What We've Learned from NKS Chapter 1: The Foundations of a New Kind of Science

all right hello everyone so this is the beginning of what will be i hope a 12-week odyssey looking at the 12 chapters of this thing that i spent a decade of my life from 1991 until well 2002 writing and um i want to i wanted to talk a little bit about what's in this book and uh how it's evolved over the last 20 years since um it will be on may 14th of this year will be the 20th anniversary of the original publication of this book and i'm i'm certainly thrilled at how much it's been possible to build on the ideas in the book and it's also i look at the online version of the book which is available to the world uh to everyone um uh every day particularly these days because there's just a lot of interesting content in it and i wanted to talk a little bit about that maybe i should mention one thing about the physical book by the way you can still get these physical books they are absolutely available from us and from bookstores and so on but you know one one fact about this physical book is it has a dust cover but actually i always think it's much better without its dust cover and i i have lots of copies of this they're convenient for um bookends as well as for their true content but i would just like to advertise the fact that the inside the um the inside cover is arguably better than the outside cover um the outside cover was more for bookstore distribution than for keeping for 20 years all right so what i want to do is talk a bit today i'm going to talk about chapter one of uh new kind of science there are 12 chapters all together let me uh just um share here to um put these up um so um that's that's the uh table of contents for new kind of science there's a website watsonscience.com um where you can find the whole book online uh actually over the course of the next few months more and more extra pieces will go live on this website in particular one thing all of the code that made all of the pictures in new kind of science it's all well from language code and uh it still runs from the 1990s although it has probably more hairy detail to do with positioning on pages and things than one will want to see and we're gradually getting it ready so that one will be able to click on any picture in the book and um the uh and be able to get the code that can reproduce that picture in a modern uh wolfram language all right well let's start off with chapter one the foundations for a new kind of science some ways this is not my favorite chapter i don't think i've reread this chapter i was just rereading some pieces of it actually i i don't think i had reread this chapter in many years i would say that in my own use of new kind of science the book um it's uh the pictures in it are very uh useful for me um the notes at the back of the book just have a huge huge amount of content in them that is in a sense uh really the the main main text of the book is kind of the overall narrative of the the story of a new kind of science um and i would say that well let me start off with the very beginning of the book because it kind of says what the point is um and what i'm going to do maybe today is go through a little bit what's in chapter one of the book and my kind reflections on it 20 years later i'll talk also a bit about the notes to um uh chapter one and maybe first time i will go back and talk about the preface and some of the uh the notes to the preface which are at least definitely fun and kind of interesting to read 20 years later all right so let's begin at the beginning here so i can i could just uh read through this just to give a sense of what's here so this is the very beginning i i think uh the first the first paragraph here really kind of says what the point was with some degree of uh forcefulness if of clarity i just said three centuries ago science was transformed by the dramatic new idea that rules based on mathematical equations could be used to describe the natural world my purpose in this book is to initiate another such transformation into to introduce a new kind of science that is based on the much more general types of rules that can be embodied in simple computer programs and i say it's taken me the better part of 20 years that means from 40 years ago now to build the intellectual structure that's needed but i've been amazed by its results for what i found is that with the new kind of science i've developed it suddenly becomes possible to make progress on a remarkable range of fundamental issues that have never successfully been addressed by any of the existing sciences before so what i go on to talk about here is what th this idea that one should be able to do theoretical science is an idea that is not obvious that it would work it's not obvious that the universe would follow definite rules or that anything would follow definite rules but as soon as you say things follow definite rules you have sort of the foundation for doing theoretical science and then the big question is what kinds of rules and as i say here there's no reason to think that systems like those we see in nature should follow only traditional mathematical rules and early in history it might have been difficult to imagine what more general types of rules could be like but today we are surrounded by computers whose programs in effect implements a huge variety of rules the programs we use in practice are mostly based on extremely complicated rules specifically designed to perform particular tasks but a program can in principle follow essentially any definite set of rules and at the core of the new kind of science that i describe in this book are discoveries i've made about programs with some of the very simplest rules that are possible one might have thought as i at first certainly did that if the rules for a program was simple then this would mean that its behavior must also be correspondingly simple for an everyday experience in building things that we tend to get the idea that the intuition that creating complexity is somehow difficult and requires rules or plans that are themselves complex but the pivotal discovery that i made some 18 years ago which is now 38 years ago is that in the world of programs such intuition is not even close to correct i did was in a sense one of the simplest among the most elementary imaginable computer experiments i took a sequence of simple programs and then systematically ran them to see how they behaved and what i found to my great surprise was despite the simplicity of their rules the behavior of the programs was often far from simple indeed some of the very simplest programs i looked at had behavior that was as complex as anything i'd ever seen it took me more than a decade to come to terms with this result and to realize just how fundamental and far-reaching its consequences are in retrospect there's no reason the result could not have been found centuries ago but increasingly i've come to to view it as one of the more important single discoveries in the history of theoretical science for in addition to opening up vast new domains of exploration it implies a radical rethinking of how processes in nature and elsewhere work these were definitely energetic and ambitious words written a bit more than 20 years ago now uh i think they're right and it's been rather lovely to see in the intervening time this really dramatic transition from a world in which when one talked about doing exact science one was typically referring to doing things with mathematical equations to a world in which the variety the majority of new models that are made of things are made with programs in just the kind of way that my opening paragraph here imagined so that's been a very a very satisfying thing to see and it's been it's been very uh significant to see what can be built on that on that foundational idea i go on to say here it could have been after all that in the natural world we well actually no i i was commenting here perhaps immediately most dramatic is that it yields a resolution to what has long been considered the single greatest mystery of the natural world what secret it is that allows nature seemingly so effortlessly to produce so much that appears to us so complex it could have been after all that in the natural world we will mostly see forms like squares and circles that we consider simple but in fact one of the most striking features of the natural world is that across a vast range of physical biological and other systems we're continually confronted with what seems to be immense complexity and indeed throughout most of history it has been taken almost for granted that such complexity being so vastly greater than the works of humans could only be the work of a supernatural being but by discovery that many very simple programs produce great complexity immediately suggests a rather different explanation for all it takes is that systems in nature operate like typical programs and then it follows that their behavior will often be complex and the reason that such complexity is not usually seen in human artifacts it's just that in building these we tend in effect to use programs that are specially chosen to give only behavior simple enough for us to be able to see that it will achieve the purposes we want one might have thought that with all their successes over the past few centuries the existing sciences would long ago have managed to address the issue of complexity but in fact they have not and indeed for the most part they have specifically defined their scope in order to avoid direct contact with it for while their basic idea of describing behavior in terms of mathematical equations works well in cases like planetary motion where the behavior is fairly simple it almost inevitably fails whenever the behavior is more complex and more or less the same is true of descriptions based on ideas like natural selection and biology but by thinking in terms of programs the new kind of science that i develop in this book is for the first time able to make meaningful statements about even immensely complex behavior in the existing sciences much of the emphasis of the past century or so has been on breaking systems down to find their underlying parts then trying to analyze these parts in as much detail as possible and particularly in physics this approach has been sufficiently successful that the basic components of everyday systems are by now completely known but just how these components act together to produce even some of the most obvious features of the overall behavior we see in the past remained almost in complete mystery within the framework of the new kind of science that i develop in this book however it finally becomes possible to address such a question so from the tradition of the existing sciences one might expect that its answer would depend on all sorts of details and be quite different for different types of physical biological and other systems but in the world of simple programs i've discovered that the basic forms of behavior same basic forms of behavior occur over and over again almost independent of underlying details and what this suggests is that there are quite universal principles that determine overall behavior and that can be expected to apply not only to simple programs but also to systems throughout the natural world and elsewhere in the existing sciences whenever a phenomenon is encountered that seems complex it's almost taken for granted that the phenomenon must be the result of some underlying mechanism that is itself complex but my discovery that simple programs can produce great complexity makes it clear that this is in fact not correct and indeed in the later parts of this book i will show that even remarkably simple programs seem to capture the essential mechanisms responsible for all sorts of important phenomena that in the past have always seemed far too complex to allow any simple explanation it's not uncommon in the history of science that new ways of thinking are what finally allow long-standing issues to be addressed but i've been amazed at just how many issues central to the foundations of the existing sciences i've been able to address by using the idea of thinking thinking in terms of simple programs for more than a century for example there's been a confusion about how thermodynamic behavior arises in physics yet from my discoveries about simple programs i've developed a quite straightforward explanation and in biology my discoveries provide for the first time an explicit way to understand just how it is that so many organisms exhibit such great complexity indeed i even have increasing evidence i said then that thinking in terms of simple programs will make it possible to construct a single truly fundamental theory of physics from which space-time quantum mechanics and all the other known features of our universe will emerge i had the first kind of signs that that would be possible it took another 20 years to sort of bring that to fruition and to get to the point where we have in our uh physics project in the last couple of years which has been very exciting to see it's kind of the the next big step and it required a number of really important ideas that build on that sort of take for granted the ideas of new kind of science but build to another level and i'll talk a bit about that later so i went on here to say when mathematics was introduced into science it provided for the first time an abstract framework in which scientific conclusions could be drawn without direct reference to physical reality yet despite all its development over the past few thousand years mathematics itself has continued to concentrate only on rather specific types of abstract systems most often ones somehow derived from arithmetic or geometry but the new kind of science i describe in this book introduces what are in a sense much more general abstract systems based on rules of essentially any type whatsoever now i will add a footnote to this that one of the things that's come out of our physics project is that a better understanding of the sort of true foundations of mathematics and in fact i think i've been working on for the last several months has been a sort of physicalization of meta mathematics that i think allows one to go quite a bit further than i was able to say 20 years ago about um understanding kind of the the role and place of mathematics um in in the structure of science anyway i went on here to say one might have thought that such systems would be too diverse for meaningful general statements to be made about them that is systems with with uh general abstract systems but the crucial idea that has allowed me to build a unified framework for the new kind of science that i described in this book is that just as the rules for any system can be viewed as corresponding to a program so also its behavior can be viewed as corresponding to a computation traditional intuition might suggest that to do more sophisticated computations would always require more sophisticated underlying rules but what launched the whole computer revolution is the remarkable fact that universal systems with fixed underlying rules can be built that can in effect perform any possible computation the threshold for such universality however generally been assumed to be high and to be reached only by elaborate and special systems like typical electronic computers but one of the surprising discoveries in this book is that in fact there are systems whose rules are simple enough to describe in just one sentence that are never less universal and this immediately suggests that the phenomenon of universality is vastly more common and important in both abstract systems and nature than has ever been imagined before and i have to say that that since the book uh in the book one of the the big results was the rule 110 cellular automaton and its universality since the book there was a another uh sort of threshold of universality question that was raised in the book about the simplest turing machine that could conceivably be universal and a few years after the uh the publication of the book i put up a prize for somebody to prove or disprove the universality of that turing machine and um a chap called alex smith was able to prove that indeed that turing machine is universal giving another sort of uh boost to the things that i say here about the thresholds universality and and there's there have been more results along similar lines there's there's one that i strongly suspect now about combinators that we put up another prize for uh as yet unclaimed um we'll see how that that goes too but i go on here to say and this is in a sense in many ways the punch line uh what i view as being a punch line of um uh of the science that um is a new kind of science i say but on the basis of many of many discoveries i have been led to a still more sweeping conclusion summarized in what i call the principle of computational equivalence that whenever one sees behavior that is not obviously simple in essentially any system it can be thought of as corresponding to a computation of equivalent sophistication and this um one very basic principle has a quite unprecedented array of implications for science and scientific thinking and i have to say that i thought there were many implications back 20 years ago but they pale in comparison with what is now clear exists anyway i go on to say uh back in in the book for a start it immediately gives a fundamental explanation for why simple programs can show behavior that seems to us complex but like other processes our own processes of perception analysis can be thought of as computations but though we might have imagined that such computations will always be vastly more sophisticated than those performed by simple programs the principle of computational equivalence implies that they are not and it is this equivalence between us as observers and the systems that we observe that makes the behavior of such systems seem to us complex one can always in principle find out how a particular system will behave just by running an experiment and watching what happens but the great historical successes of theoretical science have typically revolved around finding mathematical formulas that instead directly allow one to predict the outcome yet in effect this relies on being able to shortcut the computational work that the system itself performs and the principle of