him money? Nobody. The lesson is that if he was in a concert hall, everybody would be standing in line and playing 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.
Guy Kawasaki: The reason why I tell you is because what you should do is disguise yourself and go apply for a job at Google. When Google gives you one of these computational kind of questions, you could just smoke the answer because you’re Stephen Wolfram. What a great answer that would be.
Stephen Wolfram: I have to say, I’m not a big believer in the assessing of people. I’ve now been running my company for 33 years: more than half my life. My wife often reminds me of that. After you’ve been a CEO for more than half your life, it has all kinds of terrible effects.
Stephen Wolfram: I think one of the things I’ve managed to accumulate: wonderfully talented people. I have to say the test… “Can you write this algorithm on a whiteboard or something?”… bad idea.
Guy Kawasaki: What’s a good idea?
Stephen Wolfram: You talk to people about what they know about. My principle is if I’m talking to somebody and I spend time doing the interview, and at the end of it I can’t answer the question, “What will this person do in the job that I’m imagining they would do?”… if I’m still mystified, if I still don’t really understand the person… then I won’t hire that person. If I think I understand them and I can see how they’ll actually work and what I want them to do, then that’s a good sign for me.
Guy Kawasaki: Fair enough.
Guy Kawasaki: A few more questions about your youth. You’re at Eton. You’re at the top of everything. Who were your heroes at the time?
Stephen Wolfram: That is an interesting question. I wasn’t really a very hero-oriented character. I didn’t really have… I mean, I wanted to be a physicist at that time. I knew about a bunch of the famous physicists of then and the famous physicists of before then. I started meeting those people by the time I was 14, 15 years old. As soon as you start meeting people, the whole concept of a distant hero disappears. I was like, “These are people. Some of them seem to know what they’re talking about. They seem smart.” Some of them didn’t seem so smart.
Stephen Wolfram: I don’t think I ever really had a… It’s bad. It’s one of those deficits. No, I didn’t really have particularly a… I also didn’t really have a role model. I wanted to sort of generically be a physicist, and that was kind of a generic role model.
Guy Kawasaki: At 20, you get a Ph.D. in physics. At 21, you win a MacArthur Award. I can’t even wrap my mind around that. What is that like? At 21, you’ve accomplished what…
Stephen Wolfram: It was kind of fun. In retrospect, I should’ve made the minor effort that it would’ve taken to get my Ph.D. while I was still a teenager so I could’ve kept saying for the rest of my life that I had a Ph.D. when I was a teenager.
Stephen Wolfram: At that time, I was very focused on “I want to do science. I want to figure stuff out.” It was like, “Let me get to the point where I can just do that. I don’t want to be messing around taking classes,” which I never really did, “or those kinds of things. I just want to go and do science because that what’s what I’m interested in.” I think some people on the outside were like, “Oh gosh, what an awkward situation to be in. You’re getting all these awards and things. You’re young.” Etc., etc., etc. But I wasn’t taking these awards terribly seriously. It wasn’t like I was thinking, “Gosh…” One of the traps, I think, for people is that they do well early on and then they think there are these huge expectations; they’ve got to do this and that and the other. My own expectations, and perhaps my own opinion of myself, was always higher than I attributed to the outside world. It was one of these things where, as I say…
Stephen Wolfram: At the time, I got my Ph.D. 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. I’m 20 years old. Now I’m a physicist. Great.” Then it’s a question of the what next. I started thinking, “I’m going to make this longer-term plan. What do I want to be doing?” The first thing that I realized is I need better tools… they were computational computer tools… to do the things I wanted. So I’m like, “How am I going to get these tools?” I was interacting with the various groups that had built experimental versions of these things, but eventually, I decided that I just have to build the stuff myself.
Stephen Wolfram: Within a couple of weeks of doing my Ph.D., I got into designing this big computer system that was a forerunner of things that I’ve done more recently. Then I had to… “Okay, I’ve got to officially learn computer science,” which was a lot easier in those days because there was a lot less known. I just kept going doing things. I wasn’t really thinking, “It’s so cool that I’m in this place at this time.” I did things like I got doctor on my credit card, which I still have, but back in those days, it was much rarer. You’d go and check in for a flight or something, and the person would say, “I’ve got this ailment. Can you help me with this?” It’s like, “Sorry, wrong kind of doctor.
