MIT AGI: Computational Universe

Welcome back to Success 099 Artificial General Intelligence. Today we have Stephen Wolfram. Wow That’s of course I didn’t even get started you’re already clapping In his book a new kind of science he has explored and revealed the power of beauty and complexity of cellular automata As simple computational systems were which incredible complexity can emerge. It’s actually one of the books that really inspired me to get artificial intelligence He’s created the Wolfram Alpha computational knowledge engine created Mathematica that has now expanded to become Wolfram language Both he and his son were involved in helping analyze create the alien language from the movie arrival Of which they use the Wolfram language Please again gives Stephen a warm welcome All right, so I gather the brief here is to talk about how artificial general intelligence is going to be achieved Is that the is that the basic picture? So I maybe I’m reminded of kind of a story Which I don’t think I’ve ever told in public but that something that happened just a few buildings over from here So this was 2009 and Wolfram Alpha was was about to arrive on the scene I assume most of you have used Wolfram Alpha or seen Wolfram Alpha. Yes, the how many of you’ve used Wolfram Alpha? Okay, that’s good So I had long been a friend of Marvin minskies and Marvin was a sort of pioneer of the AI world and I kind of seen for years You know question answering systems that tried to Do sort of general intelligence question answering and so it Marvin and so I was going to show Marvin You know Wolfram Alpha. He looks at it. He’s like, okay, that’s fine Whatever said no Marvin this time it actually works You can try real questions. This is actually something useful. This is not just a toy and it was kind of interesting to see It took took about five minutes for Marvin to realize that this was finally a question answering system that could actually answer questions that were useful to people And so one question is how do we how do we achieve that? So you know you go to Wolfram Alpha and you can ask it I mean, it’s I don’t know what we can ask it. I don’t know what’s the Some random question. What is the population of Cambridge? Actually, here’s a question divided by let’s try that What’s the population of Cambridge? It’s probably going to figure out that we mean Cambridge massachusetts is going to give us some number It’s going to give us some plot actually what I want to know is number of students at MIT divided by population of Cambridge See if it can figure that out And Okay, it’s kind of interesting. Oh, no, that’s divided by ah, that’s interesting It guessed that we were talking about Cambridge University as the as the denominator there So it says the number of students at MIT divided by the number of students at Cambridge University. That’s interesting I’m actually surprised. Let’s see what happens if I say can’t Cambridge MA there Now it’ll probably fail horribly No, that’s that’s good. Okay, so No, that’s interesting. That’s a plot as a function of time of the fraction of the of Okay, so anyway So I’m glad it works the So one one question is is how did we manage to get so that many things have to work in order to get stuff like this to work You have to be able to understand the natural language you have to have data sources you have to be able to compute things from the data and so on One of the things that was a surprise to me was in terms of natural language understanding Was the critical thing turned out to be just knowing a lot of stuff the actual parsing of the natural language is Kind of I think it’s kind of clever and we use a bunch of ideas that came from my new kind of science project and so on But I think the most important thing is just knowing a lot of stuff about the world is Is really important to actually being able to to understand natural language in a useful situation I think the other thing is having Actually having access to lots of data. Let me show you a typical example here of what is needed So I ask about the ISS and hopefully it’ll wake up and tell us something here. Come on. What’s going on here? There we go. Okay, so it figured out that we probably are talking about a spacecraft not a file format And now it’s going to give us a plot that shows us where the ISS is right now so to make this work we obviously have to have some feed of You know radar tracking data about satellites and so on which we have for every satellite that’s that’s out there But then that’s not good enough to just have that feed Then you also have to be able to do celestial mechanics to work out Well, where is the ISS actually right now based on the orbital elements that have been deduced from radar and then if we want to know things like Okay, when is it going to it’s not currently visible from Boston, Massachusetts? It will next rise at 736 PM on Monday and today So you know this requires a mixture of Data about what’s going on in the world together with models about how the world is supposed to work being able to predict things and so on And I think another thing that kind of realized about about AI and so on from the Wolfmalfa Effort has been that you know One of the earlier ideas for how one would achieve AI was let’s make it work kind of like brains do and let’s make it figure stuff out and so it has to do physics Let’s have it do physics by pure reasoning like you know people At least used to do physics But in the last 300 years we’ve had a different way to do physics that wasn’t sort of based on natural philosophy It was instead based on things like mathematics And so one of the things that we were doing in in Wolfmalfa was to kind of cheat relative to what have been done in previous AI Systems which was instead of using kind of reasoning type methods We’re just saying okay. We want to compute where the ISS is going to be Well, we’ve got a bunch of equations of motion that corresponds to differential equations We’re just going to solve the equations of motion and get an answer That’s kind of leveraging the last 300 years or so of of exact science that have been done rather than trying to make use of kind of human Reasoning ideas and I might might say that in terms of the history of the Wolfmalfa project When I was a kid a disgustingly long time ago I was interested in AI kinds of things and I in fact I was kind of upset recently to find a bunch of stuff I did when I was 12 years old kind of trying to assemble a pre-version of Wolfmalfa way back before it was Technologically possible But it’s also a reminder that one just does the same thing once whole life so to speak and At some level but What happened was when when I I started off working mainly in physics and that I got involved in building computer systems to do things like mathematical computation and so on and I then sort of got interested in okay So can we generalize this stuff and can we can we really make Systems that can answer sort of arbitrary questions about the world and for example Sort of the the the the promise would be if there’s something that is Systematically known in our civilization make it automatic to answer questions on the basis of that systematic knowledge and back in the in around late 1970s early 1980s my conclusion was if you want to do something like that the only realistic path to being able to do it Was to build something much like a brain and so I got interested in neural nets and I tried to do things with neural nets back in 1980 and nothing very interesting happened. Well, I couldn’t get them to do anything very interesting and That so I kind of had the idea that the only way to get the kind of thing that now exists in Wolfmalfa for example was to build a brain-like thing and then many years later for reasons I can explain I kind of came back to this and realized actually it wasn’t true that you had to build a brain-like thing sort of mere Computation was sufficient and that was kind of what got me started actually trying to build Wolfmalfa when we started building Wolfmalfa one of the things I did was go to a sort of a field trip to a big reference library and you know You see all these shelves of books and so on and the question is can we take all of this knowledge that exists in all of these books and Actually automate being able to answer questions on the basis of it and I think we’ve pretty much done that for that at least the books You find in a typical reference library So that was it looked kind of daunting at the beginning because it’s this there’s a lot of knowledge and information out there But actually it turns out there are a few thousand domains and we’ve steadily gone through and worked on these different domains Another feature of the Wolfmalfa project was that we didn’t really You know, I’ve been involved a lot in doing basic science and trying to have sort of grand theories of the world One of my principles in building Wolfmalfa was not to start from a grand theory of the world That is not to kind of start from some global ontology of the world and then try and build down into all these different domains But instead to work up from having you know hundreds then thousands of domains that actually work whether they’re you know Information about cars or information about sports or information about movies or whatever else have each of these domains Sort of building up from the bottom in each of these domains and then finding that there were common themes in these domains That we could then build into frameworks and then sort of construct the whole system on the basis of that and that’s kind of that’s kind of how it’s worked And I can talk about some of the actual frameworks that we end up using and so on but Maybe I should explain a little bit more so so one question is how does how does Wolfmalfa actually sort of work inside and The answer is it’s a big program. It’s about it’s the core system is about 15 million lines of all from language code And it’s some number of terabytes of raw data and so the the way the thing that sort of made building wolfmalfa possible was this language wolfmalfa language which Started with Mathematica which came out in 1988 and has been sort of progressively going since then so maybe I should show you some things about wolfmalfa language and and You know, it’s easy. You can go use this MIT has a site license for it. You can use it all over the place You can find it on the web etc etc etc but Okay, the basics work The let’s let’s start off with something like let’s make a random graph and Let’s say we have a random graph with 200 nodes 400 vertices. Okay, so there’s a random graph The first important thing about wolfmalfa language is it it’s a symbolic language so I can just pick up this graph And I could say you know, I don’t want to do some analysis of this graph that graph is just a symbolic thing that I can just do computations on oh, I could say let’s let’s get a Another good thing to always do is get a current image See there we go And now I could go and say something like Let’s let’s do some basic thing. Let’s say let’s edged detect that image again This this image is just a a thing that we can manipulate we could take the image we could make it I don’t know we could take the image in partition little pieces do computations on that I don’t know simple Let’s do let’s just say sort each row of the image Assemble the image again. Oops Assemble that image again will get some some mixed up picture there if I wanted to I could for example Let’s say it’s make that the current image and let’s say make that dynamic I can be just running that code hopefully and a loop and there we can Make that work so The you know one one general point here is there’s you know This is just an image for us is just piece of data like anything else if we just have a variable a think all X It just says okay, that’s X. I don’t need to know particular value. It’s just a symbolic thing the corresponds to That’s a thing called X now you know what gets interesting when you have Symbolic language and so on is we’re interested in having it represent stuff about the world as well as just abstract kinds of things I mean, you know I can abstractly say you know find some funky integral I don’t know what you know that’s then representing Using symbolic variables to represent algebraic kinds of things, but I could also just say I don’t know something like Boston And Boston is another kind of symbolic thing that has if I say what what is it really inside? That’s it’s the entity a city Boston, Massachusetts United States Actually, you notice when I typed that in I was using natural language to to type it in and it gave me a bunch of Disambiguation here. It said assuming Boston is a city Assuming Boston, Massachusetts use Boston, New York or okay, there’s let’s use let’s use Boston and the Philippines Which I’ve never heard of but but um, uh, let’s try using that instead and now if I look at that it’ll say it’s Boston in some Province of the Philippines, etc., etc., etc. Now I might ask it of that I could say something like what’s the population of that and it Um, okay, it’s a fairly small place or I could say for example, let me let me do this. Let me say a geolist plot from that Boston Let’s take from that Boston to and now let’s type in Boston again And now let’s have it use the default meaning of the word of Boston and then let’s join those up and now this should plot Um, this should show me a plot. There we go. Okay, so there’s the um, uh, path from the Boston that we picked in the Philippines to the Boston here Oh, we could ask it. I don’t know. I could just say um, I could ask it the distance from one to another or something like that so the the one of the things here One of the things we found really really useful actually in both language First of all, there’s a way of representing stuff about the world like cities for example Or let’s say I want to say let’s let’s do this. Let’s say Let’s do something with cities. Let’s say capital cities in South America. Okay, so notice this is a piece of natural language This will get interpreted into something which is precise symbolic well from language code