computational equivalence now implies that this will normally be possible only for rather special systems with simple behavior but other systems will tend to perform computations that are just as sophisticated as those we can do even with all of our mathematics and computers and this means that such systems are computationally irreducible so that in effect the only way to find their behavior is to trace each of their steps spending about about as much computational effort as the systems themselves so this implies that there isn't a sense of fundamental limitation to theoretical science but it also shows that there's something irreducible that can be achieved by the passage of time and it leads to an explanation of how we as humans even though we may follow definite underlying rules can still in a meaningful way show free will one feature of many of the most important advances in science throughout history is that they show new ways in which we as humans are not special and at some level the principle of computational equivalence does this as well but it implies that when it comes to computation or intelligence we are in the end no more sophisticated than all sorts of simple systems and all sorts of systems in nature but from the principle of computational equivalence there also emerges a new kind of unity for across a vast range of systems from simple programs to brains to our whole universe the principle implies that there is a basic equivalence that makes the same fundamental phenomena occur and allows the same basic scientific ideas and methods to be used and is this that ultimately is ultimately responsible for the great power of the new kind of science that i described in this book so the way i would say some of these things today is uh is just a little differently i think one of the things that i talked a lot about is this notion of the computational universe of simple programs i didn't mention that specifically in this introduction this concept of this this there's our physical universe and then there's the computational universe of all possible programs something that i realized only in the last couple of years and and with uh with sort of full force only probably in the last six months is that this combination of all possible programs running uh in a sort of this entangled process of computation executed by all these possible programs this in itself is a thing i call it the ruliad and it's that's the thing that in the end underlies the physics we see in our universe and also the mathematics we see in in all in in the structure of mathematics that we we generate and i think one of the things that i've realized a new kind of science basically went from sort of a paradigm of doing science that was you just write down an equation to do science to one in which one's talking about just specify the rules in a way that is computational or just in a sense specify the rules in some precise formal way we refer to that as computational because we're used to dealing with computers as our way of thinking about those kinds of things set up those rules see what they do there's a kind of science that you can do that revolves around looking at those rules and seeing what they do i mean to me and i i certainly thought this 20 years ago there's sort of this pure nks science the science of what simple programs do the abstract science of what simple programs do and then the sort of the applications of that both in terms of making models of things and in terms of ways that one can think about things and i think recently i'd i've started calling this sort of pure nks the study of rules and what they do the science of ruleology and we're going to be rolling out some more things related to that soon but that's that's kind of the sort of the the most the core of this new kind of science that i introduced in in uh in the new kind of science book is this study ruleology understand at an abstract level how programs with simple rules behave and then and that's sort of the first part of the book if we go back to the um the the the overall table of contents for the book the the first part of the book is devoted to kind of ruleology what do simple programs actually do out there in the computational universe of all possible programs and then the second part of the book is devoted to the question of okay so we have these simple programs they are raw material for understanding things that perhaps we have looked at before in biology and physics and so on how can we use this this new source of models that we have in simple programs to inform the ways that we think about these kinds of systems and that's the sort of the second part of the book um the third part of the book is is really talking about what are the principles that we extract from this study of ruleology the study of all possible programs the the study of what those kinds of programs do and what do we learn from the ways that those seem to fit into actually modeling things in the real world and that's where i introduced the principle of computational equivalence and so on and study its consequences now for people and and i'm happy to say there have been many people who've studied this book in in a lot of detail and chapter nine is always somehow referred to in a in a very special way because it's the chapter where i talked about my ideas about fundamental physics which turned out to be uh very much on the right track but there was further to go and it took 20 years before i kind of jumped back into it um with my great young collaborators and um was able to take the next set of steps and but that really built on this foundation of this idea of the sort of computational universe of possible simple programs the principle of computational equivalence the idea of computational irreducibility those kinds of things sort of almost had to be a generation old they almost had to be things you took for granted to build the next step so to speak so to me i i view kind of i i've recently kind of viewed the sort of the big picture of the history of science as sort of these four epochs in the idea of how one makes models of things from from antiquity where it was kind of the structural idea of what are things made of to the 1600s where it was kind of the idea of uh what uh can you write down a mathematical equation to what started in the 1980s and i and i hope kind of flourished and new kind of science which was this new idea of using programs as sort of the raw material that you could where you write down a program and see its consequences and that's how you understand the kind of how things work in the world and there's a fourth paradigm which has emerged from our physics project which is what i call the multi-computational paradigm which i won't talk about here but this is the thing that's sort of new in the last year or two and it's something which in a sense you know takes nks for granted and takes another big step and it will allow one to address a whole new collection of questions i think that nks allows one to address a collection of questions that were not accessible to kind of the structural paradigm for science or the mathematical paradigm for science they are accessible in our computational paradigm for science and that's something that one has been able to see quite clearly and a lot of work people have done over the last 20 years in applying simple programs and computational models in general to all kinds of different things that show up in the natural and artificial world so okay let me let me dive in a little bit more here and look at um some more of what what i had to say here maybe i should look i i would like to just sort of advertise the importance of the notes and um the the notes were sort of succinct summaries of um uh many many different topics and i i i'm always happy to see when people have taken chunks of the notes and stuck them on wikipedia because i think they they uh they serve a good purpose there um the uh i think um let's take a look at some of these notes so i have a note here about mathematics and science and talking about um the uh uh the kind of the the arc i've talked about nks as being kind of the next step beyond kind of the idea of mathematics as a foundation for science so this note talked about uh kind of what was the idea of mathematics and science how did that how did that come about how did that um uh how did that work and as i say here the the sort of the the watershed moment was in 1687 the publication of isaac newton's book principle mathematica the mathematical principles of natural philosophy and but that that idea that you could take mathematics that have been developed for commerce for land surveying for explaining things in astronomy and one could one could use that as the foundation to build science that was kind of the core idea that emerged with galileo and then and then with newton um in the 1600s i think the um i talk here about the fact that babylonians were certainly using arithmetic to kind of make predictions about things like astronomy pythagoreans by 500 bc were talking about the idea that all is number that everything can be reduced to numbers from from musical harmony to the way that um uh the way that things work in nature some of what they had to say about number we would now think of as quite mystical um some of it was sort of the precursors of modern science the um and there were there were lots of else done in antiquity between euclid work and geometry and particularly archimedes and ptolemy uh working on on astronomy and optics and mechanics and so on uh it's interesting by by the late 1600s i talk about albertus magnus who uh says many of the geometrist figures are not found in natural bodies and many natural figures particularly those of animals and plants and not determinable by the art of geometry by which he meant geometers figures are things like triangles and and circles and and so on things that can be in euclid's sense constructed using ruler and compass um the uh what he observed was well you look at the shape of a typical animal and it's not something you could construct with ruler and compass there's a there's a thing that goes beyond kind of the the purely geometrical there but um roger bacon 1267 was writing that mathematics is the door and key to the sciences and by the 1500s it was kind of uh taken for granted that if science was going to be truly meaningful it somehow must have the systematic character of mathematics leonardo da vinci uh wrote that no human inquiry can be called science unless it pursues its path through mathematical exposition and demonstration so that was uh um in a sense now in some sense what leonardo da vinci meant by mathematics it's probably in many ways the same kind of things as meant by mathematics when it's taught in school today one could have imagined that mathematics and i'll talk about this in another one of the notes here uh that mathematics the definition of it could have expanded to talk about the kinds of things that we're describing with simple rules and computational systems but it didn't mathematics stayed talking about the kinds of things that were the geometers figures and so on so galileo actually was was famous around this end of the 1500s for saying that the universe could only be understood in the language of mathematics whose characters he said are triangles circles and other geometric figures so uh and that was kind of the you know that's the language of math with the the you know the language of nature is the language of mathematics what newton did was to say that well actually there are you can write down abstract laws that allow you to deduce things about nature purely on the basis of mathematical constructs well um i i talk here about how in uh in the period after newton for a long time that there was sort of great success as progress was made particularly in physics and so on um and uh the mathematical approach to science had limited success in in areas like biology and economics um and uh the um uh uh you know but people kind of felt that that was a limitation not of the underlying mathematical methodology but rather of those fields of biology and economics and so on if only they could be pushed further then it would be possible to have this underlying tool of mathematics succeed in them so david hilbert in 1900 uh said mathematics is the foundation of all exact knowledge of natural phenomena so um and uh alfred whitehead co-author with bertrand russell of the whitehead and russell principe mathematica foundations of mathematics some uh books in 1910 or so alfred whitehead said um it's that uh all science as it grows toward perfection becomes mathematical in its ideas now to be fair given computation which was not a thing in his day he might nowadays have said all signs as it goes towards perfection becomes formal and theoretical in its ideas and he might have intended that to encompass ideas of computation but as it was he talked about mathematics and as mathematics involved evolved it didn't pull in other kinds of things in fact the very next note in nks talks about the definition of mathematics in fact i say here when i use the term mathematics what i mean is that field of human endeavor that hasn't practiced traditionally been called mathematics one could in principle imagine defining mathematics to encompass all studies of abstract systems and indeed this was the essence of the definition that i had in mind when i chose the name mathematica yes that's a that's a long and complicated story mathematica which is now um uh just over a third of a century old uh when i named that it was not clear what the future of mathematics would be and to what extent what is now kind of computational x would be embodied would be encompassed by things one calls mathematics but it's now very clear that there's been sort of a fork in the road between that which one thinks of as purely mathematical and that which one thinks of as computational now it's somewhat ironic perhaps that my own very recent work on the physicalization of meta mathematics kind of shows that sort of underneath mathematics there's a kind of computational underpinning but what we see is is this the way in which mathematics is is a sort of a particular slice of that computational infrastructure a particular slice that is appropriate for sort of human mathematical observers but anyway i think um uh i talk here a little bit about uh the methodology of mathematics and its difference to the methodology that that i used in new kind of science i mean one of the things i i really struck i was talking to somebody just recently who was talking about um the uh the kinds of ideas that um are in the new kind of science book and was kind of trying to characterize them quite quite accurately actually and and sort of saying um you know well i guess what was there was really a new kind of science and then it paused and said actually i guess that was what you called the book and it's like yeah that was um i mean i i it was a bold title but i think an accurate title and i think it's become clearer over time about the accuracy of that title i have to say had i written the book today i think my style of writing has changed a bit more to the the more or shucks style of writing um about about discoveries and so on um perhaps perhaps says i've had more discoveries in my life i've i've taken more of an orchard's attitude towards them the the ones earlier on uh kind of loom larger and seem more significant uh personally so to speak um and um i think that that's um that's an interesting thing in fact there's a there's a note in the nks book um about clarity and modesty which maybe we'll come to in a little bit here um well i talk here about the fact that you know mathematics has had this big emphasis you know you read a mathematics paper it's all about we've got this assertion let's now prove this assertion it's all about sort of uh defining this idea of proof whereas the kind of the the approach of the sort of pure nks approach the ruliology approach is just consider a rule and see what its behavior is that's a sort of a different it's kind of a forward workflow of take this thing this rule we've defined and see what its consequences are rather than take this assertion that we have and try and work backwards and find a proof based on some pre-existing axioms and so on now by the way in in my recent work in metamathematics um we're actually are going in the forward direction so take these axioms and just you know tree out the giant collection of theorems that they produce i i talked about that a little bit in in chapter 12 a new kind of science but i've that's now much more developed all right let's see uh i talk here a little bit more about reasons for mathematics and science um it's um well i say here it's not surprising that there should be issues in science to which mathematics is relevant since until about a century ago the whole purpose of mathematics was thought to be to provide abstract idealizations for aspects of physical reality um the the issue though as i say here is that there's no reason to think that the the ideas that have emerged in mathematics in the past are ones that will cover all the kinds of things that the natural world throws at us now i have to say that one of my more recent points of view is that um the things that um uh are the things that we get to kind of talk about in our science are just that slice of kind of what is computationally possible that we humans are capable with our senses and our cognitive abilities of kind of uh of parsing and i i was just glancing at this uh earlier today and i i completely forgotten this um uh this statement here which i said down here one explanation uh for this fact often noticed that there's sort of uh uh there's unreasonable effectiveness in mathematics and natural sciences one explanation of this advanced by albert einstein was that the only physical laws we can recognize are ones that are easy to express in our system of mathematics i have to go look up where where einstein said that because i think in now from what i now understand okay probably a century after that was said from what i finally understand that's a that's a quite um that's a statement uh uh rather close to the mark i think that um uh this idea that the only physical laws we can recognize are ones that are easy to express he said in our system of mathematics but i might say in our kind of