Birthday Livestream: the Last 5 years & the Future

Hello, everyone. Welcome to an update. About five years ago, I did a live stream. I realized I wanted to make sure that little had changed. And I'm wearing the same shirt that I was five years ago. It's probably gone way out of style by now. I was kind of trying to assess what's happened in the last five years. I wrote a piece in the last couple of days about that, and I sort of summarized the story of the last five years for me in this graphic. I titled my piece five Most Productive Years. What Happened and what's Next. So. So in the what happened category, I'm showing here a few of the things that happened. Nine books got published. Okay? One of them is just the third edition of a book that had been published before, but the others are all completely new. A total of 3,939 pages that I published in my writings, which is about 1.3 million words, actually, to be compared to 580,000 words, which was the number of words in my book, New Kind of Science that I spent a decade writing. So that's a good productivity increase then. Well, I've been doing a bunch of things for the world at large. 376 podcasts for a total of 499 hours. 1310 live streams, 1369 hours. And our company has done 14 product releases with 990 new Wolfram language functions. And let's see, what else. 507 interviews and talks, 207 events, 775 student projects that I've guided across 12 different programs that we, that we've done. And then on the, on, on other things. Well, we've done two prizes that I offered. We launched one institute, and on a personal analytics level, I have sent about 63,000 emails in the last five years. I've taken about 21 and a half million steps and I have typed about 43 million keystrokes. So let me tell you in a bit more detail, five most productive years. What happened and what's next? So what happened? Well, today is my birthday for the 65th time. Five years ago, on my 60th birthday, as I mentioned, I did a live stream where I talked about some of my plans. So now question is, what happened? Well, what happened was great. In fact, I've just had the most productive five years of my life. As I mentioned, nine books, 339, 3900 pages, writings, 499 hours of podcasts and so on. And in addition to these things, a bunch of big and beautiful ideas and results. So it's been quite wonderful and unexpected. I mean, I've spent my life kind of alternating between technology and basic science, kind of progressively building a taller and taller tower of practical capabilities and intellectual concepts and sharing what I've done with the world. Five years ago, everything was going well and making steady progress. But then there were the questions I never got to. Over the years, I'd come up with a certain number of big questions, and some of them within a few years I'd answered, but others I never managed to get around to. And five years ago, as I explained in the birthday livestream that I did, I began to think, it's now or never. I had no idea how hard the questions were. Yes, I'd spent a lifetime building up tools and knowledge, but would they be enough? Or were the questions just not for our time, but only perhaps for some future century? So at several points before in my life, I'd faced such issues, and things had worked out well with a new kind of science, WolframAlpha and so on. And from this I'd gotten a certain confidence about what might be possible. In addition, as a serious student of intellectual history, I had a sense of what kind of boldness was needed. Five years ago, there wasn't really anything that made me need to do something big and new, but I thought, what the heck, I might as well try. I'll never know what's possible unless I try, though. A major theme of my work since the early 1980s have been exploring the consequences of simple computational rules, and I'd found the surprising result that even extremely simple rules could lead to immensely complex behavior. So what about the universe? Could it be that at a fundamental level, our whole universe is just following some simple computational rule? I'd begun my career in the 1970s as a teenager, studying the frontiers of existing physics. And at first I couldn't see how to make computational rules connect to what is known in physics. But in the early 1990s, I had an idea, and by the late 1990s, I'd developed it and gotten some very suggestive results. But when I published these in A New Kind of science in 2002, even my friends in the physics community didn't seem to care, and I decided to concentrate my efforts elsewhere, for example, building Wolfram Alpha Wolfram language, and so on. But I didn't stop thinking, one day I need to get back to my physics project. And in 2019, I decided, what the heck, let's try it now. It helped that I'd made a piece of technical progress the year before, and that now two young physicists were enthusiastic to work with me on the project. And so it was soon after my birthday in 2019, that we embarked on our physics project. It was a mixture of computer experiments and big concepts. But before the end of 2019, it was clear it was going to work. It was an amazing experience. Thing after thing in physics that had always been mysterious. I suddenly understood, and it was beautiful. A theory of such strength built on a structure of such incredible simplicity and elegance. We announced what we'd figured out in April 2020, right when the pandemic was in full swing. And there was. There's. Well, there's still much to do, and there still is today. But the overall picture was clear. I later learned that a century earlier, many well known physicists were beginning to think in a similar direction. Matter is discrete, light is discrete, space must be too. But back then, they didn't have the computational paradigm or the other tools they needed to move this forward. And now the responsibility had fallen on us to do this. Pleasantly enough, given our framework, many modern areas of mathematical physics seem to fit right in. And yes, figuring out the basic machine code for our universe was, of course, pretty exciting. But seeing an old idea of mine blossom like this had another very big effect on me. It made me think, okay, what about all those other projects I've been meaning to do? Maybe it's time to do those too. And something else happened as well. In doing the physics project, we developed a new way of thinking about things, not just computational, but multicomputational. And actually, the core ideas behind this were in a new kind of science too. But somehow I'd never taken them seriously enough before and never extended my intuition to encompass them. But now, with a physics project, I was doing this, and I could see that the ideas could also go much further. So, yes, I had a new and powerful conceptual framework for doing science, and I had all the technology of the modern Wolfram language. But in 2020, I had another thing too. In effect, a new distribution channel for my ideas and efforts. Early in my career, I had used academic papers as my channel, at one point in 1979, even averaging a paper every few weeks. But in the late 1980s, I had a very different kind of channel, embodying my ideas in the design and implementation of of Mathematica and what's now the Wolfram language. Then, in the 1990s, I had another channel, putting everything together into what became my book, A New Kind of Science. After that was published in 2002, I would occasionally write small Posts for the community site around the science in my book, for our corporate blog, and so on. And in 2010 I started my own blog. At first I mostly just wrote small fun pieces, but by 2015, partly driven by telling historical stories, you know, the 200th anniversary of George Boole, the 200th anniversary of Ada Lovelace, and so on, the things I was writing were getting ever meatier. There had actually already been some meaty ones about personal analytics and so on in 2012. By 2020 my pattern was set and I would routinely write 50 plus page pieces full of pictures or with immediately runnable click to copy code and intended for anyone who cared to read them. So finally I had a good channel again and I started using it. As I'd found over the years, whether with language documentation or with A New Kind of Science, the very act of exposition was a critical part of organizing and developing my ideas. And now I started producing pieces. Some were directly about specific topics around the physics project, but within two months I was already writing about a spinoff exploring ruleal space, the case of Turing Machines. I'd realized that one of the places the ideas of the physics project should apply was to the foundations of mathematics and to metamathematics. In a footnote to A New Kind of Science, I had introduced the idea of empirical metamathematics. And in the summer of 2020, fueled by my newfound Finish those old Projects mindset, I ended up writing an 80 page piece on the empirical metamathematics of euclid and beyond. December 7, 1920 was the date a certain Moses Schoenfinkel introduced what we now call combinators, the very first clear foundations for universal computation. I'd always found combinators interesting, if hard to understand. I'd used ideas from them back around 1980 in the predecessor of what's now the Wolfram language, and I had talked about them a bit in A New Kind of Science. But as the centenary approached, I decided to make a more definitive study, in particular using methods from the physics project. And for good measure, even in the middle of the pandemic, I tracked down the mysterious history of Moses Schonfinkel. In March 2021, there was another centenary, this time of Emil Post's tag system. And again I decided to finish what I'd started in A New Kind of Science and write a definitive piece, this time running to about 75 pages. One might have thought that the excursions into empirical metamathematics, combinators, tag systems, rule and multi way Turing machines would be distractions but they were not. Instead, they just deepened my understanding and intuition for the new ideas and methods that had come out of the physics project, as well as finishing projects that I'd wondered about for decades and the world had had open for a century. Perhaps not surprisingly, given its fundamental nature, the physics project also engaged with some deep philosophical issues. People would ask me about them with some regularity, and in March 2021 I started writing a bit about them, beginning with a piece on consciousness. The next month I wrote why does the Universe Exist? Some perspectives from our physics project. Actually, this piece of writing happened to coincide with the few days in my life when I've needed to do active cryptocurrency trading. So I was in the amusing position to of thinking about a philosophical question about as deep as they come, interspersed with making cryptocurrency trades. Well, everything kept weaving together. These philosophical questions made me internalize just how important the nature of the observer is in our physics project. Meanwhile, I started thinking about the relationship