Um, that we can then compute with and that will give us the cities and capital cities in South America I could for example, let’s say I say find shortest to us and I’m going to use some uh Some oops. No, I don’t want to do that What I want to do first is to say show me the geo positions of All those cities online 21 there So now it will find the geo positions and now it will say compute the shortest tour So that’s saying there’s a 10,000 mile Traveling salesman tour around those cities so I could take those cities were online 21 and I could say Order the cities according to this and then I could make another geolist plot of that join it up And this should now show us a traveling salesman tour of the of the capital cities in South America um, so You know, it’s sort of interesting to see what’s involved in making stuff like this work um, the uh Uh, one of you know, my my goal has been to sort of automate as much as possible About things that have to be computed and that means knowing as many algorithms as possible And also knowing as much data about the world as possible And I kind of view this as sort of a knowledge-based programming approach Where you have you know a typical kind of idea and programming languages is you know You have some small programming languages has a few primitives that are pretty much tied into what a machine can intrinsically do and then maybe you’ll have libraries that add on to that and so on my kind of Crazy idea of many many years ago has been to build an integrated system where all of the stuff about different domains of knowledge and so on are all just built into the system and uh and designed in a coherent way I mean, this has been kind of the story of my life for the last 30 years is trying to keep the design of the system coherent Um, even as one adds um all sorts of different areas of of uh um, of capability so As um, I mean we can go and dive into all sorts of different kinds of things here, but um Maybe as an example Well, let’s do what can we do here? We could take um, let’s try How about this is that a bone? I think so that’s a bone So let’s try uh That As a mesh region See if that works. So this will now use a completely different domain of um of human endeavor Okay, whoops, there’s two of those bones. Let’s try. Let’s just try um Uh, let’s try left humerus. Let’s try the that the mesh region for that and now we should have a bone here Okay, there’s a there’s a representation of a bone. Let’s take that bone and we could for example say um Let’s take the surface area of that as in some some units or I could let’s do some much more outrageous thing. Let’s say we take um region distance So we’re going to take the distance from some from that bone to Uh a point Let’s say zero zero z and let’s make a plot of that distance with z going from Let’s say I don’t have no idea where the where this bone is, but let’s try something like this. So that was really boring um Let’s try um So what this is doing again a whole bunch of stuff has to work in order for this to to operate this has to be this is a This is some region in 3d space that’s represented by some mesh You have to compute you know do the computational geometry to figure out where it is if I wanted to let’s try a anatomy um Uh anatomy plot 3d and let’s say something like left hand for example, and now it’s going to show us probably the the complete data that it has about the geometry of um uh Of a left hand um There we go Uh, okay, so there’s there’s the result and we could take that apart and start computing things from it and so on so what um uh So this this is um um So there’s a there’s a lot of kind of computational knowledge that’s built in here um one Uh, let’s talk a little bit about kind of the modern machine learning story So for instance if I say let’s get a picture here. Let’s say um um Let’s let’s just say picture of somebody got a favorite kind of animal What panda Okay, so let’s try okay giant panda Okay, okay, there’s a panda. Let’s see what um now let’s try saying um Let’s try for this panda. Let’s try saying image identify And now here will be embarrassed probably but let’s just see let’s see what happens if I say image identify that And now it’ll uh, hopefully Wake up wake up wake up this only takes a few hundred milliseconds. Okay, very good giant panda. Let’s let’s see what it’s We’ll see what the runners-up were to the giant panda um Uh Let’s say we want to say uh The ten runners-up in all categories for that thing okay So a giant panda a prop Kierneed which I’ve never heard of Are pandas connoirs? The bamboo shoots okay, so that was so lucky it didn’t get that one It’s really sure it’s a mammal and it’s absolutely certain it’s a vertebrate um Okay, so you might ask how did it figure this out um, and so then you can kind of look under the hood and say So we have a whole framework for representing neural nets symbolically And so this is the actual model that it’s using to do this so this is a um, so there’s a neural net and it’s got We can drill down and we can see there’s there’s a piece of the neural net We can drill down even further to one of these and we can probably see what that’s a batch normalization layer Somewhere deep deep inside the entrails of the not not panda but of of this thing Okay, so now let’s take that object which is just a symbolic object and let’s feed it the picture of the panda and we can see um and there oops I was not giving it the right thing. What did I just do wrong here? Oh, yeah, let’s let’s take oh I see what I did Okay, let’s take this thing and feed it the picture of the panda and it says a giant panda okay How about we do something more outrageous? Let’s take that neural net and let’s only use the first let’s say ten layers of the neural net So let’s just take out ten layers of the neural net and feed it the panda and now what we’ll get is something from the Insides of the neural net and I could say for example, let’s just make those into images Okay, so that’s what that’s what the neural net had figured out About the panda after ten layers of going through the neural net and maybe actually be interesting to see Let’s do a feature space plots and now we’re going to um of those intermediate things in the sort of in the brain of the neural net so to speak um this is now taking so what this is just doing is to do dimension reduction on this Space of images and so it’s not very exciting. It’s probably mostly distinguishing these by by total gray level But that’s kind of showing us the space of um of different um Of different sort of features of the insides of the neural net So it’s also what’s interesting to see here is things like the symbolic representation of the neural net And if you if you’re wondering how does that actually work inside? It’s underneath it’s using mx net which we happen to have contributed to a lot and there’s sort of a bunch of symbolic layers on top of that That feed into that and maybe I can show you here. Let me show you how you would train one of these neural nets That’s also kind of fun so We have a data repository that has all sorts of useful data one piece of data It has is a bunch of neural net training sets So this is a the standard M-ness training set of handwritten digits. Okay, so there’s M-ness and you notice that these things here That’s just an image which I could copy out and I could do you know, let’s say I could do Color negate on that image because it’s just an image Um and there’s there’s the results and so on and now I could say Let’s take let’s take a neural net like let’s take a simple neural net like lunette for example um Okay, so let’s take a lunette and then let’s take the untrained um initial evaluation network So this is now a version of lunette simple standard neural net that didn’t get trained So for example if I if I take that that symbolic representation of lunette and I could say net initialize Then it will take that and it’ll just put random weights into lunette. Okay, so if I take those random weights And I feed it a zero here. I feed it that image of a zero It will presumably produce something completely random in this particular case, too, right? So now now what I would like to do is to take this so that was just randomly initializing the weights So now what I’d like to do is to take uh the M-ness training set and I’d like to actually train Uh lunette using M-ness training sets. So let’s take let’s take this and let’s take a random sample of uh Let’s say I don’t know a thousand pieces of lunette Come on. Why is it having to load it again? There we go. Okay, so there’s a there’s a random sample there It was online 21 and now let me go down here and say Uh, where was it? Well, we can just take this this thing here So this is the uninitialized version of lunette and we can say take that and then let’s say net train of that With the thing online 21 which was that thousand instances and now what it’s doing is it’s running training on And that’s you see the loss going down and so on it’s running training for For those thousand instances of of Lunette and it will we can stop it if we want to actually this is a new display This is very nice. This is this is a new version of orphan languages is coming out next week Which I’m showing you but it’s quite similar to what exists today But because that’s one of the features of running a software companies that you always run the the very latest version of things For better or worse and that’s and this is also a good way to debug it because supposed to come out next week if I find some horrifying bug Maybe it will get delayed um, but let’s try him Let’s um, let’s try this Okay, now it says it zero Okay, and so so this is now a trained version of lunette trained with that uh with that training data um One of the things so you know we can talk about all kinds of details of of neural nets and so on But maybe I should zoom out to talk a little bit about bigger picture as as I see it so one question is Uh Sort of a question of what is in principle possible to do with computation? So you know, we have as we’re you know, we’re building all kinds of things. We’re making image identifiers We’re figuring out all kinds of things about where the international space station is and so on question is what is what is in principle possible to compute? And so The you know one of the places one can ask that question is when one looks at For example models of the natural world one can say you know, how do we make models of the natural world? Kind of a a traditional approach has been let’s use mathematical equations to make models of the natural world a question is Um if we want to kind of generalize that and say well what are all possible ways to make models of things What can we say about that question? So I spent many years in my lifetime to address that question and Basically what what I thought about a lot is That if you want to make a model of a thing you have to have definite rules by which the thing operates What’s the most general way to represent possible rules while in today’s world? We think of that as a program So the next question is well what does the space of all possible programs look like? And most of the time you know we’re writing programs like wolf and language is 50 million lines of code And it’s a big complicated program that was for built for a fairly specific purpose But the question is if we just look at sort of the space of possible programs more or less at random What’s out there in the space of possible programs? So I got interested many years ago in cellular automata which are a really good example of a very simple kind of program So let me show you an example of one of these So this is these are the rules for a typical cellular automaton and this just says you have a row of black and white squares And this just says you look at a black look at a square say what color is that square what color left or it’s left and right Neighbors decide what color the square will be on the next step based on that rule Okay, so really simple rule So now let’s let’s take a look at what what actually happens if we use that rule a bunch of times So we can take that rule the 254 is just the binary digits that correspond to those positions in this rule So now I can say this I could say let’s do 50 steps. Let me do this some And now if I run According to the rule I just defined it turns out to be pretty trivial. It’s just saying If any if any square is if we start off with a black square if any square is Um if any neighboring squares black make a black square so we’ve we’ve used a very simple program We’ve got a very simple result out Okay, let’s try a different program. We can try changing this we’ll get um that’s a program with one bit different Now we get that kind of pattern So the question is well what happens you might say Okay, if you’ve got such a trivial program. It’s not surprising. You’re just gonna get trivial results out So but you can do an experiment to test that hypothesis and you can just say let’s take all possible programs There are 256 possible programs that are based on these eight bits here Let’s just take well. Let’s just uh whoops. Let’s just take um Let’s say the first 64 of those programs and let’s just make a um Oh, let’s just make a table of the results that we get by running those first 64 programs here So here we get the result and what you see is well most of them are pretty trivial They’re like they start off with one black cell in the middle and it just tools after one side Occasionally we get something more exciting happening like here’s a nice nested pattern that we get We were to continue it longer. It would it would make uh, you know more detailed nesting but then My all-time favorite science discovery if you go on and just look at these after a while you find this one here Which is rule 30 in this in this numbering scheme and that’s doing something a bit more complicated You say well, what what’s going on here? You know, we just started off with this very simple rule Let’s see what happens maybe after a while You know if we run rule 30 long enough It will resolve into something simpler. So let’s try running it. Let’s say 500 