CHATGPT + WOLFRAM - THE FUTURE OF AI!

I just want to tell you something up front which is I started using Mathematica 2.2 and 1994 and I was blown away I thought it was awesome I was using it as an undergraduate and by about 2000 or so it had gotten so good at doing integrals that I had this uh rule of thumb that was if Mathematica couldn't do it I can't unless I had a library in probably hours and a lot of luck so yes no I think it's it's it's past humans it cost humans a while ago um welcome back to mlst so um in an era of technology and Innovation few individuals have left as indelible a mark on the fabric of modern science as our esteemed guest Dr Stephen Wolfram Dr Wolfram is a renowned polymath who has left significant contributions to the fields of physics Computer Science and Mathematics a prodigious young man too a Wolfram earned a PhD in theoretical physics from the California Institute of Technology by the age of 20. and and he became the youngest recipient of the prestigious MacArthur fellowship at the age of 21. wolfram's groundbreaking computational tool Mathematica was launched in 1988 and has become a Cornerstone for researchers and innovators worldwide and in 2002 he published A New Kind of Science a paradigm shifting work which explored the foundations of science through the lens of computational systems in 2009 Wolfram created Wolfram Alpha a computational knowledge engine utilized by millions of users worldwide and his current focus is on the Wolfram language which is a powerful programming language designed to democratize access to cutting-edge technology wolfram's numerous accolades include honorary doctorates and fellowships from prestigious institutions and as an influential thinker Dr Wolfram has dedicated his life to unraveling the mysteries of the universe and making computation accessible to all as a Trailblazer in the world of science and technology Dr Wolfram has dedicated his life to unraveling the mysteries of the universe and placing the power of computation in the hands of the masses so we invite you to join us on this captivating Journey Through the mind of one of the most influential thinkers of our time and Dr Wolfram has just dropped a new book on chat GPT which I had the pleasure of reading earlier and I highly recommend you buy it and uh actually even more exciting than that we've got the scoop on this Dr Wolfram has just announced an integration with chat GPT there's now a plugins feature and you can you can interrogate Wolfram Alpha from chatgpt but we'll get on to that in just a second but just to finish the introduction sit back folks relax prepare to have your curiosity peaked and your horizons broadened as we embark on this adventure into the extraordinary realm of Dr Stephen Wolfram Dr Wolfram welcome to mlst thank you um why don't we start with the most exciting bit which is this big announcement why don't you tell us about that first yeah well so as of today chat GPT can sort of get computational superpowers by calling on Wolfram Alpha and Wolfram language uh through its plug-in mechanism and uh the you know chat gbt is a large language model which has kind of gone through the web and lots of books and so on and it is piecing together things by sort of seeing what a reasonable piece of continuing text would be and it's amazing how far that gets and we'll we'll perhaps talk a little bit later about why I think it gets as far as it does but the thing it isn't able to do is actually do sort of serious computation it is just saying let me continue this text based on things that I've seen on the web and and things that sort of seem reasonable based on the structure of language as humans have written it but when it comes to actually Computing things that's just not something a neural net like it does but we've been building for a long time now uh well Wilson language what's talk about that probably a bunch later our sort of computational language to represent the world in a sort of precise computational way and well from alpha the kind of consumer version of that that takes natural language input converts that to welcome language to precise computational language and then does computations and very conveniently Chachi PT in a sense talks natural language too so that provides a kind of immediate Bridge through the Wolfram Alpha natural language understanding system so that chat GPT when it wants to actually do a serious computation can now reach out through or from alpha or directly to open language to our whole technology stack to get those computations done and obviously another piece of what we've done is to get sort of computable curated data about the world and sort of implement the models and methods and algorithms and so on that are needed to to sort of compute expert level answers to things and so now chat GPT has access to all of those kinds of tools I think it's kind of an exciting moment in kind of a merger of two different sort of approaches to kind of the problem of AI the sort of statistical approach of just sort of see what's out there and kind of learn from that and the kind of symbolic approach of see what you can actually compute I would say that the things we've done I don't haven't really viewed as being quote AI as such it's more the the deeper story of computation but now we've got this kind of merger of these two approaches uh that just released about an hour ago now yeah it's amazing I don't think anyone would have seen this coming even five years ago when language models started coming onto the scene that they might be used as a tool to take a natural language intelligible input and transform it into a representation that can be used on a downstream system it's absolutely amazing but why don't we go on a bit of an intellectual Journey because I was very interested reading reading your book and I wondered whether you could um we'll just start with how the language models work and kind of build from there philosophically and I mean philosophically and deeply because our audience are actually experts on on deep learning I wondered if you could talk about how language models work from from your kind of abstract deep understanding what the reasons are for their success and um you said in the book that Computing the probability of even 10 tuples of language would require more data than there are atoms in the universe um which is very interesting so language models are clearly doing some kind of clever interpolation and most importantly the thing that I'm very interested in is what does it mean to understand do you think there are language encodes enough representational Fidelity to afford a useful semantic mapping to grounded reality or you know to what most people would understand in the world and I'm sure you're aware of two major strands of thought in the philosophy of meaning which is to say meaning as reference and meaning as use so perhaps you could bring that into your answer as well well okay so so what does it mean to understand something one of the things when we built wolf malfur in 2009 people have been trying to make you know natural language understanding systems for a long time the problem that they always had which I didn't realize until after the fact I have to say was that it wasn't clear what to understand actually means you have this computer it's doing it what it's doing but what does it mean to understand well we had a very definite definition of understanding it was take the natural language and convert