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Stephen Wolfram: Computation, Physics, Going Beyond "Evolution"
September 16, 2025
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The Economist covers math, physics, philosophy, and AI in a manner that shows how different countries perceive developments and how they impact markets. They recently published a piece on China's new neutrino detector. They cover extending life via mitochondrial transplants, creating an entirely new field of medicine. But it's also not just science they analyze.
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From discrete space to Darwinian evolution to entropy and the second law, Stephen Wolfram's computational view of the universe makes claims about all of these in a unified fashion. Today's episode is a treat. If you're a fan of this channel, Theories of Everything,
Then you're likely someone who enjoys surveying large swaths of lessons from disparate fields, attempting to see how they all relate and integrate. Same with me, Kurt Jaimungal. Now today, Stephen Wolfram outlines how this polymathic disposition has helped him solve, to his satisfaction, some of the major outstanding problems in fields as diverse as computer science, fundamental physics, and biology.
This is a journey through his life in science where I tease out the lessons he's learned throughout his career and how you can apply them yourself if you also want to make contributions.
I was honored to have been invited to the Augmentation Lab Summit, a weekend of events at MIT last month, hosted by MIT researcher Dunya Baradari. The summit featured talks on the future of biological and artificial intelligence, brain-computer interfaces, and included speakers such as the aforementioned Stephen Wolfram and Andreas Gomez-Emelson. Subscribe to the channel to see the upcoming talks. Stephen, welcome. Thank you. It's a pleasure. How does one do good science?
It's an interesting question. I mean, I've been lucky enough to have done some science. I think it's fairly interesting over the course of years. And I wonder how does this happen? And I look at kind of other people doing science and I say, how could they do better science? You know, I think the first thing to understand is when does good science get done?
And the typical pattern is some new tools, some new methodology gets developed, maybe some new paradigm, some new way of thinking about things. And then there's a period when there's low hanging fruit to be pricked, last maybe five years, maybe 10 years, maybe a few decades. And then it's then some field of science gets established, it gets a name. And then there's a long grind for the next hundred years or something.
That people are doing sort of making incremental progress in that area and then maybe some new methodology gets invented things live and up again and one has the opportunity to do things in that in that period i have been lucky in my life cuz i've kind of alternated between developing technology and doing science maybe about five times in my life.
And that cycle has been very healthy. It wasn't intentional, but it's been worked out really well because I've spent a bunch of time developing tools that I've then been able to use to do science. The science shows me things about how to develop more tools and the cycle goes on, so to speak. So I've kind of had the opportunity to be sort of have first dibs on a whole bunch of new tools because I made them, so to speak. And that's let me do a bunch of things in science that have been exciting and fun to do.
I mean, I think a bunch of science I've done, I was realizing recently that it's also a consequence of sort of a paradigmatic change. This idea, taking the idea of computation seriously and by computation, the fundamental thing I mean is you're specifying rules for something and then you're letting those rules run rather than saying, I'm going to understand the whole thing at the beginning. It's kind of a, a, a more starting from the foundation's point of view.
Well what i realized actually very recently and it's always it's always surprising how long it takes one to realize these sort of somewhat obvious features of history of science even one's own history is that you know i've been working on a bunch of things in fundamental physics and foundations of mathematics foundations of biology a bunch of other areas where i'm looking at the foundations of things using a bunch of the same kinds of ideas the same kinds of paradigms
I'm realizing that a bunch of what I'm doing is kind of following on from what people did about a hundred years ago, maybe sometimes a little bit more than a hundred years ago. And I was wondering why is it that, you know, a bunch of things I'm interested in, I'm going back and looking at what people did a hundred years ago. I'm saying they got stuck. I think we can now make progress. What happened? I think what happened is that in the eighteen hundreds, there was this kind of push towards abstraction.
There was this idea that you could make formal versions of things that happen most notably in mathematics where kind of the idea emerge that sort of mathematics with just a formal game where you are defining axioms and then seeing their consequences it wasn't a thing about actual triangles or whatever else it was it was an abstract exercise.
And once people had ground things down to that level of abstraction, same kind of thing happened with atoms and so on. And in the structure of in physics, once people had ground things down to that sort of deconstructed level, they had something where they didn't know what to do next. Because what turns out to be the case is that that sort of the setup for computation is you've ground things down to these kind of elementary primitives.
And then computation takes over and that's the thing that uses those primitives to do whatever is going to happen. And so I think a lot of what got stuck then was closely related to this phenomenon of computational irreducibility that I studied for the last 40 years or so that has to do with, even though you know the rules, it will not necessarily be the case that you can kind of jump ahead and say what will happen.
You may just have to follow those rules step by step and see what happens you may not be able to make the big theory that sort of encompasses that describes everything that happens. I think what happened in a bunch of these fields is people kind of ground things down to the primitives then they effectively discovered computational irreducibility implicitly discovered it by the fact that they couldn't make progress.
I think that and things like girls theorem which are reflections of computational reducibility kind of where were kind of the other signs more direct signs of that phenomenon but the end result was people got to these parameters and i couldn't get any further and now now that we actually understand something about the kind of computational paradigm we can see to what extent you can get further and the kinds of things that you can now say.
So it's kind of interesting to me to see that. I mean, one particular area where I've only learned the history recently, and I'm kind of shocked that I didn't know the history earlier, is about the discreteness of space. So, you know, back in antiquity, people were arguing back and forth, you know, is the universe continuous or discrete? You know, are there atoms? Does everything flow? And nobody knew. End of the 19th century, finally,
One had evidence that yes, matter is made of molecules and atoms and so on. Matter is discrete. Then first decade of the 20th century became clear you could think of light as discrete. That had been another debate for a long time. And at that time, first few decades of the 20th century, most physicists were sure this whole discreteness thing was going to go all the way, that everything was going to turn out to be discrete, including space.
I didn't know that because they published very little about this. And the reason was they couldn't make it work. After relativity came in, it was like, how do we make something that is like space, but is capable of working like relativity says space should work.
And it will also somewhat confused by the the idea of space time and the similarities between time and space which were more mathematical and physical really that kind of confused confused the story but i think then the thing that that became clear was i think nineteen thirty was i think the time when when particularly when heisenberg was i think one of the ones who was really you know spaces discreet he had some i think
i need to go look at his archives and things but i think he had some kind of discrete cellular model of space and he couldn't really make it work and then eventually he said forget about all of this i'm just going to think about processes in physics as being these particles come in something happens in the middle but we're not going to talk about that we're just going to say it's an s matrix and then things go out
And so that that's when he started just saying, I'm going to look at the S matrix, this thing that just says what how what's goes in is related to what comes out. I'm not going to talk about what's in the middle, because I got really stuck thinking about sort of the ontology of what's in the middle. So
Then after that, you know, the the sort of quantum field theory and quantum mechanics and so on started working pretty well. People forgot about this idea of discrete space. And in fact, the methods that they had would not have allowed them to have much interesting to say at that time. And finally, through sort of series of events that are kind of mildly interesting in my own life, I kind of came to realize how you could think about that in computational terms and how you could actually make all of that work.
One of the cautionary tales for me is this question about is matter even discrete or continuous? The people that argued about people like Ludwig Boltzmann had gotten, you know, had sort of said towards the end of the 19th century, he kind of said he believed very much in the atomic theory of matter. He was like, nobody else believes this.
He said you know i'm gonna write down what i have to say about this he says one man can't turn back the tide of history i'm gonna write down what i know about this so that when eventually this is rediscovered eventually people realize this is the right idea they won't have to rediscover everything well in fact it's kind of a shame because in 1827 i think
A chap called Robert Brown who was a botanist had observed this little pollen grains being kicked discreetly when they were on water or something and that it was realized eventually that that Brownian motion is direct evidence for the existence of molecules. So if Boltzmann had known the botany literature,
He would have known that in fact there was evidence for molecules that existed just those connections were not made and so for me that's a kind of cautionary tale because in modern times you know i think about what does it take to kind of find experimental implications of the kinds of theories that i've worked on and you know some of those things are difficult and it's like well.
Can i be two hundred years until we can do this kind of investigation or it might cost you know ten billion dollars to set up this giant space based thing but it might also be the case that in fact you know somebody in nineteen seventy two.
Observed exactly a phenomenon that is the thing that would be what i'm looking for and you know in fact one of the things that's kind of ironic and i've seen this bunch of times in my career in science is that when when people don't kind of have a theory that says how an experiment should come out and they do the experiment and the experiment comes out differently from the way they expected they say i'm not going to publish this this this must be wrong
And so a lot of things which later on one might realize were, you know, when you have a different theory, one might realize, gosh, you know, that experiment should have come out a different way. It got hidden in the literature, so to speak, which is, you know, this is a feature of the kind of institutional structure of science, but it's something where, you know, if one's lucky, some somebody would have said, sort of done the honest experiment and just said, this is what we found.
And, you know, even though this doesn't agree with the theory that so far we understand, so to speak. And so I've actually been using LLMs quite a bit to try and do this kind of thematic searching of the scientific literature to try and figure out whether you could save the $10 billion, not do the experiment now, but just use the thing that already got figured out. But, you know, I think in terms of, I don't know, my
Efforts to do science as i was saying i think one of the things that is sort of a critical feature is methodology and when new methodologies open up and sort of i've been kind of lucky to be alive at a time when kind of computation and computers first make it possible to do kind of experiments with computers so to speak and you know that that's what i built lots of tooling to to be able to do that
I think the thing that's always interesting about doing computer experiments, and particularly this is a consequence in the end of this whole computational irreducibility idea, is almost every computer experiment I ever do comes out in a way that I didn't expect. In other words, I'll do something, been doing some things even last week or so on a particular domain where, you know, I've got some theory about how it will come out, and if I didn't have any theory about how it would come out, I wouldn't do the experiment.
First point is the experiment has to be easy enough for me to do that on a whim I can do the experiment. It can't be the case that I've got to spend a month figuring out how to do the experiment because then I'm going to be really sure about why it's worth doing. You know it's got to be something where the tooling is such that I can do the experiment easily. I happen to have spent the last 40 years building that tooling and you know it's available to everybody else in the world too but you know I'm
As far as i'm concerned i'm the number one user of the language and so on as far as i'm concerned i don't i doubt i'm the person who uses it the most of everybody out there but i'm the user i care about the most and you know it's a very nice thing to be able to take an idea that i have and be able to quickly translate that into something where i can make it real and do an experiment on it.
So I have to have some idea about what's going to happen. I always wouldn't do the experiment, but then I do it. And the thing I typically say to people who work with me is we have to understand the computational animals are always smarter than we are. So, you know, do the experiment and you find things that you never expected to find. Why don't you give an example, a specific one? Yeah. I mean, so, so let's see, I was looking at, um, uh, almost any kind of simple program.
The question is what kind of thing can it do? For instance, let's say looking at simple programs that rewrite networks. You run the network rewrite thing, what's it going to do? Well, sometimes it builds this elaborate geometrical structure. I had no idea it was going to do that.
I thought it was going to make this messy kind of sort of random looking thing, but no, actually it builds this very sort of organized, elegant structure in some particular cases. Or for example, I've been looking most recently as it happens at lambda calculus, very old model of computation that I happened to have never studied before, but have a particular reason to be interested in right now. And it's a question of, okay, I'll give you an example that happened to me just a few days ago. So
This is a very simple specification, simple program. The program will run for a while. There are different versions of this program. You can enumerate lots of different possibilities. It will typically run for a while and, well, usually it will stop. Sometimes it won't stop. It will just keep going. Sometimes it will go on repetitively and so on. And so I was looking at a bunch of these things and I was wondering, you know, what's the maximum lifetime of one that eventually stops? So
I studied a bunch of different cases and there was, you know, I thought, okay, I found it. It's a lifetime. I don't know what, a few hundred or something. And I could find that with some simple experiments.
But then there was one where I started looking at it and I started looking at pictures of it and I'm, I'm kind of a seasoned hunter at this point for these kinds of things. And there was something about it that didn't seem quite right. There's something about one that, that I thought was going to just go on forever, but it seemed like it was doing things that might not allow it to go on forever. So I pushed it a bit harder, push it harder. Sorry. What do you mean by you pushed it harder? Push the program harder. Sorry. What do you mean by you pushed it harder? Push the program harder.
I let it run for longer. I let it run overnight on a network of computers. Okay. That's that's in practice what I did. Okay. And, you know, and then come back in the morning and oops, it ran for some 10s of 1000s of steps and then stopped. Never expected that. And then there was another one where I having seen that particular phenomenon, there was another one where I kind of suspected this one is going to stop. And that one stops after some
Well, let's see that the that one I kind of found a method for figuring out how it what how it will develop and that one I think stops after a few billion steps. So these are things you just you don't expect and you know that that's this is very typical of what happens in this computational universe. It's a bit similar to the physical universe. There are things that happen in the physical universe you don't expect.
But it's particularly sort of in your face when you can see, you know, in the physical universe, you don't necessarily know what the underlying rules for something are. So you could always be wondering, you know, do I just not know enough about how snowflakes form or something? But in this case, you know, the rules, you know exactly what went in and yet you'll sort of have this forced humility of realizing you're not going to be able to figure out what happens. And sometimes you'll be quite wrong and you'll guess about what's going to happen. So, you know, one of the principles in doing that kind of science is, you know,
You'll do these experiments, they'll often come out in ways you don't expect. You kind of just have to let the chips fall where they fall, which is something in doing science that can be psychologically very difficult. You know, you had to have had some kind of theory that caused you to start doing the experiment.
I was you wouldn't have done the experiment and so there's a there's a something of a psychological pressure to say look I have this theory this theory has to be correct you know something went wrong with my experiment you know let me tweak my experiment let me you know ignore that part of the experiment or something and because I'm sure this theory must be correct one of the things that you know is a is a an important thing that I kind of learned long long long ago now is just let the damn chips fall where they'll fall
And turns out one of the things that's happened to me is sometimes some of the sometimes these chips fall in places that very much violate various kinds of prejudices that i have and it's just like i'm more interested in where the chips actually fall than in supporting some prejudice that i have and as it turned out in the end what's happened is
Sometimes several years later i realize actually the way those chips fell was more consistent with my prejudice than i could ever have imagined so i give you an example so a lot of things i've done have been so deeply deconstructive of the way the universe works.
That is, they're very non-human interpretations of what goes on in the universe and so on. They're very, you know, the universe is some giant hypergraph of things. It's a very kind of humanly meaningless object. It doesn't have sort of a resonance with kind of our human sensibilities and so on. It's deeply abstract, sort of deeply deconstructed in a sense.
And yet, as a person, I'm quite a people enthusiast, you know, I like people, I find people interesting, I work with people, I've been a company full of people and so on. And so for me, it was always something of a conflict that on the one hand, I'm interested in people. On the other hand, the things I'm doing in science are deeply deconstructive of anything sort of human about what's going on in science.
And that was the situation in my world for a couple of decades and then i realized more recently that the nature of the observer is actually critical in the end in the end sort of the ideas about the really add and so on the kind of entangled limitable possible computations that sort of the ultimately deconstructed dehumanized thing.
But what you can then realize what i realized eventually is how a perception of the laws of physics depends critically on our nature as observers within the really add another words from going from a completely dehumanized view of science.
That is this totally abstracted really add turns out the humans are actually really important and giving us the science that we have so from you know even though i was sort of unhappy and some in some sense of some sort of psychological prejudice i was unhappy with the idea that everything is deeply dehumanized cuz i kind of like humans in the end couple of decades later i realize actually the humans are kind of much more at the center of things than i had ever expected
So that was kind of it was kind of an interesting realization. Another one like that that I resisted for a long time is these things I call multiway systems, which I had invented back in the early 90s and which I had thought by the late 90s, that was a possible view of how quantum mechanics might work, that there are these kind of many parts of history and that are being followed by the universe and so on. I really resisted that idea because I felt sort of egotistically
I didn't want it to be the case that there were all these different possible paths of history and the one I was experiencing was just one of those paths or something like that. That was the assumption that I had made about what the Ford Blue Cruise hands-free highway driving takes the work out of being behind the wheel, allowing you to relax and reconnect while also staying in control.
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2019 was that in fact that isn't the right picture that the idea of multi-way systems and the idea that there are these many parts of history that's the right story but the thing to realize is we are embedded as observers in this universe that is branching all the time and the critical point then is that we are branching as well
So this from this idea from this first sort of naive idea that when you have something we have many branches of history that our sort of our experience must just go down one branch that's really not the right picture actually there are a couple of issues one is that the branches can merge and the other is that our our experience.
Can span many branches we are we are extended objects mines are extended objects in this branch real space in the space of these possible branches i didn't realize that until until twenty nineteen and that makes it that that means that my sort of ignoring multi-way systems.
for you know close to 30 years as it was a was a piece of sort of incorrect prejudice and i was kind of lucky enough that eventually kind of started thinking about look might as well try and take this seriously and uh and see how and see what its consequences actually are was something actually jonathan gorard was was one of the people who's like you should take these more seriously i'm not sure he saw what what the outcome would be but that was a you know why are you resisting this so much
It's it's pap say so that that was some but so so it's a it's it's always an interesting thing when you have kind of this the sort of you have to have a belief about how things are gonna work otherwise you don't even look there. Would you have to you know just believe the experiments are doing all the things you figure out i mean for me if i was doing i mean back in the day when i.
Was long ago when I was sort of first doing physics I worked a bunch with Dick Feynman who was a you know physicist who one of his great strengths was he was a really good human calculator and I Can't do that. I'm I'm a good computer calculator, but not a good human calculator I built these computer tools because I wasn't a very good human calculator but Dick Feynman was really good at doing these calculations and getting to the right answer and
And then he would go back and say he didn't think anybody would be impressed by the fact that he got to the right answer by doing this complicated calculation.
He thought people would only be impressed if he could come up with this really simple kind of intuitive explanation of what was going on, which he often managed to come up with. Then he would throw away the calculation, never tell anybody about the calculation. And everybody would be like, how did you possibly figure out this kind of intuitive thing? And they'd all think, oh, it must be simple to come up with this intuitive thing. It wasn't simple. It was the result of some long calculation, which he didn't think anybody would be impressed with because he found it easy to do those things.
No i don't know for me the only kind of thing where i know i know what i'm talking about is i do a computer experiment it comes out in a certain way the computer does what the computer does there's no kind of sort of i might have made a mistake somewhere type type situation. I think if i look at the kinds of things that i've tended to do in science they sort of mix what one might think of is kind of philosophy and what one might think of is this kind of very detailed kind of solid
computational experiments and so on i mean that turns out to be for me has been sort of a powerful methodology for dealing with things to go from on the one side sort of a a general almost philosophical understanding of of how things might work.
And then sort of the challenges to be able to sort of think computationally fluid fluently enough that you can go from that sort of philosophical understanding to say okay here's the program i should run that is a manifestation of that philosophical understanding and then let's see what it actually does and then i don't have to worry am i getting it wrong because the program just does what the program does and it's kind of uh you know i i find it
Kind of charming when my new kind of science book came out in 2002, people saying, but it's wrong. It's like, what does that mean? What about it was wrong? I don't know. But I mean, it's it's kind of people assume that you could sort of have got the wrong answer by doing the wrong calculation or something. But this is the nature of computer experiments. You just
You know you specify the rule you run the program the program does what the program does there's no you know no humans are involved no possibility of error exists so to speak. You can be wrong in the interpretation of what what's happening you can be wrong in the belief that what's happening in the computer experiment is relevant to something else but the actual experiment itself.
Just is what it is now you can you can be confused i will say and the number one source of confusion is when people don't look at everything that happens in the experiment. So people say is a certain tendency and science people have had this idea that you know being scientific is about generating numbers.
And so one quite common type of mistake is to say, well, you know, there's a lot of detailed stuff going on underneath, but I'm just going to plot this one curve as the result. And that means that you don't really get to see sort of the detail of what's happening. You're just seeing this one sort of summary.
And sometimes that one summary can be utterly confusing. It can just lead you into the into kind of the thinking the wrong thing. And so for me, you know, being able to have sort of the highest bandwidth thing that I think we have to kind of understand what's going on is our visual system and being able to sort of visualize what's happening in as much detail as possible. I've always found very important. And often when I do projects, in fact, I just got bitten with this in the very latest project that I was doing.
I always try to make sure that i front the effort to make the best possible visualization because if you know the the thing that one does that's a mistake is to do the project with kind of crummy visualizations and then say now i'm going to present it i'm going to make a really good visualization then you do a really good visualization and then you're like oh gosh there's something i can now see i didn't see while i was doing the project.
Just a little bit bitten with that because there was a particularly complicated kind of visualization that I didn't go to the effort to make quite early enough in the project that I'm currently doing and so I'm just having to redo a bunch of things because I realized that I can understand them much more clearly using this sort of more sophisticated visualization technique but that's a you know that that's kind of you know you have to that's that's just one of these things when you kind of start
sort of thinking computationally about things. This idea that you can see as deep into the computation as possible is important rather than saying all I care about here is this thing of plotting this one curve because that's what scientists have done for the last couple of hundred years. I think one of the things I realized only very recently about my own personal sort of scientific journey
is back in the early eighties, I started doing a bunch of computer experiments, visualizing sort of the computations that were going on, figuring things out from that. And for me, doing that in 1981 or something like that was completely obvious. It was like, how could you ever not think about doing something like that? But I was was the question was, why was that obvious to me? And it turns out what I had been doing for several years previously,
was building my first big computer system, which was a system for doing algebraic computation, symbolic algebraic computation. And I had gotten into doing that because I was doing particle physics. In particle physics, one of the things you get to spend a lot of time doing is computing Feynman diagrams. Feynman diagrams are this way of working out, well, actually this S matrix thing that I'd mentioned earlier, a particular way of doing that that's sort of the best way we know to do it.
I have to say as a footnote to this, Dick Feynman always used to say about Feynman diagrams, they're kind of the stupidest way to do this kind of calculation. There's got to be a better way, he said. I remember one time telling him, if you work out a series that you generate of Feynman diagrams, and I think at the kth order in these Feynman diagrams, I'd worked out that the computational complexity of doing these Feynman diagrams went like k factorial to the fifth power.
So as you go to higher orders, it takes unbelievably much more difficult to work things out. And so I was telling the studio five minutes like, yeah, this is a stupid way to work things out. There's got to be a better way to do it.
We haven't known what that better way is i'm sort of excited right now because i finally think i understand kind of in a bigger more foundational picture of what Simon diagrams really are and how one can think about them in a way that does allow one to sort of go underneath that formalism and potentially work things out in a much more effective way.
It's it's i mean to serve it's a it's a spoiler for some things i'm still working on but but i'm essentially in five diagrams you're drawing these diagrams that sort of say an electron goes here and then it interacts with the photon and then the photon interacts with the electron and so on it's a diagram of sort of interactions.
What i realized is that really those diagrams are diagrams about causality the diagrams that show the lines that represent his electron really the electron is basically a carrier of a causality there's a there's an event that happens when electron interacts with the photon and that event has a causal effect on some other event and that causal connection is represented by this this this electron line so to speak in this Feynman diagram
That way of understanding things allows want to connect sort of fine diagrams to a bunch of things that have come up in our physics project to do with things we call multi-way causal graphs and there's a whole rather lovely theory that's starting to emerge about these things but haven't figured it all out yet in any case that that's a that's a relevant side show but but back in the in the late seventies i was.
Trying to get computers to do these very ugly nasty calculations of Feynman diagrams because that was the only way we knew to work out the consequences of quantum field theory in those days, particularly QCD, which was a young field in those in those days. And I as the teenage me had the fun of being able to work out a bunch of calculations about QCD sort of for the first time. And now that they're well known sort of classic kinds of things, but then they were fresh and new because it was a new field.
but it was one of the things that had happened was I had built a bunch of capability to do symbolic computation algebraic computation and one of the features of doing algebraic computation is that you don't just get a number as the answer if the answer to your calculation if the computer spits out 17.4 there's not a lot you can do with 17.4 there's not a lot of intuition you can get from 17.4 on its own
But when the computer spits out this big long algebraic expression, it has a lot of structure. And one of the things that I had kind of learned to do was to get intuition from that structure.
And so, for example, if you're doing, I don't know, you're doing integrals, let's say, I've never been good at doing integrals by hand, but I became what I learned from doing thousands of integrals by computer was kind of things about the intuition about the structure of what happens in integrals. And that allows one to kind of make to sort of jump ahead and see this complicated integral. It's going to be, I think, intuitively roughly of the structure. And that's a big clue in actually being able to solve the thing on a computer.