cognitive framework um those are but that there could be other physical laws that our physical universe has the ability to be parsed in a quite different way than we parse it and you know the putative aliens could be parsing the physical universe in a quite different way so they're living in in a sense the same physical universe but have utterly different physical laws to describe how it works so it's interesting that einstein noticed this this point specifically with respect to mathematics um i talk about here you know i might say one of the things that i spent a lot of effort on in writing the nks book was all these historical notes and i i really put put a lot of primary historical effort into these notes of in many cases when they're about more recent history actually talking to the people involved in them looking at all the primary documents all this kind of thing and in more recent years i've sort of written at much greater length about historical kinds of things in the nks book i did all the work and then compressed it into two sentences so to speak now that comparable amount of work i would actually describe a bunch of that work in in pages of of of writings that i make and you know i produced this book a few years ago called idea makers which is a collection of kind of historical biography uh items uh many of which the the sort of the core uh kind of investigation of those biographies i did in connection with the anchor s book um but uh i then subsequently wrote about them in more detail um i i talked here about um this notion that um the you know could there be um programs that represent nature uh and you know given this idea of programs to represent nature can you go back in history and find places where people talked about this before and the answer is yes you can um although in slightly different terms like for example around 100 a.d uh lucretius uh uh the um uh wrote where he had this book called derarum natura on the nature of things and he had this really lovely suggestion that the universe it's a book written in latin poetry um the the universe might consist of atoms that were assembled according to grammatical rules what he thought of as grammatical rules in the same kind of way that letters and words are organized in human language and i think that's a quite poetic view of kind of what we now think about the way that one can take kind of these elements and by certain rules he thought of them as grammatical rules of sort of constraints of how grammar works organize them into things that are like what we see in the world and anyway that was sort of one early precursor that one can see um the uh a common metaphor from ptolemy to kepler and so on was this idea of sort of clockwork that there would be that things like planets would follow geometrical rules that are like the elements of a mechanical clock and that's sort of a program-like idea um but by the time newton uh came out with just you know just trust the mathematics write down the equations use calculus this idea that there might be sort of a clockwork mechanism fell kind of into disrepute and um it sort of re-emerged there were you know ideas like genetics and heredity where there were sort of simple program like rules that were talked about but really it kind of it fell by the wayside relative to this idea of let's just use calculus type mathematics and blast through the problem so to speak so uh the um 1940s and 1950s uh one gets neural networks early neural networks from the 1940s made with electronic circuits and so on cellular automata uh made both theoretically and in some cases in early things like early image processing and so on um but uh the they weren't viewed as being kind of things for themselves they were viewed in a few cases as being technological systems in many cases being things which if they were anything to do with nature there was some kind of idealization of the true mathematical equations on which nature operates so uh let's see well i talk about um i talk a bit about logic here as another kind of foundational possibility for what science could be based on i'll talk i i talked a lot more about that in the book and subsequently um it's uh uh i think logic was kind of it used to be you know back in the middle ages you know the two things taught in school were you know logic and mathematics and uh you know mathematics you know really worn out logic did not um logic as of the middle ages was was a bit closed-ended it didn't have the kind of the the reach that mathematics had it actually the reason for that was really that logic is a decidable theory it's a theory where in principle you can finitely decide any question in in basic logic whereas mathematics in the end has undecidability and girdles theorem and all that kind of thing and it's something where you can have infinitely long proofs to establish things where you can have some infinite processes so in a sense logic by its very success of being something where you can really nail everything down ended up being rather closed-ended and didn't have the kind of mileage for the next 500 years that the mathematics has had but in any case that was kind of another foundational idea and one could at some level think that logic and it did in some ways historically led to this notion of mathematical logic and computation and so on um but logic as it was kind of people were never thinking never thought i believe about you know logic as a foundation for natural science that's just not really a thing i mean there were a few tiny sort of uh tiny little sprouts that began along those lines but unlike mathematics which was for hundreds of years kind of a dominant theoretical foundation for natural science logic never made it to that it always was thought of as a as a different kind of thing maybe something related to human thinking maybe something related to mathematics but not natural science okay i had another note here about complexity and theology so i talk about um here and it's sort of interesting that a lot of the the basic thinking about uh kind of foundational questions in science was thinking that was going on in the theologians in the in the middle ages and and else and at other times um and by the time the science came along and mathematicized the theologians are sort of out of that game um and so insofar as one is kind of going back to uh rethink some of the foundations of science that existed at a time when sort of theology was the primary uh kind of intellectual frontier um one is sort of thrust back into some of the same kinds of questions that were being asked then so i i talk about here um uh that complexity and order in the natural world are cited as evidence for an intelligent creator and that's a that's a common theme the fact that there is order in the world means to some that that that that there's sort of a purpose to the world um and it's you know in a lot of early mythologies kind of things started in chaos and then some supernatural being added order and created particular natural systems um it's uh um i guess i comment here about uh complexity aristotle talked about what nature makes he said is finer than art but you know he was a big cataloguer of natural phenomena i don't think that particularly affected his discussion of that um the uh thomas aquinas 1270 uh gave a famous argument for the existence of god uh from the fact that things in nature seem to act for an end for example in respect to the fact that they always act in the same way that there are definite natural laws that determine how things act and so his inference from that is in his way of thinking about it that the things must have been specifically designed with that end in mind not that i'm not sure one would take that i think that's uh that's kind of relates to you know aristotle's um uh four different possible causes and so on you know is there a sort of final cause for things i don't think one can infer that but that's what aquinas argued so again by the time there was mathematical science this kind of whole sort of theological arguments for the way things were sort of began to recede newton for example famously said that you know um first god set the planets uh on their courses but their mathematical laws took over to govern all of their subsequent behavior um and uh but in biology even though in physics kind of you know that the role of of the supernatural being so to speak was put um sort of somewhat to the side in biology it definitely thrived i mean the the um i have a copy actually i should have brought that here of of john ray's 1691 book called the wisdom of god manifested in the works of the creation which is just a big long series of isn't it amazing that the this creature does this and that creature does that and so on um and you know basically gave the argument these are these things are so complex that they must be the work of a supernatural being and that famous quote along those lines from this field of natural theology that william paley was uh was a big proponent of in the in the early 1800s you know if it took a sophisticated human watchmaker to make a mechanical watch of the day then the only plausible explanation for the vastly greater complexity of biological organisms is they must have been created by a supernatural being and so then the question was well what other mechanism could there be and that's why natural selection and darwin's origin of species from 1859 was important as providing some kind of idea of what an alternative mechanism might be to lead to progressively to lead to complexity i mean darwin thought the last sentence of darwin's origin of species talks about how just as the earth continues to circle around the sun according to the fixed law of gravity so organisms essentially he says more and more complex continue to evolve so he i believe thought that there will be a fundamental law of biology that would say that that biological organisms by virtue of natural selection become more and more complex that never really panned out yet maybe with some of the things that we figured out from physics project we may have things to say about that question finally um but i think one of the big things in nks was this question of okay where does complexity come from darwin sort of said well it somehow must come from natural selection we didn't quite know how and what in nks i talk about a lot is the idea that you just you know pick a simple program at random just as you know random genetic mutation might pick a program at random and the point is that those simple programs have a high probability of generating what appears to us very complex behavior and in a sense it's sort of an incidental thing that you get complexity it's not something you have to deeply work towards using some very careful series of of of steps um let's see i talked here about um uh this is more more history about the notion of complexity um in uh in science uh a few few things to comment here on perhaps um the uh uh you know the this idea in the 1600s that started with newton and so on that um uh it should be possible to explain the operation of systems i actually i comment here on things like uh circulation of the blood led to this idea of sort of mechanical explanations for everything including biology and renee descartes said in 1637 that one day it should be possible to explain the operation of a tree just like we do a clock and actually he said he talked i think about it maybe in a hundred years from that time it would be possible to explain the operation of a tree just like we explained the operation of a clock um the uh um i think uh uh you know that seemed to work in physics but didn't work in things like for actual trees in biology and so on emmanuel kant 1790 another famous quote it is absurd to hope that another newton will arise in the future who will make comprehensible to us the production of a blade of grass according to natural laws so kant took a different point of view and said no no biology is simply never going to be accessible to anything that has the same kind of sort of formal structure that newton had added to mathematics and mathematical physics um but uh uh yeah talk here about complexity and so on um and uh um the the the point that in um uh by concentrating on things where mathematics worked whenever there was complexity it was just kind of a nuisance and people were like let's concentrate on what's regular you know we look at the primes we look at the sequence of primes we don't look at the fact that there's a lot of randomness apparent randomness in the actual distribution of primes rather we concentrate on the average density of primes which has some simpler form that we might be able to describe using traditional mathematical ideas i talked a little bit here about the cybernetics movement from the 1940s with people like uh well norbert weiner particularly and um uh john von neumann talked a lot about networks and things modeled after neurons and kind of the analogy between electric circuits and brains and and things like this um and and that led to practical kinds of things like control theory game theory things like this um but uh it didn't really the the kind of well what really is complexity where's it coming from and so on didn't really get that much addressed um the uh then that that kind of merged into robotics early ai um early uh things about um uh sort of management science that was a very early potential application for sort of the ideas of of understanding complexity from the 1950s was which was when management science was really big was um uh the um uh was this idea of let's use it to to sort of model uh human organizations and um i don't think i mentioned it here but in even by the in the 1800s there was this idea of sort of the social physics use newton-newtonian type ideas to have a a um a sort of a model of social science that would be on a par with the models of physics this didn't work out yet um anyway i talked a bit about some other kinds of approaches um let me see what else do we have in these notes and then maybe i can talk a little bit about some uh okay let me let me uh show you something else here so another thing that i did in um uh in the first chapter of nks is i i did something which i'm not sure it's my not my favorite part of the book um i kind of gave short summaries of the relationship of what i was doing to other areas to mathematics um i talk about the fact that there are particular kinds of mathematical systems which are studied in mathematics which are much in a sense much narrower than the set of all possible rules that one can study in studying the computational universe i talk about physics i just noticed that i had said um uh uh in the future of physics the greatest triumph would undoubtedly be a truly fundamental theory for our whole universe yet i'd say despite occasional optimism traditional approaches do not make this seem close at hand but i say with the methods and intuition that i develop in this book there is i believe finally a serious possibility that such a theory can actually be found and by you know 20 years later we're in very good shape on that i believe uh which is which is wonderful to see um i talked to already a bit about biology uh social science um i think this was mostly a kind of a a i don't apply this too quickly in social science kind of um statement here in computer science i talk about the fact that really computer science has tended to be and still is mostly about programs we write that do particular things not about the universal possible programs and uh that's this is this is not nks is not computer science ruleology is not computer science ruleology is kind of the natural science of computer science which is not the same thing as uh as the sort of the engineering science of computer science um i mentioned philosophy i mentioned um i mentioned art sort of an interesting thing i mean the nks book i think uh has been quite popular among people interested in in uh visual forms and and architecture and so on and i think one of the things to realize is that that in a sense what we're doing by finding these simple programs that uh we're trying to find the essence of what makes nature tick so to speak what the essence of how things forms are created in nature and that's and so so we get to kind of the underlying of what in many cases people have used nature as a as a inspiration for art but we are kind of going to the underlying to see what we can get both things that nature has used and things that nature might have used but hasn't used and we can make use of those to create form for things like art talked a little bit about technology here saying basically that in my book called the new kind of science then needless to say it wasn't about technology uh in the years since then it's been kind of interesting to see the extent to which sort of from a technological point of view things have emerged so for example we've made great use of the idea of just search the computational universe of possible programs for ones that are useful and i would say a much more dramatic example of that has come about from neural nets and deep learning and so on um i would say that that hasn't yet really merged with the whole nks idea um you can view it as being sort of a more complicated version of an nks like idea this notion that uh you just take a neural net which is in its original conception a fairly simple program though by the time you're actually dealing with a giant you know billion uh weight neural net it isn't such a simple thing you take that thing and you're kind of giving it all this training data and you're finding that yes it can reproduce what happens in the natural world i think there is a yet to come kind of big merger between sort of the the neural nets in a sense what it does intrinsically just leave it to its own devices will be quite simple if you make it big enough and you bash it hard enough with training data you can make it kind of interpolate and and extrapolate what happens in the world but the its intrinsic dynamics is not that complicated and there's usually a trade-off between the more complicated the intrinsic dynamics the more difficult it is to train the thing in nks we're dealing with intrinsic dynamics where the original rules are very simple the behavior is very complicated now the question is how do you train that and we don't really know that yet and i think if we can