it to our computational language from which a computation can be done we had a target for understanding we had something which was kind of the the the ultimate representation from which we could do all these things with computation and so on and that was our operational definition of understanding now when you ask a question like I think it becomes a much muddier question when you say what does a language model understand for me sort of the the sort of the gold standard of understanding is you can convert it to a computational language you can compute all sorts of things about about it you can you can kind of uh you can figure out from it anything that can be figured out from from that as from that sort of thing that is represented in this precise ways computational language I think the thing that I've sort of realized uh so I've been interested in a long for a long time and kind of how do you represent things in the world in a sort of formal computational way that's been the whole story of building uh I wasn't language computational language but the I've also been sort of interested I've been curious for a long time how do you take kind of everyday discourse and convert it also to something computational you know in our in modern language for example if well let's say I say I want to eat a piece of chocolate Okay in modern language we have a lovely representation of pieces of chocolate we know about all kinds of different kinds of chocolate we can characterize the size Etc et cetera et cetera we haven't had a characterization of the I want to eat part of that sentence but it is something I've long thought should be possible to represent in a kind of formal computational way I think what chat GPT is showing us is that yes it really is possible to think about language and kind of a and the things we represent by language in a quite structured way so I mean the way I like to see it is what what charging PT has discovered regularities in language so first first thing just to comment on this question of you know when it wants to make a continuation of a piece of text you know if it's just if it's just a single word it might be able to go out on the web and just look at the Corpus and figure out the probability based on other occurrences of the previous words but by the time as you were mentioning by the time it's any substantial number of words there's no way it can have sort of you know statistical data to just work out the probability from that so it has to have a model and one thing that's really interesting about neural Nets is when you have a model there are there's no Obvious definition of a right answer you know it could be there could be but the our operational definition of a right answer is it sounds right to a human so the thing that's really interesting about neural Nets is they do this modeling they do this continuation they do this extension beyond the merely statistical so to speak by in a way that corresponds to what we humans expect perhaps that isn't surprising because neural Nets are architecturally quite similar to brains and you know neural Nets as they exist in chat gbt are remarkably similar to the kind of original idealization invented by McCulloch and Pitts in the 1940s but they're kind of the fact that what the continuation the particular model that Chachi BT or a neural net is using is something that corresponds to what we humans expect that's the thing that's interesting you know if we were to imagine the aliens or the octopuses or whatever else it's not obvious that the thing that would be a reasonable continuation for us would also be a reasonable continuation for them but that's a feature that happens to exist that we've discovered exists for these idealized neural Nets that we use in things like chat GPT now the question then is well what you know when we see sort of uh what did it discover about language that allows it to make that model what what's in that model it could be the case that that model is just something that we humans will never understand it's something that's kind of a the insides of an automated theorem proving system or something that a kind of a lot of structure or the insides of a machine Learning System where we look at it and we say well that's interesting it works nicely it picked out some features we don't understand what the features are it's not something it's beyond sort of human understanding but it's interesting to try to see can we get some kind of narrative understanding what's happening and I think the things the two things that we know about language one is that it has a definite syntactic grammar you know we know nouns and verbs go in the places they go it's one thing we know the other thing we know is that logic Works in language I mean when logic was originally invented by Aristotle and so on back in Antiquity you know I've got my imagination of what Aristotle did is something much like a machine Learning System would do he looked at lots of examples of rhetoric and he said what are some patterns of how language Works in these pieces of rhetoric that are sort of making arguments and so on what are the patterns of language that successfully make arguments and from that he extracted your syllogistic logic the you know all men and Mortal Socrates a man therefore Socrates is a mortal type thing those those kinds of structures he he extracted from language this kind of uh superstructure that is that is the kind of uh abstract logic and and from that much later in the 1800s you know Bool and people made it more mathematical made a kind of tower out of logic that goes beyond what is what is represented directly in language but I think what we're seeing in in tragedy PT is is it sort of discovered and we don't yet know what these precise patterns are but it's discovered kind of a semantic grammar of language and I think what the way we can think about what it's doing is it is putting together essentially these puzzle pieces just like when you make us a syntactic grammar of language you're defining these are the kind of trees and so on that you can put together these are the parse trees you can put together to form a a syntactically reasonable sentence well it's finding the things that will allow you to put together a semantically reasonable sentence essay whatever else and so I think that's kind of that's my sort of uh understanding of what it's doing now if you say is there an underlying sort of understanding that it has again I I consider that a very floppy question I consider that the the sort of the really well-grounded version of that question is can we turn it into something computational from which we can then use a a precise formal system to go and and figure out things from it or even even maybe the next step might be can we once we had that structure and we can perform computation on it through Wolfram Alpha for example can we then turn around and turn that into action that we can take into the world that has you know certain desired effects and this is what I wanted to ask you is okay Bing came along and sort of provided some computation to um GPT but very restricted you know restricted to searching and and that sort of thing Wolfram Alpha now finally gives it the power of full complete Turing complete anything goes you know kind of calculation if we now allow it to feedback on itself so this this system Chad gbt plus plus Wolfram Alpha if it's now able to say put information back into the info sphere send out tweets put