So the thing i realized only very recently is that my kind of experience and doing other great computation i got me used to the idea that what a computer produces will be a thing that has structure from which you can get intuition.
and so when i started thinking about sort of actual simple programs and what they do it was sort of obvious to me really was obvious that i should just make some sort of visualization of what all the steps that were going on were because i expected to get intuition from kind of the uh the the innards of the computation so to speak you know however you know one of the things that i will say is that i started studying in that case cellular automata
Back in 1981 and I found out a bunch of things about cellular automata I thought they were pretty interesting and I had generated back in 1981 I generated a picture of this thing called rule 30 which is a particular cellular automaton that's been my all-time favorite and it has the feature that has very very simple rule but you start it off from just this one black cell it makes this complicated pattern many aspects of that pattern look for all practical purposes random
It's something that i my intuition back in nineteen eighty one said couldn't happen it said if the rule was simple enough there will be a trace of that simplicity in the behavior that's generated and so when i can i generated a picture of rule thirty even put it into paper i published but i didn't really pay attention to it because i my intuition was so strongly nothing like that can happen.
then actually sort of methodologically amusing i think june of 1984 i happened to get a high resolution laser printer they were a new thing they were big clunky objects at that time and i thought i was i was going to go on some plane flight and i thought i'll make some some cool pictures for my new laser printer so i printed out will 30 at high resolution and took it with me and i'm starting to look at it and it's like hmm what on earth is going on here
I finally sort of really started well actually some other things that happened I also studied other aspects of cellular automata and how they related to theory of computation and so on and that kind of primed me for really looking more seriously at this picture and realizing oh my gosh this is something that is completely violates the intuition that I've always had
That you know, to get get something complicated, you need complicated rules in some sense, or you need a complicated initial condition. This is something new and different. And it is kind of amusing that I realized that it really at that point, I was primed enough from the other things I'd studied particularly about computation theory, that it only took a couple of days before I was like, sort of telling people about I was I was going to some conference and actually the the I found recently there was a transcript of a q&a session at that conference, where I'm kind of
Talking as if I'd known it forever about, you know, how rules 30 works and so on. Um, but actually I'd only known it for two days, but, um, but an important point about that was first of all, it's something I had kind of quotes discovered, but I had not understood. I had not internalized it, but to be able to internalize it required me to build up a bunch of other contexts to
From studying well a bunch of things about cellular automata a bunch of things about computation theory given that priming I was able to actually understand this point about what rule 30 is and what it means
Now, for example, this phenomenon of computational irreducibility, I'm now at sort of 40 years and counting since I came up with that idea. And I'm still understanding what its implications are for whether it's for, you know, AI ethics or for, you know, proof of work for blockchains or a whole bunch of different areas. I'm only now understanding. In fact, the thing that I've understood most recently, I would say, is that I think a lot of the story of physics
is a story of the interplay between computational irreducibility and our kind of limitations as observers of the world.
But it's it's something like, for example, the simplest case is the second law of thermodynamics, where kind of the idea is you got a bunch of molecules bouncing around and the second law says most of the time it will seem like those molecules get sort of more random in the configurations that they that they take on. And that's something people wondered about since the mid eighteen hundreds. And I had wondered about it was one of the first things I got really interested in in physics.
Back when i was twelve years old or so was this phenomenon of how does randomization happen in the second law and what i finally have understood is that it happens because the sort of computational irreducibility of the underlying dynamics of these molecules bouncing around yet that sort of there's an interplay between that.
And the sort of computational limitations of us as observers because we as observers aren't capable of sort of decrypting the sort of computation that happened in this underlying struck in his underlying collisions. We just have to say oh it looks random to me so to speak and so that this phenomenon of computational irreducibility.
So at 40 years and counting, I'm still understanding sort of the implications of this, this particular idea. And I mean, another thing to say about sort of the progress of science, which I can see in my own life. And I can also see from history of science is it can take a long time. Once you've, once you've had some sort of paradigmatic idea, it can take a long time for one to understand the implications of that idea.
And I know for things I've done, I'm fully recognized the fact that it's taken me sometimes 30 years or more to understand what it was that I actually really discovered. It's kind of like if other people don't figure it out for 50 or 100 years, that's kind of par for the course because it took me 30 years to figure out what the significance of this or that thing was. I mean, I see that also in technology development.
I'm building open language we build certain paradigmatic ideas we have certain ideas about the structure of what can be in the language and then it can sometimes take a decade before we really realize given that structure is what you can build.
You kind of have to get used to the ideas you have to grind around the ideas for a long time before you kind of get to the next step and kind of seeing what what's possible from them. I kind of see it as sort of this tower that one's building of ideas and technology in that case and the the higher you are on the tower the further you built up on the tower the further you can see into the distance so to speak about what what other things might be possible. I think sometimes when when people sort of hear about
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And just figures out some big thing. Right. My own efforts in studying history of science and my own experience in my life doing science is that simply never happens.
It's this basically many years usually a decade of build up to whatever it is one is going to potentially discover i mentioned i mentioned what happened with me and will thirty once i was adequately primed. It was kind of like it all happened very quickly but that priming took many years.
And that's the thing that usually what's reported in the story book so to speak is only that final moment. After that priming of when you realize well actually this fits together in that way and so on and you can then describe the what happened when things i learned about about einstein recently was that in nineteen oh four he'd written several papers.
About a very different subject written a bunch of papers about thermodynamics particularly about the second law of thermodynamics. I think it was much influenced by Boltzmann who was a very philosophically oriented physicist person who sort of believe you could figure out things about physics just by thinking about them in the style of natural philosophy so to speak.
rather than sort of being driven by, you know, experiment to experiment type type thing. And Boltzmann figured out a bunch of things about atoms. Boltzmann had basically had the core ideas of quantum mechanics, although of discreteness and so on. That's what Planck picked up when he studied the black body radiation problem. But in any case, you know, at the time, 1904 was still very much, can you prove the second law of thermodynamics?
from some underlying principles that did not just sort of introduce the second law as a law of physics. Could you prove it from some underlying mechanical principles? And many people have been involved in that. Planck was trying to do that actually. Planck was trying to do that when he discovered the quantum mechanics, the way of sort of understanding black body radiation in terms of discrete photons. I mean that's a weird story because Planck, people had wondered why
Do things get more random and they kept on saying to get randomness you have to have some magic source of randomness so planks idea was that infrared radiation, radiative heat would be sort of the magic source of randomness that would sort of produce heat and everything and lead to that randomness.
And so he was actually studying that question when experiments came out about black body radiation and he then noticed that these calculations that Boltzmann had done a lot earlier when Boltzmann just as a matter of sort of mathematical convenience had said let's assume energy is discrete.
and had then worked out what the consequences of that were. Planck said, well, actually, if you say that energy really is discrete, you fit this data a lot better than otherwise. It took Planck another decade to actually believe that this was more than just a mathematical trick. Einstein was the one who really sort of said photons might be real a few years later. But in any case, the thing was interesting there was that Einstein was using kind of this natural philosophy, philosophical approach to science,
as a way to think about how things might work and he tried applying that to thermodynamics in 1904 and he didn't get it right you know he didn't figure out the second law he i mean in as we now know sort of the paradigmatic ideas that you need to figure out the second law come from ideas about computation and so on which were another close to 100 years in the future so to speak um but it's sort of interesting that that you know he was applying those kinds of philosophical thinking ideas
And it was a misfire in thermodynamics. It was a hit in in relativity in the photoelectric effect and the existence of photons and also in Brownian motion. But it's kind of it's sort of it's an interesting sort of footnote to the history of science. I think you know another another point that one realizes there is there are things that there is an ambient level of understanding that will allow one to make progress in that area.
And there are things where there isn't. And in fact, Einstein himself, I think 1916, he wrote to somebody, you know, in the end, space will turn out to be discrete. But right now we don't have the tools necessary to see how this works, which was very smart of him. I mean, that was that was correct. You know, it took another hundred years to have those tools. But it's it's a thing where when you think about science, another issue is
Are you at the right time in history, so to speak, is the is the ambient sort of understanding of what's going on sufficient to let you make the progress you want to make. So one area I've been interested in recently is biology, where biology has been a field which is really hasn't had a theory. You know, the the best the closest one gets to a theory in biology is natural selection from 1859. And, but that's a very
It's you know if we say well why is biology the way it is you know is there when we look at a biology textbook what's the theoretical foundation of a biology textbook we haven't known that and the question is can there be a theory of biology most biologists don't really imagine there's a theory of biology they just say we're collecting this data we do these experiments we work out these probabilities of things if you're doing medicine that's the typical approach
Sorry, why wouldn't computational irreducibility come into play in the biological case for you to say or for a biologist to say that that's the reason why there is no toe for biology?
that's uh there's some truth to that that's um am i jumping ahead no no that's good good inference i mean that that's um uh i think the reason that biology looks as complicated as it does is precisely because of computational irreducibility and the thing that surprised me this is another sort of story of my my life in science i'd worked on cellular automata back in the early 80s
It's kind of funny fact is that this was days before the internet so you couldn't look things up as easily or at least before the web and you know people had seen that I was working on cellular automata so I got invited to all the theoretical biology conferences because I figured this must be about you know
Biological kinds of things and it was kind of funny because I'm looking back now. I was kind of the period in the 1980s was a time when there was sort of a burst of somewhat interest in theoretical biology. They've been another one in the 1940s and it kept on sort of dying off. But in 1980s there was something of a burst of interest.
I realized that somebody was working with me now who's a biologist sort of went back and looked at some of these conferences and things and I keep on saying I don't really know much about biology and he kept on saying but you were there at all these key conferences just a moment it's one thing for you to be invited because of cellular automata it's another thing for you to accept it why did you go if you thought hey this is irrelevant oh no I didn't think it was irrelevant I thought it was interesting I just I just
You know had a certain I see and you know when I talked about cellular automata there and talked about the way that cellular automata are relevant to things like the growth of organisms and so on I thought it was interesting and other people thought it was interesting and it turned into a whole sort of subfield of people studying things but I didn't think when it comes to sort of the foundations of biology and things like you know why does natural selection work I didn't think it had anything much well I wasn't sure if there's anything to say about that
back in mid eighties i tried to see if it had something to say about that i tried to see if you could take cellular automata which have these definite little underlying rules those little underlying rules a little bit like genomic sequences so to speak which where the genome is specifying the rules for building an organism
So in a cellular automaton, you can think about it the same way. The underlying rules specify the rules for building these patterns that you make and cellular automata. So the obvious question was, could you evolve those rules like natural selection does? Could you make mutations and, you know, selection and so on on those underlying rules? And could you see that produce some sort of interesting patterns of growth? So I tried that in 1975.
I didn't find anything interesting
Instead of looking and it's not going to work so well with something as simple as rule 30 because you can go from rule 30 to rule 31. Rule 30 and rule 31 behave very, very differently.
By the time you're dealing with sort of rule a billion rule a billion and one potentially doesn't behave really that differently from rule a billion because many of the cases in the rule the rule is saying if you see right near where you are if you see you know cells red green white then make this and you know by the time you've got enough colors and so on you can imagine that you know some of those particular rules won't even be used when a typical pattern is produced
It'll be something so you can make some small changes to those rules and expect that maybe it won't make a huge change in what comes out. At least in the short run. Yes. Yes. I mean, well, that's a complicated issue because it depends on what what what subpart of the rules you end up selecting and as you make this pattern. I mean, it's usually the case that when you make you produce some pattern from a cellular automaton, for example, every little subpatch
Is typically using only some small subset of the rules and it's using them in some particular way and it maybe makes some periodic little sub patch and then something comes along that's using some different part of the rule sort of crashes into that and destroys that that the simplicity that existed in that region.
So it's a you're in the case of rule thirty there are only eight cases in its rule so you don't get to making any change to that is a big change. By the time you have something with like say twenty seven cases in its rule you can kind of imagine you're making a little change in that is less significant to the to the behavior that will occur.
But okay back in mid 1980s I tried this I tried looking at slightly bigger rules and making small changes and find anything interesting regarding biology yes okay yes no I was interested in kind of a model for natural selection it was a time when artificial life was first being talked about.
And worked on and, you know, Chris Langton had done a bunch of nice stuff with, with my cellular automata and thinking about artificial life and so on. And it was, it was seen sort of the obvious thing to do, but it didn't work. And so for years I was like, it's just not going to work. And then last year, actually, I was interested in the foundations of machine learning and the big lesson of machine learning that we particularly learned in 2011 was
In a neural net, if you bash it hard enough, it will learn stuff. And, you know, that wasn't obvious. Nobody knew that you could get sort of a deep learning, you know, a deep neural net to recognize images of cats and dogs and things. And so by accident, it was discovered that if you just leave the thing learning long enough, if you leave it training long enough, it comes out and it's actually succeeded in learning something.
So that's a new piece of intuition that you can have a system like a neural net. Neural net is much more complicated than one of these cellular automata. You can have a system like that. You just keep bashing it, you know, a quadrillion times or something, and eventually it will successfully achieve some fitness function, will learn something, will do the thing you want it to do. So that was a piece of new intuition.
So i thought let me just run these you know this i was writing something about actually foundations of machine learning and i thought let me just try this experiment that i you know i i thought i tried it in the 1980s and it hadn't worked but now we know something different from our studies of neural nets let me try running it a bit longer and by golly it worked
And I felt a bit silly for not having discovered this anytime in the intervening, you know, 40 years or something. But nevertheless, it was really cool that it worked. And that meant that one could actually see for the first time kind of sort of more in a more detailed way how natural selection operates. And what's really going on in the end is computational irreducibility lets one go from these quite simple rules, these very elaborate kinds of behavior.
The fitness functions, the things that sort of are determining whether you survive or not, those are fairly coarse in biology. But let's imagine you have a coarse fitness function, like what's the overall lifetime of the pattern before it dies out? Let's say, how wide does the pattern get? You're not saying how it gets wide, you're not saying particularly, but you're saying how wide does it get? It turns out with those kinds of coarse fitness functions, you can successfully achieve high fitness
But you achieve it in this very complicated way you achieve it by sort of putting together these pieces of irreducible computation and that that means that so so in the end the answer I think to why does biological evolution work is that it is the same story as what happens in physics and mathematics actually it is an interplay between underlying computational irreducibility and the computational boundedness of quotes observers of that of that computation.
So in the case of physics, the observers are us, you know, doing experiments in the physical world. In the case of mathematics, it's mathematicians looking at the structure of mathematics. In the case of biology, the observer is kind of the environment. It's the, it's the fitness function. The fitness function is kind of the, the analog of the observer and the fitness function is saying you're a success if you achieve this kind of course objective.
And the reason that biological evolution works is that kind of there's so much power in the underlying irreducible computation that you're able to achieve many of these kind of course fitness functions. So, you know, if you imagine that the only way I don't know, an organism could survive is if it breaks out of its egg and immediately it, you know, computes a thousand primes and does all kinds of other weird things.
Right it's not going to survive that right it's so i was born as one does but you know that that's one isn't going to be able to hit that particular very complicated target. What actually happens is the fitness functions are much closer than that and that's why biological evolution has been able to work.
But if you say, well, what's actually going on inside? What's going on inside is big pieces of computational irreducibility being stuck together in a way that happens to achieve this course fitness function. By the way, this is the same thing as what's going on in machine learning, I think. In machine learning, it's the same story that the fitness function in that case is you're trying to achieve some training objective. And you do that by sort of fitting together these lumps of irreducible computation
I've kind of the analogy i've been using is it's kind of like building a stone wall you're doing kind of precise engineering you might build a wall by making precise bricks and putting them together in a very precise way but the alternative is you can make a stone wall where you're just picking up random rocks off the ground and noticing well this one more or less fits in here let me stick that in that way and so on
That's what's going on in machine learning you're sort of building the stone wall if you then say well why does this particular feature of this machine learning system work the way it does it's well because we happen to find that particular rock lying around on the ground because we happen to go down this particular branch in the random numbers that we chose for the training and so it's kind of an assembly of these random lumps of irreducible computation that's what we are too.
And that's, I mean, in biology has, you know, the biology, the history of biology on earth, you know, the dice has been rolled in particular ways. We have stuck together these sort of lumps of irreducible computation and we make the organism that we are today. There are many other possible paths we could have taken, which would have also achieved a bunch of fitness objectives, but it was just a particular historical path that was taken. One of the things that's kind of a,
A sort of slightly shocking thing to do is to take one of these evolved cellular automata that looks very elaborate, has all these mechanisms in it, has all these patches that do particular things and that fit together in these interesting ways and so on. And you ask an alum, say, write a description of this pattern in the style of a biology textbook. And it's kind of shocking because it sounds just like biology.
Because it's successfully describing, you know, there's a, there's a, I don't know, it makes up sometimes it can make up names for things, you know, there's a, there's a distal, you know, distal triangle of this and so on. And, you know, interacting with this and this and this, and you go and you open a biology textbook and it
It's a description in biology and the detail in biology is a description of this particular sort of sequence of pieces of computational irreducibility that got put together by the history of life on earth and that make us as we are today. Now, you know, since we care about us,
It's very worthwhile to study that detailed history that detailed lump of computational reducibility that is us. But if you want to make a more general theory of biology, it better generalize beyond the details of us and the details of our particular history. And the thing that I've been doing actually most recently is I think we are the beginnings of finding a way to talk about
sort of any system that was adaptively evolved. So in the case of the, if we look at all possible rules that could be going on in biology, many of those rules won't be ones that would have been found by adaptive evolution with course fitness functions. So a way to think about this, so the thing
We look at sort of what is biology doing? Biology is, if nothing else, a sort of story of bulk orchestration and molecular processes. There are all these, you know, one might have thought at some time in the past that biology is sort of just chemistry. And by that I mean, in chemistry, we sort of imagine we've got liquids and so on, and they just have molecules randomly bumping into each other. But that's not what biology mostly seems to be doing. Biology is mostly a story of detailed assemblies of molecules that orchestrate
You know this molecule hooks into this one and then does this and et cetera et cetera et cetera and so discoveries in microbiology keep on being about how orchestrated things are not how this molecule randomly bumps into it's sophisticated. Yes but it's also has the sort of mechanism to it it has a it's not it's not just random collisions.
It's this molecule is guided into doing this with this molecule and so on it's a it's a big tower of things a bit like in these evolved cellular automata which also do what they do through this big tower of detailed sort of applications rules and so on.
But so what I'm extra value meals are back. That means 10 tender juicy McNuggets and medium fries and a drink are just $8 only at McDonald's for limited time only prices and participation may vary. Prices may be higher in Hawaii, Alaska and California and for delivery is to have a theory of bulk orchestration.
That's something that can tell one about what happens in any system that is sort of bulk orchestrated, which can include things like a microprocessor, let's say, which has its own sort of complicated set of things that it does. A microprocessor is not well described by the random motion of electrons. It's something different from that.
What is it what can you and does that theory that you make of the microprocessor depend on the details of the engineers who who designed it or is there other necessary features of any system that has been built to achieve certain course grained purposes. So i'm sort of i'm going down this path we're not there yet i mean the the sort of in physics the idea of statistical mechanics is an idea that like this.
The idea of statistical mechanics is once you have enough molecules in there, you can start making conclusions just by looking at averages based on what all possible configurations of molecules are like without having to have any details about the particulars of these collisions and those collisions and so on. The statistics wins out. There's lots of stuff there is more important than the details of what each of the pieces of stuff does.
In the case of biology there's one additional thing you seem to know which is that the stuff you have has rules that was subject to some kind of adaptive evolution and. Even though we don't know what the purpose was you know when when people use sort of natural selection as a theory in biology they look at some weird creature and they say it is this way because.
And sometimes that because is less convincing other times but that's been the model of how one makes a theory in biology and so i think the this this is what i'm what i'm interested in is there a theory that's independent of what the because it's just that there is a because.
is that there is some coarse-grained fitness that's achieved is that enough of a criterion to tell you that something about sort of this bulk orchestration this limit of a large number of things subject to that kind of constraint actually I figured out something this morning about this we'll see whether it pans out but I have one of the things that's really really striking about
These kinds of systems where one has done this kind of adaptive evolution on large scale is you just make pictures of them and they just look very organic strange thing.
I think I have an idea about how to sort of characterize that more precisely that may lead one to something that is sort of a I mean what one's looking for is something that's kind of a theory of bulk orchestration has for example information theory has been a general theory of just sort of the the the all possibilities type situation with with with data or with statistical mechanics and so on so you know this is but but sort of it's interesting methodologically perhaps
When I'm working on something like this, it's, it's an interesting, for me, it's an interesting mixture of thinking quite sort of philosophically about things and then just doing a bunch of experiments, which often will show me things that I absolutely didn't expect. And I suppose there's a third branch to that, which is doing my homework, which is, okay, so what have other people thought about this? And that what I found is that, you know, when I try and learn some field, I, I
often spend years kind of accumulating knowledge about that field and i'm you know i'm lucky enough i bump into the world experts on this field from time to time and i'll ask a bunch of questions and usually i'll be kind of probing the foundations of the field and one of the things i learned about sort of doing science is you might think you know the foundations of a field are always much more difficult to make progress in than some detail you know high up in the tree of that field
This is often not the case particularly not if the foundations were laid down fifty or hundred years ago or something because what's happened is you know what you will you discover is when you talk to the people sort of in the first generation of doing that field of science you say what about these foundations.
They'll say good question we wondered about that we're not sure those foundations are right you know it's a truth and now you go five academic generations later and people say of course those foundations are right how could you possibly doubt that you know it just becomes that building on top of this you know this thing that's far away from the foundations. Well often the foundations are in a sense very unprotected nobody's looked at them for decades maybe longer.
And often the ambient methodologies that exist have changed completely in modern times and things i've done a lot sort of the computational paradigm is the biggest such change and then you go look at these foundations and you realize gosh you can actually say things about these foundations which nobody has even looked at for ages because they were just building many layers above those foundations
And so I think that's, that's been one of the things I've noticed. And it's one of the, you know, for people who are sort of doing science and they want to make some, some progress in some particular area, it's like, well, what is the foundational question of this field? And, you know, sometimes people will sometimes have to think about that quite a bit, you know, what really is the question that we're really trying to answer in this field, foundationally, not the thing that is the latest thing that the latest papers we're talking about and so on, but what it really is the foundational question.
And then you say what can you make progress on that foundational question and quite often the answer is yes and even the effort to figure out what the foundational question is is often very useful but by the way when you do make progress on a foundational question the kind of the trickle down into everything else is dramatic.
Although often the stream trickles in a different area than the existing stuff have been built so in other words you make progress on the foundations and now there are new kinds of questions about that field that you get to be able to answer even if the existing questions you don't make very much progress on they've been well worked out by the existing foundations you make new foundations you can you can kind of answer a new collection of questions.
So i think the i mean that that's a that's a sort of a typical pattern and for me kind of this this effort you know i tend to try and sort of ambiently understand some field for a while and then i'll typically form some hypothesis about it hopefully i'll be able to turn it into something kind of computational.
And then i can do the experiments i can know that i actually getting the right answer and so on and then i try and go back and often at that point i'll try and understand how does this relate to what people thought about before and sometimes i'll say wow that's a great connection you know this person had figured out this thing you know fifty years ago that you know was pretty close to what i was talking about now.
i mean like the idea of computational irreducibility for example once you have that idea you can go back and say well when did people almost have that idea before like newton for example had this statement that you know he'd worked out celestial mechanics and calculus for the motion of planets and uh he made this the statement that um uh you know what did he say he said something like um to work out the motions of all these different planets
Is beyond he said if i'm not mistaken the force of any human mind. So he had figured out that there would be no even though in principle he could calculate the motions these planets he very diplomatically said it was in his time it was beyond the force of any human mind.
The force of the mind of god would be capable of working it out because after all the planets were moving the way they were moving but that was sort of a an interesting precursor that you can go back and see that that already you know he was thinking about those kinds of things but but sometimes and sometimes you find that people just utterly missed something that later seems quite obvious like for example one of the things in physics
Is the belief in space time the belief that time is something very similar to space which is something that's been quite pervasive in the last hundred years of physics and i think it's just a mistake i think einstein didn't really believe that.
The person who brought in that idea was Herman Minkowski in 1919 who kind of noticed that this thing that Einstein had defined this kind of distance metric the proper proper time was you know t squared minus x squared minus y squared or whatever I see squared and Minkowski was a number theorist and he'd been studying quadratic forms.
Sums of things with you know with squares and so on it's like this is a quadratic form it's so great and look you know time enters into this quadratic form just like space let's bundle these things together and talk about space time.
and i think that was as i said i think that was a mistake i think that misled a lot of people i think my own view and which is pretty i think it's clear this is the way things work is that sort of the nature of space as kind of the extent of some data structure effectively some hypergraph for example that the nature of that is very different from the kind of computational process that is the unfolding of the universe through time
The fact that those things seem very different at first, it's then a matter of sort of mathematical derivation to find out the relativity works and makes them enter into equations and things together, but that's not their underlying nature.
