Risto Miikkulainen: Neuroevolution and Evolutionary Computation | Lex Fridman Podcast #177
the following is a conversation with risto michelinen a computer scientist at the university of texas at austin and associate vice president of evolutionary artificial intelligence at cognizant he specializes in evolutionary computation but also many other topics in artificial intelligence cognitive science and neuroscience quick mention of our sponsors jordan harbinger show grammarly belcampo and indeed check them out in the description to support this podcast as a side note let me say that nature inspired algorithms from ant colony optimization to generic algorithms to cellular automata to neural networks have always captivated my imagination not only for their surprising power in the face of long odds but because they always opened up doors to new ways of thinking about computation it does seem that in the long arc of computing history running toward biology not running away from it is what leads to long-term progress this is the lex friedman podcast and here is my conversation with risto mcelinen if we ran the earth experiment this fun little experiment we're on over and over and over and over a million times and watch the evolution of life as it uh pans out how much variation in the outcomes of that evolution do you think we would see now we should say that you are a computer scientist that's actually not such a bad question for computer scientists because we are building simulations of these things and we are simulating evolution and that's a difficult question to answer in biology but we can build a computational model and run it million times and actually answer that question how much variation do we see when we when we simulate it uh and um you know that's a little bit beyond what we can do today but but i think that we will see some regularities and it took evolution also a really long time to get started and then things accelerated really fast uh towards the end but there are things that need to be discovered and they probably will be over and over again like manipulation uh of objects uh opposable thumbs and um and also some way to communicate uh maybe orally like whether you have speech it might be some other kind of sound and and decision making but also vision uh i has evolved many times various vision systems have evolved so we would see those kinds of solutions i believe emerge over and over again they may look a little different but they they get the job done the really interesting question is would we have primates would we have humans or something that resembles humans uh and and would that be an apex of evolution after a while uh we don't know where we're going from here but we certainly see a lot of tool use and and building our constructing our environment so i think that we will get that we get some evolution producing some agents that can do that manipulate the environment and build what do you think is special about humans like if you were running the simulation and you observe humans emerge like these like tool makers they start a fire and all stuff start running around building buildings and then running for president all those kinds of things uh what would be how would you detect that because you're like really busy as the creator of this evolutionary system so you don't have much time to observe like detect if any cool stuff came up right how would you detect humans well you are running the simulation so you also put in visualization and measurement techniques there so if you are looking for certain things like communication you'll have detectors to find out whether that's happening even if it's a lot simulation and i think that that's that's what what we would do we know roughly what we want intelligent agents that communicate cooperate manipulate and we would build detections and visualizations of those processes yeah it and there's a lot of we have to run it many times and we have plenty of time to figure out how we detect the interesting things but also i think we do have to run it many times because we don't quite know what shape those will take and our detectors may not be perfect for them to begin with well it seems really difficult to build the detector of intelligent or intelligent conv communication sort of uh if we take an alien perspective observing earth are you sure that they would be able to detect humans as the special thing wouldn't they be already curious about other things there's way more insects by body mass i think than humans by far and colonies obviously dolphins is the most intelligent uh creature on earth we all know this so it could be the dolphins that they detect it could be the rockets that we seem to be launching that could be the intelligent creature they detect uh it could be some other uh trees trees have been here a long time i just learned that sharks have been here 400 million years and that's longer than trees have been here so maybe it's the sharks they go by age like there's a persistent thing like if you survive long enough especially through the mass extinctions that could be the the thing your detector is uh detecting humans have been here for a short time and we're just creating a lot of pollution but so is the other creatures i don't know you do you think you'd be able to detect humans like how would you go about detecting in the computational sense maybe we can leave humans behind in the computational sense detect interesting things do you basically have to have a strict objective function by which you measure the performance of a system or can you find curiosities and interesting things yeah well i think that the first measurement would be to detect how much of an effect you can have in your environment so if you look at look around we have cities and that is constructed environments and that's where a lot of people live most people live so that would be a good sign of intelligence that you don't just live in an environment but you construct it to your liking yeah and that's something pretty unique i mean certainly birds build nests but they don't build quite cities termites build mounds and ice and things like that but the complexity of the human construction cities i think would stand out even to an external observer of course that's what a human would say yeah and you know you can certainly say that sharks are really smart because they've been around so long and they haven't destroyed the environment which humans are about to do which is not a very smart thing uh but we'll get over it i believe uh and and we can get over it by doing some construction that actually is benign uh and maybe even enhances uh the um resilience of nature so you mentioned that this simulation that we run over and over might start so it's a slow start so do you think uh how unlikely first of all i don't know if you think about this kind of stuff but how unlikely is step number zero which is the springing up like the origin of life on earth and second how unlikely is the anything interesting happening beyond that sort of like the start that that creates all the rich complexity that we see on earth today yeah there are people who are working on exactly that problem uh from primordial soup how do you actually get self-replicating yeah molecules and