stuff on a web page that can then come back through you know the GPT you know engine which as you say we may never understand it it may just always be kind of a black box to us but it can add its little bit of natural language magic and complete the circle we now have almost this massive turing machine that has these hybrid components to it you know is this potentially the first step towards artificial general intelligence or are there still some missing ingredients well I think that the thing you the picture you paint one of the points that I think you're making is in a sense Church EBT on its own is reconstituting what US humans have already written on the web and in books and things like that it's putting it together in novel ways but it's sort of the same pieces it's not creating new knowledge as soon as you have actual computation in the loop and what I would talk about as as irreducible computation you can build what amounts to new knowledge and so yes it surely is the case that you know you ask chat TPT plus well from some some question it goes off it it creates a piece of language code or something it goes and and runs somewhere it finds new knowledge it could then if it wanted to it could tweak that new knowledge and then that new knowledge could get incorporated into the kind of uh into the Corpus of knowledge a corpus of language that can be used by chat gbt and so on so yes I think that the the thing that is uh you know the way to perhaps think about this is uh in the end you know there's sort of a whole separate civilization of the AIS that is is in the process of developing I mean we already see that a whole bunch in uh in the things that operate on on the web today and so on but I think we'll see we'll see an increasing amount of that going forward and it's kind of uh you know the way I I've been curious thinking about sort of so what's going to happen to all of us and the you know when the civilization of the AIS is is there and what will it feel like to be in a world where a lot of what happens is happening in kind of the AI sphere and not in the human sphere already a lot of that is happening I mean already a lot of things that you know we're presented with on on the web and things like that come out of AI like processors not out of some kind of uh human decision making but I think you know the way I the the analogy is is really our experience of the natural world the natural world is full of what I think of as computational processes going on that that are ones that we don't intrinsically understand the natural world just does what it does we have found a way to exist sort of to coexist with the natural world we've had the convenient feature that we've had a few billion years of biological evolution to figure out how to do that it's a little bit of a different situation with the the kind of uh with with AI although we have the the perhaps advantage that AIS are right now things which are created as a sort of uh as a as a result of the things that the Corpus of of material that we've put on the web I mean I think the thing to understand about what's happening with computation and Ai and so on is computation is a very broad kind of thing you can you know if you just pick a program at random this is something I've been uh very much involved in over over the decades actually is asking if you take a very simple program like a cellular automaton program or sharing machine for that matter and you just say I'm going to pick the rules for this thing let's say at random and they're tiny rules let's say just you're know 405 rules six rules whatever and then you say well what does the system do you might say based on our experience with engineering and so on oh it won't do anything interesting and complicated because it's such a simple set of rules that turns out not to be true that's something I discovered in the 1980s to my great surprise it took me a number of years to kind of come to terms with that discovery that even extremely simple rules are capable of generating extremely complicated Behavior that's a phenomenon of the computational universe that we're not particularly used to from our experience in engineering because in engineering we usually we want to do things where we can foresee the output but so what happens is in the computational universe kind of there's immensely complex things that can be produced even with very simple programs the question is are those things things that we humans in a sense care about are they things for which we humans which we humans would talk about in the usual things that we want to do and so I think this idea that of sort of creativity computational creativity yes there's an infinite supply of computational creativity the challenges of those things that are created which are ones that we humans sort of care about which are ones where in the development of our civilization our natural language things like this we've actually got to the point where we where we think about those kinds of things uh you know there are plenty of examples in in history of I don't know phenomena and physics where you know who cared about liquid crystals until we realized that we could make displays out of them things like this it's sort of what becomes something about which we humans kind of care and this sort of there's a there's as I say an infinite supply of kind of computational creativity out there which is certainly is now accessible for example to chat GPT but if it goes off and starts running simple programs and produces all sorts of complicated output people will say well that's nice but but you know it's until until we get to the point where we have kind of a a human narrative that kind of encompasses the kinds of things that are going on there until we have a reason to care it's just out there in the computational universe not something pulled into kind of the human sphere I would love to explore this concept of creativity a little bit I mean first of all reading your book was um it was incredible it opened up the whole world of emergence to me and in a sense you can think of um a large language model as being a dynamical system but it's sclerotic in the sense that it's been trained and now it's frozen and something I was thinking about earlier was the irony that we almost never need to train it ever again because now we can prompt it on how to use Wolfram and how to create this neurosymbolic architectures and it's almost like we don't need to change it anymore but then if you look at um the representations it's learned and the way that um our cognitive categories emerge over time these are things that are baked into the language model but the language models aren't very good at learning new things that continual learning and this kind of brings us to this semantic grammar thing you know I just wondered what your thoughts were on the underlying model of the world that a semantic grammar assumes because when we're talking about cognitive categories having it fixed isn't necessary really what we want right interesting point so you know one of the things that in a sense I find you know I'm just sort of coming to terms with it is in a sense you know Church EBT and large language models in general are as you say taking a snapshot of the world as we humans have created it and they're just saying okay this is now the standard you know every essay should now be written this way because this is the average of What the world has produced so far and it's kind of