steps um and that’s the whoops. That’s the result we get let’s say uh Let’s just make it full screen Okay, it’s a really asking a bit on the project to there But but um you get the basic idea this is a so this just started off from one black cell at the top And this is what it made and that’s pretty weird because all this is you know This is uh sort of not the way it’s supposed things are supposed to work because what we have here is Just that little program down there and it makes this big complicated pattern here And you know, we can see there’s a certain amount of regularity on one side But for example the center column of this pattern is for all practical purposes completely random In fact, it was we used as the random number generator in mathematical and orphan language for many years It was recently retired after after excellent service because we found a somewhat more efficient one um the but um uh The um so you know, what do we learn from this what we learn from this is out in the computational universe are possible programs It’s possible to get even with very simple programs very rich complicated behavior Well, that’s important if you’re interested in modeling the natural world because You might think that there are programs that represent systems in nature that might work this way and so on It’s also important for technology Because it says okay, let’s say you’re trying to find a um let’s say you’re trying to find a program That’s a good random number generator. How are you going to do that? Well, you could start thinking very hard and you could try make up you know, you can try and Write down all kinds of flowcharts about how this random number generator is going to work Well, you can say forget that. I’m just going to search the computational universe of possible programs And just look for one that serves as a good random number generator in this particular case after you’ve searched 30 programs you’ll find one that makes a good random number generator Why does it work? That’s a complicated story. It’s not a story that I think necessarily We can really tell very well But what’s important is that this is this idea that out in the computational universe there’s a lot of rich sophisticated stuff that can be essentially mined for our technological purposes That’s the important thing whether we understand how this works is a different matter I mean, it’s like when we look at the natural world the physical world We’re used to kind of mining things You know we started using magnets to do magnetic stuff long before we understood understood the theory of fair magnetism And so on and so similarly here We can sort of go out into the computational universe and find stuff that’s useful for our purposes now in fact The the world of sort of deep learning and neural nets and so on is a little bit like this It uses the trick that there’s a certain degree of differentiability there So you can kind of hone in on let’s try and find something that’s incrementally better And for certain kinds of problems that works pretty well I think the thing that we’ve done a lot I’ve done a lot is just sort of Exorstive search in the computational universe of possible programs Just search a trillion programs and try and find one that does something interesting and useful for you Um, there’s a lot of things to say about what um well actually in in the search a trillion programs and find one that’s useful Let me show you another example of that um, let’s see so I was interested a while ago in um The I have to look something up here sorry um in um see here um in um Boolean algebra and in um I was interested in in the space of all possible mathematics is um And let me just see here ag I’m Not finding what I wanted to find sorry Uh, what is a good example I shouldn’t have memorized this but I haven’t so um here we go There it is um so I was interested in if you just look at so we talked about sort of looking at the space of all possible um uh The space of all possible programs another thing you can do is say if you’re gonna invent mathematics from nothing What possible axiom systems could we use in mathematics? So I was curious um where do and again might seem like a completely crazy thing to do To just say let’s just start enumerating axiom systems at random and see if we find one that’s interesting and useful Um, but it turns out once you have this idea that out in the computational universe of possible programs There’s actually a lot of low hanging fruit to be found It turns out you can apply that at a lot of places I mean the thing to understand is why why do we not see a lot of engineering structures that look like this? The reason is because our traditional model of engineering has been we engineer things in a way where we where we can foresee what the outcome of our engineering steps are going to be And when it comes to something like this we can find it out in the computational universe What we can’t readily foresee what’s going to happen? We can’t do sort of a step-by-step design of this particular thing And so in engineering and human engineering as it’s been practiced so far most of it has consisted of Building things where we can foresee step-by-step what the outcome of our engineering is going to be and we see that in programs We see that in in uh other kinds of engineering structures And so there’s sort of a different kind of engineering which is about mining the computational universe of possible programs And it’s worth realizing there’s a lot more that can be done a lot more efficiently by mining the computational universe of possible programs Than by just constructing things step-by-step as a human So for example if you look for optimal algorithms for things like I don’t know even something like sorting networks The optimal sorting networks look very complicated. They’re not things that you would construct By sort of step-by-step thinking about things with in a kind of in a kind of typical human way and So this this idea, you know, if you’re really going to have computation work efficiently You are going to end up with these programs that are sort of just mined from the computational universe And one one of the issues with mining things so that There this makes use of computation much more efficiently than a typical thing that we might construct now one feature of this is It’s hard to understand what’s going on and there’s actually a fundamental reason for that Which is in our efforts to sort of understand what’s going on We get to use our brains our computers our mathematics or whatever and our goal is this This this particular little program did a certain amount of computation to work out this pattern The question is can we kind of outrun that computation and say oh, I can tell that actually this particular bit down here is going to be a black Black bit You don’t have to go and do all that computation But it turns out that and again this will maybe is a digression which which um There’s this phenomenon I call computational irreducibility Which I think is really common and it’s a consequence of this thing I call principle of computational equivalence and that the principle of computational equivalence basically says as soon as you have a system Whose behavior isn’t fairly easy to analyze the chances are that the computation it’s doing is essentially as sophisticated as it could be And that has consequences like it implies that the typical thing like this will correspond to a universal computer That you can use to program anything It also has the consequence of this computational irreducibility phenomenon that says You can’t expect our brains to be able to outrun the computations that are going on inside the system If there was computational reducibility Then we can expect that this thing went to a lot of trouble and did a million steps of evolution But actually just by using our brains we can jump ahead and see what the answer will be Computational irreducibility suggests that isn’t the case if we’re going to make the most efficient use of computational resources We will inevitably run into computational irreducibility all over the place It has the consequence that we get the situation where we can’t readily sort of foresee and understand what’s going to happen So back to mathematics for a second. So this is just an axiom system um that uh, so I looked for all possible look through sort of all possible axiom systems Starting off with very really tiny ones and I asked the question What’s the first axiom system that corresponds to Boolean algebra? So it turns out this this thing here this tiny little thing here uh, uh, generates all theorems of Boolean algebra It is the simplest axiom for Boolean algebra Now something I have to show you this because it’s a new feature you see the um If I say Find equation or proof let’s say I want to prove commutativity of the nanned operation I’m going to show you something here. This is going to try to generate let’s see if this works Um, this is going to try to generate an automated proof based on that axiom system of that result. So it had 102 steps in the proof And um, let’s try and say let’s look at for example the proof network here Actually, let’s look at the proof data set um, no, that’s not what I wanted Oh, I should learn how to use this shouldn’t I? The um Let’s see what I want is the um Yeah, proof data set there we go very good. Okay, so This is actually let’s let’s say Uh, first of all, let’s say the proof graph Okay, so this is going to show me the um, how that proof was done So there are a bunch of lemurs that got proved and from those lemurs those lemurs were combined and eventually it proved the result So let’s let’s take a look at the let’s take a look at what some of those lemurs were Okay, so here’s here’s the result so after so it goes through and these are various lemurs it’s using and eventually after many pages of nonsense It will get to the result. Okay, each one of these some of these lemurs are kind of complicated there That’s so that’s that lemma to pretty complicated lemma etc etc So you might ask what on earth is going on here and the answer is so I first generated a version of this proof 20 years ago And I tried to understand what was going on and I completely failed and it’s sort of embarrassing because this is supposed to be a proof It’s supposed to be you know demonstrating some results and what we realize is that you know What does it mean to have a proof of something? What does it mean to explain how a thing is done? You know, what what is the purpose of a proof purpose of a proof is basically to let humans understand why something is true And so for example if you go to um Let’s say we go to Wolfmalfa and we do you know some random thing where we say Let’s do you know an integral of something or another it will be able to very quickly In fact, it will take it only milliseconds internally to work out the answer to that integral Okay, but then somebody who wants to hand in a piece of homework or something like that needs to explain why is this true? Okay, well we have this handy step-by-step solution This is a simple solution thing here which um Explains why it’s true now the thing I should admit about the step-by-step solution is it’s completely fake That is the steps that are described in the step-by-step solution have absolutely nothing to do with the way that internally that integral was computed These are steps created purely for the purpose of telling a story to humans about why this integral came out the way it did And now what we’re seeing and so that’s a so there’s one thing is knowing the answer the other thing is being able to tell a story about why the answer worked that way Well, what we see here is this is a proof But it was an automatically generated proof and it’s a really lousy story for us humans I mean if it turned out that one of these theorems here was one that had been proved by Gauss or something and appeared in all the textbooks We would be much happier because then we would start to have a kind of human uh, representable story about what was going on instead We just get a bunch of machine-generated lemurs that we can’t understand that we can’t kind of wrap our brains around And it’s sort of the same thing that’s going on in When we look at when he’s neural nets We’re seeing you know when we were looking wherever it was at the innards of that neural net And we say well, how is it figuring out that that’s a picture of a panda? Well, the answer is it decided that You know if we humans were saying how would you figure out if it’s a picture of a panda? We might say well Look and see if it has eyes that’s a clue for whether it’s an animal Look and see if it’s looks like it’s kind of round and furry and things That’s a version of whether it’s a panda and lents et cetera et cetera But what it’s doing is it learned to bunch of criteria for you know Is it a panda or is it one of 10,000 other possible things that it could have recognized and it learnt those criteria In a way that was somehow optimal based on the training that it got and so on But it learnt things which were distinctions which are different from the distinctions that we humans make in the language that we as humans use And so in some sense you know when when we start talking about well describe a picture We have a certain human language for describing that picture We have you know an alt human in typical human languages We have maybe 30 to 50,000 words that we use to describe things Those words are words that have sort of evolved as being useful for describing the world that we live in And when it comes to this neural net it could be using it could say well Uh, the words that it is effectively learnt which allow it to make distinctions about what’s going on in the in the analysis that it’s doing It has effectively invented words that describe distinctions But those words have nothing to do with our historically invented words that exist in our languages So it’s kind of an interesting situation that that it is It’s way of thinking so to speak if you say well, what’s it thinking about how do we describe what it’s thinking That’s a tough thing to