of methods from the physics project, the distributed computing and to economics. And in May 2021, that intersected with practical blockchain questions, which caused me to write about the problem of distributed consensus, which would soon show up again in the science and philosophy of observers. The fall of 2021 involved really leaning into the new multicomputational paradigm, among other things, giving a long list of where it might apply. Metamathematics, chemistry, molecular biology, evolutionary biology, neuroscience, immunology, linguistics, economics, machine learning, distributed computing. And yes, in a sense, this was my to do list. In many ways, half the battle was just defining this. And I'm happy to say that just three years later, we've already made a big dent in it. Well, while all this was going on, I was also energetically pursuing my day job as CEO of Wolfram Research. Version 12.1 of the Wolfram language had come out less than a month before the physics project was announced, version 12.2, right after the Combinator centenary. And in 2021 there were two new versions in all, 635 new functions, all of which I had carefully reviewed and many of which I'd been deeply involved in designing. So it's a pattern in the history of science as well as technology. Some new methodology or some new paradigm is introduced and suddenly vast new areas are opened up and there's lots of juicy, low hanging fruit to be picked. Well, that's what happened with the ideas from our physics project and the concept of multicomputation. There were many directions to go and many people Wanting to get involved. And in 2021, it was becoming clear that something organizational had to be done. This wasn't a job for a company even as terrific and innovative as ours is. It was a job for something like an institute. And yes, in 2022, we indeed launched what's now the Wolfram Institute for Computational Foundations of science. Back in 1986, I had started the very first institute, concentrating on complexity and how it could arise from simple rules. Running it hadn't been a good fit for me back then, and very quickly I started our company in 2002, when a new Kind of Science was published. I'd again thought about starting an institute, but it didn't happen. But now there really seemed to be no choice, and I started reflecting on what had happened to complexity and whether there's something to leverage from the institutional structure that had grown up around it. Nearly 20 years after the publication of A New Kind of Science, what should complexity be now? I wrote a piece called Charting a Course for Complexity, Metamodeling, Ruleology, and More. And in doing so, I finally invented a word for the pure basic science of what simple rules do. Rulology. My original framing of what became our physics project had been to try to find a computational rule that gives our universe. And I'd always found this unsatisfying, because even if we had the rule, we'd still be left asking, why this one and not another? But in 2020, there had been a dawning awareness of a possible answer. Our physics project is based on the idea of applying rules to abstract hypergraphs that represents space and everything in it. But given a particular rule, there are, in general, many ways it can be applied. And a key idea in our physics project is that somehow it's always applied in all these ways, leading to many separate threads of history that branch and merge and, importantly, give us a way to understand quantum mechanics. We talked about these different threads of history corresponding to different places in branchial space, and about how the laws of quantum mechanics are of the direct analogues in branchial space, or branch time, of the laws of classical physics and gravity in physical space or space time. But what if, instead of just applying a given rule in all possible ways, we applied all possible rules in all possible ways. What would one get? In November 2021, I came up with a name for it, the Ruliad. A year and a half earlier, I'd already been starting to talk about rulial space and the idea of us as observers, perceiving the universe in terms of our particular sampling of rulial space. But naming the ruliad really helped to crystallize the concept, and I began to realize that I had come upon a breathtakingly broad intellectual arc. The ruliad is the biggest computational thing there can be. It's the entangled limit of all possible computations. It's abstract and it's unique, and it's as inevitable in structure as two plus two equals four. It encompasses everything computational, including us. So what, then is physics? Well, it's a description of how observers like us embedded in the ruliad perceive the ruliad. Back in 1984, I'd introduced what I saw as being a very central concept, very central concept of computational irreducibility, the idea that there are many computational processes whose outcomes can be found only by following them step by step, with no possibility of doing what mathematical science was used to, and being able to jump ahead and make predictions without going through each step. At the beginning of the 1990s, when I began to work on a new kind of science, I'd invented the principle of computational equivalence, the idea that systems whose behavior isn't obviously simple Will always tend to be equivalent in the sophistication of the computations they do. Given the principle of computational equivalence, computational irreducibility was inevitable. It followed from the fact that the observer could only be as computationally sophisticated as the system they were observing, and so would never be able to jump ahead and shortcut the computation. There'd come to be a belief that eventually science would always let one predict and control things. But here, from within science, was a fundamental limitation on the power of science. All these things I'd known in some form since the 1980s and with clarity since the 1990s, but these are things I'd known since that time. But the ruliad took things to another level. But now I could see that the very laws of physics we know were determined by the way we are as observers. I'd always imagined that the laws of physics just are the way they are. But now I realized that we could potentially derive them from the inevitable structure of the ruliad and the very basic features of what we are like as observers. I hadn't seen this philosophical twist coming, but somehow it immediately made sense. We weren't getting our laws of physics from nothing. We were getting them from being the way we are. Two things seem to me critical. That as observers, we are computationally bounded, and that, somewhat relatedly, we believe we are persistent in time. In other words, we have a continuing thread of experience through time, from birthday to birthday and so on. But even as I was homing in on the idea of the RULIAD as it applied to physics, I was also thinking about another application, the foundations of mathematics. I'd been interested in the foundations of mathematics for a very long time. In fact, in the design of Mathematica and what's now Wolfram Language and its predecessor, smp. I'd made central use of ideas that I developed from thinking about the foundations of mathematics. And in a New Kind of Science. I'd included a long section on the foundations of mathematics, discussing things like the network of all possible theorems and the space of all possible axiom systems. But now I was developing a clearer picture. The RULIAD represented not only all possible physics, but also all possible mathematics. And the actual mathematics that we perceive, like the actual physics, will be determined by our nature as observers, in this case, mathematical observers. There were lots of technical details, and it wasn't until March 2022 that I published the Physicalization of Metamathematics and its Implications for the Foundations of Mathematics. In some ways, this finished what I'd started in the mid-1990s, but it went much further than I expected, in particular in providing a sweeping unification of the foundations of physics and mathematics. It talked about what the ultimate limit of mathematics would be like. And it talked about how human level mathematics, where we can discuss things like a Pythagorean theorem, rather than just the micro details of underlying axioms, emerges for observers like us, just like our human level impression of physical space emerges from the underlying network of atoms of space. One of the things I discovered in computational systems is how common computational irreducibility is, along with undecidability. And I'd always wondered why undecidability wasn't more common in typical mathematics. But now I had an answer. It just isn't what mathematical observers like us see in the Ruliad. At some level, this was a very philosophical result, but for me, it also had practical implications, notably greatly validating the idea of using higher level computational language to represent useful human level mathematics, rather than trying to drill down to kind of axiomatic machine code. October 22, 2021 had marked a third of a century of Mathematica. And May 14, 2022, was the 20th anniversary of a new kind of science. And in contextualizing my activities and planning for the future, I've increasingly found it useful to reflect on what I've done before and how it's worked out. And in both these cases, I could see that seeds I'd planted many years earlier had blossomed, sometimes in ways I'd suspected they might, and sometimes in ways that far exceeded what I had imagined. What had I done right? The key, it seemed, was drilling down to find the essence of things and then developing that, even if I hadn't been able to imagine quite what could be built on them, I'd been able to construct solid foundations that successfully encapsulated things in the cleanest and simplest ways. In talking about observers and the ruliad, and in fact, our physics project in general, I kept on making analogies to the way the gas laws and fluid dynamics emerge from the complicated underlying dynamics of molecules. And at the core of this is the Second Law of thermodynamics. Well, as it happens, the very first foundational question in physics that I ever seriously studied was the origin of the second law. But that was when I was 12 years old in 1972. For more than a century, the second law had been quite mysterious. But when I discovered computational irreducibility in 1984, I soon realized that it might be the key to the second law. And in the summer of 2022, armed with a new perspective on the importance of observers, I decided I better once and for all write down how the Second Law works. Once again. There were lots of technical details, and as a way to check my ideas, I decided to go back and try to untangle the rather confused 150 year history of the second law. It was an interesting exercise, satisfying for seeing how my new ways of thinking clarified things, but cautionary in seeing how wrong turns have been taken and solidified in the past. But in the end, there it was. The Second Law was a consequence of the interplay