train it it's kind of it's like it's like you can have the the very docile animal it's easy to train but it doesn't go very fast and doesn't do very exciting things or you can have the animal that leaps and bounds but it's really hard to train and that's we don't yet know how we take kind of the methodology of of deep learning and neural nets and apply that to kind of the the richness of kind of capabilities of the simple programs that we find in the kind of unf un un unconstrained computational universe the neural nets a bit like mathematics are a very constrained corner of the computational universe and as i say that there are methods now that we've understood for you know if you bash them hard enough you can train them we just don't know how to apply that to the to the richer kinds of things although there are a few ideas that have emerged from sort of things that come out of the physics project all right another thing that i did here which which uh probably not my favorite section here but but um uh was to kind of go through because people kept on asking me you know i i mostly when i wrote the nks book i was i was basically a hermit and i sort of disappeared for a decade to do this um longest most difficult single project i've ever done and probably will ever do um but you know whenever i would sort of stick my head out and ask people tell people what i was doing they would say and how does it relate to artificial life how does it relate to chaos theory how does it relate to this how does it relate to that in fact in some ways the the the very title a new kind of science was intended to signal this idea there's actually something new here it's not oh how does it relate to this oh i can understand it if i understand that because that was sort of a formula for getting confused and i watched that happen many times it's kind of interesting to see what i talked about i talked about ai which at the time was kind of a a submerged field i might say by the way when i mentioned technology before that for me personally one of the big outcomes technologically from nks was wolf malpha you say what on earth is the relationship between wolf and alpha and our computational knowledge engine and and nks they seem like opposite ends of the spectrum you know nks is about these simple programs uh orphan alpha is about taking the the sort of the detailed knowledge of the world and making it computable well the real thing is that and i'll talk about this in another session here is that this principle of computational equivalence of mine kind of suggests this idea that there's no bright line distinction between the computation the the intelligent and the merely computational and so i had long been interested in making kind of a computational knowledge system and i kind of got to thinking after the nks book i got to thinking look if it is the case that uh this really true that there's no bright line between the intelligent and the merely and the merely computational then it should be possible to make this thing that seemed like it would use need sort of human-like ai just using computation and that's what kind of stimulated the possibility of building wealth and alpha and it turned out yes it does indeed work but um uh i talk here about um creating technological systems capable of human-like thinking um i think the best sort of exhibit of something like that is the kinds of things that have been achieved with deep learning although that wasn't specifically what i had in mind i had in mind more something which would perhaps be a merger of those two things i actually talked about that in a section of this book um at a time when neural nets didn't seem like i had worked on neural nets back around 1980 and hadn't been able to figure out how to get them to do anything interesting it was at a time when when neural nets just didn't seem like they were they were able to do interesting things before the discoveries that were made in 2011 and so on that launched modern deep learning well at the time artificial life was kind of a big thing um that's uh this kind of idea of you know can you take um uh sort of can you take uh abstract computational systems and do they do things like living systems the answer is sort of yes um i think that's become a little less surprising in modern times but you know whether it's malware on the web acting like living systems or whether it's other kinds of things i'm not sure catastrophe here is something perhaps people have barely heard of anymore it's kind of a was a theory that was very popular in the 1970s which i talked about here chaos theory people have more often heard of um although i have to say i think that it's some very confusing because the um the original idea of chaos theory which is the sensor dependence on initial conditions that if you that there are things like uh i don't know you know um you know when you flip a coin which way it will come up depends in detail on how fast you flipped it because you're kind of excavating digits every time the coin turns over it's kind of saying be more precise to know which way it's going to end up but you have to be more precise about what the initial velocity was and that's sort of extreme versions of that you can certainly invent mathematically even though they don't seem to show up very often in practice they're things you can invent mathematically and people kind of had this idea which is a little bit muddled i think that um uh that you know you can get complexity from just excavating initial conditions of things so if you say i'm gonna have precisely pi as the initial condition for my for my thing or precisely some number that's a random collection of digits that goes on forever then as i as i look at what the system does it will gradually excavate more and more of those digits but the problem is you kind of have to know where those digits come from in the first place and you kind of have to say uh it's it's rather confusing in traditional mathematics because you sort of all real numbers are kind of equally producible but that's not you know from from alan turing's original 1936 paper was called uncomputable numbers because he wanted to make the point that some real numbers were more producible than others some were computably producible some were just numbers you would write down which we could imagine but which you could not actually produce and i think the the the greater understanding of this notion of how do you actually make that initial condition makes the sort of chaos theory idea kind of recede a bit i mean it was a bit confusing because there was a well-known and i think quite well-written book by a guy called jim glick that came out um that uh in which the end of the book he had interviewed me the end of the book talks about a bunch of stuff that that uh later on is stuff i talked about in the nks book and that was sort of bundled in a book that was called chaos and so that that made the whole thing more chaotic even than it might otherwise have been in terms of understanding what was sort of the core idea of chaos theory versus other kinds of ideas i talked about complexity theory which i'm afraid i i recently wrote a piece about sort of where has complexity theory gone um it's a it's a it's a it's an interesting story um i think it's a it's a story that uh i think looking back on nks today i think there are two key ideas i i just wrote about this a couple of months ago there are two key ideas uh what i call meta-modeling and what i call ruleology which are two key ideas that should be the things one should be talking about in studying sort of complexity um maybe i shouldn't go into this in more detail here i i did have a uh do a live stream about this a couple of months ago um but uh that's that's that's kind of i think that the the takeaway from nks is there's this kind of pure nks of studying simple programs and what they do there's this notion of meta-modeling of going sort of taking the actual models that people have made that are often quite complicated of systems and saying what is the essence of this model what is the underlying formal structure on which this model is based i have to say on this page uh you know the things i'm talking about cybernetics people don't talk about that very much anymore that was kind of an early almost you know it could have been a pre-nks but it really went off in different directions about the practicalities of control systems and so on dynamical systems theory is kind of the mathematical underpinnings for for chaos theory uh things like general systems theory which had a a good but well bit but hard to understand name but that that really mostly ended up being a question of is there sort of a scientific theory of management and so on and um uh really by the time i started working on this stuff by the late 1970s it was it was it had sort of to my you know seemed to have somewhat disappeared nanotechnology was a much bigger thing back 20 years ago than than it has seemed today i think it will be back um i think that uh a big thing that nks kind of suggests is that you can go from uh nanotechnology was often about let's take a machine that we built at a large scale and let's shrink it down to be at a molecular scale what nks suggests is that one can go from the ingredients that one has at the molecular scale and see how to make them do something interesting and in fact just recently with ideas from our physics project there's a lot of progress on thinking about how you do molecular scale computing um there's things here that i'm afraid of not whether the weathered time very well like the concept of self-organization that was kind of a big buzz word back um oh i don't know in the in the 1980s particularly maybe 70s as well of of systems that spontaneously organize themselves which is just not uh that that's uh it's i mean that that's a sort of a a small piece of the story of systems that just make themselves do complicated things um well okay so another big thing that i that i did in this um this very first chapter of nks was was to tell a little bit of the personal story of how i ended up doing the science that's in the book and i have to say the the um the sort of the the launching event was a thing was actually this book here which i have in physical form and um the uh this book if i open up the the front cover it says um i don't know if i can get that close enough to see but but um uh i used to write when i got books it says stephen wall from june 1972 and then it says three pounds and 35 pence as the as the price of this book it's probably more expensive now um if it isn't out of print but um uh anyway i got this book i was 12 years old at the time and i was uh learning physics and um the um the cover of this book i found really interesting it's like um uh this is you know this purports to be a picture of how uh kind of the um uh you have molecules in a gas bouncing around and they they start in a fairly ordered state and they progressively get more disordered it's kind of shocking that you know the things i've been writing just recently about uh the kind of foundations of matter mathematics and so on i use as an early analogy precisely the same phenomenon that this this phenomenon of kind of uh this this fundamental phenomenon of the second law of thermodynamics the law of entropy increase the tendency of systems to go from more ordered states to less ordered states anyway i found that phenomenon really interested when i was 12 i i still find that phenomenon really interesting today although i think i very deeply understand why that phenomenon happens now and it's all to do with computational irreducibility but that phenomenon is in a sense it is a place where raw computational sophistication is visible to all of us and it is a place where implicitly kind of computational irreducibility really shows up i mean it shows up uh much more explicitly when you're making you know a proof-of-work method for a for a cryptocurrency or something but it comes up as a very much more of a of a visible thing in um a very very much more all around us kind of thing in statistical mechanics in the in in in this lore of entropy increase and so on now i have to say that i was i was years after that um uh that book years after i saw that book i finally uh tracked down this person bernie alder who had made those pictures and um they were oscilloscope output from a computer at lawrence livermore what's now lawrence livermore lab and the thing that was really a bit of a shame was those pictures are a fake uh what i thought they were is pictures showing if you have hard spheres bouncing around according to deterministic rules what will happen to them but um it it didn't really work that way and a lot of what is shown in those pictures came from the use of an early random number generator middle square random number generator actually it's a random number generation method invented by von neumann um that was used to sort of set things up randomly and then kind of most of what you see is kind of just that random initial setup not the true phenomenon of the production of apparent randomness from something regular but um anyway fortunately i didn't know that back when i was 12 years old and and um uh and very interested in that book cover but i i ended up some of my first computer programs actually were intended to simulate that book cover and one thing that happened which is kind of one of those ironies of science is that i am i ended up you know i couldn't simulate all the high precision numbers and things that you need to simulate um uh kind of spheres bouncing bouncing around so i simplified it all to just integers and discrete positions and so on and i ended up with a cellular automaton this is probably in 1973. and i ran the cellular automaton and didn't do anything interesting so okay ignored it had i not made one particular very specific assumption my cellular automaton would have done lots of interesting things and uh possibly i don't think i was ready to discover all the various things that are in the nks book at that time but i would at least have had a computer experiment that i could have gone back and said oh i missed it in that computer experiment as it was my computer experiment i don't think i think it was it had one extra feature that made the thing behave in a very simple way and not show second law of thermodynamics behavior but um i i ended up working in particle physics which was sort of at that time the most uh most kind of happening area of of a basic science and what seemed like the most fundamental one but but i then you know i kept on being interested in this kind of how does complex behavior arise in um uh um in in different systems and so on and uh uh i kind of got back to after doing a bunch of particle physics and cosmology and so on i got back to questions about you know how does snowflakes end up with the shapes they do how do turbulent fluids work and so on that was the beginning of the 1980s but an important thing that happened in my life was that from 1979 to 1981 i worked on a sort of precursor of mathematica and waltham language a system called smp that was um i called it symbolic manipulation program and um it uh it pioneered a bunch of the ideas that are now um i'm which i'm sort of i kind of got those ideas partly from studying things like mathematical logic and so on and i'm actually i'm glad i didn't understand more than i did back then because if i'd understood more i would have been confused by all the different rabbit holes that one could have gone down and as it was i i managed to keep without falling down the rabbit holes so to speak and actually build a system um but uh back in 1981 i just finished sort of the first version of snp and one of the things that that had given me experience in is something very different from natural science you know in natural science you're given the world and you're told drill down and figure out how the world works when you build a software system a language computational language whatever what you're what you're doing instead is to say well you can drill down a bit and make these primitives but then most of the story is what you build up from those primitives to be able to do in the world so you're just sort of making up those primitives to be primitives that are in the end useful to humans and so on and things that computers can deal with but you're sort of making up those primitives and then seeing what the consequences are as opposed to the traditional natural science approach if you're just given you're given the answer now you're asked how did you get to that answer so to speak so that led me to a as i realized later to a somewhat different kind of um conceptual framework for thinking about making models in science and that's what kind of led me to start thinking about well what if i just take these simple programs what if i do computer experiments and run these and see what happens and then what happened was this is 1981 or two um i run these things and uh uh i get these pictures these are you know originally the line printer pictures printed out with with stars and spaces and so on um uh took a couple more years before they were nice high resolution bitmap displays and so on um that i was using but uh the big surprise was i thought these simple programs that i had which were just like one line of c code or whatever would um uh would be would just generate very simple behavior but they didn't and that was and at the beginning i was like there has to be a way you know they look complicated but they can't really be complicated there must be some some hidden uh kind of regularity here which i'm not seeing and that was sort of a big effort for several years on my part to um uh to to realize eventually to start coming to terms with this idea that from simple programs you can get very complex behavior and that's just the way everything works and it just wasn't that was not obvious to me i and i i fought it for quite a while because i thought that that there was my intuition was so strong that you know if you want to make something complicated you have to go to a lot of trouble and not just complexity just spews out from from simple rules that just wasn't the thing that um uh that i i uh had realized um anyway i i am i ended up um uh working on that for for a number of years and um i kind of my plan a was persuade the world that this was a great