Guy Kawasaki: Did you call up American Express or Visa and say, “Put my title…” How does that work?
Stephen Wolfram: I think you just fill it out on some form. I’m sure that’s what I did, and I haven’t thought about it since. At this point now, if I’m in some particular business setting, the kiss of death is if somebody refers to me as a professor, then I know they’re absolutely not taking me seriously.
Guy Kawasaki: Why is that?
Stephen Wolfram: Because I’ve spent some large part of my life actually building stuff in the software industry and so on.
Guy Kawasaki: As opposed to just studying it or teaching it.
Stephen Wolfram: If I’m in some random business meeting with the CEO of some company and they refer to me as professor, it’s like, “This is dead because they think I’m some crazy intellectual, academic nerd, not somebody that builds software that people use” type thing.
Guy Kawasaki: And makes money.
Stephen Wolfram: Yeah.
Guy Kawasaki: And makes money, yes.
Guy Kawasaki: Can we fast forward to Mathematica? Is it a product? Is it a theory? Is it a philosophy? What is it?
Stephen Wolfram: Mathematica is very much a product. The thing that Mathematica is based on… this thing called Wolfram Language is more of a thing which gets deployed in different products. Wolfram Language is basically what I’ve been working on for at least a third of a century, and the goal is to encapsulate as much computational intelligence and as much 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. I view it as being… in the long view of history… this computational language that I’ve spent all this time developing… It’s an attempt to have a definite notation for talking about computation.
Stephen Wolfram: I’ll talk about math again. A long time ago… 400 years ago something, long before we were around… 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. It was not easy to systematically communicate about math in those days. And then, mathematical notation got invented, and that led to algebra and calculus and all of these mathematical sciences that exist today. The analog of that today, I think, is the things I’ve been trying to do with computational language; can we have a notation for computation that people can understand… it didn’t happen with math, but now machines can also understand… and that we can use to crispen up our thinking and our communication about computational kinds of things.
Stephen Wolfram: That’s the kind of intellectual abstract version of what I’ve been trying to do for a long time. It has many consequences and connections to things which, yes, are a kind of philosophy. One of the things that I always find fun is when one goes from these very fancy abstract intellectual ideas and then it’s actual code, and then it’s an actual product that people use all the time. I think that’s really neat.
Stephen Wolfram: What we see a lot is things that used to be pure philosophy turn into code and products and so on. For example, one thing that 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 how do you express things about the world in an abstract way without using specific human language? A place where this comes up in modern times is things like legal contracts. When you write a legal contract, it’s more or less in English if you’re in the US, but it’s sort of in legalese because you don’t want it to be in something as vague as English. You want it to be something tighter. The question is, can you make an abstract symbolic language that can express what you want to express in a contract: a computational contract?
Stephen Wolfram: We’re getting close to being able to do this, and that’s something… You can expect these things to get executed autonomously with blockchains and all kinds of other things, but basically, it’s part of going from the philosophical idea that there’s a representation of meaning that isn’t just words and languages, but it is something deeper. That turns into this very practical thing about computational contracts and that kind of thing.
Guy Kawasaki: I have to ask you to tell us a story about Steve Jobs and helping or telling you to name Mathematica, Mathematica.
Stephen Wolfram: It’s funny because we now have this product called Wolfram Alpha. The original name for Mathematica was Omega. Over the course of many years, we went from Omega to Alpha.
Stephen Wolfram: Steve was… I started interacting with him pretty soon after we had very early versions of Mathematica because he was going to bring out this NeXT computer and it was oriented towards education. We made this deal early on to bundle what would be called Mathematica on the NeXT computer so everybody who got a NeXT could use Mathematica. It turned out that it was a pretty good deal on both sides. It was pretty smart of Steve to figure out that was a good idea. A bunch of people bought NeXTs because of that. A bunch of people used our stuff because of that.
Guy Kawasaki: You were the killer app of NeXT.
Stephen Wolfram: Thanks. I think there were some good footnotes to history, like there were these computer 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, was to buy the whole computer. Then those computers… the person who was responsible for that system was a person called Tim Berners Lee…
Guy Kawasaki: I’ve heard that name before.
Stephen Wolfram: Who ended up using those computers to build his first web setup. That was an amusing footnote to history that came out of that.