between underlying computational irreducibility and our limitations as observers. It had taken half a century, but finally I had finished the project I'd started when I was 12 years old. I was on a roll, finishing things. But I was also realizing that a bigger structure than I'd ever imagined was emerging. The Second Law project completed what I think is the most beautiful thing I've ever discovered, that all three of the core theories of 20th century physics, general relativity, quantum mechanics, and the second law statistical mechanics, have the same origin. The interplay between the underlying computational structure of the ruliad and our characteristics and limitations as observers. And I knew it didn't stop there. I'd already applied the same kind of thinking to the foundations of mathematics, and I was ready to start applying it to all sorts of deep questions in science and philosophy. And beyond. But at the end of 2022, just as I was finishing my pieces about the second law, there was a surprise. ChatGPT. I'd been following AI and neural nets for decades. I first simulated a neural net in 1981. My first company, started in 1981, had, to my chagrin, been labeled an AI company. And from the early 2010s, we'd integrated neural nets into the Wolfram language. But like the creators of ChatGPT, I didn't expect the capabilities that emerged in ChatGPT. And as soon as I saw ChatGPT, I started trying to understand it. What was it really doing? What would its capabilities be in the world at large? There was a sense of shock. If AI can do this now, soon it'll be able to do everything. But I immediately thought about computational irreducibility, and it gave us limitations. But those limitations would inevitably apply to AIs as well. There would be things that couldn't be quickly figured out by pure thought, by humans and AIs alike. And by the way, I just spent four decades building a way to represent things computationally and actually do systematic computations on them, because that was the point of the Wolfram language. So immediately I could see we were in a very interesting position. The Wolfram language had the completely unique mission of creating a full scale computational language. And now this was a crucial tool for AIs. The AIs could provide a very interesting and useful language, broad linguistic interface. But when it came to solid computation, they were, like humans, going to need a tool. Conveniently, Wolfram Alpha already communicated in natural language, and it took only a few weeks to hook up WolframAlpha and Wolfram language to ChatGPT. We'd given kind of computational superpowers to the AI. ChatGPT was everywhere, and people kept asking me about it, and over and over again I ended up explaining things about it. So at the beginning of February 2023, I decided it would be better for me just to write down once and for all what I knew. It took a little over a week. And yes, I'm a fast writer. And then I had an explainer that altogether ran to about 76 pages of ChatGPT. Partly, it talked in general about how machine learning and neural nets work, and how ChatGPT in particular works. But what a lot of people wanted to know was not how, but why ChatGPT works. Why was something like that possible? Well, in effect, ChatGPT was showing us a new science discovery about language. Everyone knows that there's a certain syntactic grammar of language like that in English, sentences typically have the form noun, verb, noun. But what ChatGPT was showing us is that there's also a semantic grammar, some pattern of rules for what words can and can be put together and make sense. I've thought about the foundations of language for a long time, which isn't too surprising given the four decades I've spent as a computational language designer. So in effect, I was well primed to think about its intersection with ChatGPT. It also helped that, as I'll talk about a little bit later, one of my long unfinished projects is precisely on a formal framework recapturing meaning that I call symbolic discourse. Language in technology and other things I always like best situations where basically nothing is known and one has to invent everything from scratch. And that's what was happening for functionality based on LLMs in the middle of 2023. How would LLM based for language functions work? How would a prompt repository work? How would LLMs interact with notebooks? Meanwhile, there was still lots of foment in the world about the AI shock. Before the arrival of physics projects in 2019, I'd been quite involved in AI philosophy, AI ethics, and so on. And in March 2023 I wrote a piece on will AIs take out all our jobs and end human history or not? In the end, after all sorts of philosophical arguments and an analysis of actual historical data, the answer was it's complicated. But along the way, computational irreducibility and the RULIAD were central elements, limiting the controllability of AIs, allowing for an infinite frontier of invention, and highlighting the inevitable meaningless of everything in the absence of human choice. By this point, and actually with remarkable speed, my explainer on ChatGPT had turned into a book that proved extremely popular, and now, for example, exists in over 10 languages. It was nice that people found the book useful, and perhaps it helped remove some of the alarming mystique of AI. But I couldn't help noticing that of all the many things I'd written, this one had been one of the fastest to write, yet it was garnering one of the largest readerships one might have imagined that AI was pretty far from our physics project, the Ruliad, and so on. But actually, it soon became clear that there were close connections and that there were things to learn in both directions. In particular, I'd come to think of minds that work in different ways as occupying different positions in the Ruliad. But how could one get intuition about what such minds would experience or observe. Well, I realized one could just look at Generative AI. In July I wrote Generative AI Space and the Mental Imagery of Alien Minds. I called this the Cats in Hats piece because yes, it has lots of pictures of often bizarrely distorted cats in hats used as examples of what happens if one moves a mind around in rulial space. But despite the whimsy of the cats, this piece provided a surprisingly useful window into what for me has been a very long standing question of how other minds might perceive things. And this fed quite directly into my piece on observer theory in December 2023. Ever since things like Turing machines, we've had a formal model for the process of computation. My goal was to do the same kind of thing with a process of observation. In a sense, computation constructs sequences of new things, say with time. Observation, on the other hand, equivalences things together so that they fit in finite minds. And just what equivalencing is done by our senses, our measuring devices, our thinking determines what our ultimate perceptions will be. Or put another way, if we characterize well enough what we are like as observers, it'll show us how we sample the ruliad and what we'll perceive the laws of physics to be. When I started the physics project, I wasn't counting on it having any applications for hundreds of years. But quite soon it became clear that actually there were going to be all sorts of near term applications, particularly of the formalism of multicomputation. And every time one used that formalism, one could get more intuition about features of the physics project, particularly related to quantum mechanics. I ended up writing a variety of ruleological pieces, all as it happens, expanding on footnotes in a new kind of science. There was multicomputation with numbers in October 2021, games and puzzles as multicomputational systems in June of 2022, and aggregation and tiling as multicomputational processes in November of 2023. And in September 2023 there was also expression evaluation and fundamental physics. Back around 1980 when I was working on SMP, I'd become interested in the theory of expression evaluation. And finally now with the physics project and my work on combinators and metamathematics. Four decades later I had a principled way to study it, potentially with immediate application in distributed computing and computational language design. Around that, and I could check off progress in another long pending project. I give many talks, do many podcasts and live streams like this one, essentially all unprepared. Except this one, which I is based on what I wrote in the last couple of days. But in October 2023, I agreed to give a TED talk, and I just didn't see any way to fit a reasonable snapshot of my activities into 18 minutes without preparation. How was I to coherently explain the physics project, the ruliad, and computational language in such a short time? I called the talk how to think computationally about AI, the universe, and Everything. And I began with what for me, was a new condensation. Human language, mathematics, logic, these are all ways to formalize the world. And in our century, there's a new and yet more powerful one. Computation. Over the years, I'd done all sorts of seemingly very different projects in science and technology, but somehow it seemed like they were all now converging. Back in 1979, for example, I'd invented the idea of transformations for symbolic expressions as a foundation for computational language. But now, more than four decades later, our physics project was saying that those kinds of transformations, specifically on hypergraphs, were just what the machine code of the universe was made of. Since the 1980s, I thought the computation was a useful paradigm with which to think about the world. But now our physics project in the ruliab was saying it wasn't just useful, it was the underlying paradigm of the world. For some time, I'd been viewing our whole Wolfram language effort as a way to provide a way to formalize computation for the purposes of both humans and machines. Four hundred years ago, mathematical notation had streamlined mathematical thinking, allowing what became the mathematical sciences to develop. I saw what we were doing with our computational language as a way to streamline computational thinking and allow computational X for all fields x to develop. I began to see computational thinking as a way to humanize the ruliad, to pick out those parts that are meaningful to humans. And I began to see computational language as the bridge between the power of raw computation and the kinds of things we humans think about. But how did AI fit in? At the beginning of 2024, lots of people were still asking, in effect, Can AI solve science? So I decided to analyze that. I certainly didn't expect AI to be able to break computational irreducibility, and it didn't. Yes, it could automate much of what humans can do in a quick look, but formalized, irreducible computation that was going to need computational language, not AI. It's easy to be original in the computational universe if you pick a rule at random. It's overwhelmingly likely nobody's ever looked at it before. But