But but in a case that's a it's a thing where where in that particular case it was you know that was a thing where when you go back and look at the history you say why do people believe in space time why do people believe that space and time the same kind of thing you eventually discover that piece of history and you say they went off in the wrong direction sometimes you're like wow they figured it out you know they really were in the right direction and or they were in the right direction but they didn't have the right tools or whatever else and for me that's a very important grounding
To know that i know what i'm talking about so to speak like recently i was studying the second law of thermodynamics i finally think after sort of 50 years of thinking about it that i finally nailed down how it really works and how it sort of arises from this kind of interplay between computational irreducibility and our nature as observers and i was like let me let me really check that i'm actually getting it right and you know i've known about the second law for a very long time and the second law is one of these things which in a textbook
You'll often see the textbook say, Oh, you can derive that entropy increases. You say, well, actually you can flip around this argument and say that the same argument will say that entropy should decrease. And the, you know, my favorite is the books where the chapter ends. This point is often puzzling to the student. It's been puzzling to everybody else too.
But you know the question was why did people come up with the things they came up with in this area and so i want to kind of untangle the whole history of the second law which i was surprised nobody had written before although after i figured it out i wasn't surprised cuz really complicated and it requires kind of understanding various points of view that people had that little bit little bit tricky to understand.
I think you know in the end i feel very confident that you know how what i figured out fits into what people have known before what people have sort of be able to do experiments on and so on and that's a you know that for me is an important sort of step in the kind of the philosophy the computational experiments the homework so to speak
I mean, I find that if I study some field and I'm like trying to read all these papers and so on and it's a complicated field with lots of complicated formalism, I just I find it difficult to absorb all that stuff. For me, it's actually easier just to work it out for myself.
And then see where those chips have fallen and then go back and figure out what the history is and see how what i've done relates to that history maybe sometimes it's not so common i have to say but sometimes i'll discover something in the history where i say that's an interesting idea and i can use that idea and something that i'm trying to do.
that's that it tends to be the case that i've kind of learned enough of the ambient kind of history beforehand that i'm not usually surprised at that level but it's always a it's a it's always a humbling experience to learn some new field because you was uh i mean i feel uh a field i've been trying to learn for a while is economics which is deeply related to kind of structure of human society and so on and i'm i'm sort of i'm i'm still at the stage it just happens whenever i learn a field that for a while
Every new person i talk to will tell me something i didn't know that makes me very confused about what what is actually going on in the field i am i feel like i'm just at the at the you know just at the crest of the hill now for economics i you know i'm not i'm just it's it's it's starting to be the case that i've heard those that idea before and i'm beginning to understand how it fits into the sort of global set of things that i'm thinking about and i do think by the way that it's it's going to turn out that uh sort of there's a
You know economics like biology, well economics thinks it has more theories than biology thinks it has. And it's sort of a question of what kind of a thing is economics? What are its foundational questions? What can one actually understand? I'm not there yet, but I think it is really clear to me that the methodologies that I've developed are going to be very relevant to that.
i don't know how it will come out that's one of these things you have to let the chips fall as they will you know i don't know how it's going to come out i don't know whether i mean i have kind of prejudices about what i'm going to learn about you know cryptocurrencies and things like this um which is an interesting case because it's kind of a case where sort of all there is is the kind of economic network there isn't the kind of obvious underlying human utility of things i mean just to give a preview of some of the thinking there
What are the questions in economics is what is value what what makes something valuable and my my proto theory of that. Which some subject to change does not not fully worked out at all is in the end the main thing that's valuable is essentially computational reducibility. What's in the world at large there's lots of computational reducibility lots of things that are unpredictable lots of things you can't do quickly and so on.
But we humans have one thing in fairly short supply and that's time because at least for the time being we're mortal we have only a limited amount of time so for us anything kind of speeds up what we can achieve.
is something that is valuable to us and computational reducibility is the possibility of finding little pockets where you can kind of jump ahead where you're not stuck just going through letting things work as they work so i see so i think the you know my proto theory is that the ultimate
Concept of the ultimate source of value is pockets of computational reusability. The fact that you can sort of put together a smartphone and it's a whole smartphone rather than having to get all the ingredients together and just go step by step, so to speak. If we had infinite time, we could build every smartphone from scratch ourselves. But because we only have finite lifetimes, it's worthwhile for us to have the finished goods, so to speak.
You know, it's it's the very beginning. I'm just trying to understand these things Roughly speaking anything that saves us time will be valuable or at least what is valuable will save us time And then what saves us time is something that's computationally reducible. Yes. Yes, that's the idea and I mean I think That sort of questions about kind of what you know when you When you invent something, how do you build on that invention?
How do you take that lump of reducibility and make use of it and so on? What is the value of that invention? That's not something usually, usually taken into account in economics. You know, there's, there's the scarcity of stuff, but not the value of this idea and so on. And in biology, we can see the same thing happening. There is some sort of piece of reducibility, some mechanism that you see being found. And then that mechanism is reused.
I don't understand how this stuff works yet, but, um, this is, uh, you know, in the end, it's kind of a theory that allows one to understand something about kind of, uh, without the function of things, as well as about the mechanism by which the things occur, so to speak. But, you know, this is, uh, so for me, I mean, in terms of doing a project like that, it's sort of this mixture of sort of the philosophy of what's going on, the kind of conceptual framework, a bunch of computer experiments.
To see what actually happens and then sort of a doing one's homework understanding of how this fits into what other people have figured out. And it's, um, you know, the thing that I've, I've done for the last 30 years or so now is, you know, in academia, it's often like you write a paper and then you're like, get some citations, you know, let's boom, boom, boom. This, this is whatever.
I have to say I find it amusing that people pointed out to me that citations to my own stuff sort of got corrupted in various databases and so people are now copying the utterly incorrect citations that are just complete nonsense and you can kind of see that they didn't look at anything they just like click click click
You know, now I've now I've papered my paper, so to speak, by putting in the right citations. The only thing that's amusing to me right now is because papers aren't on paper anymore. It's starting to be the case that you can have the citations where they'll cite all thousand authors explicitly as very nice and egalitarian. And it means that the length of the, and I'm always when I'm reading papers,
I'm always like, something exciting is about to happen. Something exciting is about to happen. Oh, shit, we reached the end of the paper. Nothing happened. Right. Now, and I thought I hadn't reached the end because the thumb on my, you know, as I'm as I'm moving down on the percentage. Yeah, yeah, right. It's like there's still a lot of way to go. But no, actually, it's just just pages and pages and pages of citations.
But, you know, what I've always thought, at least for the last 30 years, the much more interesting thing is the actual narrative history of the ideas. That's the thing that really matters. It's not, you know, it's like it's nice to be able to sort of cite your friends or whatever else you're doing. But what's much more significant for sort of the history of ideas is can you actually thread together how this relates to other ideas that have come up?
And so when I wrote you kind of science, I put a huge amount of effort into the historical notes at the back and, uh, uh, people, you know, it's like, oh, you didn't cite my thing. Well, read the frigging note. It's like, uh, did I get it right? Yes, actually. It's a very, very good portrayal of what actually happened and, um, which, uh, I think is, and I think that's, uh, for me, that's a much more useful thing than like, boom, I copied this citation from some, some database that had it wrong anyway.
I think and that's you know it's part of the story I mean to me you know when you do a piece of science there's the doing of the science and there's the kind of explaining of the science and there's the contextualizing of the science for me kind of the the effort of exposition is critical to my process in doing science I mean the fact that I'm going to write something talk about something
is very important to me actually understanding what i'm talking about so to speak and i i you know i when i write expositions of things i try and write expositions that i intend anybody to be able to understand and that is a you know that's a big sort of constraint on what one does because if one doesn't know what one's talking about it's really hard to explain it in a way that anybody has a chance to understand
And so that for me has been an important constraint in my efforts to do sciences. Can I explain it? And people sometimes think it's more impressive if you explain the science in this very elaborate technical way.
That's not my point of view. It's more impressive if you can grind it down to be able to explain it in a way that anybody can have who puts the effort in can actually understand it. And it forces you to understand what you're talking about much more clearly and it prevents the possibility of you just sort of floating over the formalism and, you know, completely missing the point. But I think, I mean, one of the things that, you know, it's sort of the doing of science
There's one, another question is who should be doing science? You know, it's, uh, I mean, I do science as a hobby, uh, and I do it because I discover interesting things and I think that's fun. If I wasn't discovering interesting things, I just wouldn't do it. It's not what I do for a living. You know, I run a tech company for a living. Um, it's, uh, it's something that has been kind of a, a, you know, I started off earlier in my life. I did science for a living, so to speak.
I wanted to understand your position, not only your views on science, but your position in the history of science and you took us through the past 50 years up until even this morning. That's great.
What's missing there is even a definition of what is science. I opened with what is good science. And then I should have said, well, what is science to begin with? And then we can also contrast that with what is bad science. To me, I think science is about has been traditionally about taking the world as it is the natural world, for example, and somehow finding a way to produce a human narrative about what's going on there.
So in other words, science is this attempt to bridge from what actually happens in the world to something that we can understand about what happens in the world. That's what the act of doing science is that effort to find this thing, which kind of is the explanation that fits in a human mind, so to speak, for what's going on in the world. That's been the traditional view of science. Now there are things that call themselves sciences. That's a computer science that really isn't about that.
That's really a different kind of thing. Computer science, if it was a science like that, will be what I now call Rulology, the study of simple rules and what they do, the kind of the computation in the wild, so to speak. So it's sort of a misnomer of a science, but that's the tradition of how it's called. But so, you know, for me, the science is this, what is it that we humans can understand that relates to what actually happens out there in the world, so to speak?
Now you know i called my big book a new kind of science for a reason because i saw it as being a different twist on that idea of what science is because when you have these simple rules and you can only know what they do by just running them you have a slightly different case you have something where you can understand the essence of what's happening the primitives but you cannot understand you can only have kind of a a meta understanding of the whole arc of what happens
You can't expect what had people have been hoping for from for example the physical sciences where you say you know now i'm going to wrap my arms around the whole thing i'm gonna be able to say everything about everything that's going to go on there so it's a slightly it's a different kind of science hence the title of the book so to speak um but uh so you know that's that's my view of sort of what science is i would say that good science tends to be science that at least science that i think is good
is science that has some sort of foundational it has some foundational connections it's you know there is science maybe i shouldn't say good science i should say say high leverage science science that's that we can be fairly certain is going to have importance in the future so to speak uh you know when you're at some tentacle some you know some detail of some detail
The you know that that is maybe the detail the detail will open up some crack that will let you see something much more foundational but the much of the time that won't happen and i think the the the the thing that to me makes make science that sort of high leverage science.
Is a science where sort of the thing you're explaining the thing you're talking about is somehow very simple very very clean very Very much the kind of thing that you can imagine will show up over and over and over again Not something where you you built this whole long description that went three pages long to say what you're studying
It's like this is a very simple thing and now there's a lot that comes out of that simple thing but it is sort of based on this kind of foundational primitive that that's at least i wouldn't necessarily say that that is some i mean we talk about good science versus not very good science you know one thing is so what would be my criteria i mean you know i would say that
Science that nobody can understand isn't very good science, since the point of science is to have a narrative that we humans can understand. If you are producing something that nobody understands, or nobody has the chance to understand, so to speak, that's not going to be a good thing. I think that there's also a lot of science that gets done that I would say is not, I don't know, it's not what it's advertised to be, so to speak.
What do you mean? Well, science is hard. And unless you, you know, it's like, did it really work that way? Or did you fudge something in the middle, so to speak? And, you know, there's a certain rate of fudging in the middle. I don't think we know what the rate is. In some fields, it's probably very high.
It's often not even like i'm nefariously fudging it in the middle it's just i knew it was going to come out this way so the mouse that didn't do that i'm going to you know that mouse must have been you know under the weather that day so we'll ignore that mouse type thing it's not you know it's not nefariously ignoring the mouse it's just ignoring the mouse because we're sure it isn't you know that mouse isn't the important mouse type thing
i think the uh that's you know i know when i was doing particle physics back in the late 70s you know what a formative experience for me was a thing i calculation i did from qcd about some particular particle interaction charm particle production in proton proton collisions okay so i had worked out it will happen at this rate there was an experiment that said
no such thing was observed at a rate i remember what it was five times below what i said it should be should happen at and so if you are the official you know scientific method operative you say well then my theory must be wrong well
I didn't think the theory was likely to be wrong because it was based on some pretty foundational things and you know i wrote some paper with a couple of other people and you know half the paper was his the calculation the other half of the paper was well this is an experiment and how come you know how could our calculation possibly be wrong well it turns out as you might guess i remember this story that you know the experiment was wrong
And, you know, that was for me an important kind of formative realization. Now, you know, do I blame the experimentalists for the fact that it was wrong? No, experiments are hard. And, you know, they had a certain set of ideas about how it would work out. And, you know, that those were not satisfied and they missed it. Just like in doing computer experiments. If you don't measure the right thing, you might miss what you're looking for. Now, just a moment. How does that jive with earlier when you're talking about the experiments, you have to let the chips fall where they may and accept it?
Fair point. I mean, you have to do a good experiment and that's not a trivial thing. And in other words, if you do a bad experiment, you'll come to the wrong conclusions. And one of the things that I suppose I've gotten so used to in doing computer experiments
Is you know how do you make a very clean experiment this is the typical problem the typical problem with experiment says you do the experiment and you get a result. And there was some effect that you didn't know that it mattered that the experiment was done you know not at sea level or something but that was the critical thing i used to know that.
And so what tends to happen with computer experiments is an awful lot easier than the physical experiments is like can you whittle it down to the point where you're doing a very very minimal experiment but there's no oh there's some complicated thing and we don't know what it came from i mean back in the 80s when people were working on some some not all but some of the artificial life stuff that was very much bitten by the experiments were just unbelievably complicated they were like well i'm going to make this model of a fish and it's going to have 100 parameters in it
well then you know you can conclude almost nothing from such an experiment and you say well you know the fish wiggles in this way but you know it could do anything with a hundred parameters and so i think the the um you know i would say
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And, you know, the most common cause of error, I suspect in experiments is prejudice about how it's going to come out and then muddiness in the experiment. When the experiment is whittled down enough, you can't kind of hide. You can't let the prejudice, you can't, you can't distort the experiment by the prejudice, so to speak, because it is so simple. You can just see this is what goes in. This is what comes out. There's no, there's nothing, you know, behind the curtain, so to speak.
I think that's some, but a lot of experiments, physical experiments, people who do physical experiments have it much worse than people like me who do computer experiments, because it's really hard to make sure you're not having any other effect you don't understand, et cetera, et cetera, et cetera. I mean, the particular mistake that was made in this experiment I mentioned before was they were studying, they were looking for tracks of particles in some emulsion stack,
and they the particle tracks were a bit shorter than they were looking for because they just didn't know about how those kinds of particles worked.
well you know that's that's something i suppose there are analogous mistakes one could make in a computer experiment but it's a lot easier to not make those mistakes in a computer experiment particularly if you do the visualization well and you're really kind of seeing every bit so to speak at least at some level visualization is something i want to get back to so i'm glad you brought it up because you brought up three or four times now is something extremely important so can you talk about how can visualization be done
The most common mistake is that there's a lot going on and you choose to only look at some small slice of what's going on. So for example, classic example, people studying snowflake growth. Okay.
They were studying they said well the thing we really gonna concentrate on is the growth rate of the snowflake okay how fast the the snowflake expands right so they had worked out that growth rate and they'd carefully calibrated it and they found out it was correct for snowflakes and so on.
But they never bothered to make a picture of what the actual snowflakes they were growing with their model looked like. They were spherical. Okay. All right. So that's an example of kind of a visualization type mistake. So the trick is, can you make a representation of things that is as faithful as possible, has as many of the details as possible, but yet is comprehensible to our visual system?
It's easier in some cases than others. Cellular automata are a particularly easy case. They're particularly suitable for our visual system. It's just at least the one-dimensional ones that I've always studied. You just have this line of cells and they go down the page. You get this picture. Boom, there it is. Now, even there, with cellular automata, what had been done before my efforts were to look at two-dimensional cellular automata, where you have a video of what's going on.
When you have that video it is really hard to tell what's happening some things like the game of life so it's on people started that at some length and you can see these gliders moving across the screen look pretty cool and you can see glider guns doing that thing and what kind of thing but when you tip it on its side and you look at the kind of space time picture of what's happening it becomes much clearer what's going on and that's a that's a case where.
our visual system yes we can see movies but we don't get sort of in one gulp the whole story of what happened and so that's another case where the more faithful visualization if you can do it it's not so trivial to do it because it's kind of a three-dimensional thing and you can't see through all the layers you have to figure out how you're going to deal with that and it's complicated for the things that i've studied for example in our models of physics where we're dealing with
On their face, trivial things to visualise?
Um, and in fact, one of the things that made the physics project possible was that a decade earlier in Wolfram language, we had developed good visualization techniques for graphs, for networks, which was a completely independent effort that had nothing directly to do with my sort of project in physics, but it was something we did for other reasons. And that was something was critically useful in doing physics project. I mean, back in the, in the, um, in the nineties, when I was doing graph layout, I, um, uh, I actually found a,
A young woman who was who was spectacularly good on a piece of paper, laying out a graph to have the, you know, the lines not crossed and so on. Later on, she became a distinguished knitwear designer. So I think that was, I don't know what was cause and what was effect, but it was kind of a unique skill. Most people are not, you know, most people can't do it. You know, you give them a bunch of a bunch of nodes and a graph and you say, untangle this thing. It's really hard to do. I can't do it at all. Um, but you know, we had found algorithms for doing that, which
I used a lot in doing things with the physics project because that was a sort of a pre-existing thing, but it's still more difficult to visualize what's happening and things I've been doing very recently last few weeks on lambda calculus. That's an area where the obvious visualizations are just
horrendous. Our visual system doesn't figure out what the heck is going on. And it's been possible. I found some reasonable ways to do that, which are very helpful in getting intuition about what's happening. But that's our highest bandwidth way of getting data into our brains is 10 megapixels of stuff that we can get through our eyes. And so that's the best. And that's the question is, can you make a sort of a faithful representation of what's going on underneath
that you can get into your brain so that your brain has the chance to get intuition about what's happening or to notice anomalies. I mean, you know, somehow I think the story of my life in science devolves every so often and like it did just a few days ago into a very large number of little pictures on the screen and going through screen after screen of these things, looking for ones that are interesting.
It's kind of a very natural history like activity. Um, it's kind of like, you know, when do I see the flightless bird or something like this? Um, and, um, and, you know, why do I do that? Obviously I've used machine learning techniques to prune these things, et cetera, et cetera, et cetera. But in the end, you know, I'm looking for the unexpected and, you know,
Two questions here. One, if you're using machine learning to sift through, I remember there's a talk from Freeman Dyson. He was saying that when the Higgs particle was discovered, he's happy that the Higgs was discovered, but he's not happy with how it was discovered because there was so much filtering out of data. And he says you want to be looking for the anomalies.
I'm happy that the particle is finally discovered after many years of effort, but I'm unhappy with the way the particle was discovered. The Large Hadron Collider is not a good machine for making discoveries. The Higgs particle was only discovered with this machine because we told the machine what we expected it to discover. It is an unfortunate deficiency of the Hadron Collider that it cannot make unexpected discoveries.
Big steps ahead in science usually come from unexpected discoveries. Do you have reservations about how some of these filtering techniques can be done? Absolutely. You get it wrong all the time. The less filtering you do, the better. That's why I end up with
You know, looking at arrays of pictures, you know, screen after screen of arrays of pictures, it used to be on paper. Um, but, uh, you know, I would print these things out and go through sheeps of paper looking for things. Yeah, it's, I mean, there are things that are fairly safe to do, although they bite you from time to time. Like I was just mentioning earlier, this very long lived Lambda creature that I found, uh, you know, the automated techniques that I had missed it.
So that was and I noticed something was wrong by looking at a visualization of what it was doing.
okay then the other question was approximately an hour and a half ago or so we were talking about bulk organization theory and natural selection so both orchestration theory yeah you made an acronym okay that's a new acronym okay got theory and the question is whether it's critical to how bots themselves work let's let's let's skip that so what i want to know is you mentioned if i recall correctly that there was a recent visualization you did in order to make it easier to see the connection to biology
Not quite. I mean, no, not quite. That was related to this. I'm doing multiple projects right now, so that was about a different project, which actually happens to have some relevance to biology, but that relevance is more related to origin of life, and it's a slightly more circuitous route, but so different kind of thing. But, you know,
Let's end the conversation on many people who email you, they email you their theory, their theory of everything. They'll say, I have a theory. You have a recent blog post about this. I have several quotes from that. We can get to that if you like, but how can someone, many of the people who watch the show, many people who are fans of yours, many people who watch Sean Carroll's show or any science show at all, they want to contribute to science and they may not have the tools to contribute to science. So they use LLMs generally speaking, or they just don't do anything, but they have the want.
How can they productively contribute to science? That's an interesting question. So I think there are a couple of... Okay, so first point is there are different areas of science, right? And at different times, people with different levels of training and expertise have been able to contribute in different ways. Like, there was a time when, in natural history, you could go and just find beetles and so on, and that was a contribution to science because, you know, every beetle you found was something which eventually there would be some systematic thing that came out from looking at that.
You're not going to find another continent.
not anymore not anymore i'm not you know in in um you know there've been times when this wasn't done so much by amateurs but but um chemistry for example there was a thing where you just you study another compound and you just keep on doing that and in the end you build up this kind
Collection of knowledge where somebody is gonna pick up you know somebody studied you know lithium hydroxide back in the day for no particularly good reason and then you know somebody at nasa realizes that's a way to scrub come dioxide or whatever and it gets used in the post spacecraft or whatever it is you know it's a it's a so i think there's this thing where there are things that you can kind of accumulate that maybe i'm not not necessarily in the not themselves they don't require sort of.
Integrating a lot of a lot of things to be able to make progress there are areas that are more difficult so for example right now physics is as it has traditionally been done is a more difficult area to contribute to because the you know back I would say in the 1700s not so difficult but now there's a pretty tall tower of stuff that's known
I mean, stuff from, you know, mathematical physics and so on that's known. And if you say, well, I'm going to have a theory of how space time works. If you don't know what's already known about space time, which is couched in quite sophisticated mathematical terms and not not capriciously, it's just that is, you know, our human, our everyday human experience doesn't happen to extend to how space time curves around a black hole. That's not everyday intuition.
And so it's inevitable that it's going to be couched in terms that are not accessible from just having everyday intuition. And there are fields where that not so much of that has happened. Physics is one where there's a pretty tall tower of things that have been figured out that get you to the sort of the description of physics as we know it today. Now it turns out that, you know, in the things that we've been able to do with our model of physics,
It's once a little bit closer to the ground again.
In some aspects of it, because the study of, you know, hypergraph rewriting and so on, that's something, you know, pretty much anybody can understand the ideas of hypergraph rewriting. It's not doesn't require that, you know, a whole bunch of stuff about, I don't know, you know, sophisticated things about partial differential equations and, you know, function spaces and all this kind of thing, which are fairly complicated abstract concepts.
It's something where, at least at the simplest level, it's like you've got this thing, you could run it on your computer, you can see what it does. Now, you know, connecting that to what's known in physics, that's more challenging. And knowing kind of how that relates to, I don't know, some result in quantum field theory or something is more challenging. The result in quantum field theory is not a waste of time. The result in quantum field theory is our best condensation of what we know about how the universe works.
It's not something where it's like forget all that stuff we can just go back to kind of the average intuition about physics that was good thing a few hundred years ago it isn't a good thing anymore cuz we already learned a bunch of stuff we already figured out a bunch of things and if you say well just throw away all those things that start from scratch. That's you know then you've got to recapitulate those few hundred years of of discoveries and that's a that's a that's a heavy that's a tall hill to climb so to speak i think the.
One of the areas where there's sort of a wonderful opportunity for people to contribute to science that is some high leverage science is in this field that I call Rulology, which is kind of studying simple rules and seeing what they do.
And whether it's cellular automator or Turing machines or lambda calculus or hypergraph rewriting. These are things where, you know, you run it on your computer. Okay, I built a bunch of tools for doing this, which, you know, make it really easy. But, you know, you do this, you're well organized, you kind of you won't immediately have intuition about how these things work.
least i never have you know it takes actually doing it for a while to have the intuition and people usually don't at the beginning they're just like oh it'll never do anything interesting we'll just run it and see what it does
and then you know if you're well organized and kind of can develop intuition you will eventually get to the point where you can say okay i see how this works i can build this thing where i can i can add some definite piece to knowledge you know i can like like for example we have this summer school every year for grown-ups and we have another summer research program for high school students so this year for the high school students we had like i don't know 80 students or something
And I'm the one who gets to figure out projects for at least almost all of them. Oh, you define them or they come to you and you... No, I define them.
Usually, during the year, I accumulate a list of ones I'd really like to see done. This year, several students I gave in projects which I've been wanting to do for decades and which are just studying particular kinds of simple systems. One was multi-way register machines was one of them. Another one this year was games between programs.