a funny time it's sort of a a standards moment for a lot of kinds of knowledge work and so on and it's a little bit of a strange thing because it's kind of it now becomes it's just like you know in the past there were probably no doubt you know all sorts of creative ways I don't know people might have written things by hand or done this or that and then we have you know the fixed idea of it's a font and everybody gets to you know set the have the thing look the same way so we've now done that for a large chunk of kind of knowledge work and the question of sort of how we of how we how we still keep doing new things that's an interesting question and I think that uh you know computation is the is the giant escape valve so to speak because it allows us to do huge numbers of new things the issue is does our understanding kind of follow along with the actual sort of ability of computation to to kind of create the news so to speak and I think that's a those are those are really interesting questions you know I think the thing to understand uh kind of if we think about the sort of computational Universe of all possibilities this is something you know I've been last few years we've had these big breakthroughs that uh I didn't think would happen in my lifetime but I'm really happy that they have with uh thinking about fundamental physics and computational terms and that's led to this kind of big idea of this thing we call the rouliad which is kind of the entangled limit of all possible computations imagine you start all possible Turing machines with all possible initial types you start them all off together and you sort of Simply look at what states those things produce so there might be two touring machines that produce different states and then later on those States will be will converge because they'll end up being the same and you get this this kind of giant structure and one of the things that's sort of a a big result is we think that giant structure is kind of the thing that's underneath all of physics and all of mathematics and kind of our experience of the physical world is our we are embedded within this rule ad and our experience of the physical world is taking some slice of that rouliad experiencing kind of the set of all possible computations in a particular slice that corresponds to the ways that we kind of that our sensory data work and that our minds work and I mean just to just to say one sort of science point the the big result I suppose is that if you put only a small set of constraints on the way that we observe what's happening in the rouliad actually really two constraints one is that we are computationally bounded we're Limited in the computations we can do the other constraint is we believe that we are persistent in time even though at every moment we get made from different atoms of space we have the belief and the experience that there's a thing that we that we persist Through Time those two constraints alone are enough to basically tell you that the slice of the rouliad that we observe will satisfy physics as we know it in fact specifically the three three big theories of 20th century physics general relativity the theory of gravity quantum mechanics and the statistical mechanics to second law of Thermodynamics and so on the the amazing thing that's become clear from stuff we've done last couple of years is that all three of those kinds of features of physics as we experience it are a consequence of us being observers of the kind we are observing this rouliad of all possible computational processes so that's sort of a a big picture version of so the physics side of things but that leads one to this view that sort of Minds experience the rouyard experience this sort of entangled limit of all possible computations it's like we are at a particular place in the rouliad experiencing sort of the world in this particular way just as we're at a particular place in physical space experiencing the world with respect to that place in physical space and so you think about sort of different Minds as being different points at different points in the rouliad so so different human Minds might be quite close in the to really add kind of the the animals might be a bit further away the AIS are somewhere in the rouliad and what does it mean when we sort of make progress in making new paradigms about thinking about things the way I see it is it's kind of a an expansion in rural space we are we are able to Encompass more ways of thinking about the world than we could before that's sort of the the advance of paradigms the increase of abstraction in in fields like like mathematics or science or whatever else and and sort of we're sort of gradually expanding in rural space able to Encompass more kinds of ways of looking at this kind of ultimate computational structure so I think that the this kind of um the thing that that we see as we you know the the the sort of the growth of our uh kind of well uh sort of the the intellectual history of our civilization can be thought of as this kind of gradual colonization of rule space and that that's the and it's something that seems to happen quite gradually it's not you know you can easily just sort of pick a random program and jump to an arbitrary place in real space the problem is that there won't be much human that you can say about it it will be just it's a program it runs uh it looks uh but we we don't have a a way of kind of describing what's going on I think that's sort of the the limiting factor and I think what will be interesting to see is you know can we can we kind of go on expanding okay we've got our large language models which are trained from the way the world is today can they tell us oh there's this New Concept that's really worthwhile can we deduce from sort of the the computational world and the world of large language models and so on there's this obvious concept that you humans should be learning about you know this is a Direction that's worth going in you know there's an analogy of this I happen to have made a big study of of the foundations of mathematics you know in mathematics it's very easy given an axiom system for some some area of mathematics for example to just enumerate all the possible theorems of of a reason principle at least to enumerate all the possible theorems of let's say logic or geometry whatever else you get this giant entangled limit of all these possible theorems the question is which ones have we humans chosen to actually talk about there's a there's sort of infinite number of possible theorems but there's only sort of an infinite space of possibilities but only some places have we chosen to sort of colonize and and describe and so sort of the you can think of the kind of the history of mathematics as being following these particular paths in kind of meta mathematical space of of all the possible things that one could be thinking about all the possible theorems one could be one could be uh considering and so I think it's sort of this interesting thing in the in the world in general what is it that we could be thinking about about the world that we aren't thinking about right now give you an example that um uh so you know fractal patterns nested patterns right things that we've known about you know sort of popularized to the last I don't know three or four decades and sort of known about for about 100 years well actually those kinds of nested patterns so far as I know were first invented by a mosaic layer in