will anyone care? They'll care if, in effect, that Part of the ruliad has been colonized if there's already a human connection to it. But what if you define some attribute that you want, then just search out there for a rule that exhibits it? That's basically what biological evolution or machine learning training seems to do. And as a kind of offhand note, I decided to just see if I could make a minimal model for that. I tried before, in the mid-1980s and in the 1990s when I was writing A New Kind of science. I'd become convinced that computational irreducibility was, in a sense, a stronger force than adaptive evolution, and that when complex behavior was seen in biology, it was computational irreducibility. That should take most of the credit, but I decided to just do the experiment and see. And although computational irreducibility in a sense, tells one to always expect the unexpected, in all these years I've never fully come to terms with that. And I'm still regularly surprised by what simple systems somehow cleverly managed to do. And so it was with my minimal model of biological evolution. I'd always wondered why biological evolution managed to work at all, why it didn't get stuck, and how it managed to come up with the ornate solutions it did. Well, now I knew. And it turned out it was once again a story of computational irreducibility. And I'd managed to finish another project that I'd started in the 1980s. But then there was machine learning. And despite all the energy around it, as well as the practical experience with it, it didn't seem like there was a good foundational understanding of what it was doing or why it worked. For a couple of years, I'd been asking all the machine learning experts. I ran into what they knew, but mostly they confirmed that, yes, it wasn't well understood. And in fact, several of them suggested that I'd be the best person to figure it out. So just a few weeks ago, starting with ideas from the biological evolution project and mixing in some things I tried to do back in 1985, I decided to embark on exploring minimal models of machine learning. I just posted the results of that last week. And yes, one seems to be able to see the essence of machine learning in systems vastly simpler than neural nets. In these systems, one can visualize what's going on. It's basically a story of finding ways to put together lumps of irreducible computation to do the tasks we want. Like stones one might pick up off the ground and. And put together in a stone wall. One gets something that works. But there's no reason for there to be any understandable structure to it. Like so many of the projects I've done in the past five years, I could in principle have done this project much earlier, even in the 1980s, but back then, I didn't have the intuition, the tools, or the intellectual confidence to actually dive in and get the project done. And what's been particularly exciting over the past five years is that I feel that I is. Is that I can feel, and even very tangibly, see how what I can do has grown with every project I've done. I further honed my intuition, developed more tools, both conceptual and practical, and built my intellectual confidence. Could I have gotten here earlier in my life? I don't think so. I think to get to where I am now required the kind of journey I've taken through science, through technology, and the other things I've done. Kind of a living example of the phenomenon of computational irreducibility. Okay, the process of getting things done. So I started my career young, usually found myself the youngest person in the room, but shockingly fast. All those years whizzed by, and now I'm usually the oldest person in the room. But somehow I still seem to feel like a young whippersnapper, not settled into some expected pattern and kind of pushing for the future. I've always done projects that are hard, projects that many people thought were impossible, projects that stretched my capabilities to the limit. And to do this has required a certain mixture of confidence and humility, confidence that it's worth me trying to do the project. Humility and not assuming that it'll be easy for me. I've learned a lot of fields by now, and with them a lot of different ways of thinking. But somehow it's never enough to make the projects I do easy. Somehow the projects are always far enough out on the frontier that I have to learn new things and new ways of thinking to succeed at them. And so there I am, often the only person in the room whose project isn't somehow easy for them and who still has to be pushing kind of whippersnapper style. At this point, a fair fraction of the projects I do are ones that I've thought about for a long time. A smaller fraction are opportunistic, coming into scope just now as a result of something I've done or something that's happened in the world at large. Before the past five years, I had a lot of projects that have languished, often for decades. Yes, I thought they would be interesting, and I gradually collected information about them. But somehow I wasn't quite in a place to tackle them. But now I feel quite differently. In the past five years, I've gone back and finished a fair fraction of all those languishing projects, and it's been great. Without exception, the projects turned out to be richer and more interesting than I had expected. Often I realized I really couldn't have done them without the tools and ideas and infrastructure I now have. And often, to my great surprise, the projects turned out to have very direct connections to big themes around the Ruliad, the physics project, and for that matter, computational language. Why was this happening? Partly it's a tribute to the breadth of the computational and now multicomputational paradigm, but partly it has to do with the specific character of projects I was choosing, always seeking what seemed like the simplest, most foundational versions of things. I've done quite a few big projects in my life, many seemingly very different. But as I look back, I realize that all my projects have a certain overall pattern to them. They're all about taking something that seems complicated, then drilling down to find the foundations of what's going on, then building up from these, often with considerable engineering style effort. And the methods and tools I've developed have, in a sense, implicitly been optimized for this pattern of work. I suppose one gets used to the rhythm of it all the time when one's drilling down slowly, trying to understand things, the time when one's doing all the work to build the big structure up. And yes, it's all hard, but by now I know the signs of progress, and they're always energizing to see. At any given time I'll have many projects gestating, often for years or decades. But once a project becomes active, it's usually the only one I'm working on, and I'll work on it with great intensity, pushing hard to keep going until it's done. Often I'll be working with other people, usually much younger than me, and I think it's always a surprise that I'll routinely be the one who works with the greatest intensity every day at all hours. I think I'm pretty efficient too. Of course, it helps that I have a tool Wolfram language that I've been building for decades to support me. And it helps that I've developed all kinds of practices around how I organize code and notebooks I create, and how I set up my process of writing about things. Of course, it also helps that I have very capable people around me to make suggestions, explore additional directions, fill in details Check things and get my write ups produced and published. Well, as I've talked about elsewhere, my life is in many ways set up to be quite simple and routine. I get up at the same time every day, I eat the same thing for breakfast and so on. But in a sense this frees me to concentrate on the intellectual things I'm doing, which are very different every day, often in unexpected ways. But how is it that I even get the time to do all these intellectual things? After all, I am, as I have been for the past 38 years, the CEO of a very active tech company, albeit one with a terrific team. Two things I think help first, organization and second, resolve. Every day I'll have tightly scheduled meetings over the course of the working day, and there are lots of details to this. I get up in late morning, then do my first two meetings while walking and so on. But somehow, mostly on evenings and weekends, I find time to work intensely on my intellectual projects. It's not as if I ignore everything else in the world, but I do have a certain drive and resolve that fills any available time with my projects and somehow seems to succeed in getting them done. And yes, there are many optimizations in the details of my life, saving me all sorts of time. And it probably helps that I've been a work from home CEO now for 33 years. One might have thought that CEOing would greatly detract from being able to do intellectual work. But I find the exact opposite. Because in my experience, the discipline of strategy and decision making, as well as communicating the thoughts and ideas to other people that comes with CEOing, is critical to being able to do incisive intellectual work. And by the way, the kind of thinking that goes with intellectual work is also incredibly valuable. Being an effective CEO there's another critical part to my formula, and that has to do with exposition. For me, the exposition of a project is an integral part of the project. Part of it is that the very definition of the question is often one of the most important parts of a project. But more than that, it's through exposition that I find I really understand things. It takes a certain discipline. It can be easy enough to make some highfalutin technical statement, but come and grind it down into truly simple pieces that one can immediately understand. Yes, that means other people will be able to understand it too. But for me, what's critical is that that's the way I can tell if I'm getting things right. And for me, the exposition is what in the end defines the backbone of a project. Normally, I write quickly and basically without revision. But whenever there's a piece I'm writing, finding unduly hard to write, I know that's where I'm muddled and need to go back and understand what's going on. Some of my projects, like creating the thing I'm just reading, end up being essentially pure writing. But most are deeply computational and full of computer experiments. And just as I put a lot of effort into making written exposition clear, I do the same for computational language and for pictures. Indeed, many of my projects are in large measure driven by pictures. Usually these are what one can think of as algorithmic diagrams created automatically with a structure optimized for exposition. And the pictures aren't just useful for presenting what I've done, they're also critical to my own efforts to figure things out. I've learned that it's important to get the presentational details of pictures right as early as possible in a project to give myself the best chance to notice things. Often