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One was, um, this year was another, uh, there were several, but, but, um, but anyway, these are things where, um, and, you know, I can say these, these high school kids, okay, they're very bright high school kids and they're using our tools and so on in two weeks were able to make quite nice progress and were able to add something, you know, I'm sure those things will turn into academic papers and things like this. And they were able to, you know, starting from.
Just being a bright high school student so to speak, not knowing.
You know eight years of mathematical physics. They don't know group theory. They don't know differential calculus or maybe some they probably know basic calculus. They probably but they don't wouldn't need to. I mean they you know, this is just a sort of be organized be careful and you know have the motivation and sort of and sort of and a little bit think foundationally enough that you're kind of drilling down to say what's the obvious experiment to do don't
Don't invent this incredibly elaborate experiment where the conclusions won't tell you anything. You know, try and have the simplest experiment. You know, what is the simplest version of this that we can look at and so on. And that, you know, it's really neat because it means it's an area. Ruliology is an area. It's a vast area. It's the whole computational universe. You know, I can, if you say, well, what's the thing that's never been studied before? I get out my computer, pick a random number, and I'm going to be able to give you something that I guarantee has never been studied before.
and you know it'll have a lot of richness to it if you study that thing that randomly generated thing the particulars of that thing may or may not show up as important in particular in the future but certainly building up that body of knowledge about things like that is something that is very high leverage science it's something that is very uh it's it's very it's something that that you can kind of be sure is something that's a solid thing that people will be able to build on
One of the things i find striking and encouraging i suppose is you know you think about something like platonic solids you know the icosahedron the dodecahedron and so on say well that's you know you have a you have a an object you know and a dodecahedron you know piece of wooden dodecahedron or something and you go back and you say well let's you know we find a dodecahedron from ancient egypt it looks exactly the same as the dodecahedron we have today this is a timeless object
It's a thing that, you know, the dodecahedron has been something that has been worth talking about from the time of ancient Egypt to today. And so similarly, these things in rheology have the same character. They're very abstract, precise, you know, simple, and they're sort of foundational. And it's something where, you know, this particular rule, it's not going to be the case that somebody is going to say, oh, we learned more about the immune system. So that model of the immune system is irrelevant now.
You know the things you measured about this are irrelevant that's not gonna happen because this is you know we're at the foundation so to speak this is an ultimate abstract thing and so anything you build there is a permanent thing and you know whether you happen to find whether you do you know there were many naturalists before Darwin who went and you know collected lots of critters around the world you know Darwin realized having collected lots of critters that there was a bigger picture that he could build
You know, it was still useful for people to have collected all those critters. And, you know, Darwin and everybody else doing evolutionary biology used a bunch of the information that had been collected by those people. It was, you know, it's a different thing to kind of integrate all those things, have the sort of philosophical integration to be able to come up with the bigger theory. But that's that's a that's a much more difficult thing to do than to add these kind of solid bricks
The science and i would say that you know as a person who's done lots of religion my time it's you know if you have a certain turn of mind it's a lot of fun because you just keep on finding stuff you keep on discovering things i mean you know i'm sure back in the day when you know the whole planet hadn't yet been explored.
You would go to you know some place in the center of africa and it's like oh my gosh there's a tree that does this or this and this is exactly the same thing you know every day you see lots of things like in this stuff about lambda calculus i've just been doing is all kinds of weird stuff i've never seen it before i don't think anybody's ever seen it before and it's i'm sure i'm sure nobody's ever seen it before and it's remarkable and it's interesting and it's kind of it feels you know it's
It's kind of you get to see for the first time something nobody's ever seen before and something that is that you know you kind of know is going to be a permanent thing that is going to be a thing that is never going to change it's never going to be oh it doesn't work that way anymore it's it is it is what it is so to speak.
I think that is a is a great example of a place where there has been a fair amount of sort of quotes amateur reality that's been done over the years it's not been as well organized as it should be and i thought myself for that and enlarge measure i mean back in the eighties. I got a bunch of people interested in this bunch of people both professional scientists and amateurs started studying these kinds of things.
I started a journal that collected some of these things, complex systems, but I would say that the rhythm of how to present Ruliology and so on, I didn't really develop as well as I should have done and I'm now hoping to do that. It's a question of, for example, back in the day when people started having academic papers, back in the 1600s, if you read those papers, they read like today's blog posts.
Much more kind of anecdotal, you know, I went to the top of this mountain I saw this and this and this and they have them They're more personal. They're actually I would say better communication Than what one gets in sort of the very cold academic paper of today Well, you know, particularly I would say math papers are one of my favorite non-favorite examples where it just starts You know, let let G be a group. It's like why are we looking at this group? Who knows? It's just you know
Because it's like it is it is beneath us or it is not appropriate or it is kind of not professional enough for us to describe why we're doing this but so you know the back in the sixteen hundreds when when academic papers kind of originated they were like.
You know, like the blog posts of today, so at least my blog posts today where there's both the content and a certain amount of the kind of the wrapping of why we're doing this and and kind of you have the purpose in mind and you're conveying it showing how what you're doing is connected to that. Yeah, right. And it's not. And it's sort of telling a story more so than it's just saying fact, fact, fact. And, you know, I'm just filing the facts, so to speak.
I think with Rulology, one of the things that is interesting is it's a place where you discover something interesting. You want to just say, this is my discovery. And you want a way to accumulate lots and lots of discoveries without having to always feel like you have to wrap a whole kind of academic story around it. And the academic system is not well set up for that.
I'm the it's you know the system it's like there's a business unit. The unit of academic achievement which is the paper so to speak and no just does this particular thing observed in these these particular characteristics that's not so much the kind of thing that one sees that.
It's the kind of thing that one should be accumulating lots of in Rulology and it's something that is very accessible to well organized people who you know who wants to sort of work cleanly on this without you know as amateurs so to speak. I think it's a powerful thing and I'm hoping to have the bandwidth to kind of put together
properly organized kind of really logical society or something where we can kind of accumulate this kind of information. I think it's a thing where I mean one of the things I do and the things that I write about science is every picture and everything I write you can click that picture you'll get a piece of Wolfram language code and at least if our QA department didn't mess up it will forever produce the picture that that I said it produced so to speak.
And you know i mentioned the q a department because it isn't actually trivial to have you know you make some piece of code and you have got to make sure it keeps on working we've been very good in both language and maintaining compatibility for the last 38 years with the language but if i use some weird undocumented feature that day that might change i don't so that usually isn't a problem but um uh you know but that fact that you kind of have it as a constraint for yourself
I like to have it as a constraint for myself, the things I write should be understandable to anybody who puts the effort into understand them. And reproducible. Yes, but also that they're understandable not only to the humans, but also to the computers, so to speak.
and that everything i do is like you can immediately reproduce it that's you know turned out in practice to be a very powerful thing because people just take you know the code and all the visualizations and so on that i've made and they just go and start from there they kind of start from that level of the tower so to speak rather than having to climb climb the tower themselves and it's uh you know i think that's a that's a powerful thing it's very it's pretty much undone
Partly because academics well they don't tend to package the sort of the kind of code in a form that will actually be reproducible and runnable they do more with our technology than anywhere else but but still it's somewhat inadequate and also I think the the motivations on the part of you know the the typical academic scientist
Is kind of in the, in the game of academia, so to speak, which involves, you know, I'm going to publish my thing. I'm going to publish another thing that leverages the thing I just published. And, you know, I'm going to get as many papers as possible and so on. For me, the calculation is actually rather different because for me, I'm trying to do a bunch of different things in my finite life, so to speak. And for me, I'll write something and I really don't want to write something about the same topic again.
It's kind of like it's a right once type activity. And so as much as possible, I'm like, I'm going to write this thing and okay, well, here it is. I hope you can do something useful with it because I'm not going to come back to this particular thing.
i mean i find actually that the you know having i do end up sort of building on the things i've done but not kind of writing sort of a an incremental version of the same document again i'm i'm i find it maybe it's just me but but i i just i can't bring myself to do that i feel the same way and it's kind of it's actually very frustrating when i've when i you know when i do a project
I like to kind of pick all the low hanging fruit and i you know i know that any fruit i don't pick first time around i'm not gonna come back and pick i'm gonna sit there is gonna be frustrating to me because it's kinda like like here's this thing and i just figured out a little bit more but i have no place to write that down i'm actually one thing i've been doing recently is in the nks book there were many notes of the back and many hundred page things that i'm writing today
Anyway, Stephen.
It's been wonderful.
but specifically i want more detail so you mentioned there are 40 high school projects maybe within two weeks there'll be a blog post out for people who want more they could say okay well it won't be two weeks i want to do it but it won't be two weeks okay well maybe whenever it's done i can put a link on the description i'll update the description yeah so if you're watching this whenever you're watching this check the description or maybe i'll have something on my sub stack about
Hi there, Kurt here. If you'd like more content from Theories of Everything and the very best listening experience, then be sure to check out my sub stack at kurtjymungle.org. Some of the top perks are that every week you get brand new episodes ahead of time
You also get bonus written content exclusively for our members. That's C-U-R-T-J-A-I-M-U-N-G-A-L dot org. You can also just search my name and the word sub stack on Google. Since I started that sub stack, it somehow already became number two in the science category. Now, sub stack for those who are unfamiliar is like a newsletter, one that's beautifully formatted. There's zero spam.
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While I remain impartial in interviews, this substack is a way to peer into my present deliberations on these topics. And it's the perfect way to support me directly. KurtJaymungle.org or search KurtJaymungle substack on Google. Oh, and I've received several messages, emails and comments from professors and researchers saying that they recommend theories of everything to their students. That's fantastic.
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▶ View Full JSON Data (Word-Level Timestamps)
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"text": " The Economist covers math, physics, philosophy, and AI in a manner that shows how different countries perceive developments and how they impact markets. They recently published a piece on China's new neutrino detector. They cover extending life via mitochondrial transplants, creating an entirely new field of medicine. But it's also not just science they analyze."
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"text": " Where senior editors argue through the news with world leaders and policy makers in twice weekly long format shows. Basically an extremely high quality podcast. Whether it's scientific innovation or shifting global politics, The Economist provides comprehensive coverage beyond headlines. As a toe listener, you get a special discount. Head over to economist.com slash TOE to subscribe. That's economist.com slash TOE for your discount."
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"text": " From discrete space to Darwinian evolution to entropy and the second law, Stephen Wolfram's computational view of the universe makes claims about all of these in a unified fashion. Today's episode is a treat. If you're a fan of this channel, Theories of Everything,"
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"text": " Then you're likely someone who enjoys surveying large swaths of lessons from disparate fields, attempting to see how they all relate and integrate. Same with me, Kurt Jaimungal. Now today, Stephen Wolfram outlines how this polymathic disposition has helped him solve, to his satisfaction, some of the major outstanding problems in fields as diverse as computer science, fundamental physics, and biology."
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"text": " This is a journey through his life in science where I tease out the lessons he's learned throughout his career and how you can apply them yourself if you also want to make contributions."
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"text": " I was honored to have been invited to the Augmentation Lab Summit, a weekend of events at MIT last month, hosted by MIT researcher Dunya Baradari. The summit featured talks on the future of biological and artificial intelligence, brain-computer interfaces, and included speakers such as the aforementioned Stephen Wolfram and Andreas Gomez-Emelson. Subscribe to the channel to see the upcoming talks. Stephen, welcome. Thank you. It's a pleasure. How does one do good science?"
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"text": " It's an interesting question. I mean, I've been lucky enough to have done some science. I think it's fairly interesting over the course of years. And I wonder how does this happen? And I look at kind of other people doing science and I say, how could they do better science? You know, I think the first thing to understand is when does good science get done?"
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"text": " And the typical pattern is some new tools, some new methodology gets developed, maybe some new paradigm, some new way of thinking about things. And then there's a period when there's low hanging fruit to be pricked, last maybe five years, maybe 10 years, maybe a few decades. And then it's then some field of science gets established, it gets a name. And then there's a long grind for the next hundred years or something."
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"text": " That people are doing sort of making incremental progress in that area and then maybe some new methodology gets invented things live and up again and one has the opportunity to do things in that in that period i have been lucky in my life cuz i've kind of alternated between developing technology and doing science maybe about five times in my life."
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"text": " And that cycle has been very healthy. It wasn't intentional, but it's been worked out really well because I've spent a bunch of time developing tools that I've then been able to use to do science. The science shows me things about how to develop more tools and the cycle goes on, so to speak. So I've kind of had the opportunity to be sort of have first dibs on a whole bunch of new tools because I made them, so to speak. And that's let me do a bunch of things in science that have been exciting and fun to do."
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"text": " I mean, I think a bunch of science I've done, I was realizing recently that it's also a consequence of sort of a paradigmatic change. This idea, taking the idea of computation seriously and by computation, the fundamental thing I mean is you're specifying rules for something and then you're letting those rules run rather than saying, I'm going to understand the whole thing at the beginning. It's kind of a, a, a more starting from the foundation's point of view."
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"text": " Well what i realized actually very recently and it's always it's always surprising how long it takes one to realize these sort of somewhat obvious features of history of science even one's own history is that you know i've been working on a bunch of things in fundamental physics and foundations of mathematics foundations of biology a bunch of other areas where i'm looking at the foundations of things using a bunch of the same kinds of ideas the same kinds of paradigms"
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"text": " I'm realizing that a bunch of what I'm doing is kind of following on from what people did about a hundred years ago, maybe sometimes a little bit more than a hundred years ago. And I was wondering why is it that, you know, a bunch of things I'm interested in, I'm going back and looking at what people did a hundred years ago. I'm saying they got stuck. I think we can now make progress. What happened? I think what happened is that in the eighteen hundreds, there was this kind of push towards abstraction."
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"text": " There was this idea that you could make formal versions of things that happen most notably in mathematics where kind of the idea emerge that sort of mathematics with just a formal game where you are defining axioms and then seeing their consequences it wasn't a thing about actual triangles or whatever else it was it was an abstract exercise."
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"text": " And once people had ground things down to that level of abstraction, same kind of thing happened with atoms and so on. And in the structure of in physics, once people had ground things down to that sort of deconstructed level, they had something where they didn't know what to do next. Because what turns out to be the case is that that sort of the setup for computation is you've ground things down to these kind of elementary primitives."
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"text": " And then computation takes over and that's the thing that uses those primitives to do whatever is going to happen. And so I think a lot of what got stuck then was closely related to this phenomenon of computational irreducibility that I studied for the last 40 years or so that has to do with, even though you know the rules, it will not necessarily be the case that you can kind of jump ahead and say what will happen."
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"text": " You may just have to follow those rules step by step and see what happens you may not be able to make the big theory that sort of encompasses that describes everything that happens. I think what happened in a bunch of these fields is people kind of ground things down to the primitives then they effectively discovered computational irreducibility implicitly discovered it by the fact that they couldn't make progress."
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"text": " I think that and things like girls theorem which are reflections of computational reducibility kind of where were kind of the other signs more direct signs of that phenomenon but the end result was people got to these parameters and i couldn't get any further and now now that we actually understand something about the kind of computational paradigm we can see to what extent you can get further and the kinds of things that you can now say."
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"text": " So it's kind of interesting to me to see that. I mean, one particular area where I've only learned the history recently, and I'm kind of shocked that I didn't know the history earlier, is about the discreteness of space. So, you know, back in antiquity, people were arguing back and forth, you know, is the universe continuous or discrete? You know, are there atoms? Does everything flow? And nobody knew. End of the 19th century, finally,"
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"text": " One had evidence that yes, matter is made of molecules and atoms and so on. Matter is discrete. Then first decade of the 20th century became clear you could think of light as discrete. That had been another debate for a long time. And at that time, first few decades of the 20th century, most physicists were sure this whole discreteness thing was going to go all the way, that everything was going to turn out to be discrete, including space."
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"text": " I didn't know that because they published very little about this. And the reason was they couldn't make it work. After relativity came in, it was like, how do we make something that is like space, but is capable of working like relativity says space should work."
},
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"text": " And it will also somewhat confused by the the idea of space time and the similarities between time and space which were more mathematical and physical really that kind of confused confused the story but i think then the thing that that became clear was i think nineteen thirty was i think the time when when particularly when heisenberg was i think one of the ones who was really you know spaces discreet he had some i think"
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"text": " i need to go look at his archives and things but i think he had some kind of discrete cellular model of space and he couldn't really make it work and then eventually he said forget about all of this i'm just going to think about processes in physics as being these particles come in something happens in the middle but we're not going to talk about that we're just going to say it's an s matrix and then things go out"
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"text": " And so that that's when he started just saying, I'm going to look at the S matrix, this thing that just says what how what's goes in is related to what comes out. I'm not going to talk about what's in the middle, because I got really stuck thinking about sort of the ontology of what's in the middle. So"
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"text": " Then after that, you know, the the sort of quantum field theory and quantum mechanics and so on started working pretty well. People forgot about this idea of discrete space. And in fact, the methods that they had would not have allowed them to have much interesting to say at that time. And finally, through sort of series of events that are kind of mildly interesting in my own life, I kind of came to realize how you could think about that in computational terms and how you could actually make all of that work."
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"text": " One of the cautionary tales for me is this question about is matter even discrete or continuous? The people that argued about people like Ludwig Boltzmann had gotten, you know, had sort of said towards the end of the 19th century, he kind of said he believed very much in the atomic theory of matter. He was like, nobody else believes this."
},
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"end_time": 643.677,
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"start_time": 621.954,
"text": " He said you know i'm gonna write down what i have to say about this he says one man can't turn back the tide of history i'm gonna write down what i know about this so that when eventually this is rediscovered eventually people realize this is the right idea they won't have to rediscover everything well in fact it's kind of a shame because in 1827 i think"
},
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"text": " A chap called Robert Brown who was a botanist had observed this little pollen grains being kicked discreetly when they were on water or something and that it was realized eventually that that Brownian motion is direct evidence for the existence of molecules. So if Boltzmann had known the botany literature,"
},
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"text": " He would have known that in fact there was evidence for molecules that existed just those connections were not made and so for me that's a kind of cautionary tale because in modern times you know i think about what does it take to kind of find experimental implications of the kinds of theories that i've worked on and you know some of those things are difficult and it's like well."
},
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"text": " Can i be two hundred years until we can do this kind of investigation or it might cost you know ten billion dollars to set up this giant space based thing but it might also be the case that in fact you know somebody in nineteen seventy two."
},
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"end_time": 734.002,
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"start_time": 705.452,
"text": " Observed exactly a phenomenon that is the thing that would be what i'm looking for and you know in fact one of the things that's kind of ironic and i've seen this bunch of times in my career in science is that when when people don't kind of have a theory that says how an experiment should come out and they do the experiment and the experiment comes out differently from the way they expected they say i'm not going to publish this this this must be wrong"
},
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"text": " And so a lot of things which later on one might realize were, you know, when you have a different theory, one might realize, gosh, you know, that experiment should have come out a different way. It got hidden in the literature, so to speak, which is, you know, this is a feature of the kind of institutional structure of science, but it's something where, you know, if one's lucky, some somebody would have said, sort of done the honest experiment and just said, this is what we found."
},
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"end_time": 784.735,
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"start_time": 760.077,
"text": " And, you know, even though this doesn't agree with the theory that so far we understand, so to speak. And so I've actually been using LLMs quite a bit to try and do this kind of thematic searching of the scientific literature to try and figure out whether you could save the $10 billion, not do the experiment now, but just use the thing that already got figured out. But, you know, I think in terms of, I don't know, my"
},
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"start_time": 785.094,
"text": " Efforts to do science as i was saying i think one of the things that is sort of a critical feature is methodology and when new methodologies open up and sort of i've been kind of lucky to be alive at a time when kind of computation and computers first make it possible to do kind of experiments with computers so to speak and you know that that's what i built lots of tooling to to be able to do that"
},
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"end_time": 839.77,
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"text": " I think the thing that's always interesting about doing computer experiments, and particularly this is a consequence in the end of this whole computational irreducibility idea, is almost every computer experiment I ever do comes out in a way that I didn't expect. In other words, I'll do something, been doing some things even last week or so on a particular domain where, you know, I've got some theory about how it will come out, and if I didn't have any theory about how it would come out, I wouldn't do the experiment."
},
{
"end_time": 865.913,
"index": 36,
"start_time": 840.52,
"text": " First point is the experiment has to be easy enough for me to do that on a whim I can do the experiment. It can't be the case that I've got to spend a month figuring out how to do the experiment because then I'm going to be really sure about why it's worth doing. You know it's got to be something where the tooling is such that I can do the experiment easily. I happen to have spent the last 40 years building that tooling and you know it's available to everybody else in the world too but you know I'm"
},
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"end_time": 892.159,
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"start_time": 865.913,
"text": " As far as i'm concerned i'm the number one user of the language and so on as far as i'm concerned i don't i doubt i'm the person who uses it the most of everybody out there but i'm the user i care about the most and you know it's a very nice thing to be able to take an idea that i have and be able to quickly translate that into something where i can make it real and do an experiment on it."
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"index": 38,
"start_time": 892.807,
"text": " So I have to have some idea about what's going to happen. I always wouldn't do the experiment, but then I do it. And the thing I typically say to people who work with me is we have to understand the computational animals are always smarter than we are. So, you know, do the experiment and you find things that you never expected to find. Why don't you give an example, a specific one? Yeah. I mean, so, so let's see, I was looking at, um, uh, almost any kind of simple program."
},
{
"end_time": 943.643,
"index": 39,
"start_time": 920.589,
"text": " The question is what kind of thing can it do? For instance, let's say looking at simple programs that rewrite networks. You run the network rewrite thing, what's it going to do? Well, sometimes it builds this elaborate geometrical structure. I had no idea it was going to do that."
},
{
"end_time": 972.892,
"index": 40,
"start_time": 944.002,
"text": " I thought it was going to make this messy kind of sort of random looking thing, but no, actually it builds this very sort of organized, elegant structure in some particular cases. Or for example, I've been looking most recently as it happens at lambda calculus, very old model of computation that I happened to have never studied before, but have a particular reason to be interested in right now. And it's a question of, okay, I'll give you an example that happened to me just a few days ago. So"
},
{
"end_time": 1000.469,
"index": 41,
"start_time": 973.746,
"text": " This is a very simple specification, simple program. The program will run for a while. There are different versions of this program. You can enumerate lots of different possibilities. It will typically run for a while and, well, usually it will stop. Sometimes it won't stop. It will just keep going. Sometimes it will go on repetitively and so on. And so I was looking at a bunch of these things and I was wondering, you know, what's the maximum lifetime of one that eventually stops? So"
},
{
"end_time": 1011.152,
"index": 42,
"start_time": 1000.572,
"text": " I studied a bunch of different cases and there was, you know, I thought, okay, I found it. It's a lifetime. I don't know what, a few hundred or something. And I could find that with some simple experiments."
},
{
"end_time": 1039.172,
"index": 43,
"start_time": 1011.527,
"text": " But then there was one where I started looking at it and I started looking at pictures of it and I'm, I'm kind of a seasoned hunter at this point for these kinds of things. And there was something about it that didn't seem quite right. There's something about one that, that I thought was going to just go on forever, but it seemed like it was doing things that might not allow it to go on forever. So I pushed it a bit harder, push it harder. Sorry. What do you mean by you pushed it harder? Push the program harder. Sorry. What do you mean by you pushed it harder? Push the program harder."
},
{
"end_time": 1069.906,
"index": 44,
"start_time": 1040.162,
"text": " I let it run for longer. I let it run overnight on a network of computers. Okay. That's that's in practice what I did. Okay. And, you know, and then come back in the morning and oops, it ran for some 10s of 1000s of steps and then stopped. Never expected that. And then there was another one where I having seen that particular phenomenon, there was another one where I kind of suspected this one is going to stop. And that one stops after some"
},
{
"end_time": 1093.746,
"index": 45,
"start_time": 1070.162,
"text": " Well, let's see that the that one I kind of found a method for figuring out how it what how it will develop and that one I think stops after a few billion steps. So these are things you just you don't expect and you know that that's this is very typical of what happens in this computational universe. It's a bit similar to the physical universe. There are things that happen in the physical universe you don't expect."
},
{
"end_time": 1124.172,
"index": 46,
"start_time": 1094.206,
"text": " But it's particularly sort of in your face when you can see, you know, in the physical universe, you don't necessarily know what the underlying rules for something are. So you could always be wondering, you know, do I just not know enough about how snowflakes form or something? But in this case, you know, the rules, you know exactly what went in and yet you'll sort of have this forced humility of realizing you're not going to be able to figure out what happens. And sometimes you'll be quite wrong and you'll guess about what's going to happen. So, you know, one of the principles in doing that kind of science is, you know,"
},
{
"end_time": 1141.869,
"index": 47,
"start_time": 1124.548,
"text": " You'll do these experiments, they'll often come out in ways you don't expect. You kind of just have to let the chips fall where they fall, which is something in doing science that can be psychologically very difficult. You know, you had to have had some kind of theory that caused you to start doing the experiment."