field of science this is mid-1980s and um uh and sort of get lots of help and um and have a have a sort of scientific army uh move forward in this direction um that was hard to get started maybe because it's just the world makes that hard maybe i maybe i'm a uh uh you know maybe that's not my leading such an army is not my forte i don't know um but uh in any case i i started some pieces of that and then kind of basically said look you know i find the science really interesting let me build the best tools and the best environment for me to just do this science myself and so that's what led me to build mathematica and now wolverine language um back in 1986 and to start our company well from research um which has been my kind of uh home for the last 35 years and has allowed me to have to take a uh in in in most of the sort of institutional areas of science it's kind of like that you know there's a big structure and incremental progress is what's expected and anything other than incremental progress is really quite alien and i think i've been fortunate that i've had this kind of structure where it's been possible to do things which are not necessarily just incremental progress um and uh anyway i talk about here by 1991 i i basically we built first versions of mathematica things have been quite successful um and uh i decided i will go off for like six months maybe a year and um uh sort of um take um uh uh take kind of um take the time to sort of work out the consequences of these things that i'd done in the in the 1980s and and write a book about those well the big mistake was the big problem was that i just ended up you know given orphan language mathematica back in in 1991 i just was able to discover all kinds of stuff and i discovered all kinds of things and i started exploring the computational universe and discovered there were all kinds of interesting things there and then i started looking at okay so what are the consequences of this for biology for physics for mathematics and so on and it's like oh my gosh there's all this stuff to discover well it took kind of ten and a half years to work through all that stuff to discover it's a very difficult i mean i i have to say that the project that i did and maybe i'll show another time some of the kind of earlier tables of contents for the book but the table of contents for the book didn't change much over that period of 10 years what happened was that um uh it was some um it was uh um the the what happened was pretty much i've got these 12 chapters this is my homework for the next as it turned out decade um now go work out um work out all of these things so let me let me just as some uh to uh um just want to show one more thing here um there are probably a bunch of notes here about um uh um well these are let's see all notes for this section ah okay um so uh i talked a little bit more detail about um about some of this um oh i see okay um about uh some of how i actually worked out these kinds of things maybe i can um i'm happy to kind of um uh take questions maybe i can just briefly point to the uh something i said i might cover which is the preface to the book um and uh you know i started off talking about just over 20 years ago that's 20 years ago from now i made what seemed like a small discovery which was uh these discoveries about simple programs and so on um and i kind of explained in the preface what my idea was in writing this book um and you know i talked about the fact that before i'd sort of done what i'd done before as a scientist and just publish papers in the scientific literature um and that worked very well actually i mean it was very successful but what i realized is if you want to have if you have a big thing to talk about you can't scatter it over 500 papers people will never understand it it's absolutely it's an incredibly kind of user-hostile approach to uh to presenting things and and i decided i would sign up for something that was in a sense very difficult which is take these ideas and if if they were as fundamental as i thought they were it should be possible to explain them not just to specialists but to anybody who's prepared to take the time to to understand what was going on and so one of my goals in in the ncaa's book was to write it in such a way that it was accessible to a wide range of people and it's um uh i talk about the fact that modern times it's almost unheard of for genuinely new science to be presented for the first time in a book that can be read by non-scientists and i think i was very well aware of the fact that um uh it's some uh you know it was more difficult for me to do that than to use technical formalism um but i'm sort of proud of the fact that i managed to do it unfortunately however i say this will no doubt mean that there are some particularly from the existing sciences who will at first assume that their existing technical knowledge must somehow already cover what is in the book and a few i feel will stop at that point and choose to learn no more etc so um and of course that happened to some extent although i have to say that i in um you know 20 years of hindsight i'm extremely glad that i went to the trouble to uh to write this book in a way that was accessible to me by you know of all the the huge number of people who've have uh told me that that um the book was important in their understanding of things and so on um that none of that would have happened um had i written it in kind of some elaborate technical formalism with all kinds of elaborate you know uh notation and so on and um so i think it's it's some uh that was sort of a big success so that was that was written january 15th i've um i i talk about at the end here that um uh you know um the uh um in the end most of what now seems surprising and remarkable in the book will come to seem familiar and commonplace there is a generation of scientists for whom that is beginning to be the case for whom things like computational irreducibility are just the way things have to be as as it finally seems to me after all these years um i just uh um let's see how is the last sentence held up the last sentence of what i i'm starting to look at questions and comments here um and i'd love to look at those some more um the uh this last sentence now i've finished building the intellectual structure that i described in this book is my hope that those who read these words can share in the excitement i've had in making the discoveries that were involved yeah i think i mean i you know that's you know another decision that i made in writing this book was to make it somewhat personal the science is not personal at all but i did talk a little bit about how the discoveries were made a little bit about my kind of uh uh sort of my my in a sense conceptual um reaction to those discoveries rather than just this is true this is true this is true it's like this is why i care about this and i think that was i think that's important i mean i i i had a bunch of notes some of these get pretty nerdy actually i was looking at these earlier and i i have to say um this is a really nerdy one i mean this must have been a consequence of my my editing staff uh and me me explaining um that uh in writing the book i had to adopt some rhetorical devices perhaps most annoying to those with a copy editing orientation will be my predilection for starting sentences with conjunctions something which i have not changed but the main reason i've done this is to break up what would otherwise be extremely long sentences i admit that it's kind of nerdy to to have actually put this in the book um but i was i was avoiding the kind of kantian page long sentence phenomenon even though the the ideas are often you know build up over the course of a long sentence and i think um oh boy this is this is nerdiness perhaps to the extreme but 20 years ago this seemed more significant that when i say billion i mean 10 to the nine and not some not 10 to the 12 which was the the earlier british usage um i think um i had a thing about clarity and modesty i mean i i've uh you know basically saying look i'm just going to say what i think is true i'm not going to kind of say well you know maybe this is true and maybe it's important and i don't really know i'm just going to say if i think it's important i'm just going to come out and say i think it's important and to me that was a uh you know probably ruffled feathers but to me that was something i thought was important because i knew when i explained this stuff to people that people would if you told them this is something big and new and it's going to be hard to understand they're kind of ready for that if you say oh well i just kind of figured out this little thing they're like oh it must be related to this it must be related to that must be related to that and they're pretty soon they're totally confused so in a sense one is the question is does one support the individual namely me or does one support the ideas if one's supporting me it's much better you know much better personal reactions if you say oh shucks i you know maybe this is interesting maybe it's not whatever but the ideas do not do not uh you know the that's not good for the ideas the ideas are much better served by just saying look this idea is important and it's uh you know the the creator of the idea can say that other people can say that but somebody's got to say it otherwise people just don't understand what's going on i would say that i had another charming note here about technology references an effort to make the main text of this book as timeless as possible i've generally avoided referring to everyday systems whose character or name i expect will change as technology advances you know reading alan turing's works he talks a bunch about brunswiggers which one might not know what they are they work that was a brand of of uh a mechanical calculator that was common at the time and so i i've kind of uh i avoided talking about that although i did notice that um there is one place i talk about personal digital assistance um which uh uh didn't survive in in those terms um let's see well there are there are lots of lots of funky things that are very nerdy in the intro in the in the notes for the preface i i think by the time one has notes for a preface one knows one's going nerdy and there are things like um uh that there's some well i talk about the historical notes which i really put a lot of trouble into um i talk about uh well all sorts of things which perhaps are interesting to see now but maybe i'll talk about them if um if we have time about um sort of my comments about how the science would develop and uh sort of the the relationship to education and so on um but uh maybe we can turn this over to um to people's comments questions etc let's go back to that all right there's a question here um uh john john says some uh commenting that um uh maybe like in the hitchhiker's guide the the you know the earth is a giant computer yeah i think that's kind of true and um the challenge is to to understand um uh yeah i mean is uh yes not in quite such literal terms but um uh yes i think that what we have learned and particularly so in the last couple of years kind of building another 20 years on top of of what was in nks it's kind of this notion that it really is computation all the way down it's a very i mean i think it's an important thing um okay eric asks after 20 years of development 20 years of reflection is there something you would fine-tune in the new edition you know i was reading the first chapter i hadn't read the i you know i look at the encase book regularly because i'm using all kinds of stuff that was that i figured out particularly in the notes which are just incredibly rich um source of of just all sorts of material and results and so on and it's kind of it's kind of crazy because a lot of times in the notes i'll just sort of say and there's the following thing and it's like okay just stated in the notes and because i avoided having notes to the notes there isn't a lot of detail on where that came from but i know because i can go back and look at the original notebooks which eventually we'll all put online um that that was a lot of work you know that was weeks of computer time and lots of programs and so on it's just stated and the result is this and so it's a very rich source of um uh sort of high density material in the notes the encased book and those are perhaps the things that i look at the most on an ongoing basis i've tended not to read so much the words because i figure i kind of know what the words say in in the book and i was i was looking at the at the introduction and i would say that well my writing style has become a little bit more informal in the last 20 years um and i think that there are probably ways that uh you know if i was writing it today i probably would have been a little bit more in the or shucks direction but that's in the in the with the wisdom of hindsight because you know at the time you kind of just have to you know bash people over the head with the fact that yes this is something new and different now 20 years later it's like well yes it was new and different because we can see it was new and different 20 years later so it becomes a little easier to do that um with the wisdom of hindsight i think that the the actual description of what the book was trying to do was really quite good i mean it was uh and there's a little bit of terminology like this idea of computational universe and so on which i didn't have at that time but i think it was not um uh that that didn't really change the exposition a lot i mean i i'm reminded i i was um you know uh if you look at the first edition of origin of species charles darwin's origin species it's got all kinds of you know energetic things to say by the time you get to the third edition there's all kinds of stuff about and responding to professor so-and-so we say blah blah blah blah and in a sense the third edition is worse than the first edition and i kind of knew this i i knew that precedent and i never intended to write another edition of the nks book i intended this to be this is it this is the thing this is what i have to say um now i have to say that that in modern times i've taken the point of view that i will write things and the main thing is just to get them written and that that you know i may move on i may i may come back and i may do more things in that area um or not but uh you know the the it was a big personal effort to spend a decade sort of as a hermit producing this one one piece of work and it's a lot easier i mean i've taken the point of view now i've i've gone total anti-hermit and we're sort of live streaming most of our working sessions you can find even the the uh uh perhaps it's excruciating i haven't tried watching it the you know i've been uh kind of recording sort of pretty much all the time i spent even on my own working on writing things figuring things out and so on and it's all on the web um it's all you can find all of it hundreds and hundreds and hundreds of hours of it um and um i think that's uh in a sense that's sort of a a a dividend of modern times that it's possible to do that and that you know live streaming and so on is a thing social media that thing and that's something that i didn't have in the 1990s um you know the web was new and uh you know i i was when i worked on the nks book i had um in my office that had was covered with piles of paper and it was kind of very exciting towards the end of the book the piles of paper gradually started disappearing as that section is finished i can put those papers away that section is finished i can put them away and so on and um that was uh but you know and a lot of the work that i'd done in particular the early 90s of tracking down a lot of very obscure information would have been much easier if i'd if i'd had 20 years to wait um and been able to use the web and so on although i have to say i don't think there's not a lot where i say oh my gosh i missed that now you can find it on the web and i missed it at the time i i dug fairly deep particularly in the historical areas of things and i think there's there's very little that i that i didn't get to that was within the scope of what i was trying to do um i think uh well the one thing that we are going to do in the in the new version um in the new online version is click to copy uh every picture has code behind it i should have put the code i mean i had all the code as i say it was a little bit hairier than it might have been because because one of the things i did in writing the book you know i did the layout for the book as i was writing the book and so if you look carefully and i even mention that in the notes to the preface if you look carefully most books you know number the figures number the pictures and they say see figure 35 i never did that i always said see the picture on the facing page see the picture below and so on that might seem trivial i kind of thought it was it was cleaner to be it to do it that way but boy was that difficult to do because that meant you had to resize pictures you had to make sure that text fit you had to rewrite the text sometimes to get it to fit it felt like felt like creating a newspaper or something but in any case then the kind of the almost the joke of it was that there were sections where i literally wanted to fill out a page and i thought i've got to generate another picture to fill out this page and the picture i ended up generating ended up being a pretty interesting piece of science and there are several of those that that uh that happened in the book so it was a book that was you know the page layout i did the page layout while i was writing the book and uh you know it was done with with with great care and um i think uh that makes it um uh you know it's a it's a different time now where where this idea of things in pages doesn't doesn't uh hold so much uh uh the weight but um that was um uh that was something i i think um um yeah that's the uh i'm i'm you know looking back on the book i'm i'm actually um i think that one of the things i spent a lot of effort on was i would say the diagrams making the diagrams as clear as possible and i wanted the diagrams to be readable without even reading the main text so you can kind of uh just look at the diagram and it's captioned and understand what's going on i think that worked really well um i think that the