the projects I do require exploring large numbers of possible systems, and somehow, with great regularity, this leads to me ending up looking at large arrays of little pictures. Yes, there's a a lot of looking at that can be automated, but in the end, computational irreducibility means that there'll always be the unexpected that basically I have to see for myself. A great thing about the Wolfram language is that it's been very stable ever since it was first released. And that means that I can take notebooks even from the 1980s and immediately run them today. And yes, given all the old projects I've worked on in the past five years, that's been very important. But in addition to being very stable, the Wolfram language is also very self contained and very much intended to be readable by humans. And the result is something that I found increasingly important. Every computational picture and everything I write has Wolfram language code behind it that you can get by clicking all the time. I find myself going back to previous things I've written and picking up Click to Copy code to run for some new case or use as the basis for something new I'm doing. And of course that Click to copy code is open for anyone to use not only for its computational content, but also the often elaborate visuals it implements. Most of my writings over the past five years have been about new basic science, but interspersed with this, along with pieces about technology and about philosophy, are pieces about history. And in fact, many of my scientific pieces have had extensive historical sections as well. Why do I put such effort into history? Partly I Just find it fun to figure out. But mostly it's to contextualize my understanding of things. Particularly in the past five years, I've ended up working on a whole sequence of projects that are in a sense, about changing long standing directions in science. And to feel confident about making such changes, one has to know why people went in those directions in the first place. And that requires studying history. Make no mistake, history, or at least good history, is hard. Often there'll be a standard, simple story about how some discovery was suddenly made, or how some direction was immediately defined. But the real story is usually much more complicated and much more revealing of the true intellectual foundations of what was figured out. Almost never did someone discover something one day. Almost always it took many years to build up the conceptual framework so that that one day the key thing could even be noticed. When I do history, I always make a big effort to look at the original documents. And I often realize that's critical because it's only with whatever new understanding I've developed that one would stand a chance of correctly interpreting what's in the documents. And even if one's mainly interested in the history of ideas, I've always found it's critical also to understand the people who were involved with them. What was their motivation? What was their practical situation? What kinds of things did they know about? What was their intellectual style in thinking about things? It has helped me greatly that I've had my own experience in making discoveries. That gives me an intuition for how the process of discovery works. It also helps that I've had my fair share of kind of worldly experiences. Still, often it's at first a mystery how some idea developed or some discovery got made. But my consistent experience is that with enough effort one can almost always solve it. Particularly with the projects I've done in recent years, it often leaves me this strange feeling of connection. For in many cases I find out that the things I've done can be viewed as direct follow ons to ideas that were thought about a century or more ago and for one reason or another, ignored or abandoned since. And I'm then usually left with a strong sense of responsibility. An idea that was someone's great achievement had been buried and lost to the world. But now I found it again, and it rests on me to bring it to the future. In addition to writing about other people's history, I've also been writing quite a bit about my own history. In the last five years, I've made a point of explaining my personal history around the science and technology I describe in doing this, it helps a lot that I have excellent personal archives that routinely let me track within minutes discoveries I made even four decades ago. My goal in describing my own history is to help other people contextualize things I write about. But I have to say that time and time again, I found the effort to piece together my own history and extremely valuable just for me. As I go through life, I try to build up a repertoire of patterns for how things I do fit together. But often those patterns aren't visible at the time, and it takes going back, often years later, to see them. I do the projects I do first and foremost for myself, but I've always liked the idea that other people can get their own pleasure and benefit from my projects. And basically, starting with a physics project, I I've tried to open to the world not just the results of my projects, but the process by which they're done. I post my working notebooks whenever practical. I live stream my working meetings. And perhaps taking things to an extreme, I record even my own solitary work, posting it in video work logs. Except as I was writing this piece, I realized that I forgot to record the writing of what I was just writing. Well, a couple of years before the physics project, I actually also opened up my technology development activities, live streaming our software design reviews and in the past five years, 692 hours of them. And yes, I put a lot of work and effort into designing the Wolfram language. At the beginning of the pandemic, I thought, there are all these kids out of school. Let me try and do a little bit of public service and live stream something about science and technology for them. And that's how I started my Science and Technology Q and A for kids and others live streams that I've now been doing for four and a half years. Along the way, I've added History of Science and Technology Q and A, Future of Science and Technology Q and A and Business Innovation and Managing Life Q and A. Altogether, I've done 272 hours of these that have generated 376 podcast episodes. Twice a week, I sit down in front of a camera, watch the feed of questions and try to answer them. It's always off the cuff, completely unprepared, and I find it a great experience. I can tell that over time I've been doing. Over the time I've been doing this, I've become a better and more fluent explainer, which no doubt helps my written exposition. Too often, in answering questions, I'll come up with a new way to explain something that I've never thought of before, and often there'll be questions that made me think about things I've never thought about at all before. Indeed, several of my recent projects actually got started as a result of questions people asked when I was younger. I always just wanted to get on with research, create things, and so on. I wasn't interested in education, but as I've gotten older I've come to really like education. Partly it's because I feel I learn a lot myself from it, but mostly it's because I find it fulfilling to use what I know and try to help people develop. I've always been interested in people, a useful attribute in running a talent rich company for four decades. I'm particularly interested actually in how people sort of develop through their lives, which led me recently, for example, to organize a 50 year reunion for my elementary school class. I've had a long time hobby of mentoring CEOs and kids, both being categories of people who tend to believe that anything is possible. But my main educational efforts are concentrated in a few weeks of the year when we do our Wolfram Summer School, which We started in 2003, and our Wolfram High School Summer Research program We started in 2012. All the students in these programs, 775 of them over the past five years, do an original project and one of my jobs is to come up with what all these projects should be. Over the course of the year, I'll accumulate ideas, though rather often when I actually meet a student, I'll invent something new. I obviously do plenty of projects myself, but it's always an interesting and invigorating experience to see so many projects get done with such intensity at our summer programs. Plus I get lots of extra practice in framing projects. That helps when I come to frame my own projects at this point. I've spent years trying to organize my life to optimize it for what I want to get out of it. I need long stretches of time when I can concentrate coherently, but I like having a diversity of activities and I'm pretty sure I wouldn't have the energy and effectiveness I do without that. Over the years I've added in little pieces like my weekly virtual sessions where I do my homework with a group of kids working on something that I need to get done but that doesn't quite fit elsewhere. Or my weekly sessions with local kids near where I live talking about things that make me and them think. Or for that matter, my sort of call while driving list of calls. It's good to make, but I Wouldn't actually wouldn't usually get quite the priority to happen without that. Doing all the things I do is hard work, but it's what I want to do. Yes, things can drag from time to time, but at this point I'm so used to the rhythm of projects that I don't think I even notice much. And yes, I work basically every hour of every day that I can do. I have hobbies. Well, back when I was an academic, business was my main hobby. When I started CEOing, science became a hobby. Writing, education, live streaming, these were all hobbies too. But somehow one of the patterns of my life is that nothing really stays quite as a true ho. What's next? The past five years have not only been my most productive ever, but they've also built more productivity momentum than I've had before. So what's next? I have a lot of projects currently in motion or ready to get into motion. Then I have many more that are in gestation, which the time may finally have come. But I know there'll also be surprises, projects that suddenly occur to me or that I suddenly realize are possible. And one of the great challenges is to be in a position to actually jump into such things. It has to be said that there's always a potentially complicated trade off. To what extent should one tend the things one has already done and to what extent one should one do new things? Of course there are some things that are never done, like the Wolven language, which I started building 38 years ago and still energetically work on every day. Or the physics project, where there's just so much still to figure out. One of the things that's worked well in most of the basic science project I've done in the past five years or so is that once I've written my piece about the project, I can usually consider the project. That particular project at least done for now. It always takes a lot of effort to