answer because just like with the with the automated theorem We’re we’re sort of stuck having to say well We can’t really tell a human story because the things that it invented are things for which we don’t even have words in our languages and so on Okay, so one thing to realize is In this kind of space of sort of all possible computations there’s a lot of stuff out there that can be done There’s this kind of ocean of sophisticated computation and then The question that we have to ask for us humans is Okay, how do we make use of all of that stuff? So what we’ve got kind of on the one hand is we’ve got the things we know how to think about Human languages our way of describing things our way of talking about stuff. That’s the one one side of things The other set of things we have is this very powerful kind of seething ocean of computation on the other side where lots of things can happen So the question is how do we make use of this sort of ocean of computation in the best possible way for our human purposes and building technology and so on And so the the way I see you know my kind of a part of what I’ve spent a very long time doing is kind of building a language that allows us to take human thinking on the one hand and describe and and sort of provide a sort of computational communication language that allows us to get the benefit of what’s possible over in the sort of ocean of computation in a way that’s rooted in what we humans actually want to do and so I kind of view of language as being sort of an attempt to make a bridge between so you know on the one hand There’s all possible computations on the other hand. There’s things we think we want to do and I view of language as being My best attempt right now to make a way to take our sort of human computational thinking and be able to actually implement it so in a sense it’s a language which works in two on two sides It’s both a language where you as a Uh as a the machine can understand. Okay. It’s it’s looking at this and that’s what it’s going to compute But on the other hand, it’s also a language for us humans to think about things in computational terms So you know if I go and I I don’t know one of these one of these things that I’m doing here whatever it is That this wasn’t that exciting but but um, you know find shortest tour of the geo position of the capital cities in South America That is a language that’s a representation and a precise language of something and the idea is that that’s a language which we humans can find useful In thinking about things in computational terms. It also happens to be a language that the machine can immediately understand and execute And so I think this is sort of a general, you know when I think about AI in general the you know what is the sort of what’s the overall problem Well part of the overall problem is so how do we tell the AI’s what to do so to speak There’s this very powerful, you know, this sort of ocean of computation is what we get to mine for purposes of building AI kinds of things But then the question is how do we tell the AI’s what to do and And the what I see what I’ve tried to do with welcome language is to provide a a way of kind of Accessing that computation and sort of making use of the knowledge that our civilization has accumulated Um and because that’s the you know, there’s the general computation on on this side And there’s the specific things that we humans have thought about and the question is to make use of the things that we’ve thought about To do do things that we care about doing actually if you’re interested in these kinds of things. I happen to just write a blog post We’re lost a couple of days ago. It’s kind of a funny blog post. It’s about some well you can see the title there It came because a friend of mine is has this crazy project to put little little sort of Disks or something that should represent kind of the best achievements if human civilization so to speak to send out It’s it’s hitchhiking on various space craft that are going out into the Solar system in the next little while and the question is what to put on this little disc that kind of represents You know the achievements of civilization. It’s kind of it’s kind of depressing when you go back and you look at what What people have tried to do on this before and Realizing how hard it is to tell even whether something is an artifact or not But this is uh, this was sort of a um, yeah, that’s a good one That’s from 11,000 years ago. Can you the question is can you figure out what an earth it is? Um, and what it means and and this is uh But but so what what’s relevant about this is the this this whole question of there are things that are out there in the computational universe And you know when we think about extraterrestrial intelligence I find it kind of interesting that Artificial intelligence is our first example of an alien intelligence We don’t happen to have found what we view as extraterrestrial intelligence right now But we are in the process of building pretty decent version of an alien intelligence here And the question is if you ask questions like well, you know, what is it thinking? Is it does it have a purpose and what it’s doing and so on and you’re confronted with things like this It’s very we you can kind of do a test run of you know, what’s what’s its purpose? What is it trying to do? Um in a way that is very similar to the kinds of questions you would ask about about extraterrestrial intelligence but in any case the the um The main point is that I see there’s sort of ocean of computation There’s the let’s describe what we actually want to do with that ocean of computation And that’s where you know, that’s one of the primary problems we have now people talk about you know AI and what is AI going to allow us to automate And my basic answer to that would be we’ll be able to automate everything that we can describe The problem is it’s not clear what we can describe or put another way You know, you imagine various jobs and people are doing things their repeated judgment jobs things like this They’re where we can readily automate those things But the thing that we can’t really automate is saying well, what are we trying to do? That is what are our goals? Because in a sense when when we see one of these systems, you know, let’s say Let’s say it’s a it’s a cellular automaton here, okay? The question is what is the cellular automaton trying to do? Maybe I can maybe I’ll give you another cellular automaton that is a little bit more exciting here Let’s do this one. So that the um The question is what is the cellular automaton trying to do? You know, it’s got this whole big structure here and things are happening with it We can go we can run it for a couple thousand steps We can ask it’s a nice example of kind of undecidability and action What’s going to happen here? This is kind of the halting problem. Is this kind of halt? What’s it going to do? There’s computational irreducibility so we actually can’t tell This is the case where we know this is a universal computer in fact Eventually, well, I won’t even spoil it for you if I went on long enough it would it would Go into some kind of cycle but We can ask what is this thing trying to do? What is it? You know, is it what’s it thinking about? What’s it? Um, you know, what’s its goal? What’s its purpose? And you know, we get very quickly in a in a big mess thinking about those kinds of things I’ve I’ve um one of the things that comes out of this principle of computational equivalence is thinking about what kinds of things have are capable of of sophisticated computation so so I mentioned a Well-back here Sort of my personal history with wealth malphur of having thought about doing something like wealth malphur when I was a kid And then believing that you sort of had to build a brain to make that possible and so on and One of the things that I then thought was that there was some kind of bright line Between what is intelligent and what is merely computational so to speak In other words that there was something which is like oh, we’ve got this great thing that we humans have that You know, it’s intelligence and all these things in nature and so on and all the stuff that’s going on there It’s just computation or it’s just you know things operating according to rules that’s different There’s some bright line distinction between these things Well, I think the thing that came about after I’d looked at all these cellular automata and all kinds of other things like that is I sort of Came up with this principle of computational equivalence Idea which we’ve now got quite a lot of evidence for which I talk about people are interested in But that basically there isn’t a that once you reach a certain level of of computational sophistication everything is equivalent And that means that that implies that there really isn’t a bright line distinction between for example the computations going on on our brains And the computations going on in these simple cellular automata and so on and that essentially philosophical point Is what actually got me to start trying to build both mouthhaw because I realized that gosh, you know I’ve been looking for this sort of the the magic bullets of intelligence and I just decided probably there isn’t one And actually it’s all just computation And so that means we can actually in practice build something that does this kind of intelligent like thing And so that’s what I think is the case is that there really isn’t sort of a bright line distinction And that has that has more extreme consequences like people will say things like you know the weather has a mind of its own Okay, sounds kind of silly sounds kind of animistic primitive and so on but in fact the you know fluid dynamics of the weather is as computationally sophisticated as the stuff that goes on in our brains But we can start asking but then you say but the weather doesn’t have a purpose You know, what’s the purpose of the weather? Well, you know Maybe the weather is trying to equalize the temperature between the you know the the north pole and the tropics or something And then we have to say well, but that’s not a purpose in the way that we think about purposes That’s just you know and we get very confused and in the end what we realize is when we’re talking about things like purposes We have to have this kind of chain of Providence that goes back to humans and human history and all that kind of thing And I think it’s the same type of thing when we talk about computation and AI and so on the thing that We this question of sort of purpose goals things like this That’s a thing which is intrinsically human and not something that we can ever sort of automatically generate It makes no sense to talk about automatically generating it because these computational systems they do all kinds of stuff You know, we can say they’ve got a purpose we can attribute purposes to them etc etc But you know ultimately it’s sort of the human thread of purpose that we have to have to deal with So that means for example when we talk about AI’s and we we’re interested in things like so how do we tell you know like like we’d like to be able to tell But we talk about AI ethics for example We’d like to be able to make a statement to the AI’s like you know, please be nice to to us humans Um, and that’s a you know, that’s something So one of the issues there is so so talking about that kind of thing um One of the issues is how are we going to make a statement like be nice to us humans? What’s the you know in how are we going to explain that to an AI? And this is where again, you know my My efforts to build a language of computational communication language that bridges The world of what we humans think about and the world of what is possible in computation is important And so one of the things I’ve been interested in is actually building what I call a symbolic discourse language That can be a general representation for sort of the kinds of things that we might want to uh Put in That we might want to to say in things like be nice to humans So sort of a little bit background to that So you know in in the modern world people are keen on smart contracts They often think of them as being deeply tied into blockchain, which I don’t think is really quite right The the important thing about smart contracts is it’s a way of having sort of an agreement between parties Which can be executed automatically and that agreement maybe you know you may choose to sort of Anker that agreement on blockchain you may not but the whole point is you have to what you you know when people write legal contracts They write them in an approximation to English they write them in legal ease typically because they’re trying to write them in something a little bit more Precise than regular English But the limiting case of that is to make a symbolic discourse language in which you can write the contract and code basically And the I’ve been very interested in using wolf language to do that because in wolf language We have a language which can describe things about the world and we can talk about the kinds of things that people actually talk about in contracts and so on and we’re most of the way there to being able to do that um and Then when you start thinking about that you start thinking about okay, so we’ve got this language to describe Things that we that we care about in the world and so when it comes to things like tell the AI’s to be nice to the humans We can imagine using wolf language to sort of build an AI constitution that says this is how the AI is supposed to work But when we talk about sort of just the the untethered you know the untethered AI Doesn’t have any particular. It’s just gonna do what it does and if we want it to you know if we want to somehow align it with human purposes We have to have some way to sort of talk to the AI and that’s that’s a you know, I view My efforts to build wolf language as as way to do that. I mean, I you know as I was showing at the beginning You can use you can take natural language and with natural language you can build up a certain amount of You can say a certain number of things in natural