Stephen Wolfram: But in terms of naming your products… I had thought of the name Mathematica, but I thought it was too long, too ponderous, etc. I had this whole list of other names. I put that list on the web some number of years ago. What’s funny is all these names… including really horrifying awful ones… have been used as product names in the intervening years. But Steve had a theory of naming at that point, which was take the generic word for something, and I think he said: “Romanticize it.” He used the example of Trinitron, which was a now long-lost brand probably from Sony, which was a television brand and represented the three cathode-ray tube guns or something in color television, which younger people who are listening to this have probably never heard of. It makes me feel old.
Stephen Wolfram: In any case, at that time, the killer app of the thing we were building was for mathematical computation. In the end, the bigger picture is all about computation in general, but at that time, math was the first killer app for that type of approach. So Steve was… “You’ve got to call it Mathematica because it’s like math, but it’s romanticizing that word.”
Guy Kawasaki: Did he do this in a civil manner, or did he just tell you that…
Stephen Wolfram: No, it was perfectly civilized. He was just like, “I think you should do this.” It wasn’t a petulant, “You’ve got to do this.” It was, “I think you should do this.”
Stephen Wolfram: I always had very civilized interactions with Steve, actually. I also liked the fact that I would tell him something, and he would say, “I don’t care,” and then sometime later, he would, “Actually, I do care.” That happened with Wolfram Alpha. I showed it to him before I released it, and he says, “I don’t know why I care about this.” A little while goes by and this little company called Siri that had licensed our stuff and had put a wrapper around it and made a thing that could be thought of as an intelligent assistant rather than the use case that we were primarily dealing with, which was ask questions on the web. Then he looks at this, and he says, “Okay, now I get it.”
Stephen Wolfram: There are a bunch of things that… When we were working on the 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.
Guy Kawasaki: Did he, at any point, try to explain math or physics to you?
Stephen Wolfram: No, I don’t think so. It turns out I know somebody who knew Steve in high school. The person who I know who knew Steve in high school is now a physicist. I actually saw him recently at Washington. He was like, “Yeah, Steve was always kind of a weird person in high school. 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.” I knew that bit about Steve, that even though he was like, “I don’t know anything about this math stuff,” etc., he actually had learned calculus when he was in high school. That was one of those kind of outer band pieces of information that I happen to have.
Guy Kawasaki: I fully expected you to tell me a story of Steve trying to explain physics to you.
Stephen Wolfram: No. I’m trying to remember. A lot of times in the tech industry there are people who are really quite intellectually interested in a lot of these science things. Sometimes they are very… 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 can’t just make stuff up. If you want the science to mean something, it has to correspond to how our universe happens to work.
Stephen Wolfram: I think sometimes the mentality of the people who are used to the tech world is a bit different. Having said that, I have to say that some of the science that I’ve done and that I’m about to start doing again is very much of the… It’s sort of a tech-informed approach to science that perhaps is methodically 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 to find what’s happening in pieces of it.” My approaches tended to be there’s a whole giant 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 universe an example of these. That’s a somewhat different approach.
Guy Kawasaki: Is this what to refer to as the new kind of science?
Stephen Wolfram: That’s related to that. It’s an outgrowth of that. The new kind of science is all about… We’re back to talking about math again. 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. That was what Newton and Galileo… as these people… that’s what made them famous, was doing that stuff. It’s been a successful thing for 300 years.
Stephen Wolfram: What I got interested in a long time ago now is there are things we can’t explain using mathematical equations. There are things in physics but also in biology and in other places. How can we generalize what we do and to something more general than just mathematical equations and still be making precise theories of things? What I realized is that you can use programs instead of equations to represent how things work in some particular system. So you say, “This system, there’s a program that 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 algebraic operations and standard mathematical kind of things.
Stephen Wolfram: That was the starting point. Then the question was, “If we’re using programs to talk about the world, what kinds of programs might represent things in the world?” That got me into the question of… So we just look at the universe of all possible programs. What’s it like? 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. If you just think about programs in the wild, programs that are natural programs, programs that if you just started enumerating programs at random, little tiny programs… It’s like we get to this program, and what does it do?
Stephen Wolfram: I had assumed that if you had a very simple program it would do very simple things, but it isn’t true. This was the big thing that I discovered in the early 1980s, was if you just look at the universe of all possible simple programs, you very quickly find ones that are very complicated in their behavior. The program itself is tiny.