get a project to the point where I can write about it, but I work hard to make sure I only have to do it once. But I've just sort of picked all the low hanging fruit, so I don't feel I have to come back to add a little more. I put a lot of effort into the pieces. I write about my projects and also give talks, do interviews, et cetera. About 500 in the past five years. But I certainly don't market my efforts as much as I could. It's a decision I've made that at this point in my life, particularly with the burst of productivity I'M experiencing. I want to spend as much time as possible doing new things, and so I need to count on others to follow up and spread knowledge about what I've done, whether in the academic world, on Wikipedia, the web, and so on. And by the way, the pieces I write and the pictures they contain are set up to be kind of immediately reproducible wherever appropriate. So feel free to do it. So what specific new things are currently in my pipeline? Well, there's lots of science and related intellectual things, and there's also lots of technology. Let's talk about the science first. A big story is the physics project, where there's a lot to be done in many different directions. There's foundational theory to be developed, and there are experimental implications to be found. It'd be great if we could find experimental evidence of the discreteness of space, or maximum entanglement speed, or a host of other unexpected phenomena in our models. A century or so ago, it was something of a stroke of luck that atoms were big enough that they could be detected. We don't know if the discreetness of space is something we'll be able to detect now or only centuries from now. There are phenomena particularly associated with black holes that might effectively serve as powerful space time microscopes. And there are phenomena like dimension fluctuations that could potentially show up in a variety of astrophysical settings. But one direction I'm particularly interested in is exploring what one might call spacetime heat, the effect of detailed microscopic dynamics in the hypergraph that makes up space time. Could dark matter, for example, not be matter at all, but instead be associated with spacetime heat? Part of investigating this involves building practical simulation software to investigate our models on as large a scale as possible. Part of it involves good old fashioned physics, figuring out how to go from underlying foundational effects to observable phenomena. And there's a foundational piece to this too. How does one set up mathematics and mathematical physics when one's starting from a hypergraph? A traditional manifold is ultimately built up from Euclidean space. But what kind of object is the limit of a hypergraph? To understand this, we need to construct what I'm calling infrageometry, an infrastructure infra calculus alongside it. Infra geometry, as its name suggests, starts from something lower level than traditional geometry. And the challenge is, in effect, to build a 21st century Euclid, then Newton and so on, eventually finding generalizations of things like differential geometry and algebraic topology that answer questions like what three and a quarter dimensional curvature tensors are like, or how we might distinguish local gauge Degrees of freedom from from spatial ones in a limiting hypergraph. Another direction has to do with particles like electrons. The fact that existing quantum field theory, in a sense only really deals with particles indirectly by thinking of them as perturbations in a field, which in turn is full of usually unobservable zero point fluctuations. In our models, the structure of everything from space time up is determined by the fluctuating structure of the underlying hypergraph, or more accurately, by the whole multi way graph of possible fluctuations. And what this suggests is that there's in a sense a much lower level version of the Feynman diagrams we use in quantum field theory and where we can discuss the effect of particles without having to say exactly what a particle is. I must say that I expected we have to know what particles were even to talk about energy. But it turned out there was a bulk one way to do that, and maybe similarly, though there's an indirect way to talk about interactions between particles. My guess is that in our model, particles are structures a bit like black holes. But we may be able to go a very long way without having to know the details. One of the important features of our models is that quantum mechanics is inevitable in them. And one of the projects I'm hoping to do is to finally really understand quantum mechanics in general terms. It's connected to the way branching observers like us perceive branching universes. But how do we get intuition for this? And what effects can we expect? Several projects over the past years, like multi way Turing machines, multi way games, multi way aggregation, and so on, I've done in large part to bolster my intuition about branch real space and quantum mechanics. I first worked on quantum computers back in 1980, and at the time I thought that the measurement process, whose mechanism isn't really described in the standard formulas of quantum mechanics, will be a big problem for them. Years have gone by and enthusiasm for quantum computers has skyrocketed. In our models, there's a rather clear picture that inside a quantum computer there are many threads of history that can affect, in effect, do computations in parallel. But for an observer like us to know what the answer is, we have to knit those threads together. And in our models, particularly with my observer theory efforts, we start to be able to see how that might happen and what the limitations might be. Meanwhile, in the world at large, there are all sorts of experimental quantum computers being built. But what are their limitations? I have a suspicion that there's some as yet unknown fundamental physics associated with their limitations. It's like building telescopes. You polish the Mirror Keep on making engineering tweaks, but unless you know about diffraction, you won't understand why your resolution is limited. And I have a slight hope that even existing results on quantum computers may be enough to see limitations perhaps associated with maximum entanglement speed in our models and the way our models work. Knowing the speed, you can, for example, immediately deduce the discreteness scale of space. Back in 1982, I and another physicist wrote two papers on properties of the vacuum. Part one was mechanical properties, part two was electrodynamic. We announced a part three on gravitational properties, but we never wrote it. Well, finally it looks as if our physics project shows us how to think about such properties. So perhaps it's finally time to write part three and respond to all those people who sent preprint request cards for it or decades ago. One of the great conclusions of our physics project and the concept of the RULIAD is that we have the laws of physics. We do because we are observers of the kind we are, and just knowing very coarsely about us as observers seems to already imply the major laws of 20th century physics. And to be able to say more, I think we need more characterization of us as observers. And my guess, for example, is that some feature of us that we probably consider completely obvious is what leads us to perceive space as at least roughly three dimensional. And indeed, I increasingly suspect that the whole structure of our physics project can be derived a bit like early derivations of special relativity, from certain axiomatic assumptions about our nature as observers and fundamental features of computation. There's plenty to do on our physics project, and I'm looking forward to making progress with all of it. But the ideas of the physics project and multi computation in general apply to lots of other fields too, and I have many projects planned on these. Let's talk first about chemistry. I never found chemistry interesting as a kid, but as we've added chemistry functionality in the Wolfram language, I've understood more about it and why it's interesting. And I've also followed molecular computing since the 1980s, and now, largely inspired by thinking about multicomputation, I've become very interested in what one might call the foundations of chemistry. Actually, what I'm interested in is what I'm calling sub chemistry. I suppose one can think of it as having a similar kind of relation to chemistry as infrageometry has to geometry. In ordinary chemistry, one thinks about reactions between different species of molecules, and to calculate rates of reactions, one multiplies concentrations of different species, implicitly assuming that there's perfect randomness in which specific molecules interact. But what if one goes to a lower level and starts talking about the interactions not of species of molecules, but individual molecules? My physics project, we get the idea of making causal graphs that represent the causal relations between different specific interaction events. In a gas, the assumption of molecular level randomness will probably be pretty good, but even on a liquid, it's much more questionable. And in more exotic materials, it'll be a completely different story. And I suspect there are subchemical processes that can potentially be important, perhaps, in a sense, finding a new slice of computational reducibility within the general computational irreducibility associated with the second law. But the most important potential application of subchemistry is in biology. If we look at a biological tissue, a basic question might be, what phase of matter is it? One of the major takeaways from molecular biology in the last few decades has been that in biological systems, molecules, or at least large ones, are basically never just bouncing around randomly. Instead, their motion is typically carefully orchestrated. So when we look at biological tissue or a biological system, we're basically seeing the result of bulk orchestration. But what are the laws of bulk orchestration? We don't know, but I want to find out. I think the mechanoidal phase that I identified in studying the second law is potentially a good test case. If we look at a microprocessor, it's not very useful to describe it as containing a gas of electrons. And similarly, it's not useful to describe a biological cell as being liquid inside. But just what kind of a theory is needed to have a more useful description? We don't know. And my guess is that there'll be some new level of abstraction that's needed to think about this, Perhaps a little like the new abstraction that was needed to formulate information theory. Biology is not big on theory. Yes, there's natural selection, and there's a digital nature of biomolecules, but mostly biology has ended up just accumulating vast amounts of data using ever better instrumentation, without any overarching theory. But I suspect that, in fact, there's another foundational theory to be found in biology. And if we find it, a lot of the data that's been collected will suddenly fall into place. There's