},
{
"end_time": 1170.06,
"index": 48,
"start_time": 1142.159,
"text": " I was you wouldn't have done the experiment and so there's a there's a something of a psychological pressure to say look I have this theory this theory has to be correct you know something went wrong with my experiment you know let me tweak my experiment let me you know ignore that part of the experiment or something and because I'm sure this theory must be correct one of the things that you know is a is a an important thing that I kind of learned long long long ago now is just let the damn chips fall where they'll fall"
},
{
"end_time": 1191.596,
"index": 49,
"start_time": 1170.145,
"text": " And turns out one of the things that's happened to me is sometimes some of the sometimes these chips fall in places that very much violate various kinds of prejudices that i have and it's just like i'm more interested in where the chips actually fall than in supporting some prejudice that i have and as it turned out in the end what's happened is"
},
{
"end_time": 1209.155,
"index": 50,
"start_time": 1191.937,
"text": " Sometimes several years later i realize actually the way those chips fell was more consistent with my prejudice than i could ever have imagined so i give you an example so a lot of things i've done have been so deeply deconstructive of the way the universe works."
},
{
"end_time": 1233.575,
"index": 51,
"start_time": 1209.582,
"text": " That is, they're very non-human interpretations of what goes on in the universe and so on. They're very, you know, the universe is some giant hypergraph of things. It's a very kind of humanly meaningless object. It doesn't have sort of a resonance with kind of our human sensibilities and so on. It's deeply abstract, sort of deeply deconstructed in a sense."
},
{
"end_time": 1256.459,
"index": 52,
"start_time": 1234.053,
"text": " And yet, as a person, I'm quite a people enthusiast, you know, I like people, I find people interesting, I work with people, I've been a company full of people and so on. And so for me, it was always something of a conflict that on the one hand, I'm interested in people. On the other hand, the things I'm doing in science are deeply deconstructive of anything sort of human about what's going on in science."
},
{
"end_time": 1278.387,
"index": 53,
"start_time": 1257.312,
"text": " And that was the situation in my world for a couple of decades and then i realized more recently that the nature of the observer is actually critical in the end in the end sort of the ideas about the really add and so on the kind of entangled limitable possible computations that sort of the ultimately deconstructed dehumanized thing."
},
{
"end_time": 1294.036,
"index": 54,
"start_time": 1278.831,
"text": " But what you can then realize what i realized eventually is how a perception of the laws of physics depends critically on our nature as observers within the really add another words from going from a completely dehumanized view of science."
},
{
"end_time": 1323.404,
"index": 55,
"start_time": 1294.497,
"text": " That is this totally abstracted really add turns out the humans are actually really important and giving us the science that we have so from you know even though i was sort of unhappy and some in some sense of some sort of psychological prejudice i was unhappy with the idea that everything is deeply dehumanized cuz i kind of like humans in the end couple of decades later i realize actually the humans are kind of much more at the center of things than i had ever expected"
},
{
"end_time": 1351.92,
"index": 56,
"start_time": 1323.404,
"text": " So that was kind of it was kind of an interesting realization. Another one like that that I resisted for a long time is these things I call multiway systems, which I had invented back in the early 90s and which I had thought by the late 90s, that was a possible view of how quantum mechanics might work, that there are these kind of many parts of history and that are being followed by the universe and so on. I really resisted that idea because I felt sort of egotistically"
},
{
"end_time": 1375.93,
"index": 57,
"start_time": 1352.176,
"text": " I didn't want it to be the case that there were all these different possible paths of history and the one I was experiencing was just one of those paths or something like that. That was the assumption that I had made about what the Ford Blue Cruise hands-free highway driving takes the work out of being behind the wheel, allowing you to relax and reconnect while also staying in control."
},
{
"end_time": 1398.507,
"index": 58,
"start_time": 1377.056,
"text": " Enjoy the drive in Blue Cruise-enabled vehicles like the F-150, Explorer, and Mustang Mach-E. Available feature on equipped vehicles. Terms apply. Does not replace safe driving. See Ford.com slash Blue Cruise for more details. The implications of this idea of multi-way systems would be what I realized when we did the physics project"
},
{
"end_time": 1419.718,
"index": 59,
"start_time": 1398.831,
"text": " 2019 was that in fact that isn't the right picture that the idea of multi-way systems and the idea that there are these many parts of history that's the right story but the thing to realize is we are embedded as observers in this universe that is branching all the time and the critical point then is that we are branching as well"
},
{
"end_time": 1440.674,
"index": 60,
"start_time": 1420.094,
"text": " So this from this idea from this first sort of naive idea that when you have something we have many branches of history that our sort of our experience must just go down one branch that's really not the right picture actually there are a couple of issues one is that the branches can merge and the other is that our our experience."
},
{
"end_time": 1459.36,
"index": 61,
"start_time": 1440.93,
"text": " Can span many branches we are we are extended objects mines are extended objects in this branch real space in the space of these possible branches i didn't realize that until until twenty nineteen and that makes it that that means that my sort of ignoring multi-way systems."
},
{
"end_time": 1487.363,
"index": 62,
"start_time": 1459.718,
"text": " for you know close to 30 years as it was a was a piece of sort of incorrect prejudice and i was kind of lucky enough that eventually kind of started thinking about look might as well try and take this seriously and uh and see how and see what its consequences actually are was something actually jonathan gorard was was one of the people who's like you should take these more seriously i'm not sure he saw what what the outcome would be but that was a you know why are you resisting this so much"
},
{
"end_time": 1513.114,
"index": 63,
"start_time": 1487.363,
"text": " It's it's pap say so that that was some but so so it's a it's it's always an interesting thing when you have kind of this the sort of you have to have a belief about how things are gonna work otherwise you don't even look there. Would you have to you know just believe the experiments are doing all the things you figure out i mean for me if i was doing i mean back in the day when i."
},
{
"end_time": 1540.094,
"index": 64,
"start_time": 1513.473,
"text": " Was long ago when I was sort of first doing physics I worked a bunch with Dick Feynman who was a you know physicist who one of his great strengths was he was a really good human calculator and I Can't do that. I'm I'm a good computer calculator, but not a good human calculator I built these computer tools because I wasn't a very good human calculator but Dick Feynman was really good at doing these calculations and getting to the right answer and"
},
{
"end_time": 1546.817,
"index": 65,
"start_time": 1540.486,
"text": " And then he would go back and say he didn't think anybody would be impressed by the fact that he got to the right answer by doing this complicated calculation."
},
{
"end_time": 1573.2,
"index": 66,
"start_time": 1547.056,
"text": " He thought people would only be impressed if he could come up with this really simple kind of intuitive explanation of what was going on, which he often managed to come up with. Then he would throw away the calculation, never tell anybody about the calculation. And everybody would be like, how did you possibly figure out this kind of intuitive thing? And they'd all think, oh, it must be simple to come up with this intuitive thing. It wasn't simple. It was the result of some long calculation, which he didn't think anybody would be impressed with because he found it easy to do those things."
},
{
"end_time": 1602.244,
"index": 67,
"start_time": 1573.2,
"text": " No i don't know for me the only kind of thing where i know i know what i'm talking about is i do a computer experiment it comes out in a certain way the computer does what the computer does there's no kind of sort of i might have made a mistake somewhere type type situation. I think if i look at the kinds of things that i've tended to do in science they sort of mix what one might think of is kind of philosophy and what one might think of is this kind of very detailed kind of solid"
},
{
"end_time": 1617.875,
"index": 68,
"start_time": 1602.534,
"text": " computational experiments and so on i mean that turns out to be for me has been sort of a powerful methodology for dealing with things to go from on the one side sort of a a general almost philosophical understanding of of how things might work."
},
{
"end_time": 1643.626,
"index": 69,
"start_time": 1618.234,
"text": " And then sort of the challenges to be able to sort of think computationally fluid fluently enough that you can go from that sort of philosophical understanding to say okay here's the program i should run that is a manifestation of that philosophical understanding and then let's see what it actually does and then i don't have to worry am i getting it wrong because the program just does what the program does and it's kind of uh you know i i find it"
},
{
"end_time": 1665.282,
"index": 70,
"start_time": 1644.462,
"text": " Kind of charming when my new kind of science book came out in 2002, people saying, but it's wrong. It's like, what does that mean? What about it was wrong? I don't know. But I mean, it's it's kind of people assume that you could sort of have got the wrong answer by doing the wrong calculation or something. But this is the nature of computer experiments. You just"
},
{
"end_time": 1686.903,
"index": 71,
"start_time": 1665.52,
"text": " You know you specify the rule you run the program the program does what the program does there's no you know no humans are involved no possibility of error exists so to speak. You can be wrong in the interpretation of what what's happening you can be wrong in the belief that what's happening in the computer experiment is relevant to something else but the actual experiment itself."
},
{
"end_time": 1708.507,
"index": 72,
"start_time": 1687.244,
"text": " Just is what it is now you can you can be confused i will say and the number one source of confusion is when people don't look at everything that happens in the experiment. So people say is a certain tendency and science people have had this idea that you know being scientific is about generating numbers."
},
{
"end_time": 1726.869,
"index": 73,
"start_time": 1708.814,
"text": " And so one quite common type of mistake is to say, well, you know, there's a lot of detailed stuff going on underneath, but I'm just going to plot this one curve as the result. And that means that you don't really get to see sort of the detail of what's happening. You're just seeing this one sort of summary."
},
{
"end_time": 1755.179,
"index": 74,
"start_time": 1727.108,
"text": " And sometimes that one summary can be utterly confusing. It can just lead you into the into kind of the thinking the wrong thing. And so for me, you know, being able to have sort of the highest bandwidth thing that I think we have to kind of understand what's going on is our visual system and being able to sort of visualize what's happening in as much detail as possible. I've always found very important. And often when I do projects, in fact, I just got bitten with this in the very latest project that I was doing."
},
{
"end_time": 1778.643,
"index": 75,
"start_time": 1755.179,
"text": " I always try to make sure that i front the effort to make the best possible visualization because if you know the the thing that one does that's a mistake is to do the project with kind of crummy visualizations and then say now i'm going to present it i'm going to make a really good visualization then you do a really good visualization and then you're like oh gosh there's something i can now see i didn't see while i was doing the project."
},
{
"end_time": 1803.746,
"index": 76,
"start_time": 1778.882,
"text": " Just a little bit bitten with that because there was a particularly complicated kind of visualization that I didn't go to the effort to make quite early enough in the project that I'm currently doing and so I'm just having to redo a bunch of things because I realized that I can understand them much more clearly using this sort of more sophisticated visualization technique but that's a you know that that's kind of you know you have to that's that's just one of these things when you kind of start"
},
{
"end_time": 1828.012,
"index": 77,
"start_time": 1804.224,
"text": " sort of thinking computationally about things. This idea that you can see as deep into the computation as possible is important rather than saying all I care about here is this thing of plotting this one curve because that's what scientists have done for the last couple of hundred years. I think one of the things I realized only very recently about my own personal sort of scientific journey"
},
{
"end_time": 1853.353,
"index": 78,
"start_time": 1828.422,
"text": " is back in the early eighties, I started doing a bunch of computer experiments, visualizing sort of the computations that were going on, figuring things out from that. And for me, doing that in 1981 or something like that was completely obvious. It was like, how could you ever not think about doing something like that? But I was was the question was, why was that obvious to me? And it turns out what I had been doing for several years previously,"
},
{
"end_time": 1880.811,
"index": 79,
"start_time": 1853.746,
"text": " was building my first big computer system, which was a system for doing algebraic computation, symbolic algebraic computation. And I had gotten into doing that because I was doing particle physics. In particle physics, one of the things you get to spend a lot of time doing is computing Feynman diagrams. Feynman diagrams are this way of working out, well, actually this S matrix thing that I'd mentioned earlier, a particular way of doing that that's sort of the best way we know to do it."
},
{
"end_time": 1907.654,
"index": 80,
"start_time": 1881.203,
"text": " I have to say as a footnote to this, Dick Feynman always used to say about Feynman diagrams, they're kind of the stupidest way to do this kind of calculation. There's got to be a better way, he said. I remember one time telling him, if you work out a series that you generate of Feynman diagrams, and I think at the kth order in these Feynman diagrams, I'd worked out that the computational complexity of doing these Feynman diagrams went like k factorial to the fifth power."
},
{
"end_time": 1918.695,
"index": 81,
"start_time": 1907.978,
"text": " So as you go to higher orders, it takes unbelievably much more difficult to work things out. And so I was telling the studio five minutes like, yeah, this is a stupid way to work things out. There's got to be a better way to do it."
},
{
"end_time": 1938.524,
"index": 82,
"start_time": 1919.087,
"text": " We haven't known what that better way is i'm sort of excited right now because i finally think i understand kind of in a bigger more foundational picture of what Simon diagrams really are and how one can think about them in a way that does allow one to sort of go underneath that formalism and potentially work things out in a much more effective way."
},
{
"end_time": 1956.152,
"index": 83,
"start_time": 1938.524,
"text": " It's it's i mean to serve it's a it's a spoiler for some things i'm still working on but but i'm essentially in five diagrams you're drawing these diagrams that sort of say an electron goes here and then it interacts with the photon and then the photon interacts with the electron and so on it's a diagram of sort of interactions."
},
{
"end_time": 1983.933,
"index": 84,
"start_time": 1956.152,
"text": " What i realized is that really those diagrams are diagrams about causality the diagrams that show the lines that represent his electron really the electron is basically a carrier of a causality there's a there's an event that happens when electron interacts with the photon and that event has a causal effect on some other event and that causal connection is represented by this this this electron line so to speak in this Feynman diagram"
},
{
"end_time": 2007.159,
"index": 85,
"start_time": 1983.933,
"text": " That way of understanding things allows want to connect sort of fine diagrams to a bunch of things that have come up in our physics project to do with things we call multi-way causal graphs and there's a whole rather lovely theory that's starting to emerge about these things but haven't figured it all out yet in any case that that's a that's a relevant side show but but back in the in the late seventies i was."
},
{
"end_time": 2036.578,
"index": 86,
"start_time": 2007.568,
"text": " Trying to get computers to do these very ugly nasty calculations of Feynman diagrams because that was the only way we knew to work out the consequences of quantum field theory in those days, particularly QCD, which was a young field in those in those days. And I as the teenage me had the fun of being able to work out a bunch of calculations about QCD sort of for the first time. And now that they're well known sort of classic kinds of things, but then they were fresh and new because it was a new field."
},
{
"end_time": 2062.278,
"index": 87,
"start_time": 2037.039,
"text": " but it was one of the things that had happened was I had built a bunch of capability to do symbolic computation algebraic computation and one of the features of doing algebraic computation is that you don't just get a number as the answer if the answer to your calculation if the computer spits out 17.4 there's not a lot you can do with 17.4 there's not a lot of intuition you can get from 17.4 on its own"
},
{
"end_time": 2072.21,
"index": 88,
"start_time": 2062.602,
"text": " But when the computer spits out this big long algebraic expression, it has a lot of structure. And one of the things that I had kind of learned to do was to get intuition from that structure."
},
{
"end_time": 2099.633,
"index": 89,
"start_time": 2072.534,
"text": " And so, for example, if you're doing, I don't know, you're doing integrals, let's say, I've never been good at doing integrals by hand, but I became what I learned from doing thousands of integrals by computer was kind of things about the intuition about the structure of what happens in integrals. And that allows one to kind of make to sort of jump ahead and see this complicated integral. It's going to be, I think, intuitively roughly of the structure. And that's a big clue in actually being able to solve the thing on a computer."
},
{
"end_time": 2112.79,
"index": 90,
"start_time": 2099.633,
"text": " So the thing i realized only very recently is that my kind of experience and doing other great computation i got me used to the idea that what a computer produces will be a thing that has structure from which you can get intuition."
},
{
"end_time": 2140.316,
"index": 91,
"start_time": 2113.251,
"text": " and so when i started thinking about sort of actual simple programs and what they do it was sort of obvious to me really was obvious that i should just make some sort of visualization of what all the steps that were going on were because i expected to get intuition from kind of the uh the the innards of the computation so to speak you know however you know one of the things that i will say is that i started studying in that case cellular automata"
},
{
"end_time": 2168.831,
"index": 92,
"start_time": 2140.725,
"text": " Back in 1981 and I found out a bunch of things about cellular automata I thought they were pretty interesting and I had generated back in 1981 I generated a picture of this thing called rule 30 which is a particular cellular automaton that's been my all-time favorite and it has the feature that has very very simple rule but you start it off from just this one black cell it makes this complicated pattern many aspects of that pattern look for all practical purposes random"
},
{
"end_time": 2193.592,
"index": 93,
"start_time": 2169.155,
"text": " It's something that i my intuition back in nineteen eighty one said couldn't happen it said if the rule was simple enough there will be a trace of that simplicity in the behavior that's generated and so when i can i generated a picture of rule thirty even put it into paper i published but i didn't really pay attention to it because i my intuition was so strongly nothing like that can happen."
},
{
"end_time": 2222.398,
"index": 94,
"start_time": 2194.155,
"text": " then actually sort of methodologically amusing i think june of 1984 i happened to get a high resolution laser printer they were a new thing they were big clunky objects at that time and i thought i was i was going to go on some plane flight and i thought i'll make some some cool pictures for my new laser printer so i printed out will 30 at high resolution and took it with me and i'm starting to look at it and it's like hmm what on earth is going on here"
},
{
"end_time": 2242.944,
"index": 95,
"start_time": 2222.705,
"text": " I finally sort of really started well actually some other things that happened I also studied other aspects of cellular automata and how they related to theory of computation and so on and that kind of primed me for really looking more seriously at this picture and realizing oh my gosh this is something that is completely violates the intuition that I've always had"
},
{
"end_time": 2273.37,
"index": 96,
"start_time": 2243.439,
"text": " That you know, to get get something complicated, you need complicated rules in some sense, or you need a complicated initial condition. This is something new and different. And it is kind of amusing that I realized that it really at that point, I was primed enough from the other things I'd studied particularly about computation theory, that it only took a couple of days before I was like, sort of telling people about I was I was going to some conference and actually the the I found recently there was a transcript of a q&a session at that conference, where I'm kind of"
},
{
"end_time": 2297.073,
"index": 97,
"start_time": 2273.831,
"text": " Talking as if I'd known it forever about, you know, how rules 30 works and so on. Um, but actually I'd only known it for two days, but, um, but an important point about that was first of all, it's something I had kind of quotes discovered, but I had not understood. I had not internalized it, but to be able to internalize it required me to build up a bunch of other contexts to"
},
{
"end_time": 2308.473,
"index": 98,
"start_time": 2297.534,
"text": " From studying well a bunch of things about cellular automata a bunch of things about computation theory given that priming I was able to actually understand this point about what rule 30 is and what it means"
},
{
"end_time": 2335.077,
"index": 99,
"start_time": 2308.882,
"text": " Now, for example, this phenomenon of computational irreducibility, I'm now at sort of 40 years and counting since I came up with that idea. And I'm still understanding what its implications are for whether it's for, you know, AI ethics or for, you know, proof of work for blockchains or a whole bunch of different areas. I'm only now understanding. In fact, the thing that I've understood most recently, I would say, is that I think a lot of the story of physics"
},
{
"end_time": 2342.108,
"index": 100,
"start_time": 2335.213,
"text": " is a story of the interplay between computational irreducibility and our kind of limitations as observers of the world."
},
{
"end_time": 2369.053,
"index": 101,
"start_time": 2342.449,
"text": " But it's it's something like, for example, the simplest case is the second law of thermodynamics, where kind of the idea is you got a bunch of molecules bouncing around and the second law says most of the time it will seem like those molecules get sort of more random in the configurations that they that they take on. And that's something people wondered about since the mid eighteen hundreds. And I had wondered about it was one of the first things I got really interested in in physics."
},
{
"end_time": 2389.94,
"index": 102,
"start_time": 2369.053,
"text": " Back when i was twelve years old or so was this phenomenon of how does randomization happen in the second law and what i finally have understood is that it happens because the sort of computational irreducibility of the underlying dynamics of these molecules bouncing around yet that sort of there's an interplay between that."
},
{
"end_time": 2410.196,
"index": 103,
"start_time": 2390.196,
"text": " And the sort of computational limitations of us as observers because we as observers aren't capable of sort of decrypting the sort of computation that happened in this underlying struck in his underlying collisions. We just have to say oh it looks random to me so to speak and so that this phenomenon of computational irreducibility."
},
{
"end_time": 2434.991,
"index": 104,
"start_time": 2410.503,
"text": " So at 40 years and counting, I'm still understanding sort of the implications of this, this particular idea. And I mean, another thing to say about sort of the progress of science, which I can see in my own life. And I can also see from history of science is it can take a long time. Once you've, once you've had some sort of paradigmatic idea, it can take a long time for one to understand the implications of that idea."
},
{
"end_time": 2461.305,
"index": 105,
"start_time": 2435.606,
"text": " And I know for things I've done, I'm fully recognized the fact that it's taken me sometimes 30 years or more to understand what it was that I actually really discovered. It's kind of like if other people don't figure it out for 50 or 100 years, that's kind of par for the course because it took me 30 years to figure out what the significance of this or that thing was. I mean, I see that also in technology development."
},
{
"end_time": 2476.135,
"index": 106,
"start_time": 2461.527,
"text": " I'm building open language we build certain paradigmatic ideas we have certain ideas about the structure of what can be in the language and then it can sometimes take a decade before we really realize given that structure is what you can build."
},
{
"end_time": 2503.473,
"index": 107,
"start_time": 2476.34,
"text": " You kind of have to get used to the ideas you have to grind around the ideas for a long time before you kind of get to the next step and kind of seeing what what's possible from them. I kind of see it as sort of this tower that one's building of ideas and technology in that case and the the higher you are on the tower the further you built up on the tower the further you can see into the distance so to speak about what what other things might be possible. I think sometimes when when people sort of hear about"
},
{
"end_time": 2526.049,
"index": 108,
"start_time": 2503.985,
"text": " Think Verizon, the best 5G network is expensive? Think again. Bring in your AT&T or T-Mobile bill to a Verizon store today and we'll give you a better deal. Now what to do with your unwanted bills? Ever seen an origami version of the Miami Bull?"
},
{
"end_time": 2555.811,
"index": 109,
"start_time": 2526.51,
"text": " And just figures out some big thing. Right. My own efforts in studying history of science and my own experience in my life doing science is that simply never happens."
},
{
"end_time": 2574.821,
"index": 110,
"start_time": 2556.425,
"text": " It's this basically many years usually a decade of build up to whatever it is one is going to potentially discover i mentioned i mentioned what happened with me and will thirty once i was adequately primed. It was kind of like it all happened very quickly but that priming took many years."
},
{
"end_time": 2600.469,
"index": 111,
"start_time": 2574.821,
"text": " And that's the thing that usually what's reported in the story book so to speak is only that final moment. After that priming of when you realize well actually this fits together in that way and so on and you can then describe the what happened when things i learned about about einstein recently was that in nineteen oh four he'd written several papers."
},
{
"end_time": 2620.674,
"index": 112,
"start_time": 2600.759,
"text": " About a very different subject written a bunch of papers about thermodynamics particularly about the second law of thermodynamics. I think it was much influenced by Boltzmann who was a very philosophically oriented physicist person who sort of believe you could figure out things about physics just by thinking about them in the style of natural philosophy so to speak."
},
{
"end_time": 2649.599,
"index": 113,
"start_time": 2620.998,
"text": " rather than sort of being driven by, you know, experiment to experiment type type thing. And Boltzmann figured out a bunch of things about atoms. Boltzmann had basically had the core ideas of quantum mechanics, although of discreteness and so on. That's what Planck picked up when he studied the black body radiation problem. But in any case, you know, at the time, 1904 was still very much, can you prove the second law of thermodynamics?"
},
{
"end_time": 2678.524,
"index": 114,
"start_time": 2650.009,
"text": " from some underlying principles that did not just sort of introduce the second law as a law of physics. Could you prove it from some underlying mechanical principles? And many people have been involved in that. Planck was trying to do that actually. Planck was trying to do that when he discovered the quantum mechanics, the way of sort of understanding black body radiation in terms of discrete photons. I mean that's a weird story because Planck, people had wondered why"
},
{
"end_time": 2698.353,
"index": 115,
"start_time": 2678.78,
"text": " Do things get more random and they kept on saying to get randomness you have to have some magic source of randomness so planks idea was that infrared radiation, radiative heat would be sort of the magic source of randomness that would sort of produce heat and everything and lead to that randomness."
},
{
"end_time": 2714.07,
"index": 116,
"start_time": 2698.78,
"text": " And so he was actually studying that question when experiments came out about black body radiation and he then noticed that these calculations that Boltzmann had done a lot earlier when Boltzmann just as a matter of sort of mathematical convenience had said let's assume energy is discrete."