Guy Kawasaki: Can you give an example of this?
Stephen Wolfram: My favorite example is this thing called Rule 30, which is a thing that 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 the step before and the color of its two neighbors. It’s a very simple thing. You can write it down. It’s my favorite science discovery. It’s on my business card. It’s a tiny thing.
Guy Kawasaki: That and doctor.
Stephen Wolfram: Right. Actually, the doctor is not on my business. Only credit cards. In business, doctor is a term of disrespect in some ways.
Stephen Wolfram: In any case, the Rule 30 is a very simple rule. You can state it. You can say it in a sentence, although it’s a boring sentence that involves ands and ors and things like that. Then you start it off from just one black cell, and you see what it does, and it makes this incredibly complicated pattern. That was a major a-ha moment, discovering that. You look in this computational universe of programs, and you’re finding a phenomenon that you absolutely didn’t expect. People would think simple programs simple behavior.
Stephen Wolfram: When we build things with engineering, if we want to make something complicated, it takes us a lot of effort. We have to have complicated plans. It’s got a 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. Why does one care about that? 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 constraint that we 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’s not under a constraint of being understandable from the outset.
Stephen Wolfram: In this computational universe, the big discovery is that there’s a lot of complexity that’s easy to get, and it seems to be the same essential idea that nature uses to make complicated things. It’s also something we can use for technology. When you have this simple program that does this very complicated thing, sometimes that complicated thing will be something that we humans find useful. Even my Rule 30 cellular automaton thing: we used it as a random number generator for a long time because it’s a very simple process but generates something which for all practical purposes, seems random. 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.”
Stephen Wolfram: In ordinary physical technology, we find liquid crystals, and we say, “Gosh, this is a cool scientific physics phenomenon,” or something. Actually, we realize that we can use that to make displays. It’s the same kind of thing in the computational universe. You find these programs, and they do remarkable things, and sometimes they do things which we humans find useful for things that we want to do.
Guy Kawasaki: What is computational paradigm? Or is this the same thing?
Stephen Wolfram: It’s thinking about things in computational terms, so thinking about… Given a question, trying to formulate it with the kinds of thinking that you could talk to a smart computer about.
Stephen Wolfram: You have this giant display on your wall of a rhinoceros. I’m thinking about how do I make that rhinoceros computational? I’ll give you an example: it’s got a couple of horns at the front. One question would be what’s the diversity of rhinoceros horns? How do I think about the space of shapes of rhinoceros horns? That’s something which we can then engaging with computationally. If that’s the signature of a rhinoceros, is the shape of its horn or something, then we can say there’s the space of sames, and maybe there are actually sub-breeds of rhinoceros that are separated from other ones because their horns are a different shape and so on. This is how you start engaging with some questions about the world computationally.
Guy Kawasaki: Does all of this mean we’re a simulation? Are we kidding ourselves here?
Stephen Wolfram: This whole simulation argument thing, it’s kind of charming how some of these theological, religious ideas and so on get reiterated in these very different, bizarre wrappings. It’s kind of like if we look at our universe. One thing about our universe… this is almost a theological fact about our universe… which is that it has definite laws, might not be the case. Might be the case that there are 10^90 particles in the universe. They might all do their own thing. There might be no order to the universe.
Stephen Wolfram: The first thing that is surprising, and that early theologians made a lot of hay out of was that the universe is an orderly place. It doesn’t need to be an orderly place. We don’t know why it should be an orderly place, but it is. When it comes to thinking about what would it mean if the universe was a simulation… It’s a philosophically wrong idea, but it takes a few steps to explain why that’s the case. Basically, if the universe has definite rules, then the universe is just doing what it does and running according to those rules. If you ask, “Where do those rules come from?” well, the universe has the rules the universe has. You can say, “Why does it have those rules? For example, why doesn’t it have much more complicated rules?” We don’t actually know how simple the rules for the universe are. 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. I think there’s a possibility that it’s three lines long.
Stephen Wolfram: 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 that I’ve got to do this now or never… to actually make a serious assault on can we find the fundamental theory of physics? Can we find the fundamental theory of our universe? Might be the wrong century to try this, but if it turns out the rule for the universe is simple, it’d be pretty embarrassing… if we have the technology now to find it… if we just hadn’t bothered to look for it. It might not be simple enough to find. To might be that there are things about validating whether it’s correct that are things we can’t do yet with the current state of science.