Italy in the 1200s early 1200s made this thing that particular person died the uh you know the the art of making that particular nested pattern disappeared the art historians wrote about many of the mosaics done by this family of Mosaic layers they never mentioned the nested patterns they talked about you know the birds and lions and so on but they were completely blind to this idea of nested patterns which then in recent times everybody would see this pattern say oh that's a fractal pattern um but uh you know this is the kind of example of how sort of when you have a new paradigm for thinking about things you kind of noticed things you didn't notice before and a very interesting question is what things are there in the world that we have noticed before in my own life the the sort of the biggest one that was this phenomenon that I already mentioned about how simple programs can lead to extremely complicated Behavior that's a very fundamental scientific result that has all kinds of implications but that's a result which as I say after I had first done computer experiments that plainly showed that result it still took me several years to kind of realize oh that's a real you know that's a real thing and when I look back I realized there were zillions of examples of this that existed for hundreds of years where people just ignored it I mean you know you look at it yeah yeah I wonder if I could jump in here because what you're saying is it's truly fascinating it's the idea that that these AIS could help us navigate this impossibly vast you know space of possible com you know computation the ruly ad but there's also the other way around which is us helping them navigate and as you pointed out in this kind of wonderful essay back in February you know what was yeah what is GPT doing and why does it work you know in this impossibly vast space they're sort of total pure chance there's complete Randomness and at this narrow boundary between there's really chaos which is where all the interesting stuff happens it's where the irreducibly complex computations are it's where Turing machines are it's where all this Rich behavior is kind of on that on that boundary and it's almost the sliver and like you said finding the programs in this vast space that are useful to humans or do something interesting is really difficult and and we've kind of been discovering over time that as you build systems that are more and more capable they become harder and harder to train and you talk about this a lot in your essay and you're optimistic that we're going to find ways to to train you know these ever more capable systems effectively and efficiently and uh I'm wondering how you think we're going to achieve that like what what's going to be the key to it what Avenues of research are promising to take us down that path of navigating the ruly ad space well so so first point to make is let me this notion of computational irreducibility which I think is really a pretty core notion that sort of we're starting to see kind of pop up in a lot of different a lot of different places it's an important piece of kind of intuition about science the question is this if you have a program you have a set of rules you know the set of rules does that immediately tell you what the consequences of those rules will be in traditional science well particularly mathematical science practice sort of since the 1600s kind of the big thing people are always excited about is science lets you predict stuff given that you know the the equations the rules by which something works science will let you say okay this is what's going to happen sometime in the future it's it's just once you know the rules you can predict the future that's been the kind of paradigmatic idea of that people have about science for the last 300 years or so what we realized from thinking about kind of computational universe computational thinking about science based on programs rather than on equations is that isn't always true in fact it's often not true instead what can happen is you've got the rules you run the rules you can explicitly run them you run them for a billion steps you see what happens but then you ask can you jump ahead and say oh I know you know can you figure out what's going to happen a billion steps in the future without having to explicitly run those steps and the whole point is that you in general can't do that and the reason you can't do that well as a principle that I kind of came up with quite a long time ago now called the principle of computational equivalence which gives you sort of an understanding of why you can't do that principle of computational equivalence says that as soon as you see a system which is not obviously simple in its Behavior it will tend to be equivalent in the sophistication of computations it can do so you know we've known since the 1920s 1930s that there are Universal computers there are systems that are capable of doing uh that that can you know that can be programmed to do any computation what has not been obvious is that that phenomenon and actually its generalization are quite ubiquitous it's a thing that's been sort of interesting for me is you look at cellular automata you look at Turing machines you say you look at you just start enumerating let's say possible Turing machines the very smallest ones only do very simple things as soon as you have a turing machine with just let's say three colors on its tape and two states for its head so six little cases in its rule turns out the first turing machine that doesn't do obviously simple things is computation Universal so that's kind of evidence for this principle of computational equivalence that sort of as soon as the behavior is not obviously simple it will be equivalent in its computational sophistication so what does that mean well when we're trying to predict something we say we can predict it what we mean is we with our brain with our mathematics whatever else we can jump ahead of it it takes it a billion steps to figure out what it's going to do do but we have this clever way of just saying I know what you're going to do after a billion steps the fact is once we know about computational equivalence we know that won't be possible in general we know that there will be computational irreducibility the only way to figure out what the system is going to do is effectively to follow through each step and see what happens and that's sort of an important realization in science it's an important sort of limitation of science coming from within science it's kind of also a little bit the explanation for why bugs are hard to find and things like this they're always sort of unexpected consequences there are always things you couldn't readily foresee from the underlying rules that you had but I think that that um so this this phenomenon of computational irreducibility that is that's kind of the hard that's hard computation that's computation you can't reduce in a sense we should consider ourselves lucky that there is irreducible computation because you know we lead Our Lives we do all these different things it will be really bad in a sense but one might feel it would be really bad if somebody could just say you know okay you don't need to lead your life I know what the answer is it's 42 or whatever else there's nothing sort of achieved by the passage of time what computational irreducibility tells one is that there's sort of something achieved by the passage of time some some sort of