language You can then say well, how do we make this more precise in a precise symbolic language? If you want to build up more complicated things It gets hard to do that in natural language and so you have to kind of build up more serious programs in in In symbolic language and I’ve probably been Been yacking a while here, and I’m happy to um, I can talk about all kinds of different things here, but but maybe um I’ve not seen as many reactions as I might have expected to think so I’m not sure which things people are interested in Which they’re not but so maybe I should maybe I should Uh stop here and we can have discussion questions comments. Yes Two microphones if you have questions please come up So I have a quick question. It’s close to the earlier part of your talk where you um say you don’t build a top-down ontology You actually build from the bottom up with disparate domains What do you feel are the core technologies of the knowledge representation which you use within wolfram alpha? Uh that allows you uh, you know different domains to reason about each other to come up with solutions And is there any feeling of different shability for example? So if you were to Uh come up with a plan to do something new uh within wolfram alpha language You know, how would you go about doing that me? Okay, so We’ve done maybe a couple of thousand domains, okay? The what is actually involved in doing one of these domains? It’s it’s a gnarly business every domain has some crazy different thing about it I tried to make up actually a while ago We um let me show you something a kind of a hierarchy of what it means to make um see if I can find this here Kind of a hierarchy of what it means to make a domain computable Uh, where is it? There we go um Let’s okay here we go So this is sort of a hierarchy of levels of what it means to make a domain computable from Just you know, you’ve got some a you know, you got some array of data. That’s quite structured forget You know that the separate issue about extracting things from unstructured data But let’s imagine that you were given you know a A bunch of data about Landing sites of meteorites or something, okay? So you go through various levels So you know things like um, okay the landing sites of the meteorites are the are the positions just strings or are there some kind of canonical representation of geoposition? Is the you know is the type of meteorite? You know some of them are iron meteorites some of them are stone meteorites Have you made a canonical representation? Have you made some kind of a way to um To identify what um Sorry, go ahead. No, no, I mean to to do that so so my question is like yeah If you did have positions as a string as well as a canonical representation Do you have redundant pieces of the same uh redundant representations of the same information in the different uh No, I mean I’ll go is everything canonical that you have yeah, you have a minimal representation of everything Yeah, our goal is to make everything canonical now that’s you know, there is a lot of complexity in doing that I mean if you you know and each okay, so another feature of these domains, okay? So here’s another another thing to say um You know it will be lovely if one could just automate everything and cut the humans out of the loop Turns out this doesn’t work and in fact whenever we do these domains It’s fairly critical to have expert humans who really understand the domain or you simply get it wrong And it’s also having said that once you’ve done enough domains You can do a lot of cross checking between domains and we are the the number one reporters of error and Of errors and in pretty much all standardized data sources because we can do that kind of cross checking But I think you know if you ask the question what’s involved in um Uh in bringing online a new domain It’s you know that those sort of hierarchy of things, you know some of those take a few hours You can get to the point of of having you know We’ve got good enough tools for ingesting data Uh figuring out oh those are names of cities in that column. Let’s you know, let’s canonicalize those Um, you know some may be questions, but many of them will be able to to nail down and to get to the full level of you’ve got some complicated domain and it’s fully computable is probably a year of work um and uh and you might say well gosh Why are you wasting your time? You’ve got to be able to automate that so you can probably tell we’re fairly sophisticated about machine learning kinds of things and so on And we have tried you know to automate as much as we can and we have got a pretty efficient pipeline But if you actually want to get it right and you see it is an example of what what happens There’s a level even going between wolf mouthful and wolf and language There’s a level of so for example, let’s say you’re looking at you know lakes in Wisconsin Okay, so people are querying about lakes in Wisconsin and wolf mouthful They’ll name a particular lake and they want to know, you know, how big is the lake? Okay, fine And wolf and language they’ll be doing a systematic computation about lakes in Wisconsin So if there’s a lake missing you’re going to get the wrong answer and so that’s a kind of higher level of of difficulty Okay, but there’s yeah, I think you’re asking some more technical questions about ontologies and I can try and answer those Actually one quick question. Can you No, that’s there’s a lot of the questions Okay, all right, thank you very much Cyclists as he left here. I got a simple question. Um, who or what are your key influences? Oh gosh In terms of of language design for wolf language In the context of machine intelligence if you like if you want to make it tailored to this audience I don’t know I’ve been absorbing stuff forever. I think my main um in terms of language design probably a list of an APL were my sort of early um influences, but in terms of of um thinking about AI You know In I mean I’m kind of Quite knowledgeable. I like history of science. I’m pretty knowledgeable about the the early history of kind of Mathematical logic symbolic kinds of things. I would say okay. Maybe I can answer that in the negative Okay, I have for example in building wolf malpha um I thought gosh let me do my homework. Let me learn all about computational linguistics Let me hire some computational linguistics PhDs That will be a good way to get the started turns out we used almost nothing from the from the previous sort of history of computational linguistics partly because what we were trying to do namely short question natural language understanding is different from a lot of the natural language processing Which has been is down in the past I also have made to my disappointment Very little use of you know people Like Marvin Minsky for example. I really don’t think I mean I knew Marvin for years and in fact some of his early work on simple touring machines and things Those are probably more influential to me than his work on on AI um, and you know probably my my mistake of not understanding that better But really I would say that I’ve been been rather uninfluenced by by sort of the traditional AI kinds of things I mean it probably hasn’t helped that I’ve kind of lived through a time when when sort of AI went from You know when I was a kid AI was going to solve everything in the world and then you know It kind of decayed for a while and then sort of come back so I So I would say that I can describe my negative my non-influences better than my impression The impression you give is that you made it up out of your own head and it sounds as though that’s pretty much right Yeah, I mean yes, I I mean insofar as there’s things to me. I mean look things like the um Uh, you know, okay, so for example studying simple programs as a and trying to understand the universe of simple programs actually the personal history of that sort of interesting. I mean I I you know I used to do particle physics when I was a kid basically and um Then I actually got interested. Okay, so I’ll tell you the history of that just as an example of how sort of interesting is a sort of history of ideas type thing So I was interested in in how order arises in the universe So you know you start off from the hot big bang and then pretty soon you end up with a bunch of humans and galaxies and things like this How does this happen? So I got interested in that question I was also interested in in things like neural networks For sort of AI purposes and I thought let me make a minimal model that encompasses sort of how complex things arise from from other stuff and I ended up sort of making simpler and simpler and simpler models and eventually wound up with cellular automata And which I didn’t know were called cellular automata when I started looking at them and then found they did interesting things And the two areas where cellular automata have been singularly Unuseful in analyzing things are large-scale structure in the universe and your own networks So turned out but but that by the way the fact that I kind of even imagined that one could just start Yeah, I should say you know, I’ve been doing physics and Physics the kind of intellectual concept is you take the world as it is and you try and drill down and find out what You know what makes the world out of primitives and so on. It’s kind of a you know reduced to find things Then I built my first computer language I think of the SMP which went the other way around where I was just like I’m just going to make up this computer language and you know Just make up what I want the primitives to be and I’m going to build stuff up from it I think that the fact that I kind of had the idea of doing things like making up cellular automata as possible models for the world Was a consequence of the fact that I worked on this computer language Which was a thing which worked the opposite way around from the way that one is used to doing natural science Which is sort of this reductionist approach and that’s I mean, so that’s just an example of it You know, I found I happen to have spent a bunch of time studying as I say history of science and one of my one of my hobbies is Sort of history of ideas. I even wrote this little book called idea makers Which is about biographies of a bunch of people who for one reason or another I’ve written about and so I’m always curious about this thing about How do people actually wind up figuring out the things they figure out and you know one of the one of the conclusions of my You know investigations of many people is there are very rarely moments of inspiration Usually it’s long multi-decade kinds of things which only later get compressed into something short and also the path is often much You know, it’s it’s it’s it’s quite What can I say that the steps are quite small and you know, but the path is often kind of complicated And that’s what that’s what’s been for me. So I simple question complex answer. Sorry So When I basically see from the Wolfram language, it’s a way to describe all of objective reality It’s kind of Formalizing just about the entire domain of discourse use a philosophical term and you kind of hinted at this in your Electure where that where it sort of leaves off is that when we start to talk about more esoteric philosophical concepts purpose I guess this would lead into things like epistemology because essentially you only have science there and as amazing as science Is there other things that are talked about not you know like idealism versus materialism etc Do you have an idea of how Wolfram might or might not be able to branch into those discourses because I’m hearing echoes in my head of that time Ballstrom said that an AI needs a you know when you give an AI a purpose There’s like I think he said Philosophers are divided completely evenly between the top four ways to measure how good something should be It’s like utilitarianism and sure. You’re the formation of Japanese Yeah, right. Well, so the first thing is I mean this problem of making what okay about 300 years ago people like Leibniz were interested in the same problem that I’m interested in which is how do you formalize Sort of everyday discourse and Leibniz had the original idea, you know He was originally trained as a lawyer and he had this idea if he could only reduce all law all legal questions to matters of Logic he could have a machine that would basically describe every you know Answer every legal case, right? He was unfortunately a few hundred years too early Even though he did have you know, he tried to he tried to do all kinds of things very similar things I’ve tried to do like he tried to get various dukes to assemble big libraries of data and stuff like this but but the point So what he tried to do was to make a A formalized representation of everyday discourse for whatever reason for the last 300 years basically people haven’t tried to do that There’s it’s a almost completely barren landscape. There was this period of time in the 1600s when people talked about philosophical languages Leibniz was one a guy called John Wilkins was another Um, and they tried to you know break down human thought into something symbolic people haven’t done that for a long time Um in terms of what can we do that with? Um, you know, I’ve been trying to figure out what the best way to do it is I think it’s actually not as hard as one might think these areas one thing you have to understand these areas like philosophy and so on Are there on the harder end? I mean things like good example typical example, you know, I want to have a piece of chocolate Okay, they in more than language right now. We have a pretty good description of pieces of chocolate We know all sorts of you know, we probably know a hundred different kinds of chocolate We know how big the pieces are all that kind of thing the I want part of that sentence. We can’t do that right now Um, but I don’t think that’s that hard Um, and I’m you know, that’s now if you ask um, let’s say we had I think the different thing you’re saying is Let’s say we had the omnipotent AI so to speak that was able to you know where we turn over the control of the central