},
{
"end_time": 2742.363,
"index": 117,
"start_time": 2714.65,
"text": " and had then worked out what the consequences of that were. Planck said, well, actually, if you say that energy really is discrete, you fit this data a lot better than otherwise. It took Planck another decade to actually believe that this was more than just a mathematical trick. Einstein was the one who really sort of said photons might be real a few years later. But in any case, the thing was interesting there was that Einstein was using kind of this natural philosophy, philosophical approach to science,"
},
{
"end_time": 2772.278,
"index": 118,
"start_time": 2742.637,
"text": " as a way to think about how things might work and he tried applying that to thermodynamics in 1904 and he didn't get it right you know he didn't figure out the second law he i mean in as we now know sort of the paradigmatic ideas that you need to figure out the second law come from ideas about computation and so on which were another close to 100 years in the future so to speak um but it's sort of interesting that that you know he was applying those kinds of philosophical thinking ideas"
},
{
"end_time": 2800.742,
"index": 119,
"start_time": 2772.278,
"text": " And it was a misfire in thermodynamics. It was a hit in in relativity in the photoelectric effect and the existence of photons and also in Brownian motion. But it's kind of it's sort of it's an interesting sort of footnote to the history of science. I think you know another another point that one realizes there is there are things that there is an ambient level of understanding that will allow one to make progress in that area."
},
{
"end_time": 2825.742,
"index": 120,
"start_time": 2801.015,
"text": " And there are things where there isn't. And in fact, Einstein himself, I think 1916, he wrote to somebody, you know, in the end, space will turn out to be discrete. But right now we don't have the tools necessary to see how this works, which was very smart of him. I mean, that was that was correct. You know, it took another hundred years to have those tools. But it's it's a thing where when you think about science, another issue is"
},
{
"end_time": 2851.34,
"index": 121,
"start_time": 2826.084,
"text": " Are you at the right time in history, so to speak, is the is the ambient sort of understanding of what's going on sufficient to let you make the progress you want to make. So one area I've been interested in recently is biology, where biology has been a field which is really hasn't had a theory. You know, the the best the closest one gets to a theory in biology is natural selection from 1859. And, but that's a very"
},
{
"end_time": 2875.64,
"index": 122,
"start_time": 2851.783,
"text": " It's you know if we say well why is biology the way it is you know is there when we look at a biology textbook what's the theoretical foundation of a biology textbook we haven't known that and the question is can there be a theory of biology most biologists don't really imagine there's a theory of biology they just say we're collecting this data we do these experiments we work out these probabilities of things if you're doing medicine that's the typical approach"
},
{
"end_time": 2894.872,
"index": 123,
"start_time": 2875.879,
"text": " Sorry, why wouldn't computational irreducibility come into play in the biological case for you to say or for a biologist to say that that's the reason why there is no toe for biology?"
},
{
"end_time": 2920.555,
"index": 124,
"start_time": 2896.237,
"text": " that's uh there's some truth to that that's um am i jumping ahead no no that's good good inference i mean that that's um uh i think the reason that biology looks as complicated as it does is precisely because of computational irreducibility and the thing that surprised me this is another sort of story of my my life in science i'd worked on cellular automata back in the early 80s"
},
{
"end_time": 2939.411,
"index": 125,
"start_time": 2920.862,
"text": " It's kind of funny fact is that this was days before the internet so you couldn't look things up as easily or at least before the web and you know people had seen that I was working on cellular automata so I got invited to all the theoretical biology conferences because I figured this must be about you know"
},
{
"end_time": 2957.944,
"index": 126,
"start_time": 2939.838,
"text": " Biological kinds of things and it was kind of funny because I'm looking back now. I was kind of the period in the 1980s was a time when there was sort of a burst of somewhat interest in theoretical biology. They've been another one in the 1940s and it kept on sort of dying off. But in 1980s there was something of a burst of interest."
},
{
"end_time": 2982.807,
"index": 127,
"start_time": 2957.944,
"text": " I realized that somebody was working with me now who's a biologist sort of went back and looked at some of these conferences and things and I keep on saying I don't really know much about biology and he kept on saying but you were there at all these key conferences just a moment it's one thing for you to be invited because of cellular automata it's another thing for you to accept it why did you go if you thought hey this is irrelevant oh no I didn't think it was irrelevant I thought it was interesting I just I just"
},
{
"end_time": 3012.159,
"index": 128,
"start_time": 2982.807,
"text": " You know had a certain I see and you know when I talked about cellular automata there and talked about the way that cellular automata are relevant to things like the growth of organisms and so on I thought it was interesting and other people thought it was interesting and it turned into a whole sort of subfield of people studying things but I didn't think when it comes to sort of the foundations of biology and things like you know why does natural selection work I didn't think it had anything much well I wasn't sure if there's anything to say about that"
},
{
"end_time": 3030.538,
"index": 129,
"start_time": 3012.637,
"text": " back in mid eighties i tried to see if it had something to say about that i tried to see if you could take cellular automata which have these definite little underlying rules those little underlying rules a little bit like genomic sequences so to speak which where the genome is specifying the rules for building an organism"
},
{
"end_time": 3055.879,
"index": 130,
"start_time": 3030.794,
"text": " So in a cellular automaton, you can think about it the same way. The underlying rules specify the rules for building these patterns that you make and cellular automata. So the obvious question was, could you evolve those rules like natural selection does? Could you make mutations and, you know, selection and so on on those underlying rules? And could you see that produce some sort of interesting patterns of growth? So I tried that in 1975."
},
{
"end_time": 3079.718,
"index": 131,
"start_time": 3056.288,
"text": " I didn't find anything interesting"
},
{
"end_time": 3091.101,
"index": 132,
"start_time": 3080.094,
"text": " Instead of looking and it's not going to work so well with something as simple as rule 30 because you can go from rule 30 to rule 31. Rule 30 and rule 31 behave very, very differently."
},
{
"end_time": 3119.787,
"index": 133,
"start_time": 3091.596,
"text": " By the time you're dealing with sort of rule a billion rule a billion and one potentially doesn't behave really that differently from rule a billion because many of the cases in the rule the rule is saying if you see right near where you are if you see you know cells red green white then make this and you know by the time you've got enough colors and so on you can imagine that you know some of those particular rules won't even be used when a typical pattern is produced"
},
{
"end_time": 3146.374,
"index": 134,
"start_time": 3120.128,
"text": " It'll be something so you can make some small changes to those rules and expect that maybe it won't make a huge change in what comes out. At least in the short run. Yes. Yes. I mean, well, that's a complicated issue because it depends on what what what subpart of the rules you end up selecting and as you make this pattern. I mean, it's usually the case that when you make you produce some pattern from a cellular automaton, for example, every little subpatch"
},
{
"end_time": 3163.677,
"index": 135,
"start_time": 3146.954,
"text": " Is typically using only some small subset of the rules and it's using them in some particular way and it maybe makes some periodic little sub patch and then something comes along that's using some different part of the rule sort of crashes into that and destroys that that the simplicity that existed in that region."
},
{
"end_time": 3183.558,
"index": 136,
"start_time": 3163.985,
"text": " So it's a you're in the case of rule thirty there are only eight cases in its rule so you don't get to making any change to that is a big change. By the time you have something with like say twenty seven cases in its rule you can kind of imagine you're making a little change in that is less significant to the to the behavior that will occur."
},
{
"end_time": 3202.432,
"index": 137,
"start_time": 3183.968,
"text": " But okay back in mid 1980s I tried this I tried looking at slightly bigger rules and making small changes and find anything interesting regarding biology yes okay yes no I was interested in kind of a model for natural selection it was a time when artificial life was first being talked about."
},
{
"end_time": 3230.35,
"index": 138,
"start_time": 3202.824,
"text": " And worked on and, you know, Chris Langton had done a bunch of nice stuff with, with my cellular automata and thinking about artificial life and so on. And it was, it was seen sort of the obvious thing to do, but it didn't work. And so for years I was like, it's just not going to work. And then last year, actually, I was interested in the foundations of machine learning and the big lesson of machine learning that we particularly learned in 2011 was"
},
{
"end_time": 3252.875,
"index": 139,
"start_time": 3230.657,
"text": " In a neural net, if you bash it hard enough, it will learn stuff. And, you know, that wasn't obvious. Nobody knew that you could get sort of a deep learning, you know, a deep neural net to recognize images of cats and dogs and things. And so by accident, it was discovered that if you just leave the thing learning long enough, if you leave it training long enough, it comes out and it's actually succeeded in learning something."
},
{
"end_time": 3274.855,
"index": 140,
"start_time": 3253.183,
"text": " So that's a new piece of intuition that you can have a system like a neural net. Neural net is much more complicated than one of these cellular automata. You can have a system like that. You just keep bashing it, you know, a quadrillion times or something, and eventually it will successfully achieve some fitness function, will learn something, will do the thing you want it to do. So that was a piece of new intuition."
},
{
"end_time": 3294.65,
"index": 141,
"start_time": 3275.23,
"text": " So i thought let me just run these you know this i was writing something about actually foundations of machine learning and i thought let me just try this experiment that i you know i i thought i tried it in the 1980s and it hadn't worked but now we know something different from our studies of neural nets let me try running it a bit longer and by golly it worked"
},
{
"end_time": 3323.302,
"index": 142,
"start_time": 3294.838,
"text": " And I felt a bit silly for not having discovered this anytime in the intervening, you know, 40 years or something. But nevertheless, it was really cool that it worked. And that meant that one could actually see for the first time kind of sort of more in a more detailed way how natural selection operates. And what's really going on in the end is computational irreducibility lets one go from these quite simple rules, these very elaborate kinds of behavior."
},
{
"end_time": 3352.312,
"index": 143,
"start_time": 3323.968,
"text": " The fitness functions, the things that sort of are determining whether you survive or not, those are fairly coarse in biology. But let's imagine you have a coarse fitness function, like what's the overall lifetime of the pattern before it dies out? Let's say, how wide does the pattern get? You're not saying how it gets wide, you're not saying particularly, but you're saying how wide does it get? It turns out with those kinds of coarse fitness functions, you can successfully achieve high fitness"
},
{
"end_time": 3382.022,
"index": 144,
"start_time": 3352.619,
"text": " But you achieve it in this very complicated way you achieve it by sort of putting together these pieces of irreducible computation and that that means that so so in the end the answer I think to why does biological evolution work is that it is the same story as what happens in physics and mathematics actually it is an interplay between underlying computational irreducibility and the computational boundedness of quotes observers of that of that computation."
},
{
"end_time": 3406.442,
"index": 145,
"start_time": 3382.022,
"text": " So in the case of physics, the observers are us, you know, doing experiments in the physical world. In the case of mathematics, it's mathematicians looking at the structure of mathematics. In the case of biology, the observer is kind of the environment. It's the, it's the fitness function. The fitness function is kind of the, the analog of the observer and the fitness function is saying you're a success if you achieve this kind of course objective."
},
{
"end_time": 3435.043,
"index": 146,
"start_time": 3406.817,
"text": " And the reason that biological evolution works is that kind of there's so much power in the underlying irreducible computation that you're able to achieve many of these kind of course fitness functions. So, you know, if you imagine that the only way I don't know, an organism could survive is if it breaks out of its egg and immediately it, you know, computes a thousand primes and does all kinds of other weird things."
},
{
"end_time": 3458.404,
"index": 147,
"start_time": 3435.043,
"text": " Right it's not going to survive that right it's so i was born as one does but you know that that's one isn't going to be able to hit that particular very complicated target. What actually happens is the fitness functions are much closer than that and that's why biological evolution has been able to work."
},
{
"end_time": 3485.998,
"index": 148,
"start_time": 3458.831,
"text": " But if you say, well, what's actually going on inside? What's going on inside is big pieces of computational irreducibility being stuck together in a way that happens to achieve this course fitness function. By the way, this is the same thing as what's going on in machine learning, I think. In machine learning, it's the same story that the fitness function in that case is you're trying to achieve some training objective. And you do that by sort of fitting together these lumps of irreducible computation"
},
{
"end_time": 3507.892,
"index": 149,
"start_time": 3485.998,
"text": " I've kind of the analogy i've been using is it's kind of like building a stone wall you're doing kind of precise engineering you might build a wall by making precise bricks and putting them together in a very precise way but the alternative is you can make a stone wall where you're just picking up random rocks off the ground and noticing well this one more or less fits in here let me stick that in that way and so on"
},
{
"end_time": 3533.148,
"index": 150,
"start_time": 3507.892,
"text": " That's what's going on in machine learning you're sort of building the stone wall if you then say well why does this particular feature of this machine learning system work the way it does it's well because we happen to find that particular rock lying around on the ground because we happen to go down this particular branch in the random numbers that we chose for the training and so it's kind of an assembly of these random lumps of irreducible computation that's what we are too."
},
{
"end_time": 3559.036,
"index": 151,
"start_time": 3533.899,
"text": " And that's, I mean, in biology has, you know, the biology, the history of biology on earth, you know, the dice has been rolled in particular ways. We have stuck together these sort of lumps of irreducible computation and we make the organism that we are today. There are many other possible paths we could have taken, which would have also achieved a bunch of fitness objectives, but it was just a particular historical path that was taken. One of the things that's kind of a,"
},
{
"end_time": 3581.135,
"index": 152,
"start_time": 3559.258,
"text": " A sort of slightly shocking thing to do is to take one of these evolved cellular automata that looks very elaborate, has all these mechanisms in it, has all these patches that do particular things and that fit together in these interesting ways and so on. And you ask an alum, say, write a description of this pattern in the style of a biology textbook. And it's kind of shocking because it sounds just like biology."
},
{
"end_time": 3600.094,
"index": 153,
"start_time": 3581.425,
"text": " Because it's successfully describing, you know, there's a, there's a, I don't know, it makes up sometimes it can make up names for things, you know, there's a, there's a distal, you know, distal triangle of this and so on. And, you know, interacting with this and this and this, and you go and you open a biology textbook and it"
},
{
"end_time": 3620.64,
"index": 154,
"start_time": 3600.811,
"text": " It's a description in biology and the detail in biology is a description of this particular sort of sequence of pieces of computational irreducibility that got put together by the history of life on earth and that make us as we are today. Now, you know, since we care about us,"
},
{
"end_time": 3643.763,
"index": 155,
"start_time": 3621.032,
"text": " It's very worthwhile to study that detailed history that detailed lump of computational reducibility that is us. But if you want to make a more general theory of biology, it better generalize beyond the details of us and the details of our particular history. And the thing that I've been doing actually most recently is I think we are the beginnings of finding a way to talk about"
},
{
"end_time": 3669.07,
"index": 156,
"start_time": 3644.241,
"text": " sort of any system that was adaptively evolved. So in the case of the, if we look at all possible rules that could be going on in biology, many of those rules won't be ones that would have been found by adaptive evolution with course fitness functions. So a way to think about this, so the thing"
},
{
"end_time": 3699.275,
"index": 157,
"start_time": 3669.804,
"text": " We look at sort of what is biology doing? Biology is, if nothing else, a sort of story of bulk orchestration and molecular processes. There are all these, you know, one might have thought at some time in the past that biology is sort of just chemistry. And by that I mean, in chemistry, we sort of imagine we've got liquids and so on, and they just have molecules randomly bumping into each other. But that's not what biology mostly seems to be doing. Biology is mostly a story of detailed assemblies of molecules that orchestrate"
},
{
"end_time": 3721.22,
"index": 158,
"start_time": 3699.275,
"text": " You know this molecule hooks into this one and then does this and et cetera et cetera et cetera and so discoveries in microbiology keep on being about how orchestrated things are not how this molecule randomly bumps into it's sophisticated. Yes but it's also has the sort of mechanism to it it has a it's not it's not just random collisions."
},
{
"end_time": 3736.135,
"index": 159,
"start_time": 3721.22,
"text": " It's this molecule is guided into doing this with this molecule and so on it's a it's a big tower of things a bit like in these evolved cellular automata which also do what they do through this big tower of detailed sort of applications rules and so on."
},
{
"end_time": 3755.418,
"index": 160,
"start_time": 3736.493,
"text": " But so what I'm extra value meals are back. That means 10 tender juicy McNuggets and medium fries and a drink are just $8 only at McDonald's for limited time only prices and participation may vary. Prices may be higher in Hawaii, Alaska and California and for delivery is to have a theory of bulk orchestration."
},
{
"end_time": 3775.213,
"index": 161,
"start_time": 3755.93,
"text": " That's something that can tell one about what happens in any system that is sort of bulk orchestrated, which can include things like a microprocessor, let's say, which has its own sort of complicated set of things that it does. A microprocessor is not well described by the random motion of electrons. It's something different from that."
},
{
"end_time": 3801.101,
"index": 162,
"start_time": 3775.213,
"text": " What is it what can you and does that theory that you make of the microprocessor depend on the details of the engineers who who designed it or is there other necessary features of any system that has been built to achieve certain course grained purposes. So i'm sort of i'm going down this path we're not there yet i mean the the sort of in physics the idea of statistical mechanics is an idea that like this."
},
{
"end_time": 3830.23,
"index": 163,
"start_time": 3801.374,
"text": " The idea of statistical mechanics is once you have enough molecules in there, you can start making conclusions just by looking at averages based on what all possible configurations of molecules are like without having to have any details about the particulars of these collisions and those collisions and so on. The statistics wins out. There's lots of stuff there is more important than the details of what each of the pieces of stuff does."
},
{
"end_time": 3852.295,
"index": 164,
"start_time": 3830.623,
"text": " In the case of biology there's one additional thing you seem to know which is that the stuff you have has rules that was subject to some kind of adaptive evolution and. Even though we don't know what the purpose was you know when when people use sort of natural selection as a theory in biology they look at some weird creature and they say it is this way because."
},
{
"end_time": 3869.735,
"index": 165,
"start_time": 3852.585,
"text": " And sometimes that because is less convincing other times but that's been the model of how one makes a theory in biology and so i think the this this is what i'm what i'm interested in is there a theory that's independent of what the because it's just that there is a because."
},
{
"end_time": 3891.51,
"index": 166,
"start_time": 3870.06,
"text": " is that there is some coarse-grained fitness that's achieved is that enough of a criterion to tell you that something about sort of this bulk orchestration this limit of a large number of things subject to that kind of constraint actually I figured out something this morning about this we'll see whether it pans out but I have one of the things that's really really striking about"
},
{
"end_time": 3903.336,
"index": 167,
"start_time": 3891.766,
"text": " These kinds of systems where one has done this kind of adaptive evolution on large scale is you just make pictures of them and they just look very organic strange thing."
},
{
"end_time": 3932.91,
"index": 168,
"start_time": 3903.66,
"text": " I think I have an idea about how to sort of characterize that more precisely that may lead one to something that is sort of a I mean what one's looking for is something that's kind of a theory of bulk orchestration has for example information theory has been a general theory of just sort of the the the all possibilities type situation with with with data or with statistical mechanics and so on so you know this is but but sort of it's interesting methodologically perhaps"
},
{
"end_time": 3961.408,
"index": 169,
"start_time": 3933.643,
"text": " When I'm working on something like this, it's, it's an interesting, for me, it's an interesting mixture of thinking quite sort of philosophically about things and then just doing a bunch of experiments, which often will show me things that I absolutely didn't expect. And I suppose there's a third branch to that, which is doing my homework, which is, okay, so what have other people thought about this? And that what I found is that, you know, when I try and learn some field, I, I"
},
{
"end_time": 3988.49,
"index": 170,
"start_time": 3961.63,
"text": " often spend years kind of accumulating knowledge about that field and i'm you know i'm lucky enough i bump into the world experts on this field from time to time and i'll ask a bunch of questions and usually i'll be kind of probing the foundations of the field and one of the things i learned about sort of doing science is you might think you know the foundations of a field are always much more difficult to make progress in than some detail you know high up in the tree of that field"
},
{
"end_time": 4004.445,
"index": 171,
"start_time": 3988.746,
"text": " This is often not the case particularly not if the foundations were laid down fifty or hundred years ago or something because what's happened is you know what you will you discover is when you talk to the people sort of in the first generation of doing that field of science you say what about these foundations."
},
{
"end_time": 4032.363,
"index": 172,
"start_time": 4004.445,
"text": " They'll say good question we wondered about that we're not sure those foundations are right you know it's a truth and now you go five academic generations later and people say of course those foundations are right how could you possibly doubt that you know it just becomes that building on top of this you know this thing that's far away from the foundations. Well often the foundations are in a sense very unprotected nobody's looked at them for decades maybe longer."
},
{
"end_time": 4055.469,
"index": 173,
"start_time": 4032.705,
"text": " And often the ambient methodologies that exist have changed completely in modern times and things i've done a lot sort of the computational paradigm is the biggest such change and then you go look at these foundations and you realize gosh you can actually say things about these foundations which nobody has even looked at for ages because they were just building many layers above those foundations"
},
{
"end_time": 4085.06,
"index": 174,
"start_time": 4055.93,
"text": " And so I think that's, that's been one of the things I've noticed. And it's one of the, you know, for people who are sort of doing science and they want to make some, some progress in some particular area, it's like, well, what is the foundational question of this field? And, you know, sometimes people will sometimes have to think about that quite a bit, you know, what really is the question that we're really trying to answer in this field, foundationally, not the thing that is the latest thing that the latest papers we're talking about and so on, but what it really is the foundational question."
},
{
"end_time": 4102.773,
"index": 175,
"start_time": 4085.299,
"text": " And then you say what can you make progress on that foundational question and quite often the answer is yes and even the effort to figure out what the foundational question is is often very useful but by the way when you do make progress on a foundational question the kind of the trickle down into everything else is dramatic."
},
{
"end_time": 4126.732,
"index": 176,
"start_time": 4103.063,
"text": " Although often the stream trickles in a different area than the existing stuff have been built so in other words you make progress on the foundations and now there are new kinds of questions about that field that you get to be able to answer even if the existing questions you don't make very much progress on they've been well worked out by the existing foundations you make new foundations you can you can kind of answer a new collection of questions."
},
{
"end_time": 4144.991,
"index": 177,
"start_time": 4127.005,
"text": " So i think the i mean that that's a that's a sort of a typical pattern and for me kind of this this effort you know i tend to try and sort of ambiently understand some field for a while and then i'll typically form some hypothesis about it hopefully i'll be able to turn it into something kind of computational."
},
{
"end_time": 4168.063,
"index": 178,
"start_time": 4144.991,
"text": " And then i can do the experiments i can know that i actually getting the right answer and so on and then i try and go back and often at that point i'll try and understand how does this relate to what people thought about before and sometimes i'll say wow that's a great connection you know this person had figured out this thing you know fifty years ago that you know was pretty close to what i was talking about now."
},
{
"end_time": 4194.241,
"index": 179,
"start_time": 4168.404,
"text": " i mean like the idea of computational irreducibility for example once you have that idea you can go back and say well when did people almost have that idea before like newton for example had this statement that you know he'd worked out celestial mechanics and calculus for the motion of planets and uh he made this the statement that um uh you know what did he say he said something like um to work out the motions of all these different planets"
},
{
"end_time": 4211.749,
"index": 180,
"start_time": 4194.548,
"text": " Is beyond he said if i'm not mistaken the force of any human mind. So he had figured out that there would be no even though in principle he could calculate the motions these planets he very diplomatically said it was in his time it was beyond the force of any human mind."
},
{
"end_time": 4234.684,
"index": 181,
"start_time": 4212.125,
"text": " The force of the mind of god would be capable of working it out because after all the planets were moving the way they were moving but that was sort of a an interesting precursor that you can go back and see that that already you know he was thinking about those kinds of things but but sometimes and sometimes you find that people just utterly missed something that later seems quite obvious like for example one of the things in physics"
},
{
"end_time": 4247.961,
"index": 182,
"start_time": 4234.991,
"text": " Is the belief in space time the belief that time is something very similar to space which is something that's been quite pervasive in the last hundred years of physics and i think it's just a mistake i think einstein didn't really believe that."
},
{
"end_time": 4268.848,
"index": 183,
"start_time": 4248.268,
"text": " The person who brought in that idea was Herman Minkowski in 1919 who kind of noticed that this thing that Einstein had defined this kind of distance metric the proper proper time was you know t squared minus x squared minus y squared or whatever I see squared and Minkowski was a number theorist and he'd been studying quadratic forms."
},
{
"end_time": 4281.22,
"index": 184,
"start_time": 4269.155,
"text": " Sums of things with you know with squares and so on it's like this is a quadratic form it's so great and look you know time enters into this quadratic form just like space let's bundle these things together and talk about space time."