computational achievement from the passage of time now the so now the question might be well how does that relate to things like training of neural Nets and so on well essentially computational irreducibility is the enemy of neural net training because as soon as there is computational irreducibility as soon as there's actually something you have to explicitly compute you just can't do this approach of saying well you know training is about sort of knowing what will happen you know being able to sort of explicitly burn in a prediction of what will happen and that is explicitly kind of that's that's sort of the opposite direction from computational irreducibility so the thing that's exciting about for example our uh chat Plus World from effort of today is that um that it's a place where we're combining something capable of doing irreducible computation with something that is sort of the the interface to us humans I mean I kind of view sort of what chat GPT is doing and a lot of its potential uses as being kind of a new form of user interface it's kind of a linguistic user interface and the way that we've had in the past kind of you know we have graphically certificates Etc I mean in a sense both malfo was I think the first sort of serious example of a linguistic user interface on the input side we were not dealing with the output so we're not writing essays we're we're just generating essentially more like a GUI kind of output a linguistic input so to speak but now we've got something where we where this is kind of uh humanizing uh kind of a lot of things by providing this kind of linguistic interface and language seems to be our richest form of communication and so having that linguistic interface is very important in having a richer form of communication with sort of the computational world but I think this this question of you know how do you find the things that are interesting to us humans well that's something that I think these neural Nets are are beginning to actually know a lot about because they just were trained on the things we humans produce they in a sense have a very finely tuned sense of sort of what the current state of how we humans think about things is and so if you say well what did we miss that becomes a thing that it can potentially answer you know the the question of uh and this is something uh uh I've been curious about and I did some experiments but they haven't been very successful yet it's kind of of all possible let's say mathematical theorems which ones are ones that a human is likely to consider interesting we know some other ways to try and figure that out but to be able to say okay you know large language model you just learned all these things from what humans have cared about and put on the web now tell us which things exist in the computational universe but we haven't yet sort of for given names to and so on which ones should we give names to yes I I wholeheartedly agree that the gradient of interestingness is very important but I also want to bring in the gradient of conceivability just before I do that I wanted to comment on you referring to it as a linguistic interface and I'm glad that you did because um it's a it's an interface that that can be used by many people in many different situations but the key thing is that it's about being intelligible rather than being a conversational interface but on this notion of conceivability you said something fascinating which is that there's this Mosaic and and it was it was interesting to people let's say hundreds or even thousands of years ago and then it and then it became lost and Nagel's thought experiment um about the bat is is a subjective form of conceivability and Noam Chomsky talks as a nativist about the domain of things which are conceivable as a function of the cognitive prize which are in our brain so there's this trajectory space of things which are conceivable and it's almost like we're cheating by creating these computational models which can subject to irreducibility can compute all of these different things but the the key question is whether or not we would recognize something as being interesting or whether it would be conceivable even if it were present right so I think this is the thing progress is gradual and slow in other words if you jump to some random place in the rouliad you pick a random program you look at it you know when you first look at it it's like I don't know why I care about this it's something where you we have to have a a kind of a I don't know whether you can call it cognitive or or intellectual history kind of path that gets there we don't we don't get to talk about the you know until we've we've built sort of the ambient ideas until we built this kind of intellectual path it isn't really it doesn't really work to just jump to that place it's like I could make up a word and uh I don't know I could make up a word for I don't know let's say look at look at clouds in the sky and they have all kinds of weird different shapes and we have a few names for a few of those shapes right now we don't have a reason to care about the particular details of those shapes but you know I can make up a word for one of those things but until I can sort of tell you why we care about that it becomes something that's very hard to it's something we're sort of not connected to I I kind of think it's one of these things where it is there's sort of a a a a sense of gradual progress but you kind of can't it's a it's it's you can't kind of jump ahead it's kind of like the computational irreducibility story actually that you can't kind of jump to a random place and expect to have understanding there well it may be the case too maybe we don't need to understand it right because as long as let's say the the uh AI is producing structured Concepts even if we don't understand those Concepts if they're structured enough that that uh programs in Wolfram Alpha can make use of them to produce actions let's say in the world that are good for us then we may not even need to understand those Concepts at least not yet um right I think I think this is the this is the kind of question of you know once we Define the goals how those goals get implemented is something that is a matter of interest for science but it's not necessarily a matter of relevance for the people using it and I think this is the this is the thing which is sort of an interesting phenomenon in well the history of computing particularly is what is it that you're trying to get out of a computer well you're trying to achieve certain goals you're trying to and and this is where you know my sort of lifelong story of kind of the programming languages versus the the full-scale computational language idea you know the programming languages which are really talking to computers in terms that computers intrinsically understand you know set this um set up this array you know and uh move this you know do this Loop you know change this value from this to this to this that's that's the sort of the computer's intrinsic understanding I think that uh uh you know the thing I've long been interested in is how do we go from something that is the way we think about things how do we make a bridge between the way we think about things and what is possible computationally and the thing that has been my long time effort is to build our computational language well from language to be able to sort of be that bridge between the way we think about things the things we think about