um uh i had a lot of kind of i found it very interesting doing the historical notes and that was something i put a lot of effort into the other thing i put a ridiculous amount of effort into was trying to clean the ideas to the point where i could whittle down the idea so i could really explain it in a couple of sentences and i would say that um uh the effort to learn to do that and i suppose it's been a a thing that i've been interested in throughout my life is is kind of you know getting ideas down to their essence and i suppose that that comes with the the okay i want to find the fundamental theory of physics um instinct too but um and it comes with the i'm going to design a computational language instinct but i would say the nks book was a was a major effort in doing that that i'm pretty satisfied with and when i look back at the things that i managed to clean as in you know take these ideas that started off pretty complicated things with a lot of kind of technical formalism and so on and yes that was pretty clean i think i've gotten better at uh explaining ideas and cleaning them the nks book was kind of a big training exercise in some ways although it was a training exercise that came after previous ones like the the first mathematica book and things like this but it was um that that was uh something that um i'm i'm fairly happy i i you know most of those things i look back on them i don't say oh that was a messy explanation there's a better one now no i actually that was usually pretty good pretty minimal explanations um danny is asking is there a formal notation system for the ruliad how are simple programs represented well you have to have kind of a a representation language you know as a practical matter wolfram language is what what and its kind of idea of symbolic transformations for patterns that's that's a really good way to represent a lot of these things but in the end you could be using turing machine solar automata you know network rewriting systems all kinds of different things these are all coordinate systems in a sense to to sort of describe these pieces of the ruliad that represents the kind of entangled limit of all possible computations each one is a is kind of a coordinate system a reference frame for for describing these things kovas hello kovas bogota is asking can you speak to transition the title of the book from its original title that's a that's a good question that's that's a good one so the original title for this book was a science of complexity that's what i called it in the early 90s when i was starting to write it at the time the my original focus had been understand this thing that is complexity and i kind of realized and i would say that perhaps if you ask how has the things transitioned since then perhaps even more strongly than um uh this idea of computation as sort of the key anchor concept i certainly very much understood that in writing the nks book i would say that the the the kind of the the wording the description of of um uh is is computation has become for me at least a more central descriptive term than it was in the time of nks but in the early days of nks my my early concept was that this was the the title of the book was a science of complexity one of the things that happened with that title had two problems one is i don't think it really it didn't really live up to quite to that because in a sense science is at odds with complexity in a sense science complexity and eventually computational irreducibility is the story of limitations of science in some ways it's also the story of a big leg up for some new kinds new new kinds of science so to speak but it's a it's it's um and so in a sense that sort of a a um uh that was one thing there was another very practical thing you know hermit as i was i would occasionally talk to people and i would say oh i'm writing this book it's called a science of complexity what everybody said after i said that was oh that sounds very complicated that was always the response and so i'm like this is just not going to work because people are just going to say it sounds very complicated i'm not going to read it even if it tries to explain things in a simple way it sounds by its title it sounds like it's full of complexity in its explanation whereas the whole point is this is about simplicity and its consequences which happen to include complexity so that led to me to make the change to calling it a new kind of science i think the original i i wrote a book blurb for um uh sort of this was probably early to mid 90s i wrote a proto book blurb um for some publisher i ended up not using them in the end but but um uh the um that's a whole story in its own right but but um the uh uh you know i wrote a book blurb and the book blog kept on talking about a new kind of science and it's like hey wait a minute why don't they just use that for the title of the book and when i did that and i would mention oh i'm writing this book it's called a new kind of science people would say oh what's new about it much better question than just the statement that sounds very complicated i'm out of here so that was that was encouraging i would say that one of the things that i was trying to do in the book title was there's sort of a tradition of the you know scientist rights popular book and the you know some of these popular books by virtue of their marketing by virtue of their content are uh you know they they say things that scientists don't say in their technical publications sometimes scientists you know it's one of the things i've learned as you try and learn lots of different fields often you know there'll be some some very distinguished scientists in some field who'll write an elementary textbook and in the introduction to that elementary textbook they'll say some very crisp things about the kind of overall conceptual framework of the field but they never say them anywhere else in a technical paper they never say them because it's kind of like no no this is a technical paper here we don't want to talk about these elementary things but if they write a textbook even if much of the textbook is quite technical the introduction will have things that are really getting at the essence of what's going on and i think that that the the sort of the tradition particularly around 20 years ago and 30 years ago when i was sort of starting out writing the nks book there was kind of this this whole kind of idea of the kind of the the popular science book that maybe had a few extra things that it was saying that kind of made it useful from a research point of view my my kind of idea had always been this is all going to be about new stuff and or stuff that maybe i had done in the 1980s and was kind of uh sort of packaging together but it was going to be about sort of a conceptual framework that was really pretty much new to this book and so it was sort of a tension because on the one side were the popular science books and we kind of did these surveys because i was interested i'm a you know practical guy so i actually got some surveys done in bookstores back in the early 90s of why were people buying popular science books and the main discovery was because they might in another time have bought philosophy books but the philosophy books were all pretty hard to understand and the science books were interesting but in any case the the um uh the thing so you know on one side it was like there's this thing of you know if you write about science clearly enough there's a whole bunch of people who are interested in it and who will do things on the basis of it on the other side though is the there's the very technical communication of technical ideas that traditionally has been done very incrementally in things like academic journals and i was trying to to to to sort of walk this middle path between those two things and so the title was sort of an attempt to do that and i i didn't want to have something to kind of i didn't want to have something whimsical i didn't want to have something i i thought for a long long long time about can i come up with a word you know a cybernetics-like word but it's a huge risk to come up with a word i think now this term rooliology that we're just starting to use now um i figure that uh uh nothing to lose and i like the word anyway um and um that um uh but but it really ology is a much more whimsical i mean it's kind of the the study of simple programs for their own sake which i view as being a very definite thing it's not the whole story of this new kind of science it's it's a story of one of its underpinnings an important underpinning an important methodology but it's not the whole story and um so that that's um uh i think i might even have some pictures of um the uh let's see i really should for another one of these live streams i think i should um uh uh get out some of the earlier drafts i've been looking at different times because i'm trying to figure out when did i actually figure out about like multi-way systems which would become a big thing in our physics project and i you know i just have a shelf full of these different drafts so let me see if i can find this here for one second um um ah let's see and perhaps talk about some uh yeah there are so many stories actually from the from the creation of the nks book which i should be one day told i i think that the um um uh let's see let me just share something here um just from my scrapbook uh so there's a page of leaves in the nks book and um uh yeah that was that was me taking a picture of what turned out to be a very big leaf that was um um but those were some of the earlier kind of i guess this was all post oh no there we go there's the science of complexity yeah that was another early title complexity and computation in nature that was a very short-lived title um but i'm afraid these were some of the these were some of the reasons i didn't use an outside publisher because these were that was their their finest production of a of a cover which i'm i'm happy was not the one that actually happened these were from our design department and the final one was from um uh from our um uh folks um in um uh who do um uh graphic design at our company um and uh let's see what else is there here there's a there's there's a lot of fun stuff with them uh to do with the actual printing of the book maybe i'll talk about that another time um that was a huge challenge because these are high resolution pictures that were really pushing the envelope of technology to actually have a a bitmap that was at that resolution and even found a bug in postscript that had been there for 10 years that was found over a holiday weekend as these pages were actually getting printed that is an interesting story in its own right um but let's see other other questions comments here um okay so mori asks why is mathematics so effective for natural science um and uh the okay so i think the answer is it depends what you're studying in natural science if what you choose to study and call physics is those things that can be studied using the methods of mathematics then it is a self-fulfilling prophecy that mathematics will be successful and if you look at the history of physics that's a lot of what's happened for example turbulence in fluids good example smooth flow of fluids study it with mathematics is all good turbulent fluid flow it's really complicated mathematics doesn't tell you very much about it it's been a big challenge to get mathematics to say nothing much at all about it but for a long time that was something which oh it's not really physics maybe it's engineering we just have to deal with it in practice it's not something that we can have a theory about so in that sense mathematics physics is sort of concentrated on those things which are mathematicizable now i have to say my current point of view is even more bit more extreme than that it's to say that that in this ruliad this this sort of entangled limit of all possible computations there's a lot of wild stuff going on but we as human observers with bounded uh where we have sort of bounded computational abilities we have this idea that we're persistent through time even though our underlying structure may be continually updating itself we have this idea that we persist through time those two things are enough to constrain those at which aspects of this ruliad of all all possible computations we actually observe and that slice that we are capable of observing is a particular slice and that particular slice has more characteristics that are like mathematics than that are like the things we've talked about as mathematics than perhaps i had realized before and sort of the big thing that comes about is the realization that our mathematics is also based on this ruliad idea and the mathematics that we have is also based on kind of our ability to observe the ruliad our physics is based on our ability to observe the ruliad our mathematics is based on our ability to observe the ruliad but it's the same us that's doing the observation in the two cases and so that means that there are constraints that are associated with our particular uh characteristics like computational boundaries like this notion of persistence through time that apply themselves both in physics and mathematics and i've only really recently realized this and it's a it's a very deeply platonic thought in the sense that it really is that there is this you know this universe of ideal forms it's kind of the the ruliad and we are seeing these particular slices that are very human slices you know the aliens might see completely different slices but yes that's the that's kind of the the thing and in a sense the the effectiveness at some level the effectiveness of mathematics and the fact that mathematics well a recent realization is the fact that mathematics and physics have the same foundation and you know they're things like for example if you believe that you are persistent in time that is you believe that you now is the same as you a second ago well in our theories of physics now the atoms of space that make up me now are completely new atoms of space than the ones that made me up 10 to the minus 90 seconds ago um they're they're you know they're completely re they're they're reprocessed just like you know you have a vortex moving through a fluid the actual molecules that make it up will be completely different molecules but yet the vortex moves on and it's the same thing with us kind of existing in the physical universe and but yet if we have the idea that we have a consistent persistent existence between this moment in time and the next moment in time in a sense that immediately tells one that there is a a conception of continuity there's a conception of of the continuum because that moment in time it's not like i'm here for this moment in time then uh i'm nowhere i'm nowhere and then i'm back for the next moment in time it's rather there's this concept that i am consistently there through these moments in time and that leads one immediately to something perception of the continuum even if there isn't a continuum in some sense really there nikolai asks do i think that the widely recognized term theory of everything overlaps with with uh my ideas um i don't know i don't really think about that term i don't like it much at all and i i it's it's almost i mean it's it's said i think almost uh uh i haven't really thought about that actually i mean i i suppose the things that i'm talking about are as close to a quotes theory of everything as you're likely to ever find um but i i would uh tend to avoid that term for it because i think it's been a a um it's a it's a term almost said in mockery um for many kinds of theories but i i suppose we're we're yes we're probably signing up for for that in some sense although it is some uh um i think as you take apart that term a theory of everything it's not obvious there would be a theory of everything it's you know everything is a lot of things and a theory might suggest that it is something where we are capable of human statements about it that is in fact in fact i would say okay i'm going to take apart that term okay the problem with that term is that the very notion of a a theory is is this you know what is a theory and a theory is presumably something that is a a thing that bridges between what we humans can think about and the way a system actually works and in some sense i think it's almost sort of a mockery of that idea to say that everything has a theory in that sense in fact the very notion of things like computational irreducibility is a whole story of things for which there cannot be a sort of a a short human narrative for what's going on so i i sort of i'm not i'm not a big fan of that of that term but perhaps some more for the for uh you know maybe maybe i haven't really thought about it actually it's interesting i haven't um a question from rbs what mathematical fields should we know and study to to look at specific things like uh cellular automaton rules and their behavior um let's talk about that another time i think that the um uh the first step is this kind of whole idea of ruliology and this whole idea of doing computer experiments and understanding their consequences and kind of doing clean experiments there are mathematical methods like dynamical systems theory statistical mechanics discrete mathematics these are all things which for example i've used at different times to analyze certain aspects of that that kind of behavior but i would say that there's a a collection of methods that have emerged that many of them were in the nks book most of them were in the ncaa book actually that are sort of the raw material methods of ruleology that i think the powerful things to use for those purposes um okay richard asks richard hacker asks you think of any particular criticisms of the book that have been demolished in the interceding years you know one of the things i haven't done is read all the reviews of the book and i was kind of thinking of doing that in in celebration of the 20th anniversary you know i'm i'm a person who i i kind of just do what i do and it's i'm more interested in the doing of it than i am in the in the kind of uh uh the kind of the the um uh the feedback on it um i mean i love seeing other people build on what i've done i think that's wonderful and it's it's very very terrific thing to see and i it's also nice to hear that people uh found