},
{
"end_time": 4308.148,
"index": 185,
"start_time": 4281.596,
"text": " and i think that was as i said i think that was a mistake i think that misled a lot of people i think my own view and which is pretty i think it's clear this is the way things work is that sort of the nature of space as kind of the extent of some data structure effectively some hypergraph for example that the nature of that is very different from the kind of computational process that is the unfolding of the universe through time"
},
{
"end_time": 4323.251,
"index": 186,
"start_time": 4308.387,
"text": " The fact that those things seem very different at first, it's then a matter of sort of mathematical derivation to find out the relativity works and makes them enter into equations and things together, but that's not their underlying nature."
},
{
"end_time": 4351.254,
"index": 187,
"start_time": 4323.575,
"text": " But but in a case that's a it's a thing where where in that particular case it was you know that was a thing where when you go back and look at the history you say why do people believe in space time why do people believe that space and time the same kind of thing you eventually discover that piece of history and you say they went off in the wrong direction sometimes you're like wow they figured it out you know they really were in the right direction and or they were in the right direction but they didn't have the right tools or whatever else and for me that's a very important grounding"
},
{
"end_time": 4379.582,
"index": 188,
"start_time": 4351.596,
"text": " To know that i know what i'm talking about so to speak like recently i was studying the second law of thermodynamics i finally think after sort of 50 years of thinking about it that i finally nailed down how it really works and how it sort of arises from this kind of interplay between computational irreducibility and our nature as observers and i was like let me let me really check that i'm actually getting it right and you know i've known about the second law for a very long time and the second law is one of these things which in a textbook"
},
{
"end_time": 4400.93,
"index": 189,
"start_time": 4379.923,
"text": " You'll often see the textbook say, Oh, you can derive that entropy increases. You say, well, actually you can flip around this argument and say that the same argument will say that entropy should decrease. And the, you know, my favorite is the books where the chapter ends. This point is often puzzling to the student. It's been puzzling to everybody else too."
},
{
"end_time": 4423.49,
"index": 190,
"start_time": 4401.391,
"text": " But you know the question was why did people come up with the things they came up with in this area and so i want to kind of untangle the whole history of the second law which i was surprised nobody had written before although after i figured it out i wasn't surprised cuz really complicated and it requires kind of understanding various points of view that people had that little bit little bit tricky to understand."
},
{
"end_time": 4443.251,
"index": 191,
"start_time": 4423.865,
"text": " I think you know in the end i feel very confident that you know how what i figured out fits into what people have known before what people have sort of be able to do experiments on and so on and that's a you know that for me is an important sort of step in the kind of the philosophy the computational experiments the homework so to speak"
},
{
"end_time": 4458.575,
"index": 192,
"start_time": 4443.626,
"text": " I mean, I find that if I study some field and I'm like trying to read all these papers and so on and it's a complicated field with lots of complicated formalism, I just I find it difficult to absorb all that stuff. For me, it's actually easier just to work it out for myself."
},
{
"end_time": 4478.507,
"index": 193,
"start_time": 4458.985,
"text": " And then see where those chips have fallen and then go back and figure out what the history is and see how what i've done relates to that history maybe sometimes it's not so common i have to say but sometimes i'll discover something in the history where i say that's an interesting idea and i can use that idea and something that i'm trying to do."
},
{
"end_time": 4506.783,
"index": 194,
"start_time": 4478.848,
"text": " that's that it tends to be the case that i've kind of learned enough of the ambient kind of history beforehand that i'm not usually surprised at that level but it's always a it's a it's always a humbling experience to learn some new field because you was uh i mean i feel uh a field i've been trying to learn for a while is economics which is deeply related to kind of structure of human society and so on and i'm i'm sort of i'm i'm still at the stage it just happens whenever i learn a field that for a while"
},
{
"end_time": 4536.118,
"index": 195,
"start_time": 4506.783,
"text": " Every new person i talk to will tell me something i didn't know that makes me very confused about what what is actually going on in the field i am i feel like i'm just at the at the you know just at the crest of the hill now for economics i you know i'm not i'm just it's it's it's starting to be the case that i've heard those that idea before and i'm beginning to understand how it fits into the sort of global set of things that i'm thinking about and i do think by the way that it's it's going to turn out that uh sort of there's a"
},
{
"end_time": 4560.828,
"index": 196,
"start_time": 4536.493,
"text": " You know economics like biology, well economics thinks it has more theories than biology thinks it has. And it's sort of a question of what kind of a thing is economics? What are its foundational questions? What can one actually understand? I'm not there yet, but I think it is really clear to me that the methodologies that I've developed are going to be very relevant to that."
},
{
"end_time": 4589.531,
"index": 197,
"start_time": 4561.101,
"text": " i don't know how it will come out that's one of these things you have to let the chips fall as they will you know i don't know how it's going to come out i don't know whether i mean i have kind of prejudices about what i'm going to learn about you know cryptocurrencies and things like this um which is an interesting case because it's kind of a case where sort of all there is is the kind of economic network there isn't the kind of obvious underlying human utility of things i mean just to give a preview of some of the thinking there"
},
{
"end_time": 4616.886,
"index": 198,
"start_time": 4589.804,
"text": " What are the questions in economics is what is value what what makes something valuable and my my proto theory of that. Which some subject to change does not not fully worked out at all is in the end the main thing that's valuable is essentially computational reducibility. What's in the world at large there's lots of computational reducibility lots of things that are unpredictable lots of things you can't do quickly and so on."
},
{
"end_time": 4630.657,
"index": 199,
"start_time": 4617.312,
"text": " But we humans have one thing in fairly short supply and that's time because at least for the time being we're mortal we have only a limited amount of time so for us anything kind of speeds up what we can achieve."
},
{
"end_time": 4649.94,
"index": 200,
"start_time": 4631.101,
"text": " is something that is valuable to us and computational reducibility is the possibility of finding little pockets where you can kind of jump ahead where you're not stuck just going through letting things work as they work so i see so i think the you know my proto theory is that the ultimate"
},
{
"end_time": 4678.729,
"index": 201,
"start_time": 4650.367,
"text": " Concept of the ultimate source of value is pockets of computational reusability. The fact that you can sort of put together a smartphone and it's a whole smartphone rather than having to get all the ingredients together and just go step by step, so to speak. If we had infinite time, we could build every smartphone from scratch ourselves. But because we only have finite lifetimes, it's worthwhile for us to have the finished goods, so to speak."
},
{
"end_time": 4707.654,
"index": 202,
"start_time": 4679.104,
"text": " You know, it's it's the very beginning. I'm just trying to understand these things Roughly speaking anything that saves us time will be valuable or at least what is valuable will save us time And then what saves us time is something that's computationally reducible. Yes. Yes, that's the idea and I mean I think That sort of questions about kind of what you know when you When you invent something, how do you build on that invention?"
},
{
"end_time": 4735.52,
"index": 203,
"start_time": 4707.978,
"text": " How do you take that lump of reducibility and make use of it and so on? What is the value of that invention? That's not something usually, usually taken into account in economics. You know, there's, there's the scarcity of stuff, but not the value of this idea and so on. And in biology, we can see the same thing happening. There is some sort of piece of reducibility, some mechanism that you see being found. And then that mechanism is reused."
},
{
"end_time": 4764.974,
"index": 204,
"start_time": 4736.032,
"text": " I don't understand how this stuff works yet, but, um, this is, uh, you know, in the end, it's kind of a theory that allows one to understand something about kind of, uh, without the function of things, as well as about the mechanism by which the things occur, so to speak. But, you know, this is, uh, so for me, I mean, in terms of doing a project like that, it's sort of this mixture of sort of the philosophy of what's going on, the kind of conceptual framework, a bunch of computer experiments."
},
{
"end_time": 4788.422,
"index": 205,
"start_time": 4765.469,
"text": " To see what actually happens and then sort of a doing one's homework understanding of how this fits into what other people have figured out. And it's, um, you know, the thing that I've, I've done for the last 30 years or so now is, you know, in academia, it's often like you write a paper and then you're like, get some citations, you know, let's boom, boom, boom. This, this is whatever."
},
{
"end_time": 4806.937,
"index": 206,
"start_time": 4788.916,
"text": " I have to say I find it amusing that people pointed out to me that citations to my own stuff sort of got corrupted in various databases and so people are now copying the utterly incorrect citations that are just complete nonsense and you can kind of see that they didn't look at anything they just like click click click"
},
{
"end_time": 4830.213,
"index": 207,
"start_time": 4807.329,
"text": " You know, now I've now I've papered my paper, so to speak, by putting in the right citations. The only thing that's amusing to me right now is because papers aren't on paper anymore. It's starting to be the case that you can have the citations where they'll cite all thousand authors explicitly as very nice and egalitarian. And it means that the length of the, and I'm always when I'm reading papers,"
},
{
"end_time": 4850.708,
"index": 208,
"start_time": 4830.52,
"text": " I'm always like, something exciting is about to happen. Something exciting is about to happen. Oh, shit, we reached the end of the paper. Nothing happened. Right. Now, and I thought I hadn't reached the end because the thumb on my, you know, as I'm as I'm moving down on the percentage. Yeah, yeah, right. It's like there's still a lot of way to go. But no, actually, it's just just pages and pages and pages of citations."
},
{
"end_time": 4876.886,
"index": 209,
"start_time": 4851.032,
"text": " But, you know, what I've always thought, at least for the last 30 years, the much more interesting thing is the actual narrative history of the ideas. That's the thing that really matters. It's not, you know, it's like it's nice to be able to sort of cite your friends or whatever else you're doing. But what's much more significant for sort of the history of ideas is can you actually thread together how this relates to other ideas that have come up?"
},
{
"end_time": 4906.425,
"index": 210,
"start_time": 4877.312,
"text": " And so when I wrote you kind of science, I put a huge amount of effort into the historical notes at the back and, uh, uh, people, you know, it's like, oh, you didn't cite my thing. Well, read the frigging note. It's like, uh, did I get it right? Yes, actually. It's a very, very good portrayal of what actually happened and, um, which, uh, I think is, and I think that's, uh, for me, that's a much more useful thing than like, boom, I copied this citation from some, some database that had it wrong anyway."
},
{
"end_time": 4933.37,
"index": 211,
"start_time": 4906.715,
"text": " I think and that's you know it's part of the story I mean to me you know when you do a piece of science there's the doing of the science and there's the kind of explaining of the science and there's the contextualizing of the science for me kind of the the effort of exposition is critical to my process in doing science I mean the fact that I'm going to write something talk about something"
},
{
"end_time": 4955.52,
"index": 212,
"start_time": 4933.473,
"text": " is very important to me actually understanding what i'm talking about so to speak and i i you know i when i write expositions of things i try and write expositions that i intend anybody to be able to understand and that is a you know that's a big sort of constraint on what one does because if one doesn't know what one's talking about it's really hard to explain it in a way that anybody has a chance to understand"
},
{
"end_time": 4967.381,
"index": 213,
"start_time": 4955.862,
"text": " And so that for me has been an important constraint in my efforts to do sciences. Can I explain it? And people sometimes think it's more impressive if you explain the science in this very elaborate technical way."
},
{
"end_time": 4996.937,
"index": 214,
"start_time": 4967.79,
"text": " That's not my point of view. It's more impressive if you can grind it down to be able to explain it in a way that anybody can have who puts the effort in can actually understand it. And it forces you to understand what you're talking about much more clearly and it prevents the possibility of you just sort of floating over the formalism and, you know, completely missing the point. But I think, I mean, one of the things that, you know, it's sort of the doing of science"
},
{
"end_time": 5024.667,
"index": 215,
"start_time": 4997.21,
"text": " There's one, another question is who should be doing science? You know, it's, uh, I mean, I do science as a hobby, uh, and I do it because I discover interesting things and I think that's fun. If I wasn't discovering interesting things, I just wouldn't do it. It's not what I do for a living. You know, I run a tech company for a living. Um, it's, uh, it's something that has been kind of a, a, you know, I started off earlier in my life. I did science for a living, so to speak."
},
{
"end_time": 5046.749,
"index": 216,
"start_time": 5024.94,
"text": " I wanted to understand your position, not only your views on science, but your position in the history of science and you took us through the past 50 years up until even this morning. That's great."
},
{
"end_time": 5074.155,
"index": 217,
"start_time": 5048.268,
"text": " What's missing there is even a definition of what is science. I opened with what is good science. And then I should have said, well, what is science to begin with? And then we can also contrast that with what is bad science. To me, I think science is about has been traditionally about taking the world as it is the natural world, for example, and somehow finding a way to produce a human narrative about what's going on there."
},
{
"end_time": 5102.978,
"index": 218,
"start_time": 5074.906,
"text": " So in other words, science is this attempt to bridge from what actually happens in the world to something that we can understand about what happens in the world. That's what the act of doing science is that effort to find this thing, which kind of is the explanation that fits in a human mind, so to speak, for what's going on in the world. That's been the traditional view of science. Now there are things that call themselves sciences. That's a computer science that really isn't about that."
},
{
"end_time": 5131.954,
"index": 219,
"start_time": 5103.302,
"text": " That's really a different kind of thing. Computer science, if it was a science like that, will be what I now call Rulology, the study of simple rules and what they do, the kind of the computation in the wild, so to speak. So it's sort of a misnomer of a science, but that's the tradition of how it's called. But so, you know, for me, the science is this, what is it that we humans can understand that relates to what actually happens out there in the world, so to speak?"
},
{
"end_time": 5161.22,
"index": 220,
"start_time": 5132.244,
"text": " Now you know i called my big book a new kind of science for a reason because i saw it as being a different twist on that idea of what science is because when you have these simple rules and you can only know what they do by just running them you have a slightly different case you have something where you can understand the essence of what's happening the primitives but you cannot understand you can only have kind of a a meta understanding of the whole arc of what happens"
},
{
"end_time": 5189.65,
"index": 221,
"start_time": 5161.22,
"text": " You can't expect what had people have been hoping for from for example the physical sciences where you say you know now i'm going to wrap my arms around the whole thing i'm gonna be able to say everything about everything that's going to go on there so it's a slightly it's a different kind of science hence the title of the book so to speak um but uh so you know that's that's my view of sort of what science is i would say that good science tends to be science that at least science that i think is good"
},
{
"end_time": 5215.077,
"index": 222,
"start_time": 5190.094,
"text": " is science that has some sort of foundational it has some foundational connections it's you know there is science maybe i shouldn't say good science i should say say high leverage science science that's that we can be fairly certain is going to have importance in the future so to speak uh you know when you're at some tentacle some you know some detail of some detail"
},
{
"end_time": 5232.346,
"index": 223,
"start_time": 5215.179,
"text": " The you know that that is maybe the detail the detail will open up some crack that will let you see something much more foundational but the much of the time that won't happen and i think the the the the thing that to me makes make science that sort of high leverage science."
},
{
"end_time": 5251.374,
"index": 224,
"start_time": 5232.79,
"text": " Is a science where sort of the thing you're explaining the thing you're talking about is somehow very simple very very clean very Very much the kind of thing that you can imagine will show up over and over and over again Not something where you you built this whole long description that went three pages long to say what you're studying"
},
{
"end_time": 5274.309,
"index": 225,
"start_time": 5251.647,
"text": " It's like this is a very simple thing and now there's a lot that comes out of that simple thing but it is sort of based on this kind of foundational primitive that that's at least i wouldn't necessarily say that that is some i mean we talk about good science versus not very good science you know one thing is so what would be my criteria i mean you know i would say that"
},
{
"end_time": 5302.295,
"index": 226,
"start_time": 5274.582,
"text": " Science that nobody can understand isn't very good science, since the point of science is to have a narrative that we humans can understand. If you are producing something that nobody understands, or nobody has the chance to understand, so to speak, that's not going to be a good thing. I think that there's also a lot of science that gets done that I would say is not, I don't know, it's not what it's advertised to be, so to speak."
},
{
"end_time": 5323.609,
"index": 227,
"start_time": 5302.756,
"text": " What do you mean? Well, science is hard. And unless you, you know, it's like, did it really work that way? Or did you fudge something in the middle, so to speak? And, you know, there's a certain rate of fudging in the middle. I don't think we know what the rate is. In some fields, it's probably very high."
},
{
"end_time": 5348.285,
"index": 228,
"start_time": 5324.07,
"text": " It's often not even like i'm nefariously fudging it in the middle it's just i knew it was going to come out this way so the mouse that didn't do that i'm going to you know that mouse must have been you know under the weather that day so we'll ignore that mouse type thing it's not you know it's not nefariously ignoring the mouse it's just ignoring the mouse because we're sure it isn't you know that mouse isn't the important mouse type thing"
},
{
"end_time": 5372.261,
"index": 229,
"start_time": 5348.558,
"text": " i think the uh that's you know i know when i was doing particle physics back in the late 70s you know what a formative experience for me was a thing i calculation i did from qcd about some particular particle interaction charm particle production in proton proton collisions okay so i had worked out it will happen at this rate there was an experiment that said"
},
{
"end_time": 5387.978,
"index": 230,
"start_time": 5372.534,
"text": " no such thing was observed at a rate i remember what it was five times below what i said it should be should happen at and so if you are the official you know scientific method operative you say well then my theory must be wrong well"
},
{
"end_time": 5411.664,
"index": 231,
"start_time": 5388.302,
"text": " I didn't think the theory was likely to be wrong because it was based on some pretty foundational things and you know i wrote some paper with a couple of other people and you know half the paper was his the calculation the other half of the paper was well this is an experiment and how come you know how could our calculation possibly be wrong well it turns out as you might guess i remember this story that you know the experiment was wrong"
},
{
"end_time": 5441.101,
"index": 232,
"start_time": 5412.073,
"text": " And, you know, that was for me an important kind of formative realization. Now, you know, do I blame the experimentalists for the fact that it was wrong? No, experiments are hard. And, you know, they had a certain set of ideas about how it would work out. And, you know, that those were not satisfied and they missed it. Just like in doing computer experiments. If you don't measure the right thing, you might miss what you're looking for. Now, just a moment. How does that jive with earlier when you're talking about the experiments, you have to let the chips fall where they may and accept it?"
},
{
"end_time": 5456.084,
"index": 233,
"start_time": 5441.527,
"text": " Fair point. I mean, you have to do a good experiment and that's not a trivial thing. And in other words, if you do a bad experiment, you'll come to the wrong conclusions. And one of the things that I suppose I've gotten so used to in doing computer experiments"
},
{
"end_time": 5476.118,
"index": 234,
"start_time": 5456.34,
"text": " Is you know how do you make a very clean experiment this is the typical problem the typical problem with experiment says you do the experiment and you get a result. And there was some effect that you didn't know that it mattered that the experiment was done you know not at sea level or something but that was the critical thing i used to know that."
},
{
"end_time": 5506.237,
"index": 235,
"start_time": 5476.357,
"text": " And so what tends to happen with computer experiments is an awful lot easier than the physical experiments is like can you whittle it down to the point where you're doing a very very minimal experiment but there's no oh there's some complicated thing and we don't know what it came from i mean back in the 80s when people were working on some some not all but some of the artificial life stuff that was very much bitten by the experiments were just unbelievably complicated they were like well i'm going to make this model of a fish and it's going to have 100 parameters in it"
},
{
"end_time": 5520.145,
"index": 236,
"start_time": 5506.63,
"text": " well then you know you can conclude almost nothing from such an experiment and you say well you know the fish wiggles in this way but you know it could do anything with a hundred parameters and so i think the the um you know i would say"
},
{
"end_time": 5550.879,
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"start_time": 5521.049,
"text": " Hola, Miami. When's the last time you've been in Burlington? We've updated, organized, and added fresh fashion. See for yourself Friday, November 14th to Sunday, November 16th at our Big Deal event. You can enter for a chance to win free wawa gas for a year, plus more surprises in your Burlington. Miami, that means so many ways and days to save. Burlington. Deals. Brands. Wow. No purchase necessary. Visit bigdealevent.com for more details. Perhaps the right thing to say is that"
},
{
"end_time": 5578.916,
"index": 238,
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"text": " And, you know, the most common cause of error, I suspect in experiments is prejudice about how it's going to come out and then muddiness in the experiment. When the experiment is whittled down enough, you can't kind of hide. You can't let the prejudice, you can't, you can't distort the experiment by the prejudice, so to speak, because it is so simple. You can just see this is what goes in. This is what comes out. There's no, there's nothing, you know, behind the curtain, so to speak."
},
{
"end_time": 5608.183,
"index": 239,
"start_time": 5579.343,
"text": " I think that's some, but a lot of experiments, physical experiments, people who do physical experiments have it much worse than people like me who do computer experiments, because it's really hard to make sure you're not having any other effect you don't understand, et cetera, et cetera, et cetera. I mean, the particular mistake that was made in this experiment I mentioned before was they were studying, they were looking for tracks of particles in some emulsion stack,"
},
{
"end_time": 5615.776,
"index": 240,
"start_time": 5608.507,
"text": " and they the particle tracks were a bit shorter than they were looking for because they just didn't know about how those kinds of particles worked."
},
{
"end_time": 5641.715,
"index": 241,
"start_time": 5616.271,
"text": " well you know that's that's something i suppose there are analogous mistakes one could make in a computer experiment but it's a lot easier to not make those mistakes in a computer experiment particularly if you do the visualization well and you're really kind of seeing every bit so to speak at least at some level visualization is something i want to get back to so i'm glad you brought it up because you brought up three or four times now is something extremely important so can you talk about how can visualization be done"
},
{
"end_time": 5667.688,
"index": 242,
"start_time": 5641.715,
"text": " The most common mistake is that there's a lot going on and you choose to only look at some small slice of what's going on. So for example, classic example, people studying snowflake growth. Okay."
},
{
"end_time": 5682.585,
"index": 243,
"start_time": 5668.131,
"text": " They were studying they said well the thing we really gonna concentrate on is the growth rate of the snowflake okay how fast the the snowflake expands right so they had worked out that growth rate and they'd carefully calibrated it and they found out it was correct for snowflakes and so on."
},
{
"end_time": 5707.892,
"index": 244,
"start_time": 5682.995,
"text": " But they never bothered to make a picture of what the actual snowflakes they were growing with their model looked like. They were spherical. Okay. All right. So that's an example of kind of a visualization type mistake. So the trick is, can you make a representation of things that is as faithful as possible, has as many of the details as possible, but yet is comprehensible to our visual system?"
},
{
"end_time": 5734.838,
"index": 245,
"start_time": 5708.302,
"text": " It's easier in some cases than others. Cellular automata are a particularly easy case. They're particularly suitable for our visual system. It's just at least the one-dimensional ones that I've always studied. You just have this line of cells and they go down the page. You get this picture. Boom, there it is. Now, even there, with cellular automata, what had been done before my efforts were to look at two-dimensional cellular automata, where you have a video of what's going on."
},
{
"end_time": 5759.036,
"index": 246,
"start_time": 5734.838,
"text": " When you have that video it is really hard to tell what's happening some things like the game of life so it's on people started that at some length and you can see these gliders moving across the screen look pretty cool and you can see glider guns doing that thing and what kind of thing but when you tip it on its side and you look at the kind of space time picture of what's happening it becomes much clearer what's going on and that's a that's a case where."
},
{
"end_time": 5787.09,
"index": 247,
"start_time": 5759.36,
"text": " our visual system yes we can see movies but we don't get sort of in one gulp the whole story of what happened and so that's another case where the more faithful visualization if you can do it it's not so trivial to do it because it's kind of a three-dimensional thing and you can't see through all the layers you have to figure out how you're going to deal with that and it's complicated for the things that i've studied for example in our models of physics where we're dealing with"
},
{
"end_time": 5789.735,
"index": 248,
"start_time": 5787.449,
"text": " On their face, trivial things to visualise?"
},
{
"end_time": 5820.009,
"index": 249,
"start_time": 5790.145,
"text": " Um, and in fact, one of the things that made the physics project possible was that a decade earlier in Wolfram language, we had developed good visualization techniques for graphs, for networks, which was a completely independent effort that had nothing directly to do with my sort of project in physics, but it was something we did for other reasons. And that was something was critically useful in doing physics project. I mean, back in the, in the, um, in the nineties, when I was doing graph layout, I, um, uh, I actually found a,"
},
{
"end_time": 5850.128,
"index": 250,
"start_time": 5820.282,
"text": " A young woman who was who was spectacularly good on a piece of paper, laying out a graph to have the, you know, the lines not crossed and so on. Later on, she became a distinguished knitwear designer. So I think that was, I don't know what was cause and what was effect, but it was kind of a unique skill. Most people are not, you know, most people can't do it. You know, you give them a bunch of a bunch of nodes and a graph and you say, untangle this thing. It's really hard to do. I can't do it at all. Um, but you know, we had found algorithms for doing that, which"
},
{
"end_time": 5869.121,
"index": 251,
"start_time": 5850.589,
"text": " I used a lot in doing things with the physics project because that was a sort of a pre-existing thing, but it's still more difficult to visualize what's happening and things I've been doing very recently last few weeks on lambda calculus. That's an area where the obvious visualizations are just"
},
{
"end_time": 5898.609,
"index": 252,
"start_time": 5869.428,
"text": " horrendous. Our visual system doesn't figure out what the heck is going on. And it's been possible. I found some reasonable ways to do that, which are very helpful in getting intuition about what's happening. But that's our highest bandwidth way of getting data into our brains is 10 megapixels of stuff that we can get through our eyes. And so that's the best. And that's the question is, can you make a sort of a faithful representation of what's going on underneath"
},
{
"end_time": 5923.251,
"index": 253,
"start_time": 5899.189,
"text": " that you can get into your brain so that your brain has the chance to get intuition about what's happening or to notice anomalies. I mean, you know, somehow I think the story of my life in science devolves every so often and like it did just a few days ago into a very large number of little pictures on the screen and going through screen after screen of these things, looking for ones that are interesting."