and what is computationally possible so I I think the um this this uh uh this question of of um uh sort of relating well let's see the the I think I didn't respond to what you you would say something interesting and I think I lost my thread of what what you were uh what what you were uh what was it was really um I think what I'm what I'm trying to find out is what role human beings you know are actually going to play in building the better architectures of tomorrow because because you made a point in in that article from February that you know right now to be honest the the construction of these architectures is an art it's really just people do some trial and error they come up with some horseshoes or whatever shape you know of uh of architecture happens to work you know gpts and that's you know same kind of category you point out there's no real science you know behind that and on the one hand we could defer all that science to AIS potentially and maybe we'll never understand the science that they do to build the next better versions of themselves but I'm wondering if you also have any space in your vision for us developing human intelligible sciences that can do a better job of this and if so like what might they look like and what other Sciences might they resemble zoology you know I don't know right so I think the thing okay so this question of uh uh sort of so this thing that's going on inside that's very complicated that's computationally irreducible what can we say about it and you know we can still have something where there's complicated computations going on they achieve goals we're interested in we're happy we're done we can then go in as you as you point out and ask what's the science of what's Happening inside in a sense the Neuroscience has been that story of we know what we humans do you know how does we know something about how the microscopic brain uh you know works and you know neuron firings and so on we don't have an Intermediate Language of Neuroscience that is kind of the thing that is above the level of individual neuron firings and below the level of psychology basically and I think that that's um the question of can you find that and when can you find that it's a very interesting question I think one of the things that is a science point that when you have computational irreducibility one of of the necessary consequences of computational irreducibility is within a computational irreducible system that will always be an infinite number of specific little pieces of computational reusability that you can find in other words even though you can't say everything there are lots of little pieces that little things you can say in a sense that's why you know if we take it at a more General level if you say well what inventions can we make given the physical world which has computational irreducibility what things can we predict what things can we invent which have foreseeable behavior and so on and the answer is there will be there will be an infinite collection of these things we'll never run out of inventions little pieces of computational reducibility that we can find in this ultimately computational irreducible Universe in which we live so in a sense we're always we're always finding these little jumps little pieces of computational reducibility and those pieces of computational reducibility may be big enough that we can kind of live in them you know the fact that we believe space is continuous that is a feature of a slice of computational reducibility from the underlying computational irreducibility of this complicated as you know the the way we've set it up with this you know rewriting rules for some hypergraph that's going on underneath that's a very complicated thing if we were stuck you know experiencing the world in terms of this typograph being Rewritten all the time we wouldn't be able to sort of go and Lead our lives in a simple ways it's like you know when you have molecules and a gas bouncing around if we had to follow every molecule that would be a very different thing from our typical experience of a gas which is just this sort of continuous fluid so I think the the thing that that um uh in we're we're kind of we we experience it's very important to find these sort of slices of computational reducibility because they're the things that we can build engineering we can make a predictable life out of and so on and those are things which again in uh but but many of those you know the the many of those pieces of computational reducibility we haven't yet found we don't yet have names for as we find them we may be able to make use of them and we may be able to kind of uh expand our experience of of the world um in a way which which makes use of those things but I think in um uh and you know is it the case that sort of uh we'll be able to use uh well you're asking kind of for example one one question you can ask is will there be a science of machine learning that would be a reasonable kind of question you know is it is it going to be the case you know right now people say well uh what might a science of machine learning look like well that's an interesting question it's uh the you know when uh here's an example so typically a machine learning you you know you do a training you're finding you know you're finding a particular path where you get to a a good point where you have a nice trained neural Nets imagine you followed all possible paths it'd be a computationally absurd you know thing to do but the universe does something very much like that in the way that quantum mechanics Works in physics one of the things that's taken me a while to get used to is the idea that the universe is a profitably waster of computational resources it's constantly following all these different Paths of History which we conflate together when we actually experience the world but it followed all these different paths so imagine that you did that for for machine learning not necessarily practical at first but imagine that you could do that and imagine you could start thinking about kind of that space of possible path that were followed imagine you could start using ideas from physics to think about that that set of possible paths imagine you could think about things like you could think about sort of the formation of something like Continuum space out of all of those different possible paths that might be followed you know there are there are ways that you could imagine doing some kind of science where you start saying well there's an event horizon that will fall form in this part of sort of training space things like this now right now it's it's that might be something where we would say oh that's really quite interesting science it's irrelevant to the practicalities of machine learning or it might turn out that understanding things at that level with that kind of sort of generalization and then going back to the specifics might turn out to be really valuable I don't know yet I think that what we can say from our experience in understanding physics and fundamental physics is there are some very powerful techniques that one can use that in a sense have already been discovered in physics that once we know that physics is computational they can be applied those things that have been discovered in physics can be transported to lots of other areas like for example machine learning but in terms of uh you know do I do I expect that there will be many uh another thing to say about about