what i had to say interesting but um uh i would say that the um uh the kind of um the oh this is terrible because um is uh uh i'm i'm i'm less if i was always concerned about those things very hard to do new innovative stuff if you're always kind of looking over your shoulders somebody you know is somebody coming and attacking you in in um uh and i've tended personally not not really to do that i would say that um uh you know the the thing that is kind of most ironic in some sense is that the i would say beyond the very basic oh you know uh in in if you're a cynical academic or something you would say you know everybody always says about things either it's wrong or it's been done before or it's both wrong and it's been done before so one of the things when people would say the nks book is wrong it's like what does that even mean you know these are you just take a program and you run it and it makes this picture what do you mean it's like saying you know two is green or something it's a it's a it's a mistake of of uh of categories so to speak it's not a kind of thing that you could say it's wrong you could say its implications are incorrect you could say something like that but to say that it's content is quotes wrong it's just you know doesn't make any sense because unlike most science where you could say you know you made this conclusion from some you know you did this experiment and you did the experiment wrong that's not a thing in in computer experiments you just run the experiment and it does what it does and you know you can any computer anytime you can run it it'll do what it does so that was that was kind of a a thing the the other thing is you know it's wrong it's been done before you know one of the things that i did and i think i was kind of i have to say if there was one thing that sort of annoyed me about um people's responses to the nks book was people complaining that oh you didn't you know give appropriate sort of recognition of history really look at the history notes in the back of the book i did more careful history than you know people doing research uh work in academia basically ever do unless they're historians um and uh and i have to say that many people when i said that to them they said well i did look at the note and actually well yes those notes are really quite good um so but i think that the um there was sort of a a um uh and so you know i think i really did know and do know in considerable detail you know what was new what was not new et cetera et cetera et cetera um i think that the thing that was interesting in terms of the response the book as i look at it from 20 years later and i was just realizing this in connection with our physics project almost all of the kind of pitchforking came from people who were physicists and there was a lot of a lot of very positive response from a lot of places including in physics but particularly outside of physics i think it was almost uniformly positive response outside of physics and that was really odd because i'd been a physicist it was kind of well known in that field and i knew many of these people and it's like you know i would say the physicists and to some extent the early complexity people there was another there's another crowd who were particularly ironic because i i think i you know got many of them into the field and then i sort of came back you know 15 years later and um uh i hadn't really realized that returning to a field that you have left is um is such a shocking thing to do but anyway the the um the thing that um was uh from the physicists the kind of the main pitch forking was like it just doesn't look like physics it's like well yes that's why the title the book is a new kind of science it's not physics you know et cetera et cetera et cetera but i think a thing that i just realized actually is two people who were um uh you know i suppose the the um i think i referred to them in a piece i wrote about physics project as nobel prize winners with pitchforks there are two people steve weinberg and phil anderson um uh who wrote these kind of attack reviews which i have to admit i have not read word for word and i and i will i think um now but their main point was oh it's not physics it's never going to be relevant for physics et cetera et cetera et cetera the thing that was ironic which i only just realized is those two were locked in mortal kombat for much of the 90s because steve weinberg was pushing for the super colliding particle accelerator philanderson was saying don't spend all that money on the particle physicists condensed metaphysics as me and so it's kind of ironic that they both were like attacking a new kind of science we don't want a new kind of science in fact i remember steve weinberg telling me explicitly you know i hope you don't work on this stuff it was after the book came out um you know i hope you don't work on it because you know if you're right he says of course you're not right but if you are right it's going to demolish the last 50 years of work that we've done in physics and i have to say i said to him i really don't think that's going to happen as it's turned out with our physics project it not only hasn't happened it has been something where the synergy between what has been done and what we're doing is just wonderful it worked out a lot better than i expected but i would say that this it's never going to be relevant to physics yeah that that's i think we can we can put that in the in the not not relevant uh category and the that was wrong idea but um the other thing i suppose was people saying you know the this this kind of notion that you know we've got mathematical equations they're our models for science and many people said that the only way you make a real model is to use a mathematical equation well you know in practice people have voted with their models so to speak and new models are mostly made with programs these days not with equations and so that that really well i would say did demolish that although it demolished it in a rather in a sense a rather quiet way because just the models have moved in this direction and that's where the the sort of the the thrust of what people do in science has moved um i think somewhat not not with a lot of fanfare not that visibly but that's what's happened um so i you know i kind of knew that was going to happen that wasn't a big surprise at all that's why i wrote this whole book that um you know whose first sentence talks about exactly that phenomenon but i think it was a surprise to to some other people um i think that the uh yeah so probably the most dramatic thing is uh the oh it's never gonna be relevant to physics that i think we can say was was not a correct assertion i mean i wasn't sure you know i have to say when when i you know i i was really thought there was some interesting things and and definitely had the right direction in terms of thinking about discrete space and so on but i didn't know how it was all going to come together and it did require several more layers of ideas to make more progress on that so memes is asking if someone used tools i've developed and found fundamental theory of physics would i feel excited disappointed thoughts oh i mean i you know it'd be great um you know i think we are at this point we have the framework for such a theory i will be completely amazed if as we nail down you know how does physics really work and you know what are electrons and all this kind of thing i'll be amazed if the framework that we have is not you know is is not the right thing and i think that that it's kind of inevitable at this point because the framework we have is clear that sort of a machine code for five other frameworks that people have developed that are pretty much the complete set of of serious frameworks and mathematical physics i think i think sort of we're all right um but we happen to have built a machine code layer that's sort of lower level than the other things that have been done and and somewhat more explicit so it's easier to see what's what's happening but i think you know as that gets tightened up um it will be and i think the thing that i've realized is you know when we tighten it up and we find the electron we will find the electron as we observers observe the universe aliens so to speak who we would not recognize at all because they're they are incoherently different from us we'll have a different electron so in a sense we are what we find when we find a fundamental theory of physics is a bridge between the way that we humans are and the way that nature fundamentally is in some sense and so that that's kind of the way to understand what we're looking for and yeah it's i mean it'll be great more progress is made and i'm you know it's great that there are a lot of people now you know we've had a couple of summer schools couple of winter schools devoted to our physics project a lot of people working on things related to a lot of people uh in academic physics and mathematical physics and mathematics working on related things um it's uh it's really nice to see and i have to say it's been um you know it's been interesting that that the kind of uh the sort of you know there haven't been pitchforks at all it's been a very positive and synergistic experience i would say um so you know in in a from a um uh yeah i'm i'm i'm a big enthusiast i want somebody else to do it i'm i'm i'm i'm i'm happy with things i've done you know somebody else gets to discover the electron in this in these models if i have to do it i'll be a little bit disappointed um because it's uh it's kind of um in a sense development of of um uh i mean there are a lot of things that were in the nks book that were said i think with very good clarity in the book i like to believe where they were like world you should just do this there's things to do here you know just go do it like the things about physics for example it's like just go do it i mean i gave a ted talk for example in 2010 which quite a lot of people watched where i i somebody pointed this out to me i sort of said you know in a decade you know i hope that we'll be able to sort of hold in our hands of theory of physics and it's like you know like million people watch two million whatever it was and you know there were a few you know there were a couple of young people who did follow up and who i worked with in on that project um and uh the um uh but you know that was a very attenuated number and i certainly would have hoped and expected that given the you know given the launch pad so to speak that there would be more done but you know it's hard to get these things to happen i think now uh you know we are planning to launch this really illogical society to help collect the large number of researchers who've done interesting things uh that many of them building on things i did in the 80s things that i did in the new kind of science but it hasn't been sort of collected in a way in a coherent way and that's something i hope we'll be able to help with um let's see uh a couple of questions here a lot of interesting things okay let me try and go through these um flamio asks how did a will and case influence analog computing well in a sense you know our theory of physics kind of says well things are computational and digital sort of all the way down you know i don't know i haven't really thought much about analog computing in a long time i think that um uh this question of when that's actually an interesting question and it relates to this kind of fourth paradigm for thinking about modeling um and i should think about it i don't have a good answer right now uh crypto is asking who was my greatest influence or source of inspiration and what's my opinion of benoit mandelbrot's work well let's um uh about benoit i can say i i think i knew him decently well um he uh i wrote a sort of biographic biographical piece about him i i have to say i exchanged many letters with him and um when he died i was going to write an obituary but i i looked at some of these letters and my i i was kind of showing them to my staff and they're like you cannot write this obituary these letters you know that he wrote to you are so outrageous that you know any obituary you try to write that even has fragments of these is just going to be really horrible um now having said that i think benoit and i've said this and you know benoit did great science um i would say that he uh he had some kind of tactical mistakes in the way that he did it he also i think uh you know this idea of fractals as the sort of intermediary between uh the periodic and the sort of much more elaborate and random is a very interesting waypoint it's an important waypoint and it's a you know it's a surviving waypoint now i know that benoit felt that um uh the things i did in nks for example uh he he said uh to others if and in a filtered way to me i suppose too that you know he thought that in the end sort of the kinds of things i was doing in nks would sort of overwhelm the whole fractal story and that you know knowing the sort of full things that could happen with computation was going to sort of uh you know dwarf what was known about sort of nested structures and fractals and yeah he was right about that at some level but that doesn't mean that that fractals aren't a very important uh contribution to uh to science and a very sort of singular and forceful contribution to science i think also benoit you know i sort of tracked down i i had to hound him to get this history in some cases but um i sort of tracked down how did he come to make such a uh sort of visually interesting book which was in some ways a uh uh kind of a an inspiration of a sort for some of my work in terms of just the visual presentation of things because benoit's early papers have been about power laws and all there were plots and actually his publisher was the one who told him this these plots just don't cut it you've got to have something more sort of uh more arresting so to speak to get people to pay attention to this and and that's how he ended up at ibm with dick vos and other people uh making all of those all of those nice pictures um but uh i think you know one of the things that i sort of did learn from a negative learning from benoit was that you know he would take the idea of fractals and he would kind of enter various different fields and kind of write papers with peoples and people in those fields and i think he you know he sort of entered in a sense the politics of those fields in a way that i think was not in the end positive um and i kind of i haven't done that i haven't written papers with people well i haven't written papers with i haven't written academic papers since 1986. um i i don't i i prefer the style that i'm able to write in in which i'm i'm just saying what i think so to speak rather than sort of uh presenting it in this ponderous way that that um is is the tradition and is a convenient tradition in academic writing but in any case i i think um this notion of uh you know sort of going to a field and and collaborate with the with the locals so to speak um is is fraught with difficulty if you're bringing new methodology and i think it's much better to just provide the methodology and do what you can and then let people in different parts of that field take the methodology and do what they're going to do with it but in terms of of um my sort of influence the source of inspiration you know in the preface to the nks book i list a large number of people who uh i have known over the years um who um uh have um uh who i've learned a lot from i i would say that the um uh um in terms of sort of a single uh you know i'm trying to emulate this person i don't really think i have had that i mean i'm i'm fairly aware of history and so i'm fairly aware of you know where important contributions have come from and i like to understand you know how did this manage to become an important contribution how did people kind of shake off the kind of oh i don't know what this is all about to really understand this is the crisp message so to speak um how did that really happen and that's been very useful to see kind of in the in the in from a distance even for people who you know died before i was born and so on uh for people that i've known in person um i mean there are many people who i would say uh i'm i'm um uh you know people who kind of go for the clarity of thought to um uh to kind of um uh to to to pull things out i mean like a physicist i knew well was i was richard feynman who was who was big on that although he did this thing that that is not what i i suppose i do a version of this but in a very different way i mean he was a very good calculator of mathematical things by hand and so on he would do these very elaborate mathematical calculations and come up with some conclusion the amazing thing was he got the right answer from his mathematical calculations but then he thought the mathematical calculations were easy to everybody they weren't they were really hard and he was really good at doing them and so he would kind of throw those away and then he would go back and say let me understand this intuitively and come up with some very nice elementary explanation and people would wonder through me uh you know how did you figure that out why did you know that that was how it was going to go rather than the other way well he said well i just did all these calculations and it's like then i threw those away but it's like but but those are you know a big big part of the value and i suppose in a sense the the things that i've done i suppose i could be accused of the same phenomenon because i you know what i tend to do is some kind of mixture of do computer experiments get intuition from computation do in a sense philosophical level thinking and try and bring those two things together and in a sense that's a and so in the end it becomes a quite simple argument although at least in my case i'm able to show the computer pictures rather than saying well there's a bunch of mathematics but i threw it all away type thing um but uh you know i i would say that the um uh yeah i know i i mean probably anybody who is in my idea maker's book um is somebody who from whom i think i i feel some kind of that i learnt something from kind of the content or style of what they've done um but i haven't had um i would say i haven't had sort of specific uh this is what i want to be like um kinds of inspirations i