bank to the AI We turn over all these other things to the AI then the question is we say to the AI now do the right thing And then the problem with that is and this is why I talk about you know creating AI constitutions and so on We have absolutely no idea what do the right thing is supposed to mean and philosophers have been arguing about that You know utilitarianism as an example of that of one of the answers to that although it’s not a complete answer by any means It’s not not really an answer. It’s just a way of posing the question Um, and so I think that the you know one of one of the features of um, so I think it’s a really hard problem to You know You think to yourself what should the AI constitution actually say so first thing you might think is oh There’s going to be you know something like azimuth’s laws of robotics. There’s gonna be one You know golden rule for a eyes and if we just follow that golden rule all the well, okay? I think that that is Absolutely impossible and in fact, I think you can even sort of mathematically prove that that’s impossible Because I think as soon as you have a system that You know essentially what you’re trying to do is you’re trying to put in constraints that Okay, basically as soon as you have a system that shows computational irreducibility I think it is inevitable that you have Aces of have unintended consequences of things which means that you never get to just say put everything in this one very nice box You always have to say let’s put in a patch here. Let’s put in a patch there and so on a version of this much more abstract version of this Gurdles theorem So Gurdles theorem is you know it starts up by taking the uh, you know Gurdles theorem is trying to talk about integers and says start off with pionos axioms pionos axioms you might say in pionos thought Describe the integers and nothing but the integers, okay? So anything that’s provable from pionos axioms will be true about integers and vice versa, okay? What Gurdles theorem shows is that you can that will never work that they’re an infinite hierarchy of patches That you have to put on to pionos axioms if you want to describe the integers and nothing but the integers And I think the same is true if you want to have a legal system effectively that has no Bizarre unintended consequences I don’t think it’s possible to just say you know if you when you’re describing something in the world That’s complicated like that. I don’t think it’s possible to just have a small set of rules That will always do what we want so to speak I think it’s inevitable that you have to have a long Essentially code of laws and that’s what you know So my guess is that what will actually have to happen is you know as we try and describe what you want the eyes to do You know, I don’t know the socio-political aspects of how will Figure out whether it’s one AI constitution or one per You know city or whatever We can talk about that. That’s a separate issue, but but um, you know I think what will happen is it’ll be much like human laws. It’ll be a complicated thing that gets progressively patched And so I think it’s it’s some and these ideas like um, you know, oh, we’ll just make the eyes, you know Run the world according to you know Mills you know chance to it mills idea It’s not gonna work As we’re just not surprising this philosophy has has has made the point that it’s not as easy as not an easy problem for the last 2000 years And they’re right. It’s not an easy problem Thank you I um you talked about computational irreducibility and computational equivalence and also that earlier on in your intellectual adventures you’re interested in particle physics and things like that Um, I’ve heard you make the comment before in other contexts that Things like molecules Compute and I was just to ask you exactly you know what you mean by that and what’s since does a Molecule I mean Uh, what would you like to compute so to speak? I mean in other words you It what what is the case is that you know one definition of your computing is given a particular computation like I don’t know finding square roots or something You know you can program a You know the the surprising thing is that an awful lot of stuff Can be programmed to do any computation you want that’s some and uh, you know when it comes to I mean I think for example when you look at nanotechnology and so on the the um the current You know one of the current belief says to make very small Computers you should take what we know about making big computers and just you know Make them smaller so to speak I don’t think that’s the approach you have to use I think you can take the components that exist at a level of molecules and say how do we assemble those components To be able to do complicated computations. I mean it’s like the cellular automata that the you know the underlying Uh rule for the cellular automaton is very simple yet When that rule has applied many times it can do a sophisticated computation. So I think that that’s the that’s the sense in which um What can I say the raw material that you need for computation can be uh, you know There’s a great diversity in the raw material that you can use for computation our particular human development, you know stack of of um uh of technologies that we use for computation right now is just one particular path and we can you know So a very practical example of this is algorithmic drugs So the question is right now drugs pretty much work by most drugs work by you know There is a binding site on a molecule drug fits into binding site does something Question is can you imagine having something where the molecule you know is something which has Computations going on in it where it goes around and it looks at that You know that thing it’s supposed to be binding to and it figures out. Oh, there’s this knob here and that knob there It reconfigures itself. It’s computing something. It’s trying to figure out, you know Is this likely to be a tumor cell or whatever based on some more complicated thing? That’s the type of thing that I mean by by computations happening at molecular scale. Okay. I guess I meant to ask If it follows from that if In your view like the the molecules in the chalkboard and in my face and in the table are in any sense Currently during doing computer. I mean the question of what computation look one of the things to realize if you look at kind of That sort of past and future of things the the Okay, so here’s an observation actually I was about livenets actually and livenets this time livenets made a A calculator type computer out of brass took him 30 years, okay? So in his day There was you know at most one computer in the world as far as he was concerned, right? Today’s world there may be 10 billion computers maybe 20 billion computers. I don’t know The question is what’s that going to look like in the future And I think the answer is that in time probably everything we have will be made of computers in the following sense that Basically it won’t be you know in today’s world things are made of you know Metal plastic whatever else, but actually that won’t make it there won’t be any points in doing that once we know how to do You know molecular scale manufacturing and so on We might as well just make everything out of programmable stuff And I think that’s a that’s a sense in which you know the um and you know the one example We have molecular computing right now is us in biology You know biology does a reasonable job of specific kinds of molecular computing It’s kind of embarrassing. I think that the only you know molecule We know that sort of a memory molecule is DNA and it’s kind of you know, which is kind of the you know the particular biological solution In time we’ll know lots of others and um, you know, I think the the sort of the the end point is So if you’re asking is you know is computation going on in you know in this water bottle the answer is absolutely It’s probably even many aspects of that computation are pretty sophisticated if we wanted to know what would happen To particular molecules here. It’s going to be hard to tell there’s going to be computationally reduced ability and so on Can we make use of that for our human purposes? Can we piggyback on that to achieve something technological? That’s different issue and that’s the for that we have to build up this whole sort of chain of Of technology to be able to connect it, which is what I’ve kind of been been keep on talking about is how do we connect Sort of what is possible computationally in the universe to what we humans Can kind of conceptualize that we want to do in computation? And that’s you know, that’s the bridge that we have to make and that’s the hard part But getting the intrinsic getting the computation done is is um, you know, there’s computation going on all over the place The Maybe a couple more questions. I was hoping you could elaborate on What you were talking about earlier of like searching the entire space of possible programs Um, so that’s very broad so maybe like What kind of searching of that space were good at and like what we’re not and I guess what the yeah right So I mean, I would say that we’re at an early stage in knowing how to do that. Okay, so I’ve done lots of these things and they are The thing that I’ve noticed is If you do an exhaustive search Then you don’t miss even things that you weren’t looking for If you do a non exhaustive search there is a tremendous tendency to miss things that you weren’t looking for um, and so you know We’ve done searches or a bunch of function evaluation and wolf and language is done by was done by searching for optimal Approximations in some big space a bunch of stuff with hashing is done that way a bunch of image processing is done that way Where we’re just sort of searching this you know doing exhaustive searches and maybe trillions of programs to find things now You know there is on the other side of that story is the incremental improvements story with with deep learning And neural networks and so on where because there is differentiability Um, you’re able to sort of incrementally get to a better solution now in fact people are making less and less differentiability in deep learning neural nets And so I think eventually there’s going to be sort of a a grand unification of these kinds of approaches Um, right now we’re still you know, I don’t really know what the you know the exhaustive search side of things which you can use for all sorts of purposes I mean the reason the surprising thing that makes the exhaustive search not crazy is that there is rich sophisticated stuff near at hand in the computational universe if you had to go you know quadrillions You know through a quadrillion cases before you ever found anything exhaustive search will be hopeless But you don’t in many cases um, and uh, you know I would say that we are in a fairly primitive stage of the of the science of how to do those searches well My guess is that there’ll be some sort of unification which needless to say I thought a bunch about and uh Between kind of the neural net so you know the tradeoff typically in neural net says you can have a neural net that is very good at That is you know uses its computational resources well, but it’s really hard to train Or you can have a neural net that doesn’t use its computational resources so well, but it’s very easy to train because it’s very you know smoothly um, and you know my guess is that somewhere in the uh, you know harder to train But makes use of things that are closer to the complete computational universe Is is where one’s going to see progress, but it’s it’s a it’s a really interesting area and You know, I consider us only at the at the beginning of figuring that out The one last question Hi, I’m good. You go. Yeah, okay. Let’s do it Thank you for your talk. I just to give a bit of context for my question I research how we could teach AI to kids and developing platforms for that how we could teach artificial intelligence and machine learning to children And I know you develop resources for that as well. So I was wondering like where do you think it’s problematic that we have computation that is very efficient and can you know from Utility, Italian and problem solving perspective it achieves all the goals, but we don’t understand how we how it works So we have to create this fake steps and if you could think of scenarios where that could become very problematic over time And why do we approach it such in a such a deterministic way and when you mentioned that computation and intelligence are Differentiated by this like very thin line. How does that affect the way you learn and how do you think that will affect the way We kids learn we learn right, so I mean look my general principle about You know future generations and what they should learn. I mean first point is you know Very obvious point that you know for every field that people study You know archaeology to zoology There either is now a computational X or there will be soon So you know every field the paradigm of computation is Becoming important perhaps the dominant paradigm in that field. Okay, so how do you teach kids? To be useful in a world where everything is computational Um, I think the the number one thing is to teach them how to think in computational terms What does that mean that doesn’t mean writing code? Necessarily. I mean in other words one of the things that’s happening right now is a practical matter Is you know there been these waves of enthusiasm for teaching coding of various kinds You know, we’re in a we’re in a actually we’re in the end of a of an uptick wave I think it’s going down again Um, you know, it’s been up and down for 40 years or so Um, okay, why doesn’t that work? Well, it doesn’t work because while there are people like people who are students at MIT For example for whom they really want to learn you know engineering style coding and it really makes sense to them to learn that The vast majority of people it’s just not going to be relevant because they’re not going to write a low-level C program or something and it’s the same thing that’s happened in math education