Stephen Wolfram: Imagine that we have the rules for the universe. Imagine that we succeeded. We’ve got the rules. We can write them down. I could tell you them in a few sentences. You say, “Okay, that’s the universe.” Now you say, “What do we conclude from that?” What would it mean for the universe to be a simulation? You say, “What are those rules running on?” They’re not running on anything. They’re just rules that describe how our 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 philosophical and a bit complicated.
Stephen Wolfram: Essentially, the other side of it is to say… when we look at these rules… do these rules feel like they’re an artifact? Do they feel like they were produced by some intelligence on purpose making these rules, or are they just rules our universe happens to have? This then goes into the question of how can you tell if a thing was made for a purpose? That’s another huge can of worms. Even when we look at something like Stonehenge, for instance. We say, “What was Stonehenge for?” 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 we found some extraterrestrial radio signal and we say, “Is 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. That’s one reason there isn’t even a meaning to saying, “The rules for the universe: were they made on purpose? Were they made as a thing by some other entity and then we exist as a simulation with respect to that entity?” It doesn’t really make sense.
Stephen Wolfram: It’s a complicated area.
Guy Kawasaki: Do I dare ask what’s God’s role in this or if there is a God?
Stephen Wolfram: It’s a funny question because, as I was mentioning, in early theology, the very fact the universe is orderly was seen as evidence for the existence of God. But then, if the universe is just you have these rules and you run them, that doesn’t leave any place for a God, so to speak, because it’s like running a program. There’s no God needed to run this program, it’s just a program and it’s running and there are no miracles that come from the outside.
Guy Kawasaki: But maybe God was the programmer.
Stephen Wolfram: That comes back to the question of why this universe and not another one? First, we have to find what the rules for our universe are, which we haven’t succeeded in doing, and it may be the wrong century to try to find them. If we find them, then that really is an in-your-face question. Why these rules? Why not other ones? What can we conclude from this? Is this evidence for something beyond our universe? Is it just what we happened to get? We happened to get this universe, not another universe. I don’t know. It’s a curious question.
Stephen Wolfram: When people like Isaac Newton back in the 1680s figured out a bunch of stuff about the planets and the motion of planets, at that time he was talking about once the planets are set in motion it’s just a matter of mathematics to decide what will happen. How the planets started off, he said, “That’s the hand of God that determines that.” It always used to be the case. People would say the fact that we had nine planets… now it’s eight but it used to be nine… is one of these random facts about the world, not something you could ever from mathematics or something. It’s interesting that now that we know about zillions of exoplanets, we 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.
Stephen Wolfram: In Newton’s time, he had no choice but to say, “The way it started, it’s just God.” 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, we kind of understand this one.” If we find a fundamental theory of physics, I’m sure there will interpretations on many sides about what that actually means.
Guy Kawasaki: What is your reaction to the reputation of science these days, particularly at the highest levels of our government? It seems like truth and science and facts… It seems to be it’s whatever you agree with is true. So how do you…
Stephen Wolfram: Science is in some ways its own worst enemy in that regard because what’s happened is that 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. People would say… for example, evolution. “Evolution is the whole story of biology,” people would say. Well, it’s not. There’s other things going on, and some of them I’ve figured out in the science of how complicated forms arise in biology, which is something where people say, “It must just be evolution because evolution is all there is.”
Stephen Wolfram: Actually, evolution on its own can’t explain why there are complicated forms. That’s a computational phenomenon that is similar to this Rule 30 phenomenon, where the rules can be simple, and the forms that are produced aren’t simple. 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.” That’s an overreach in the sense that actually there is something else going on, which is non-trivial science. It’s not that the other thing that’s going on is not science. It’s absolutely science. It’s probably cleaner science than evolution, in many ways. But it’s something where people say, “We’ve got one piece of science, let’s carry that all the way.”
Stephen Wolfram: I think the other thing that happens… there’s an important phenomenon not yet well understood, although I happened to testify for a Senate sub-committee a couple of months ago and now this term is in the congressional record. I don’t know what that means about it. The term is computational irreducibility. What does that mean? You say, “I know the rules for how some system behaves. I can figure out everything about what the system does.” 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? You’d have to run all those steps and see what happens.