think that the main thing is just the realization that it is possible to clean ideas to the point where they are where they're really crisp and the value of doing that and i've seen that in just a lot of different examples um i think perhaps in some ways there's sometimes some sort of negative um uh um you know anti um uh role modeling that goes on in in the sense of people saying you'll never be able to make that work and i don't know i i it's if it has an effect it's more yeah yeah i'll be able to make it work and you know you'll see and not that not that there's any particular uh you know i i'm not um i i view that mostly as a sign of yes i'm actually doing something new and unexpected um and that's kind of that's kind of good um because if it's like everybody says of course you're gonna make that work of course it's gonna work it's like okay i don't need to do this somebody else can do this it's sort of more satisfying if you do things where people say that's crazy that'll never work and then you manage to make it work and it's kind of like that's neat it's it's not kind of i would say for me in my particular psychology i mean it's not oh i proved you wrong i don't care about that it's it's more and actually it's often interesting to understand why was the person wrong what was the thing that they didn't get what was the the sort of the shift you had to make because one of the things for me is very interesting is when i see that either sort of firsthand in that way or in history it's like how do i avoid making the mistake that person made you know how do i avoid not seeing the obvious thing not seeing what's important about this thing you know i have this whole structure how do i avoid for example i mean i could have generated and did you know all these pictures of cellular automata and all this kind of thing which had all kinds of complex behavior and i could have just said let me concentrate on this one line that i can pull out of this picture where i can make some mathematical theory about it that's what i could have done but the important point was to realize that wasn't the important point of the pic of what one was seeing the important point was the complexity and the kinds of phenomena that were associated with that um nikolai asks deduction or induction what's more important in nks in what proportions very interesting question i mean i would say that uh ruliology is a is a deductive business mostly in the sense you start from rules you see what they do but now when you go back and say what does it mean that's an inductive process and that's sort of the meta modeling idea of you see what happens you go back you try and find well the metamodeling is more going from models that have already been constructed but it's an interesting question i mean i think that um uh in the actual nks book there's you know a chunk of it that is pure ruleology pure pure nks of just looking at simple programs and what they do and then there's another part that's trying to relate that to what does it mean for biology what does it mean for fluid flow and so on that is i suppose a more inductive kind of kind of act um and when it comes to what it has to say about mathematics well that's an interesting story of the sort of relationship between the inductive and deductive views of mathematics and in fact my my recent work in metamathematics kind of you know tangles these up even more but interesting question um anthony asks will you ever will you eventually continue trying to write fiction yeah i you know i think i may have mentioned on one of these live streams that i i made one attempt based on a a piece that i wrote about um uh you know what kind of beacons could our civilization leave that uh indicates sort of the the meaning of things um that i i tried i i spent one evening writing a science fiction ish story which uh i sent to a couple of friends of mine who were fairly well-known science fiction writers and they said you know it's terrible i don't know maybe they're not right i don't know i i mean i i would say that the thing that is difficult for me is i'm interested in people you know i i um and i've even written quite a lot about you know historical biography and things um for some reason when i'm writing about making people you know make up a person i i have a i have i have a sort of some kind of block about doing that i've asked writers i know about that um and um i asked them about why doesn't it make them feel very uh kind of exposed about how they think about things about actual people they know and they say well yes it does and and sometimes they say and and that's why you know the book that i wrote about my time in country x will never get translated into the language of country x because uh because i'm too afraid of of what people will recognize in themselves there and so on but but um uh you know i think i i was trying to write a little piece that is about um what it's like to be a computer that i suppose one could view as fiction it's a weird kind of fiction uh as i was trying to understand sort of the notion of consciousness seen from the inside and seen from the point of view of a present-day computer and i i hopefully i'll get to do that next uh month or two um i have to say that that um uh i in um i you know sometimes i feel like you know one has ideas and how should one express them and um you know i tend to be very straight that is i'm trying to i try to write it with kind of as clearly as i can without with this the minimum dressing and in a sense sometimes ideas are probably more clearly communicated when they do have a certain amount of dressing and fiction in terms of ideas at least is is you know provides a kind of dressing i have to say my friend rudy rooker has written quite a bit of fiction that has base season and chaos he actually has a book called um uh the life box this the what is it called the um seashell snail the lifeboat i've forgotten i'm sorry um he wrote it about some 15 years ago it's a nice book that is a kind of it's a exposition of nks told among other things with through some pieces of fiction um and it's like it's really interesting to me i mean you know i i was um in fact some things that i was writing about the rouliad uh you know rudy rooker had written an early book called infinity in the mind which was a book based on his experience in mathematical logic um uh which was this kind of original academic field um and uh uh then he's written some science fiction about uh transfinite numbers and things like that i mean who writes science fiction about transfinite numbers it's really a kind of a cool thing to do um and i realized as i was writing some stuff about the ruliad i realized that and in fact something that rudy had recently written it's like rudy has actually written fiction about um things which are these kind of limiting you know infinite infinite objects and so on um and he's also written stuff that that makes direct use of ideas in nks so he's he's and he's probably done a better job than i'll ever do of those things so i recommend his his work um let's see uh sahak asks about um max tegmark's work i i don't know i i know max tag mark i haven't seen him in a few years um i i i simply don't know um i think um uh yeah i'll simply say something silly by saying anything um the max claims that he's understood what i've done but i don't know whether he actually has um so we can that's at least a one-way dependency chain um wilf asks will neural nets and ai eventually tell you whether you're right or wrong about computational universe theory and no i don't think so i think that that this kind of sort of the idea that you can neural nets probably work for us because they're a bit like us that is if you want to recognize objects in the world that are things that we recognize in visual objects and so on it is the case that if you make something it's kind of like the visual system you know like our visual cortex then it's sort of not surprising it was surprising it is still surprising but it's sort of um uh the fact that a thing like that that we can recognize is something it can recognize if if there was a thing that was very different from anything that something that we pull out of the world i mean there are plenty of things that happen in the world where we don't say that's an object we have a word to describe it and so on there are plenty of things where there's sort of complicated behavior we just say that's complicated i don't know what's going on as opposed to that's a bird that's flying or something or that's a um oh that's a leaf or something like this um and so i think that some part of that story has to do with the fact that neural nets are a bit like us now another part of the story there are cases where i don't know protein folding something like this where it seems like neural nets are doing things that are just pulling things out of nature but i think there's also a little bit of what features are you trying to get out what are you trying to get right they're ones that we care about there's a certain self-fulfillingness to this i think um and i think that's a and i think in some sense nor that's maybe a good meta model for some of what we as observers do and that's pretty interesting from the point of view of understanding sort of going backwards and saying well what does that imply about what we can deduce about physics and so on but i don't think in terms of just saying oh neural net you know tell me if i'm right or not i don't think that's really the pattern of what's what's going on um it's a question about what do i think about a book not only in a dynamics perspective about nks you know this is it's a terrible thing about that i you know if there's something i kind of regret it's that i don't have the opportunity to read all the things that people do that are based on things i've created i mean at some level part of the point there that you know if i can help somebody to go further with something that they've done then like i've got a reason to see what they've done and i'm you know i'm i'm excited to do that but it's otherwise i i tend to be uh and you know i sort of i kind of regret it in some ways i tend to be more oriented towards if i'm going to have x amount of time to do something i'm going to use that time to do a new thing rather than to see what has been done with things that i've done before and uh you know it's it's a thing i wish i had more time to do that and i wish that there was a sort of better mechanism for learning about what people do because you know one of the things you learn this you know orphan language and mathematica and well from alpha you know millions of people do all kinds of stuff with it and you know sometimes a decade or two later somebody will come up to me and say by the way did you know that this cool thing that got done you know originally was done in some awesome language program it's like okay that's nice but the fact is when you make tools and ideas you put them out in the world and you don't necessarily get any feedback at all about what's happened with them it's just like it's kind of like a a um you know it's kind of like giving a talk to a to a completely blackened room so to speak you just don't have any idea what's out there and it's it's very nice sometimes when one gets back kind of um uh sort of feedback and i suppose one can go looking for feedback and in fact as part of the 20th anniversary of the nks book we are trying to get i'm trying to go through and make a bibliography of all the papers that have come out and and so on and actually this is a thing for for anybody here who's who's um uh actually written papers and books and things based on on nks or building on nks we're planning to actually link those things as kind of forward links from pages of the online nks book sort of forward linking to things that that are sort of new updates news about things that are on this page or that page and so we'd really like to get any of that material if we can um let's see a question from carson about the design of the nks book um that um i said that i'd spent a lot of time doing layout and formatting did i personally do the layout what program did i use um uh the just wondering since uh so few technically sophisticated books are well designed where did the aesthetic sense come from okay so the answer is what i used in um the actual layout program is a program called frame maker um had i started it in the mid-1990s i would have used our wolfram notebook technology um but it wasn't well enough developed by the early 1990s so i started off using frame maker and i kept on using frame maker in the end we processed the whole book through wolfram language and through notebooks and so on um but the actual layout day by day was done in frame maker now i think the big thing was the notion of algorithmic diagrams the notion of a diagram which was really made by a computer which had packed a lot of information in i mean people had the idea of you know having diagrams where you know you would design the diagram and somebody would draw the diagram um you know i have to say uh i personally have been interested in kind of diagrammatic presentation of information i mean you can find on the web i could bring it up stuff i did when i was like 12 years old and so on of of uh sort of collecting physics facts and drawing pictures about them so i i suppose if you ask where um uh you know what was the um i mean i should say by the way the algorithmic diagrams are all made with wilson language so that that's some and that was sort of a key part in the symbolic graphics descriptions that are available in our language were very important in making those algorithmic diagrams i should also say that the um actually the person who did the book design uh andre kuznerick um is a by now what 30-year employee of our company and now uh has some actually is in a very senior role with us um but um uh he um was the uh uh would always give me a hard time for for the level of micro precision although he admitted that the results were really nice uh the level of sort of micro precision of uh with which the you know the pages were composed and so on um but yes i mean i i don't think one could have done that layout if i hadn't done it myself because it really relied on you know words and pictures fitting in the right way it was a little bit crazy to do that i mean today i wouldn't do that because it's just a big long scroll in you know and you don't have to worry about the fitting of this and the fitting of that but um uh back when i thought i was still writing a book which i was and i still like the fact that it's in the form factor of a book and i still like the fact that it has it's paginated and so on um i mean i think um the um um i'll just show maybe to to end here i just for fun i can show um uh let's see if i can pull it up for a second here i'll just show you ask the the aesthetic and you can you can decide for yourself or not um what um uh let's see here um yeah let me um let's see early unpublished books okay let's go to the 12 year old production um so this was uh my efforts at um uh yes i was interested in physics then um okay there was that's not bad but you can see that this was um uh i i had a certain interest i i've never learned sadly i've never learned to do proper 3d drawings i you know it's one of the one of those things that i haven't um and you know these these there's a certain people always only do one thing in their lives kind of attitude towards the fact that yes i was collecting tables of data and yes walter alpha is involved in collecting large tables of data and yes when i kind of made wolf now for i went back and and typed in random numbers from my 12 year old effort and found out that yes world malfoy really gives those results but yeah i mean i i i've sort of been interested in in um uh invisible presentation of things for a long time um and uh i think it's it's um i consider it an important part of kind of communicating i mean to me today there are sort of three pieces of communication uh natural language text english text in my case uh computational language and pictures and i think that those are all three of those are important forms of communication and i i'm very interested in communication and so i i try to have a decent degree of proficiency at all three of those things and uh uh you know and also i think perhaps realistically i'm something of a perfectionist and i like to i like to try and see whether i both in ideas and in that presentation i can kind of make things as as perfect as possible the challenge for the uh you know the challenge of the perfectionist is is you know how do you make something like the ncaa's book in only 10 years and not an infinite number of years and never get it finished and that's uh that's a challenge and i have to say when i was writing the nks book one of the things that was a good driver you know i had the 12 chapters that i was planning to write but in the end you know the book it's um if you look very carefully you'll find out it's 1285 pages long and you will discover the the binding technology that we used kind of maxed out at that number of pages so i couldn't have written more in the book because it just wouldn't have fit um okay well we should wrap up here and uh i i want to say by the way we we've um we're using uh uh we're streaming to a new um platform today twitter as well as um uh as well as twitch and um youtube and facebook uh and um so welcome to anybody who's joining us through through twitter um we'll be using twitter as well as our other platforms going forward um and uh uh i'd like to um um we are um uh i'm planning to go on to um to chapter two uh same time next week chapter two is called the i think i know it by heart but i'm going to just check it but to make sure i don't get it wrong um chapter two is called the um uh the crucial experiment so that's that's what we'll be doing next time and i think i may learn progressively maybe by the time we reach chapter 12 i will have figured out exactly how to do these um uh for the best sort of communication but um i uh chapter two i would say chapter one is not my favorite chapter two might be my favorite so tune in uh same time next week for chapter two i look forward to uh being with you again then

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Length: 158min 15sec (9495 seconds)

Published: Mon Feb 07 2022