the, frankly, molecular level of biology, and there's the more functional level. And I was surprised recently to be able to find a very simple model that seems to capture functional aspects of biological evolution. It's a surprisingly rich model, and there's much more to explore with it notably about how different ideas get propagated and developed in the process of adaptive evolution, and what kinds of sort of tree of lifestyle branchings occur. And then there's the question of self replication, a core feature of biology. Just how simple a system can exhibit it in a biologically relevant way. I had thought that self replication was just relevant for biology, but in thinking about the problem of observers in the wild, I've come to realize that it's also relevant at a foundational level. There it's no good to just have one observer. You have to have a whole kind of rule flock of similar ones, and to get similar ones you need something like self replication. Talking of societies, of observers brings me to another area I want to study economics. How does a coherent economic system emerge from all the microscopic transactions and other events in a society? I suspect it's a story that's in the end similar to the theories we've studied in physics, from the emergence of bulk properties and fluids to the emergence of continuous space time and so on. But now in economics we're dealing not with fluid density or metric, but instead with things like price. I don't yet know how it will all work out. Maybe computational reducibility will be associated with value. Maybe computational irreducibility will be what determines robustness of value. But I suspect that there's a way of thinking about economic observers in the RULIAD and figuring out what natural laws they'll inevitably observe. And maybe some of those natural laws will be relevant in thinking about the kinds of questions we humans care about in economics. It's rather amazing in how many different areas one seems to be able to apply the kind of approach that's emerged from the physics project RULIAD and so on. One that I very recently tackled is machine learning. And in my effort to understand its foundations, I've ended up coming up with some very minimal models. My purpose was to understand the essence of machine learning. But somewhat to my surprise, it looks as if these minimal models could actually be practical ways to do machine learning. Their hardware level trade offs are somewhat different. But given my interest in practical technology, I want to see if one can build out a practical machine learning framework that's based on these fundamentally discrete models. And while I'm not planning to investigate this myself, I suspect that the approach I've used to study machine learning can also be applied to to neuroscience and perhaps to linguistics. And yes, there'll probably be a lot of computational irreducibility in evidence. And once again, one has to hope that the Pockets of computational reusability that exist will give rise to natural laws that are useful for what we care about in these fields. In addition to these big projects, I'm also hoping to do a variety of smaller projects. Many I started decades ago and in fact mention a new kind of science. But now I feel I have the tools intuition, intellectual momentum to finally finish them. Nestedly recursive functions, deterministic random tilings, undecidability in the three body problem, meta engineering in the game of life. These might on their own seem esoteric, but my repeated experience, particularly in the past five years, is that by solving problems like these, one builds examples and intuition that have surprisingly broad application. And then there are history projects. Just what did happen to theories of discrete space in the early 20th century? And how close did people like Einstein get to the ideas of our physics project? What was the ancient history of neural nets? Why did people come to assume that they should be based on continuous real numbers? I fully expect that as I investigate these things, I'll encounter all sorts of if only situations where, for example, some unpublished note languishing in an archive or an attic would have changed the course of science if it had seen the light of day long ago. When I find something like this, it's yet more motivation to actually finish those projects of mine that have been languishing so long in the file system of my computer. There's a lot I want to do down in the computational trenches in physics, chemistry, biology, economics, et cetera. But there are also things at a more abstract level in the ruliad. There's more to study about metamathematics and about how mathematics that we humans care about can emerge from the ruliad. There are also foundational questions in computer science. P versus np, for example, can be formulated as an essentially geometric problem in the ruliad. And conceivably there are mathematical methods, say from higher category theory, that might give insight into it. Then there are questions about hyperruleads and hypo ruleiads. In a hyper ruleiad that's based on hyper computation, there will be hyper observers. But is there a kind of rule of relativity that makes their perception of things just the same as ordinary observers in the ordinary ruliad? A way to get some insight into this may be to study hyporuliads, versions of the ruliad in which there are only limited levels of computation possible. A bit like the way a space time singularity is associated with a black hole, supports only limited time histories or a decidable axiomatic theory supports only proofs of limited length. There will be limitations in the hypo ruliad, and by studying them, there's a possibility that we'll be able to see more about issues like what kind of mathematical axioms can be compatible with observers like us. It's worth commenting that our physics project and the ruliad have all sorts of connections and resonances with long studied ideas in philosophy. Didn't Kant talk about that? Isn't that similar to Leibniz? And so on? I've wanted to try to understand these historical connections, but while I've done a lot of work on the historical development of ideas, the ideas in question have tended to be more focused and more tied to concrete formalism than they usually are in philosophy. Did Kant actually mean that or something completely different? You might have to understand all his works to know, and that's more than I think I can do. I invented the concept of the ruliad as a matter of science, but it's now clear that the ruliad has all sorts of connections and resonances, not only with philosophy, but also with theology. Indeed, in a great many belief systems, there's always been the idea that somehow, in the end, everything is one. In cases where this gets slightly more formalized, there's often some kind of combinatorial enumeration involved. Think the I Ching, or various versions of counting the names of God, and so on. There are all sorts of examples where long surviving ancient beliefs end up having something to them, even if the specific methods of post 1600s science don't have much to say about them. One example is the notion of a soul, which we might now see as an ancient premonition of the modern notion of abstract computation. And whenever there's a belief that's ancient, there's likely to have been lots of thinking about it done over the millennia. So if we can, for example, see a connection to the ruliad, we can expect to leverage that thinking and perhaps also be able to provide new input that can refine the belief system in interesting and valuable ways. I'm always interested in different viewpoints about things, whether from science, philosophy, theology, wherever. An extreme version of this is to think about how other alien minds might view things. Nowadays, I think of different minds as effectively being at different places. In the Ruliad, humans with similar backgrounds have minds that are close. In rulial space, cats and dogs have minds that are further away, and the weather, with its mind of its own, is still further. Now that we have AIs, we potentially have A way to study the correspondence and communication between different minds. I looked at one aspect of this in my Katz piece. But my recent work on the foundations of machine learning suggests a broader approach that can potentially tell us also things about the fundamental character of language and about how it serves as a medium that can transport thoughts from one mind to another. Many non human animals seem to have at least some form of language, though mostly, in effect, just a few standalone words. But pretty unquestionably, the greatest single invention of our species is language, and particularly compositional language, where words and phrases can fit together in an infinite number of ways. But is there something beyond compositional language? And for example, where might we get if our brains were bigger? With 100 billion neurons in our brains, we seem to be able to handle about 50,000 words. If we had a trillion neurons, we'd probably be able to handle more words, though perhaps more slowly, in effect letting us describe more things more easily. But what about something fundamentally beyond compositional language, Something perhaps higher order with a word? We're in effect conflating all instances of a certain concept into a single object that we can then work with. But typically with ordinary words, we're dealing with what we might call static concepts. So what about ways of thinking or paradigms? They're more like active functional concepts and a bit like dogs versus us. Dogs will deal with a few standalone words. We package those together into whole sentences and beyond, and the next level we can imagine, in effect, packaging things like generators of meaningful sentences. Interestingly enough, we have something of a preview of ideas like this in computational language. This is one of those places where my efforts in science and philosophy start to directly intersect with my efforts in technology. The foundation of the Wolfram language is the idea of representing everything in computational terms, particular in symbolic computational terms. And one feature of such a representation is that it can encompass both data and code, that is, both things one might think about and ways one might think about them. I first started building Wolfram language as a practical tool, the one very much informed by my foundational ideas. And now, four decades later, Wolfram language has emerged as the largest single project of my life, and something that, yes, I expect to always put immense effort into. It wasn't long ago that we finally finished my 1991 to do list for Wolfram language, and we have many projects running now that will take years to complete. But the mission has always remained the same, to take the concept of computation and apply it as broadly as possible through the medium of computational language. Now, however, I have some additional context for that, viewing computational language as a bridge from what we humans think about to what's possible in the