Which has been sort of a disaster there which is the number one takeaway for most people from the math they learn in school is I don’t like math and You know that’s not for all of them obviously, but that’s the you know if you ask no general scale You know what people and why is that well part of the reason is because what’s been taught is rather low level and mechanical It’s not about mathematical thinking particularly. It’s mostly about you know what teachers can teach and what assessment processes can Assess and so on okay, so how should one teach Computational thinking I mean I’m I’m kind of excited about what we can do with wolf and language because I think we have a high enough level language That people can actually write you know that for example I reckon by age 11 or 12 and I’ve done many experiments on this so I have some The only problem with my experiments is most of my experiments end up being with kids who are high achieving kids Despite many efforts to reach lower achieving kids. I always ends up with the kids who actually do the things that I set up Of the high achieving kids, but but you know setting that aside you know you take the typical um You know 11 12 13-year-olds and so on and they can learn how to write stuff in this language and what’s interesting is They learn to start thinking here. I’ll show you let’s be very practical. I can show you I was doing every Sunday I do a little little thing with some middle school kids and I might even be able to find my stuff from yesterday This is this is um, okay, let’s see probably my adventures January 28th, okay, let’s see what I did. Oh look at that. That was that was why I thought of the South America thing here because I just done that with these kids um, the um And so What are we doing? We were trying to figure out uh, this this some I Trying to figure out the shortest tour thing that I that I just showed you which is this is how I got where I got what Show you is is what I was doing with these kids, but this this was my version of this but the kids all had various different versions of this And we had um somebody suggested, you know, let’s just enumerate Let’s just look at all possible permutations of these these cities and figure out what their distances are there’s the histogram of those That’s what we get from those. Okay. How do you get the largest distance from those etc etc etc And this is okay. This was my version of it, but the kids had similar stuff and this is you know This is I think and that probably went off into oh yeah, there we go There’s there’s the one for the whole whole earth and then they wanted to know how do you do that in 3D? So I was showing them how to convert to xyz coordinates in 3D and make the corresponding thing in 3D So what’s This maybe isn’t the This is a random example from yesterday. So it’s not not a highly considered example, but but um What I think is interesting is that we seem to have finally reached the point where we automated enough of the actual doing of the computation That the kids can be exposed mostly to the thinking about what you might want to compute And you know part of our role in language design as far as I’m concerned is to get it as much as possible to the point where for example You can do a bunch of natural language input you can do things which make it as easy as possible For kids to not get mixed up in the kind of what the you know how the computation gets done But rather to just think about how you formulate the computation So for example the typical example I’ve used a bunch of times in you know What does it mean to do right code versus do other things like a typical sort of test Example would be I don’t know you you asked somebody You’re gonna there’s practical problem we had a mouth-mouth here You give a lat long position on the earth and you say you’re gonna make a map of that lat long position What what scale of maps should you make? So if the lat long is in the middle of the Pacific making a 10 mile You know radius map isn’t very interesting If it’s in the middle of Manhattan a 10 mile radius map might be quite quite a sensible thing to do So the question is come up with an algorithm come up with even a way of thinking about that question What do you do? You know, how should you figure that out? Well, you might say you know Oh, let’s look at the visual complexity of the image Let’s look at how far it is to another city. That’s far you know They’re various different things but thinking about that as a kind of computational thinking exercise that um Uh is um, you know, that’s the kind of thing so in terms of what one automates and whether people whether people need to understand how it works inside um Okay, main point is you’ll In the end it will not be possible to know how it works inside So you might as well stop having that be a criterion I mean that is there are plenty of things that one teaches people that uh let’s say in in um Lots of areas of biology medicine whatever else You know, maybe we’ll know how it works inside one day But you can still there’s an awful lot of useful stuff you can teach without knowing how it works inside And I think also as we get computation to be more efficient inevitably we will be dealing with things where you don’t know how it works inside Now, you know, we’ve seen this in math education because I’ve happened to made tools that automate a bunch of things that people do in math education And I think Well to tell a silly story I mean my My older daughter who at some point in the past was doing you know calculus You know and learning doing integrals and things and I was saying to her, you know, I didn’t think humans still did that stuff And anymore Which was a very unindirring comment The um but but in any case, I mean the you know, there’s a question of whether Do humans need to know how to do that stuff or not? So I haven’t done an integral by hand in probably 35 years That’s true more or less true then but when I was using computers to do them The I was for a while, you know, I used to do physics and so on. I used computers to this stuff I was a really really good integrator Except that it wasn’t really me. It was me plus the computer So how did that come to be while the answer was that because I was doing things by computer I was able to try zillions of examples and I got a much better intuition the most people got For how these things would work roughly how what you did to make the thing go and so on whereas people who are like I’m just working this one thing out by hand. You got a different, you know, you don’t get that intuition So I think you know two points first of all, you know, this how do you think about things computationally? How do you formulate the question computationally? That’s really important and something that we are now in a position I think to actually teach and it is not really something you teach by You know teaching, you know traditional quotes coding because a lot of that is okay We’re gonna make a loop. We’re gonna define variables. I just as I think I probably have a copy here Yeah, I wrote this book for this is a book for kind of for kids about other language except it seems to be useful to adults as well But I wrote it for kids. So it’s One of the amusing things in this book is it doesn’t talking it talk about assigning values to variables until chapter 38 So in other words, that will be a thing that you would find in chapter one of most, you know Low-level programming coding type type things turns out it’s not that relevant to know how to do that It’s also kind of confusing and Not necessary and so You know in terms of the you asked where will we get in trouble when people don’t know how the stuff works inside? That’s I mean, you know, I think one just has to get used to that because it’s like, you know You might say well we live in the world and it’s full of natural processes Well, we don’t know how they work inside but somehow we managed to survive and we go to a lot of effort to do natural science to try and figure out how Stuff works inside But it turns out we can still use plenty of things even when we don’t know how they work inside But we don’t need to know and I think the I mean, I think the main point is computational irreducibility guarantees that we will be using things Where we don’t know and can’t know how they work inside And you know, I think the the perhaps The thing that is a little bit you know to me a little bit Unfortunate as a you know as a typical human type thing the fact that I can readily see that you know The AI stuff we build is sort of effectively Creating languages and things that are completely outside our domain to understand and where by that I mean You know our human language with its 50,000 words or whatever has been developed over the last however many You know tens of thousands of years and as a society we’ve developed this way of communicating and explaining things You know the aIs are reproducing that process very quickly But they’re coming up with a an a historical You know something you know their way of describing the world But it doesn’t happen to relate at all to our historical way of doing it and that’s um You know, it’s a little bit disquieting to me as a human that that you know The things going on inside where I know it is you know in principle I could learn that language but It’s you know not the historical one that we’ve all learnt and it really wouldn’t make a lot of sense to do that because you learn it for one AI and then another one gets trained and it’s going to use something different So it’s some but my main I guess my main point for for education So another point about education I just make which is something I haven’t figured out but but um just is um You know when do we get to make a good model for the human learner using machine learning So in other words, you know part of what we’re trying to do like like I’ve got that automated proof I would really like to manage to figure out a way What is the best way to present that proof so human can understand it and basically for that We have to have a bunch of heuristics about how humans understand things So as an example if we’re doing let’s say a lot of visualization stuff from both of them language, okay? We have tried to automate do automated aesthetics So what we’re doing is you know we’re laying out a graph What way of laying out that graph is most likely for humans to understand it and we’ve done that you know by building a bunch of heuristics and so on But that’s an example of you know if we could do that for learning and we say what’s the optimal path given that the person is trying to understand this proof for example What’s the optimal path to lead them through understand that proof? I suspect we will learn a lot more and probably fairly small number of years about that and it will be the case that you know for example if you’ve got Oh, I don’t know you can do simple things like you know you go to Wikipedia and you look at what the path of You know how do you if you want to learn this concept what other concepts you have to learn we have much more detailed symbolic information about what Is actually necessary to know in order to understand this and so on it is I think Reasonably likely that we will be able to I mean you know if I look at I was interested recently in the history of math education So I wanted to look at the complete sort of path of math textbooks You know for the past well basically the like 1200 you know peven archie produced this one of the early math textbooks So there’ve been these different ways of teaching math and you know I think we’ve we’ve gradually evolved a fairly optimized way for the typical person though It’s probably the variation of the population is not well understood for you know how to explain certain concepts And we’ve gone through some pretty weird ways of doing it from the 1600s and so on Where which have gone out of style and possibly you know who knows whether that’s for us because of Well, but anyway, so so you know we’ve kind of learned this path of what’s the optimal way to explain adding fractions or something For humans for the typical human, but I think we’ll learn a lot more about how you know by essentially making a model for the human A machine model for the human will learn more about how to um, you know how to optimize how to explain stuff to humans A coming attraction, but Ah, by the way, do you think we’re close to that at all because you you said that there’s a something in will from alpha that That presents the human a nice away. Are we how far said coming interaction ten years? Yeah, right. So I mean in in um That explaining stuff to humans thing is a lot of human work right now right being able to automate Explaining stuff to humans. Okay, so some of these things Let’s see. I mean, so an interesting question Actually just today I was working on something that’s related to this. Yeah, it’s it’s it’s being able to Um, the question is given a whole bunch of can we for example train a machine learning system from explanations that it can see roughly Can we train it to give explanations that are likely to be understandable? Maybe I think the um, okay, so an example that I’d like to do Okay, I’d like to do a debugging assistant where the typical thing is program runs program gives wrong answer human says why did you get the wrong why did it give the wrong answer? Well the first piece of information to the computer is that was the human thought That was the wrong answer because the computer just did what it was told and it didn’t know that was supposed to be the wrong answer So the only question is can you in fact, you know in that domain? Can you actually have a Reasonable conversation in which the human is explaining the computer what they thought it was supposed to do the computer is explaining what happened and why did it happen and so on Same kind of thing for math tutoring Um, you know, we have a lot of you know, we’ve got a lot of stuff You know, we’re sort of very widely used for people who want to understand the steps in math You know, can we make a thing where people tell us I think it’s this okay? I’ll tell you