},
{
"end_time": 5943.66,
"index": 254,
"start_time": 5923.592,
"text": " It's kind of a very natural history like activity. Um, it's kind of like, you know, when do I see the flightless bird or something like this? Um, and, um, and, you know, why do I do that? Obviously I've used machine learning techniques to prune these things, et cetera, et cetera, et cetera. But in the end, you know, I'm looking for the unexpected and, you know,"
},
{
"end_time": 5970.35,
"index": 255,
"start_time": 5944.497,
"text": " Two questions here. One, if you're using machine learning to sift through, I remember there's a talk from Freeman Dyson. He was saying that when the Higgs particle was discovered, he's happy that the Higgs was discovered, but he's not happy with how it was discovered because there was so much filtering out of data. And he says you want to be looking for the anomalies."
},
{
"end_time": 5997.517,
"index": 256,
"start_time": 5971.323,
"text": " I'm happy that the particle is finally discovered after many years of effort, but I'm unhappy with the way the particle was discovered. The Large Hadron Collider is not a good machine for making discoveries. The Higgs particle was only discovered with this machine because we told the machine what we expected it to discover. It is an unfortunate deficiency of the Hadron Collider that it cannot make unexpected discoveries."
},
{
"end_time": 6013.234,
"index": 257,
"start_time": 5997.807,
"text": " Big steps ahead in science usually come from unexpected discoveries. Do you have reservations about how some of these filtering techniques can be done? Absolutely. You get it wrong all the time. The less filtering you do, the better. That's why I end up with"
},
{
"end_time": 6039.838,
"index": 258,
"start_time": 6013.541,
"text": " You know, looking at arrays of pictures, you know, screen after screen of arrays of pictures, it used to be on paper. Um, but, uh, you know, I would print these things out and go through sheeps of paper looking for things. Yeah, it's, I mean, there are things that are fairly safe to do, although they bite you from time to time. Like I was just mentioning earlier, this very long lived Lambda creature that I found, uh, you know, the automated techniques that I had missed it."
},
{
"end_time": 6047.108,
"index": 259,
"start_time": 6041.118,
"text": " So that was and I noticed something was wrong by looking at a visualization of what it was doing."
},
{
"end_time": 6075.452,
"index": 260,
"start_time": 6047.483,
"text": " okay then the other question was approximately an hour and a half ago or so we were talking about bulk organization theory and natural selection so both orchestration theory yeah you made an acronym okay that's a new acronym okay got theory and the question is whether it's critical to how bots themselves work let's let's let's skip that so what i want to know is you mentioned if i recall correctly that there was a recent visualization you did in order to make it easier to see the connection to biology"
},
{
"end_time": 6099.565,
"index": 261,
"start_time": 6076.049,
"text": " Not quite. I mean, no, not quite. That was related to this. I'm doing multiple projects right now, so that was about a different project, which actually happens to have some relevance to biology, but that relevance is more related to origin of life, and it's a slightly more circuitous route, but so different kind of thing. But, you know,"
},
{
"end_time": 6129.718,
"index": 262,
"start_time": 6100.282,
"text": " Let's end the conversation on many people who email you, they email you their theory, their theory of everything. They'll say, I have a theory. You have a recent blog post about this. I have several quotes from that. We can get to that if you like, but how can someone, many of the people who watch the show, many people who are fans of yours, many people who watch Sean Carroll's show or any science show at all, they want to contribute to science and they may not have the tools to contribute to science. So they use LLMs generally speaking, or they just don't do anything, but they have the want."
},
{
"end_time": 6158.166,
"index": 263,
"start_time": 6129.94,
"text": " How can they productively contribute to science? That's an interesting question. So I think there are a couple of... Okay, so first point is there are different areas of science, right? And at different times, people with different levels of training and expertise have been able to contribute in different ways. Like, there was a time when, in natural history, you could go and just find beetles and so on, and that was a contribution to science because, you know, every beetle you found was something which eventually there would be some systematic thing that came out from looking at that."
},
{
"end_time": 6173.66,
"index": 264,
"start_time": 6158.166,
"text": " You're not going to find another continent."
},
{
"end_time": 6190.725,
"index": 265,
"start_time": 6174.121,
"text": " not anymore not anymore i'm not you know in in um you know there've been times when this wasn't done so much by amateurs but but um chemistry for example there was a thing where you just you study another compound and you just keep on doing that and in the end you build up this kind"
},
{
"end_time": 6218.37,
"index": 266,
"start_time": 6190.725,
"text": " Collection of knowledge where somebody is gonna pick up you know somebody studied you know lithium hydroxide back in the day for no particularly good reason and then you know somebody at nasa realizes that's a way to scrub come dioxide or whatever and it gets used in the post spacecraft or whatever it is you know it's a it's a so i think there's this thing where there are things that you can kind of accumulate that maybe i'm not not necessarily in the not themselves they don't require sort of."
},
{
"end_time": 6243.439,
"index": 267,
"start_time": 6218.37,
"text": " Integrating a lot of a lot of things to be able to make progress there are areas that are more difficult so for example right now physics is as it has traditionally been done is a more difficult area to contribute to because the you know back I would say in the 1700s not so difficult but now there's a pretty tall tower of stuff that's known"
},
{
"end_time": 6273.37,
"index": 268,
"start_time": 6243.916,
"text": " I mean, stuff from, you know, mathematical physics and so on that's known. And if you say, well, I'm going to have a theory of how space time works. If you don't know what's already known about space time, which is couched in quite sophisticated mathematical terms and not not capriciously, it's just that is, you know, our human, our everyday human experience doesn't happen to extend to how space time curves around a black hole. That's not everyday intuition."
},
{
"end_time": 6301.152,
"index": 269,
"start_time": 6273.763,
"text": " And so it's inevitable that it's going to be couched in terms that are not accessible from just having everyday intuition. And there are fields where that not so much of that has happened. Physics is one where there's a pretty tall tower of things that have been figured out that get you to the sort of the description of physics as we know it today. Now it turns out that, you know, in the things that we've been able to do with our model of physics,"
},
{
"end_time": 6304.821,
"index": 270,
"start_time": 6301.596,
"text": " It's once a little bit closer to the ground again."
},
{
"end_time": 6331.937,
"index": 271,
"start_time": 6305.247,
"text": " In some aspects of it, because the study of, you know, hypergraph rewriting and so on, that's something, you know, pretty much anybody can understand the ideas of hypergraph rewriting. It's not doesn't require that, you know, a whole bunch of stuff about, I don't know, you know, sophisticated things about partial differential equations and, you know, function spaces and all this kind of thing, which are fairly complicated abstract concepts."
},
{
"end_time": 6358.933,
"index": 272,
"start_time": 6332.312,
"text": " It's something where, at least at the simplest level, it's like you've got this thing, you could run it on your computer, you can see what it does. Now, you know, connecting that to what's known in physics, that's more challenging. And knowing kind of how that relates to, I don't know, some result in quantum field theory or something is more challenging. The result in quantum field theory is not a waste of time. The result in quantum field theory is our best condensation of what we know about how the universe works."
},
{
"end_time": 6387.756,
"index": 273,
"start_time": 6358.933,
"text": " It's not something where it's like forget all that stuff we can just go back to kind of the average intuition about physics that was good thing a few hundred years ago it isn't a good thing anymore cuz we already learned a bunch of stuff we already figured out a bunch of things and if you say well just throw away all those things that start from scratch. That's you know then you've got to recapitulate those few hundred years of of discoveries and that's a that's a that's a heavy that's a tall hill to climb so to speak i think the."
},
{
"end_time": 6402.773,
"index": 274,
"start_time": 6387.995,
"text": " One of the areas where there's sort of a wonderful opportunity for people to contribute to science that is some high leverage science is in this field that I call Rulology, which is kind of studying simple rules and seeing what they do."
},
{
"end_time": 6423.78,
"index": 275,
"start_time": 6403.217,
"text": " And whether it's cellular automator or Turing machines or lambda calculus or hypergraph rewriting. These are things where, you know, you run it on your computer. Okay, I built a bunch of tools for doing this, which, you know, make it really easy. But, you know, you do this, you're well organized, you kind of you won't immediately have intuition about how these things work."
},
{
"end_time": 6435.794,
"index": 276,
"start_time": 6424.104,
"text": " least i never have you know it takes actually doing it for a while to have the intuition and people usually don't at the beginning they're just like oh it'll never do anything interesting we'll just run it and see what it does"
},
{
"end_time": 6464.087,
"index": 277,
"start_time": 6436.254,
"text": " and then you know if you're well organized and kind of can develop intuition you will eventually get to the point where you can say okay i see how this works i can build this thing where i can i can add some definite piece to knowledge you know i can like like for example we have this summer school every year for grown-ups and we have another summer research program for high school students so this year for the high school students we had like i don't know 80 students or something"
},
{
"end_time": 6475.555,
"index": 278,
"start_time": 6464.189,
"text": " And I'm the one who gets to figure out projects for at least almost all of them. Oh, you define them or they come to you and you... No, I define them."
},
{
"end_time": 6499.872,
"index": 279,
"start_time": 6475.93,
"text": " Usually, during the year, I accumulate a list of ones I'd really like to see done. This year, several students I gave in projects which I've been wanting to do for decades and which are just studying particular kinds of simple systems. One was multi-way register machines was one of them. Another one this year was games between programs."
},
{
"end_time": 6529.224,
"index": 280,
"start_time": 6500.52,
"text": " This episode is brought to you by State Farm. Listening to this podcast? Smart move. Being financially savvy? Smart move. Another smart move? Having State Farm help you create a competitive price when you choose to bundle home and auto. Bundling. Just another way to save with a personal price plan. Like a good neighbor, State Farm is there. Prices are based on rating plans that vary by state. Coverage options are selected by the customer. Availability, amount of discounts and savings, and eligibility vary by state."
},
{
"end_time": 6557.039,
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"text": " One was, um, this year was another, uh, there were several, but, but, um, but anyway, these are things where, um, and, you know, I can say these, these high school kids, okay, they're very bright high school kids and they're using our tools and so on in two weeks were able to make quite nice progress and were able to add something, you know, I'm sure those things will turn into academic papers and things like this. And they were able to, you know, starting from."
},
{
"end_time": 6560.725,
"index": 282,
"start_time": 6557.654,
"text": " Just being a bright high school student so to speak, not knowing."
},
{
"end_time": 6587.927,
"index": 283,
"start_time": 6561.101,
"text": " You know eight years of mathematical physics. They don't know group theory. They don't know differential calculus or maybe some they probably know basic calculus. They probably but they don't wouldn't need to. I mean they you know, this is just a sort of be organized be careful and you know have the motivation and sort of and sort of and a little bit think foundationally enough that you're kind of drilling down to say what's the obvious experiment to do don't"
},
{
"end_time": 6617.807,
"index": 284,
"start_time": 6587.927,
"text": " Don't invent this incredibly elaborate experiment where the conclusions won't tell you anything. You know, try and have the simplest experiment. You know, what is the simplest version of this that we can look at and so on. And that, you know, it's really neat because it means it's an area. Ruliology is an area. It's a vast area. It's the whole computational universe. You know, I can, if you say, well, what's the thing that's never been studied before? I get out my computer, pick a random number, and I'm going to be able to give you something that I guarantee has never been studied before."
},
{
"end_time": 6645.128,
"index": 285,
"start_time": 6618.063,
"text": " and you know it'll have a lot of richness to it if you study that thing that randomly generated thing the particulars of that thing may or may not show up as important in particular in the future but certainly building up that body of knowledge about things like that is something that is very high leverage science it's something that is very uh it's it's very it's something that that you can kind of be sure is something that's a solid thing that people will be able to build on"
},
{
"end_time": 6673.336,
"index": 286,
"start_time": 6645.128,
"text": " One of the things i find striking and encouraging i suppose is you know you think about something like platonic solids you know the icosahedron the dodecahedron and so on say well that's you know you have a you have a an object you know and a dodecahedron you know piece of wooden dodecahedron or something and you go back and you say well let's you know we find a dodecahedron from ancient egypt it looks exactly the same as the dodecahedron we have today this is a timeless object"
},
{
"end_time": 6703.456,
"index": 287,
"start_time": 6673.695,
"text": " It's a thing that, you know, the dodecahedron has been something that has been worth talking about from the time of ancient Egypt to today. And so similarly, these things in rheology have the same character. They're very abstract, precise, you know, simple, and they're sort of foundational. And it's something where, you know, this particular rule, it's not going to be the case that somebody is going to say, oh, we learned more about the immune system. So that model of the immune system is irrelevant now."
},
{
"end_time": 6731.323,
"index": 288,
"start_time": 6703.916,
"text": " You know the things you measured about this are irrelevant that's not gonna happen because this is you know we're at the foundation so to speak this is an ultimate abstract thing and so anything you build there is a permanent thing and you know whether you happen to find whether you do you know there were many naturalists before Darwin who went and you know collected lots of critters around the world you know Darwin realized having collected lots of critters that there was a bigger picture that he could build"
},
{
"end_time": 6757.79,
"index": 289,
"start_time": 6731.647,
"text": " You know, it was still useful for people to have collected all those critters. And, you know, Darwin and everybody else doing evolutionary biology used a bunch of the information that had been collected by those people. It was, you know, it's a different thing to kind of integrate all those things, have the sort of philosophical integration to be able to come up with the bigger theory. But that's that's a that's a much more difficult thing to do than to add these kind of solid bricks"
},
{
"end_time": 6777.193,
"index": 290,
"start_time": 6757.79,
"text": " The science and i would say that you know as a person who's done lots of religion my time it's you know if you have a certain turn of mind it's a lot of fun because you just keep on finding stuff you keep on discovering things i mean you know i'm sure back in the day when you know the whole planet hadn't yet been explored."
},
{
"end_time": 6804.855,
"index": 291,
"start_time": 6777.193,
"text": " You would go to you know some place in the center of africa and it's like oh my gosh there's a tree that does this or this and this is exactly the same thing you know every day you see lots of things like in this stuff about lambda calculus i've just been doing is all kinds of weird stuff i've never seen it before i don't think anybody's ever seen it before and it's i'm sure i'm sure nobody's ever seen it before and it's remarkable and it's interesting and it's kind of it feels you know it's"
},
{
"end_time": 6825.981,
"index": 292,
"start_time": 6805.094,
"text": " It's kind of you get to see for the first time something nobody's ever seen before and something that is that you know you kind of know is going to be a permanent thing that is going to be a thing that is never going to change it's never going to be oh it doesn't work that way anymore it's it is it is what it is so to speak."
},
{
"end_time": 6849.394,
"index": 293,
"start_time": 6825.981,
"text": " I think that is a is a great example of a place where there has been a fair amount of sort of quotes amateur reality that's been done over the years it's not been as well organized as it should be and i thought myself for that and enlarge measure i mean back in the eighties. I got a bunch of people interested in this bunch of people both professional scientists and amateurs started studying these kinds of things."
},
{
"end_time": 6876.664,
"index": 294,
"start_time": 6849.77,
"text": " I started a journal that collected some of these things, complex systems, but I would say that the rhythm of how to present Ruliology and so on, I didn't really develop as well as I should have done and I'm now hoping to do that. It's a question of, for example, back in the day when people started having academic papers, back in the 1600s, if you read those papers, they read like today's blog posts."
},
{
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"index": 295,
"start_time": 6877.125,
"text": " Much more kind of anecdotal, you know, I went to the top of this mountain I saw this and this and this and they have them They're more personal. They're actually I would say better communication Than what one gets in sort of the very cold academic paper of today Well, you know, particularly I would say math papers are one of my favorite non-favorite examples where it just starts You know, let let G be a group. It's like why are we looking at this group? Who knows? It's just you know"
},
{
"end_time": 6922.244,
"index": 296,
"start_time": 6906.954,
"text": " Because it's like it is it is beneath us or it is not appropriate or it is kind of not professional enough for us to describe why we're doing this but so you know the back in the sixteen hundreds when when academic papers kind of originated they were like."
},
{
"end_time": 6949.224,
"index": 297,
"start_time": 6922.534,
"text": " You know, like the blog posts of today, so at least my blog posts today where there's both the content and a certain amount of the kind of the wrapping of why we're doing this and and kind of you have the purpose in mind and you're conveying it showing how what you're doing is connected to that. Yeah, right. And it's not. And it's sort of telling a story more so than it's just saying fact, fact, fact. And, you know, I'm just filing the facts, so to speak."
},
{
"end_time": 6977.875,
"index": 298,
"start_time": 6949.957,
"text": " I think with Rulology, one of the things that is interesting is it's a place where you discover something interesting. You want to just say, this is my discovery. And you want a way to accumulate lots and lots of discoveries without having to always feel like you have to wrap a whole kind of academic story around it. And the academic system is not well set up for that."
},
{
"end_time": 6996.715,
"index": 299,
"start_time": 6978.2,
"text": " I'm the it's you know the system it's like there's a business unit. The unit of academic achievement which is the paper so to speak and no just does this particular thing observed in these these particular characteristics that's not so much the kind of thing that one sees that."
},
{
"end_time": 7021.476,
"index": 300,
"start_time": 6997.039,
"text": " It's the kind of thing that one should be accumulating lots of in Rulology and it's something that is very accessible to well organized people who you know who wants to sort of work cleanly on this without you know as amateurs so to speak. I think it's a powerful thing and I'm hoping to have the bandwidth to kind of put together"
},
{
"end_time": 7048.37,
"index": 301,
"start_time": 7022.415,
"text": " properly organized kind of really logical society or something where we can kind of accumulate this kind of information. I think it's a thing where I mean one of the things I do and the things that I write about science is every picture and everything I write you can click that picture you'll get a piece of Wolfram language code and at least if our QA department didn't mess up it will forever produce the picture that that I said it produced so to speak."
},
{
"end_time": 7075.043,
"index": 302,
"start_time": 7048.37,
"text": " And you know i mentioned the q a department because it isn't actually trivial to have you know you make some piece of code and you have got to make sure it keeps on working we've been very good in both language and maintaining compatibility for the last 38 years with the language but if i use some weird undocumented feature that day that might change i don't so that usually isn't a problem but um uh you know but that fact that you kind of have it as a constraint for yourself"
},
{
"end_time": 7089.309,
"index": 303,
"start_time": 7075.538,
"text": " I like to have it as a constraint for myself, the things I write should be understandable to anybody who puts the effort into understand them. And reproducible. Yes, but also that they're understandable not only to the humans, but also to the computers, so to speak."
},
{
"end_time": 7119.036,
"index": 304,
"start_time": 7089.616,
"text": " and that everything i do is like you can immediately reproduce it that's you know turned out in practice to be a very powerful thing because people just take you know the code and all the visualizations and so on that i've made and they just go and start from there they kind of start from that level of the tower so to speak rather than having to climb climb the tower themselves and it's uh you know i think that's a that's a powerful thing it's very it's pretty much undone"
},
{
"end_time": 7141.766,
"index": 305,
"start_time": 7119.036,
"text": " Partly because academics well they don't tend to package the sort of the kind of code in a form that will actually be reproducible and runnable they do more with our technology than anywhere else but but still it's somewhat inadequate and also I think the the motivations on the part of you know the the typical academic scientist"
},
{
"end_time": 7168.763,
"index": 306,
"start_time": 7142.125,
"text": " Is kind of in the, in the game of academia, so to speak, which involves, you know, I'm going to publish my thing. I'm going to publish another thing that leverages the thing I just published. And, you know, I'm going to get as many papers as possible and so on. For me, the calculation is actually rather different because for me, I'm trying to do a bunch of different things in my finite life, so to speak. And for me, I'll write something and I really don't want to write something about the same topic again."
},
{
"end_time": 7181.834,
"index": 307,
"start_time": 7169.189,
"text": " It's kind of like it's a right once type activity. And so as much as possible, I'm like, I'm going to write this thing and okay, well, here it is. I hope you can do something useful with it because I'm not going to come back to this particular thing."
},
{
"end_time": 7205.913,
"index": 308,
"start_time": 7182.159,
"text": " i mean i find actually that the you know having i do end up sort of building on the things i've done but not kind of writing sort of a an incremental version of the same document again i'm i'm i find it maybe it's just me but but i i just i can't bring myself to do that i feel the same way and it's kind of it's actually very frustrating when i've when i you know when i do a project"
},
{
"end_time": 7235.009,
"index": 309,
"start_time": 7205.913,
"text": " I like to kind of pick all the low hanging fruit and i you know i know that any fruit i don't pick first time around i'm not gonna come back and pick i'm gonna sit there is gonna be frustrating to me because it's kinda like like here's this thing and i just figured out a little bit more but i have no place to write that down i'm actually one thing i've been doing recently is in the nks book there were many notes of the back and many hundred page things that i'm writing today"
},
{
"end_time": 7264.428,
"index": 310,
"start_time": 7235.401,
"text": " Anyway, Stephen."
},
{
"end_time": 7283.473,
"index": 311,
"start_time": 7264.923,
"text": " It's been wonderful."
},
{
"end_time": 7309.701,
"index": 312,
"start_time": 7284.002,
"text": " but specifically i want more detail so you mentioned there are 40 high school projects maybe within two weeks there'll be a blog post out for people who want more they could say okay well it won't be two weeks i want to do it but it won't be two weeks okay well maybe whenever it's done i can put a link on the description i'll update the description yeah so if you're watching this whenever you're watching this check the description or maybe i'll have something on my sub stack about"
},
{
"end_time": 7335.469,
"index": 313,
"start_time": 7309.855,
"text": " Hi there, Kurt here. If you'd like more content from Theories of Everything and the very best listening experience, then be sure to check out my sub stack at kurtjymungle.org. Some of the top perks are that every week you get brand new episodes ahead of time"
},
{
"end_time": 7363.626,
"index": 314,
"start_time": 7335.691,
"text": " You also get bonus written content exclusively for our members. That's C-U-R-T-J-A-I-M-U-N-G-A-L dot org. You can also just search my name and the word sub stack on Google. Since I started that sub stack, it somehow already became number two in the science category. Now, sub stack for those who are unfamiliar is like a newsletter, one that's beautifully formatted. There's zero spam."
},
{
"end_time": 7391.92,
"index": 315,
"start_time": 7363.968,
"text": " This is the best place to follow the content of this channel that isn't anywhere else. It's not on YouTube. It's not on Patreon. It's exclusive to the Substack. It's free. There are ways for you to support me on Substack if you want and you'll get special bonuses if you do. Several people ask me like, hey Kurt, you've spoken to so many people in the field of theoretical physics, of philosophy, of consciousness. What are your thoughts, man? Well,"
},
{
"end_time": 7422.056,
"index": 316,
"start_time": 7392.227,
"text": " While I remain impartial in interviews, this substack is a way to peer into my present deliberations on these topics. And it's the perfect way to support me directly. KurtJaymungle.org or search KurtJaymungle substack on Google. Oh, and I've received several messages, emails and comments from professors and researchers saying that they recommend theories of everything to their students. That's fantastic."
},
{
"end_time": 7447.551,
"index": 317,
"start_time": 7422.534,
"text": " If you're a professor or a lecturer or what have you and there's a particular standout episode that students can benefit from, or your friends, please do share. And of course, a huge thank you to our advertising sponsor, The Economist. Visit economist.com slash totoe to get a massive discount on their annual subscription. I subscribe to The Economist and you'll love it as well."
},
{
"end_time": 7471.903,
"index": 318,
"start_time": 7448.012,
"text": " Toe is actually the only podcast that they currently partner with. So it's a huge honor for me. And for you, you're getting an exclusive discount. That's economist.com slash toe. And finally, you should know this podcast is on iTunes. It's on Spotify. It's on all the audio platforms. All you have to do is type in theories of everything and you'll find it."
},
{
"end_time": 7497.483,
"index": 319,
"start_time": 7472.21,
"text": " I know my last name is complicated, so maybe you don't want to type in Jymungle, but you can type in theories of everything and you'll find it. Personally, I gain from rewatching lectures and podcasts. I also read in the comment that toe listeners also gain from replaying. So how about instead you re-listen on one of those platforms like iTunes, Spotify, Google podcasts, whatever podcast catcher you use. I'm there with you. Thank you for listening."
}
]
}No transcript available.