Stephen Wolfram: That’s important when it comes to predicting the climate or something. The question is, can you just say, “This is the answer,” or is this computational irreducibility phenomenon that means there’s an irreducible amount of computational work you have to do to figure out what’s going to happen, and where it’s very hard to make simple “Oh, it’s just going to do this” claims. One of the things that happens is people will say, “We have this science. We know something about this scientifically. We will take that particular idea in science and take it to its end conclusion. Because it’s science, that must be the whole story.” But actually, it’s not. You’ve 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 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.
Stephen Wolfram: I think what has tended to happen is that people have said, “Science is the new religion in many ways,” in the sense that people say, “We believe in science.” In some ways, they believe in a version of science that isn’t well informed by things like computational irreducibility. They believe in a version of science that has simple cut-and-dried answers to things. Other people say, “Common sense tells me this can’t be right,” and they’re right. It’s not right. It’s a piece of the story, and there are certainly places where that story comes through for science spectacularly. But there are other ones… including some of these most controversial ones, whether it’s in a medical area or in climate… where it’s not so obvious. It’s not something where it’s a cut-and-dried story.
Stephen Wolfram: Now, people can come up with crazy conclusions. I’m not arguing that all the things that are said that are on the other side from the cut-and-dried science make sense, but it isn’t true that the cut-and-dried science is really the whole story. I feel that actually 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. Some people are rightly suspicious and say, “It can’t be the whole story.” Then there’s an attack on science as a whole, which is also unfair.
Stephen Wolfram: I think it’s a more complicated picture. I always kind of wince when I hear “Science has proved that blank blank blank,” when it’s like, “I know how the science works. You can’t possibly have proved that.” It’s a much more complicated story. There are things you can say, but there are also a lot of footnotes and caveats and so on.
Guy Kawasaki: How about the other extreme where I know the Earth is flat?
Stephen Wolfram: You have to be a more sophisticated consumer of science. It’s no good to say… It’s the same thing that has happened in past years with religion. You could be an unsophisticated consumer of religion and just say, “The Earth is 6,000 years old, and it says that in the Bible and therefore it must be true.” There’s more to religion than that statement. You have to take from it what actually makes sense and not just take the most obvious things.
Stephen Wolfram: In this whole computational thinking thing, as that finally gets well-absorbed, some of these pieces of intuition like computational irreducibility will become quite common place. In marketing and things like that, you talk about force and momentum and acceleration. These are all terms which were invented by people like Newton and Galileo to describe physics. They’re terms that come from science that have found their way into our general way of thinking about the world. There are similar ideas that come from the computational way of thinking about the world, like computational irreducibility, and as we really start to absorb these, we get to have a more nuanced way of thinking about these kinds of scientific questions.
Guy Kawasaki: 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.” I could barely keep up with you. I have some kind of off-the-wall questions. Number one: you’re kind of off the scale on intelligence. Do you ever have a moment where you… “God, I just wish I was a regular person. I’m not thinking about all this stuff. I’m not thinking about irreducibility. I’m not thinking about whether it’s a simulation. I’m just drinking beer watching football.” Do you ever have moments like that?
Stephen Wolfram: Well, I don’t like either beer or football.
Guy Kawasaki: Cricket?
Stephen Wolfram: No. I’m not into cricket either.
Stephen Wolfram: People tell me, “You’re so smart about this or that thing.” To me, that’s not what it feels like. I’m always trying to figure out things that are difficult for me to figure out. Maybe some of those things are really difficult for other people to figure out, but I’m always struggling to figure stuff out. The internal perception is not one of… The fact that I’m always trying to figure stuff out…
Guy Kawasaki: Do you feel it’s a burden?
Stephen Wolfram: Absolutely not. I like what I do. I like figuring stuff out.
Stephen Wolfram: Actually, in recent years I’ve been really interested in the question of how does one find talent in the world, because I’ve spent a lot of time building a great collection of people in the company and things, but I’ve also been… I like interacting with talented people. I suppose one thing I’ve noticed is… I’ve interacted with kids a bunch and we have various programs for high school kids and things. One of the things that I have noticed is that it’s surprisingly difficult to break out of the elite bubble that one tends to live in. It’s like, there probably are kids out there who would really benefit from exposure to all this stuff and I don’t have connection to any of these. It’s also the case that I sometimes worry that I have a particular way of thinking about stuff and it may not be the way that kid in place X wants to think about things. Am I serving as a missionary for my ways of thinking about things, and is that the right thing to do?