one one little factoid which I which it did work out So if you do multi-digit arithmetic multi-digit addition, okay? Okay, so the the basis of this is It’s kind of silly silly thing, but but you know if you get the right answer for an addition sum, okay? You don’t get very much information the student gives the wrong answer The question is can you tell them where they went wrong So I’d say you have a four-digit addition sum and the student gives the wrong answer Can you backtrace and figure out what they likely did wrong and the answer is you can You know, you just make this graph of all the different things that can happen You know, when did they you know? There’s certain things that are more common transposing numbers and things or you know Having a one and a seven mixed up those kinds of things you can with very high probability given a four-digit addition sum with the wrong answer You can say this is the mistake you made Which is sort of interesting and that’s you know being done in a fairly symbolic way Whether one can do that in a you know more machine learning kind of way for more complicated Derivations, I’m not sure, but that’s a you know, that’s one that works I Just had a follow-up question So do you think you know like in the future it is is it possible to simulate virtual environments Which can actually understand how the human mind works and then build you know like finite state machines inside of this virtual environment to To provide a better learning experience and a more personalized learning experience Well, I mean so the question is if if if you’re going to You know, can you optimize if you’re playing a video game or something and that video game is supposed to be educational Can you optimize the the experience based on a model of you so to speak? Yeah, I’m sure the answer is yes, and I’m sure the you know the question of how complicated the model of you will be Is an interesting question that I don’t know the answer to I mean I’ve I’ve kind of wondered a similar question So I I’m a kind of personal analytics enthusiast so I collect tons of data about myself and I mean I do it most because it’s been super easy to do and I’ve done it for like 30 years And I have you know every keystroke I’ve typed on a computer like every keystroke I’ve typed here and I the screen at my computer every every 30 seconds or so of maybe 15 seconds I’m not sure it there’s a screenshot. It’s a super boring movie to watch But anyway, I’ve been collecting all this stuff and so a question that I’ve asked is is there enough data that a bot of me could be made? In other words, do I have enough data about you know, I’ve got I’ve written a million emails I have all of those I’ve received three million emails Over that period of time. I’ve got you know endless, you know things I’ve typed etc etc. etc. You know, is there enough data to reconstruct? You know me basically I think the answer is probably yes Not sure but I think the answer is probably yes and so the question is in an environment where you’re interacting with some video game Trying to learn something whatever else, you know How long is it going to be before it can learn enough about you to change that environment in a way that’s useful for Explain in the next thing to you. I would guess I would guess that have done that this is comparatively easy I might be wrong, but but um and that the um I mean, I think you know, it’s an interesting thing because you know one’s dealing with you know There’s a space of human personalities. There’s a space of human learning styles You know, I’m sort of always interested in the space of all possible xyz and there’s you know, there’s that question But how do you parameterize the space of all possible human learning styles? And is there a way that we will learn you know Like can we do that symbolically and say these are 10 learning styles or is it something? I think that’s a case where it’s better to use you know sort of soft machine learning type methods to kind of feel out that space Thank you. Yeah Maybe very last question. I was just intuitively thinking when you spoke about an ocean I thought of Isaac Newton when he said uh Uh, I You know the famous quote I might not and I thought instead of Newton on the beach. What if Franz list were there? Uh, what question would he ask? What would he say and I’m trying to understand your um, the alien Ocean and humans Through maybe Franz list and music Well, so I mean the the quote from Newton is is um uh, it’s sort of an interesting quote. I think it goes something like this if you know Uh People are talking about how wonderful calculus and all that kind of thing are and and Newton says Uh, you know to others. I may seem like I’ve done a lot of stuff But to me, I seem like a child who who picked up a particularly elegant You know seashell on the on the beach and I’ve been studying this the seashell for a while Even though there’s this ocean of truth out there waiting to be discovered. That’s roughly the quote Okay, I find that quote interesting for the following reason the what Newton did was you know calculus and things like it If you look at the computational universe of all possible programs There is a small corner Newton was exactly right in what he said that is he picked off calculus Which is a corner of the possible things that can happen in the computational universe that happened to be an elegant seashell So to speak they happen to be a case where you can figure out what’s going on and uh and so on while there is still the sort of ocean of Of other Sort of computational possibilities out there, but but when it comes to you know, you’re asking about music I oh, I think my computer stopped being able to get anywhere but but um sort of interesting the Oh see if we can get to the site. Yeah, so this is a um This is a website that um we made years ago and now my computer isn’t playing anything but Let’s try that Okay, so these things are created by basically just searching computational universe’s possible programs It’s sort of interesting because every one has kind of a story some of them are more interesting in others That’s right that one Anyway the the um what’s what’s interesting Actually what was interesting to me about this was this is a very trivial You know what this is doing is very trivial at some level It’s just it actually happens to use cellular automata you can even have it show you I think someplace here Whereas it’s somewhere there’s a way of showing your show the evolution. This is this is showing the behind the scenes what was actually happening What it chose to use to generate that musical piece um and What I thought was interesting about the site um I thought well, you know How would computers be relevant to music etc etc etc well, you know what would happen is a human would have an idea And then the computer would kind of dress up that idea and then you know a bunch of years go by and I Talked to people you know who are composers and things and they say oh, yeah, I really like that wolf and tone site Okay, that’s nice. They say it’s a very good place for me to get ideas So that’s sort of the opposite of what I would have expected namely what’s happening is you know human comes here You know listens to some 10-second fragment and And they said oh that’s an interesting idea and then they kind of embellish it Using kind of something that is humanly meaningful But it’s like you know you’re taking a photograph and you see some interesting configuration and then kind of your you know You’re filling that with kind of some human sort of context um but uh but so I’m not quite sure what um Uh You’re asking about I mean back to the Newton quote the thing that I think is some Another way to think about that quote is us humans You know with our sort of historical development of you know our intellectual history Have explored this very small corner of what’s possible in the computational universe and everything that we care about Is contained in the small corner and that means that you know you could say well G You know I want to You know what what we what we end up Wanting to talk about other things that we as a society have decided we care about and what there’s an interesting feedback loop I’ll just mention this should end but but um so you might say So here’s here’s a funny thing. So let’s take language for example language evolves We you say we we make up language to describe what we see in the world. Okay fine. That’s a fine idea I imagine the you know in paleolithic times people make up language They probably didn’t have a word for table because they didn’t have any tables Um, they probably had a word for rock But then we end up as a result of the particular You know development that our Civilization has gone through we build tables and There was sort of a as a synergy between coming up with a word for table and deciding tables were a thing And we should build a bunch of them and so there’s this sort of complicated interplay between the things that we learn how to describe and how to think about The things that we build and put in our environment and then the things that we end up Wanting to talk about because they’re things that we have experience of in our environment And so that’s the you know, I think as we look at sort of the progress of civilization There’s you know there’s various layers of first we you know We invent a thing that we can then think about and talk about Then we build an environment based on that Then that allows us to do more stuff and we build on top of that and that’s why for example when we talk about computational thinking and teaching it to kids and so on That’s one reason that’s kind of important because we’re building a layer of Things that people are then familiar with that’s different from what we’ve had so far and they give people a way to talk about things I give you one example that um see did I have that still up the um Uh, yeah, okay one one example here um Are From this blog post of mine actually so There Where is it? Okay, so that That thing there is a nested pattern, you know, it’s a it’s a sapinski um That um That tile pattern was created in 1210 AD Okay, and it’s the first example. I know of a fractal pattern Okay, well The um artist historians wrote about these patterns there a bunch of this particular style of pattern they wrote about these for years They never discussed that nested pattern these patterns also have you know pictures of lions and you know Elephants and things like that in them they wrote about those kinds of things But they never mentioned the nested pattern Until basically about 25 years ago when fractals and song became a thing And then it’s are I can now talk about that. It’s a nested pattern. It’s a fractal And then you know before that time the artist historians were blind to that particular part of this pattern Which is like I don’t know what that is that there’s no you know, I don’t have a word to describe it. I’m not going to um I’m not gonna talk about it. So that’s a you know, it’s part of this feedback loop of of things that we We learn to describe them we build in terms of those things then we build another layer I think one of the things I mean you talk about you know just in the to sort of The thing I one thing I’m really interested in is the evolution of purposes So, you know if you look back in human history There’s a you know what was thought to be worth doing a thousand years ago is different from what’s thought to be worth doing today and part of that is is um Uh, you know good examples of things like you know walking on a treadmill or Buying goods in virtual worlds both of these are hard to explain to somebody from a thousand years ago Um because each one ends up being a whole sort of societal story about we’re doing this because we do that As we do that and so the question is how will these purposes evolve in the future? And I think one of the things that I view as a sort of sobering thought is that that um Uh actually one thing I found While the disappointing and then I became less pessimistic about it is you know if you think about the future of the human condition And you know we’ve been successful in making our AI systems and we can read out brains and we can upload consciousnesses and things like that And we’ve eventually got this box with trillions of souls in it and the question is what are these souls doing? And to us today it looks like they’re playing video games for the rest of eternity Right and that seems like a kind of a bad outcome It’s like we’ve gone through all of this long history and what do we end up with? We end up with a trillion souls in a box playing video games Okay Um and I thought this is a very you know depressing outcome so to speak And then I realized that that actually you know if you look at the sort of arc of human history people Are there any given time in history people have been Uh, you know they’ve My main conclusion is that any time in history the things people do Seem meaningful and purposeful to them at that time in history and History moves on and you know like a thousand years ago. There were a lot of purposes That people had that you know what to do with weird superstitions and things like that that we say why the heck were you doing that? That just doesn’t make any sense Right but to them at that time it made all the sense in the world And I think that you know the thing that makes me sort of less depressed about the future So to speak is that at any given time in history You know You can still have meaningful purposes Even though they may not look meaningful from a different point in history and that the sort of a whole theory You can kind of build up based on kind of the trajectories that you’ve followed through the space of purposes and sort of interesting If you can’t jump like you say let’s get cryonically frozen for you know 300 years and then you know Be back again the the interesting cases then you know all the purposes that you sort of You know that you find yourself in ones that have any continuity with what we know today. I should stop with that That’s the beautiful way to end it please give us a big hand.