architectures of neural Nets and so on is you know the principle of computational equivalence kind of tells you in the end it doesn't really matter in the end if you kind of bash it hard enough it's going to be able to sort of learn quotes learn anything now there may be when I say bash it hard enough there might be a factor of a billion difference in practice and that makes big difference in in the world as it is but I think and the other question is the thing that we've been sort of Lucky about is that as I was mentioning before the neural Nets that we have when they generalize they generalize in Fairly human ways and I think we don't fully understand that I think that's a fact about Neuroscience as much as it's a fact about machine learning I don't think that's something you can't prove that it's not something where you can get a mathematical proof this thing is going to generalize in a human way because it depends how we humans work and that's something that is really a matter of natural science of Neuroscience and so on to determine but you know there happens to be an alignment there possibly because the underlying sort of McCulloch pits level architecture is quite similar between our brains and what have happens in neural Nets and I think that that's uh so you know that that's a that's something that has been fortunate I don't think uh and uh you know I mean for me you know I first looked at neural Nets back in 1981 or 1980 and uh you know they didn't do anything interesting I couldn't really get them to do anything I I decided they were way too complicated to understand scientifically so I started looking at things like cellular automata where you just have these fixed cells that have a very simple behavior and you don't have the sort of connection to everything else and all these weights and so on and uh turned out that you know I managed to figure out a very rich set of behaviors that happen even in this much much much simpler systems and those kinds of behaviors certainly will happen even more so in neural Nets well at least in the neural Nets as they exist in something like church EBT are just essentially these feed forward kind of networks that that don't um uh that don't have the possibility of kind of have having sort of infinite computations they're just you know just put in the input and and let it Ripple forward so to speak but uh but something in combination with Wolfram Alpha is what you know yeah yeah right but once you can do that once once you've got sort of actual computation in the loop and the thing that that to to emphasize perhaps about what I think we're adding to the world of of kind of large language models with with both language and modern Alpha is it's both the depth of computation the kind of possibility of irreducible computation and the structured knowledge of the world that we actually do have there's a there there so to speak There's an actual representation of you know a city a you know a chemical element a molecule something like that there's a hard computational representation of that it's not well I saw that a few times on the web and of course that's not how the neural net actually presents it but you know it's it's sort of you know somewhere inside that neural net is some collection of Weights we don't understand exactly how but some collection of Weights that represent the hundred times that this particular molecule showed up on on the web um of course that if you say well what about the molecule which didn't show up on the web well the the extrapolation from what did show up is an extrapolation based on this kind of extrapolation that neural Nets do that humans seem to also do and whether that is what is the kind of scientific physical extrapolation who's to say the thing that has been a you know one of the surprises was that neural Nets an even large language models have been successful in things like protein folding and that's it's it's a bit unclear that that's a place where that's really physics that wasn't just us talking about things or at least probably that's the case I mean in in actuality the way those things tend to work is they're they're sort of just making a correspondence with what's been physically observed to large chunks of the of the protein and then the big effort of the machine learning model is to figure out how to fit together those sort of physically known chunks so to speak and how to sort of smooth out the correspondence between those and it's sort of I you know I do kind of Wonder the extent to which the uh sort of there's the the proteins that well the proteins that biology happens to use which is a tiny set of all possible proteins and sort of what the correspondence is between kind of what happens to get used in biology and what we happen to be thinking about and representing in human language you know I think that's the uh you know when it comes to you were mentioning that the sort of the um the kind of what's interesting so to speak between the the two random and the two simple and so on and uh you know biology I think has has also picked out some particular domain of things and it's I've been actually a project that I that I started a depressingly long time ago when I was a kid um of trying to understand the second law of Thermodynamics which is you know this law of entropy increased the fact that things always tend to get more random you know I finally think I finally sort of understood that and I finally was writing something about it and that's when chat GPT came out and and I started to uh uh to think a bit about that but but in any case one of the things that's come out of that thinking about the second law is that you know we all know about solids liquids and gases and so on but when when you have biological tissue what is that is it is it a liquid is it a solid well it's really neither of those things it's a it's a weird phase of matter that is kind of a computational phase of matter where we increasingly know from molecular biology that biology sort of explicitly transports molecules around and sort of quotes meaningful ways they don't just run around randomly not that they're really random in a liquid but they they seem random to us in a liquid but we can we can already recognize that there is sort of structure in the way that things are transported around inside cells inside molecular biological processes and it's sort of interesting the extent to which that in even in the the sort of that low level of biology once once again seeing these things which somehow have this sort of they read to us as meaningful structure and exactly how that sort of all fits together with with what we can sort of how we understand what we can understand and so on I don't know how that all works yet thank you wonderful um Dr Wolfram unfortunately we've come to time this has been an absolute honor thank you so much for joining us today we we really appreciate it lots of interesting questions we didn't get we didn't get deeply technical but uh uh this was really really fun thank you very much

Channel: Machine Learning Street Talk

Views: 425,336

Keywords:

Id: z5WZhCBRDpU

Length: 57min 30sec (3450 seconds)

Published: Thu Mar 23 2023

Reddit Comments

[u/CertainMiddle2382]

This man will lead the resistance and send Kyle back in time to destroy SkyGPT:-)

Do you think openAI does a countdown when they put their models online like in the movies?

[u/Prymu]

I always thought the name came from the metal not that is was a surname