computational universe. And this helps in framing some of the ways to expand the foundations of our computational language, for example to multi computation or to hypergrapher based representations. It also helps in understanding the character of current AI and how it needs to interact with computational language. In the Wolfram language, we've been steadily trying to create a representation for everything. And when it comes to definitive objective things, we've gotten a long way. But there's more than that in everyday discourse. For example, I might say I'm going to drink a glass of orange juice. Well, we do just fine at representing a glass of orange juice in the Wolfram language, and we can compute lots of things like nutrition content about it, but what about I'm going to drink? For that we need something different. And actually I've been thinking about for a shockingly long time about what one might need. I first considered the question in the early 1980s in connection with extending SMP to AI. I learned about the attempts to make philosophical languages in the 1600s, and about some of the thinking around modern conlangs constructed languages. Something that always held me back though was use cases. Yes, I could see how one could use things like this for tasks like customer service, but I wasn't myself too excited about that. But finally there was blockchain, and with it smart contracts. And around 2015 I started thinking about how one might represent contracts in general, not in legalese, but in some precise computational way. And the result was that I began to crispin my ideas about what I called symbolic discourse language. I thought about how this might relate to questions like a constitution for AIs and so on. But I never quite got around to actually starting to design the specifics of the symbolic discourse language. But then along came LLMs, together with my theory that their success had to do with the semantic grammar and of language. And finally now we've launched a serious project to build a symbolic discourse language. And yes, it's a difficult language design problem, deeply entangled with a whole range of foundational issues in philosophy and so on. But as by now at least the world's most experienced language designer, for better or worse, I feel a responsibility to try to do it. In addition to language design, there's also the question of making all the various symbolic calcul that describe in appropriately coarse terms the operation of the world. Calculi of motion, calculi of life, eating, dying, and so on. Calculi of human desires all that, as well as calculi that are directly supported by the computation and knowledge in the Wolfram language. And just as LLMs can provide a kind of conversational linguistic interface to the Wolfram language, one can expect them also to do this to our symbolic discourse language. So the pattern will be similar to what it is for orphan language. The symbolic discourse language will provide a formal, and at least within its purview, correct underpinning for the LLM. It may lose the poetry of language that the LLM handles, but from the outset it'll get its reasoning straight. The Symbolic Discourse language is a broad project, but in a sense breadth is what I have specialized in, because that's what's needed to build out the Wolfram language, and that's what's needed to in my efforts to pull together the foundations of so many fields and in maintaining a broad range of interests. There are some where I imagine that someday there'll be a project I can do. But there may be, for example, been many years of ambient technology that are needed before that project will be feasible. Usually, though, I have some conceptual idea of what the project might be. For example, I followed robotics, imagining that one day there'll be a way to do general purpose robotics, perhaps constructing everything out of modular elements. I follow biomedicine partly out of personal self interest and partly because I think it'll relate to some of the foundational questions I'm asking in biology. But in addition to all these projects where the goal is basic research or technology development, I'm also hoping to pursue my interests in education. Much of what I hope to do relates to content, but some of it relates to access and motivation. I don't have perfect evidence, but I strongly believe that there's a lot of young talent out there in the world that never manages to connect, for example, with things like the educational programs we put on. We and I have tried quite hard over the years to bridge the gap, but with the world as it is, it's proved remarkably difficult. But it's still a problem I'd like to solve. And I'll keep picking away at it, hoping to change for the better some kids trajectories. But about content, I believe my path is clearer with the modern Wolfram language. I think we've gone a long way towards being able to take computational thinking about almost anything and being able to represent it in a formalized way and compute from it. But how do people manage to do the computational thinking in the first place? Well, like mathematical thinking and other formalized kinds of thinking they'll have to learn how to do it for years, people have been telling me I should write the book to teach this. Finally, in January of this year, I started. I'm not sure how long it will take, but I'll soon be starting to post sections that I've written so far. My goal is to create a general book and course that's an introduction to computational thinking at a level suitable for typical first year college students. Lots of college students these days say that they want to study computer science, but really it's computational X for some field X that they're ultimately interested in. And neither the theoretical nor the engineering aspects of typical computer science are what's most relevant to them. What they need to know is computational thinking as it might be applied to computational X, not CS, but what one might call CX. So what will CX101 be like? In some ways more like a philosophy course than a CS1, because in the end it's about generally learning to think, albeit in the new paradigm of computation. And the point is that once someone has a clear computational conceptualization of something, then it's our job in the Wolfram language to make sure that it's easier for them to concretely implement it. But how does one teach computational conceptualization? What I've concluded is that one needs to anchor it in actual things in the world. Geography, video, genomics. Yes, there are principles to explain, but they need practical context to make them useful or even understandable. And what I'm finding is that framing everything computationally makes things incredibly much easier to explain than before. A test example coming soon is whether I can easily explain math ideas like algebra and calculus this way. Okay, so that's a lot of projects, but I'm excited about all of them. Can't wait to make them happen. At an age when many of my contemporaries are retiring, I feel like I'm just getting started. And somehow the way my projects keep on connecting back to things I did decades ago makes me feel, in a computational irreducible kind of way, that there's something necessary about all the steps I've taken. I feel like the things I've done have let me climb some hills, but now there are more hills that have come into view, and I look forward to being able to climb those too, for myself and for the world. Thanks very much. Let's see, I have to go to some birthday festivities soon, but let me see. Maybe I can try taking a few questions I could maybe try and expand on on a few things. Let's see. Ciao Asks how do you manage to find so much time? I always feel like I have less time as I get older. I honestly don't know how I should find so much time. I think it's by being somewhat organized and disciplined. And I think it's a question as much of motivation as a practicality. It's somehow. It's like there are always scraps of time. The question is whether you're motivated enough to fill them with something that's something that corresponds to a project you really want to do and so on. And yeah, you know, I'm asleep eight hours a day type of person. I don't skimp on those kinds of things. I do skimp on having a very ordered, simple life and not. And doing things and also sometimes doing things like, you know, spending money to make my life easier type thing. I think that's a, that for me has been a, A, a kind of a calculation that's usually worked out in. Even if it takes effort to, you know, make the money, spend a little bit of it to make things easier so that you can get to do the things you want. That's. That's been a thing that's worked quite well for me. Let's see, Elsie is commenting on, on CX101, saying now that the technology is there, teaching that kind of thinking is a great direction. Yes, I think one of the things that is sometimes frustrating for me to see is that while all sorts of sophisticated, highly placed people in, for example, the tech industry use Wolfram language every day, they're kind of underlings who are more kind of rank and file programmers don't do it as much. And I sort of realized only rather recently that part of the reason for that is because the folks who are sort of highly placed and in charge and so on, they tend to have lots of ideas that they've thought out computationally and they just need a quick way to implement them and then they use Wolfram language. But the people who are kind of doing programming in the trenches are much more oriented towards. I will be given a specific and I'll spend my week or my month implementing that spec. And the idea that the fraction of time spent doing computational thinking is actually quite small. It's mostly sort of computational implementation. And that's something where when they see Wolfram language, they say, oh my gosh, I could sort of squash that computational implementation part. But it seems really hard to do that computational thinking part. And yes, it is hard, but it's something one has to learn. And that's sort of the point of this kind of CX101 approach. Well, I think it's time for me to. Somebody was telling me, go eat cake. And I think I have 15 minutes before that. But thank you very much. So thanks for. Thanks for joining me. And I'll try and keep this shirt. To be able to come back in five years and give you a report on what's happened over that period of time. I'm looking forward to it. Thanks very much and thank you for all your good wishes. And if people are asking what's a good gift? I suppose if there might be any gift, it's to fill in what I'm not doing, which is, well, both in terms of developing ideas, but also in terms of spreading the things that I have actually done and being able to take the things that I've tried to create and actually have them be distributed in the world, so to speak, in as effective a way as possible. That I. I feel that would be something that I'd like to see. I think it's good for the world and that would be a great gift. All right, thank you very much and bye for now.