Stephen Wolfram: The fact that I’ve lived in this elite bubble I sometimes find a bit frustrating, because I’m curious. That means I’m curious about what the rest of the world is like.
Guy Kawasaki: 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…
Stephen Wolfram: These are things I don’t do because I don’t find them… Well, surfing I’d just be useless. I’m not into sports at all. I’m not into playing it. I’m not into watching it. Why? I don’t know. Perhaps because I don’t know enough about it to know why I care. I like figuring stuff out. I like building stuff. Those activities don’t satisfy those particular… for whatever reason… the drive that I have to do those kinds of things.
Guy Kawasaki: Two last questions. Question one: who’s the smartest person you ever met?
Stephen Wolfram: I don’t rank people by smartness. You’ve got to realize 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 able to figure it out. Then you’d say, “Okay, that’s a smarter person than whatever.” I have to say, in my own life… Ever since I was in kindergarten… I happened to go to a kindergarten in Oxford, England, where there were a bunch of smart kids. I’ve always had this thought, “Eventually, I’m going to go to this place where I’m going to find all these people who are fundamentally smarter than me.” I went through different kinds of places: the fancy universities, Silicon Valley, this, that, whatever. I never had that moment where I said, “I’ve finally found the place where everybody is smarter than me.”
Guy Kawasaki: This is a high-quality problem.
Stephen Wolfram: Actually, it’s a bit disorienting to realize there’s no place… you asked about heroes… It’s like, I’ve got to figure stuff out because it’s not like there are other people out there who are going to figure all this stuff out. If there are things that I think about where they’re kind of difficult for me… If our species is going to figure them out at this time, I’m it in terms of doing that.
Stephen Wolfram: The thing I’ve noticed… and I’ve noticed from leading people a lot over the course of years… is that this who’s-smarter-than-who is really not the point, because there’s so many different ways in which people can be thinking about things. Somebody can be super good at analytical figuring out of stuff, and super useless at conceptualizing what to do. They’re great if you give them a specific problem. They do a great job. If something goes wrong with that problem and they have to take a turn somewhere, they completely can’t do that. They just don’t have the initiative and the creativity to do that.
Guy Kawasaki: Okay. And my last question: what do you want your legacy to be?
Stephen Wolfram: I don’t know. It’s an interesting question. Now that I’m getting old, I’m supposed to think about questions like that.
Stephen Wolfram: There are things that I’ve done… particularly, understanding the computational universe, building this computational language. These are things that, if nothing 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 a good question for me. For example, there are things on this side of science and thinking about the computational universe that inexorably will happen and that I can jump up and down and tell people how important it is and so on, and maybe that will make it happen some number of years earlier, but these are things which inevitably, inexorably, this is the direction that science will go in. I’ve already seen that over the last couple of decades.
Stephen Wolfram: I have to sort of deconstruct what the concept of a legacy really is. That’s terrible. That’s not what one is supposed to say. There’s the genetic legacy; I’ve got four kids. Hopefully, they’ll do interesting things. Then there’s the intellectual legacy of things I’ve figured out that might not have been figured out for a lot longer in our history, although it might eventually have been figured out. Then there are things where I created things where they were created the way they were created because I happened to do them.
Stephen Wolfram: When you do science, in some sense, there’s never anything you can uniquely contribute. All you can do is accelerate the process because the world is the way the world is, and eventually it’s going to be found out. When you do things like writing or creating computational language, there are things which are more creative acts, where there’s 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 of a personal imprint on the world than something which inevitably gets discovered at some point.
Guy Kawasaki: 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.
Guy Kawasaki: I’m Guy Kawasaki, and this was Remarkable People. This podcast was produced by Jeff Seih and Peggy Fitzpatrick for her awesome promotion. In the next episode, I’m interviewing Margaret Atwood, author of The Handmaid’s Tale and 59 other books.
Guy Kawasaki: This is Remarkable People.
The post Stephen Wolfram: Fascinating Interview with a Modern Day Genius appeared first on Guy Kawasaki.