they are very close uh with a little bit of help you can make that happen so we of course we know what we want so they can set up the conditions and try out conditions that are conducive to that for evolution to discover that that took a long time for us to recreate it probably won't take that long and the next steps from there um i think also with some hand-holding i think we can make that happen um but if with evolution what was really fascinating was eventually the runaway evolution of the brain that created humans and created well also other higher animals that that was something that happened really fast and that's a big question is that something replicable is that something that can happen and if it happens does it go in the same direction that is a big question to ask even in computational terms i think that it's relatively possible to come up here create an experiment where we look at the primordial soup and the first couple of steps of multicellular organisms even but to get something as complex as the brain we don't quite know the conditions for that and how to even get started and whether we can get this kind of runaway evolution happening from a detector perspective if we're observing this evolution what do you think is the brain what do you think is the let's say what is intelligence so in terms of the thing that makes humans special we seem to be able to reason we seem to be able to communicate but the core of that is this something in the broad category we might call intelligence so it's uh if you put your computer scientists add on uh is their favorite ways you like to think about that question of what is intelligence well my goal is to create agents that are that are intelligent not to define what and and that that is a way of defining it and that means that it's some kind of an um object or or a program um that has limited sensory and uh effective capabilities interacting with the world and then also a mechanism for making decisions so with limited abilities like that can it survive um survival is the simplest goal but it could you could also give it other goals can it multiply can it solve problems that you give it uh and that is quite a bit less than human intelligence there are animals would be intelligent of course with that definition and you might have even even some other forms of of life even so what so intelligence in that sense is a survival um skill uh given resources that you have and using using your resources so that you will stay around do you think death mortality is fundamental to an agent so like there's a i don't know if you're familiar there's a philosopher named ernest becker who wrote the denial of death and his whole idea and there's folks psychologists cognitive scientists that work on terror management theory and they think that one of the special things about humans is that we're able to sort of foresee our death right we can we can realize not just as animals do sort of constantly fear in an instinctual sense respond to all the dangers that are out there but like understand that this ride ends eventually yeah and that in itself is the most is a is the force behind all of the creative efforts of human nature yeah that's that's the philosophy i think that makes sense a lot of sense i mean animals probably don't think of death the same way but humans know that your time is limited and you want to make it count and you can make account in many different ways but i think that has a lot to do with creativity and the need for humans to do something beyond just surviving and now going from that simple definition to something that's the next level i think that that could be a second decision a second level of definition that um intelligence means something and you do something that stays behind you that's more than uh your existence um something you create something that um is useful for others is useful in the future not just for yourself and i think that that's a nice definition of intelligence in a next level uh and it's also nice because it doesn't require that they are humans or biological they could be artificial agents that intelligence they could they could achieve those kind of goals so particular agent the uh the ripple effects of of their existence on the entirety of the system is significant so like they leave a trace where there's like uh yeah like ripple effects it's the but see then you go back to the the butterfly with the flap of a wing and then you can uh trace a lot of uh like nuclear wars and all the conflicts of human history somehow connected to that one butterfly that created all the the chaos so maybe that's not maybe that's a very poetic way to think uh that's something we humans in a human-centric way want to hope we have this impact like that is the the the secondary effect of our intelligence we've had the long-lasting impact on the world but maybe the entirety of physics in the universe has a very long lasting effect sure but you can also think of it what if um like the wonderful life what if you're not here will somebody else do this is it is it something that you actually contributed because you had something unique to compute that contribute that's a pretty high bar though uniqueness yeah yeah so you know you have to be mozart or something to actually reach that level that nobody would have developed that but other people might have solved this equation um if you didn't do it but but also within limited scope i mean during your lifetime or next year you could contribute something that unique that other people did not see and um and then that could change the way things move forward for a while uh so i don't think we have to be mozart to be called intelligence but we have this local effect that is changing if you weren't there that would not have happened and it's a positive effect of course you want it to be a positive effect do you think it's possible to engineer in to uh computational agents a fear of mortality like uh does that make any any sense so there's a very trivial thing whereas like you could just code in a parameter which is how long the life ends but more of a fear of mortality like awareness of the the way that things end and somehow encoding a complex representation of that fear which is like maybe as it gets closer you become more terrified i mean there seems to be something really profound about this fear that's not currently encodable in a trivial way into our programs well i think you're you're referring to the emotion of fear something because we are cognitively we know that we have limited lifespan and most of us cope with it by just hey that's what the world is like and i make the most of it but sometimes you can have a like a a fear that's not healthy that paralyzes you you can't do anything uh and and uh somewhere in between they're not caring at all and and getting paralyzed because of fear is a normal response which is a little bit more than just logic and and it's emotion so now the question is what good are emotions i mean they are quite uh complex and they are multiple dimensions of emotions and they probably do serve as survival function heightened focus for instance and fear of death might be a really good emotion when you are in danger that you recognize it even even if it's not logically necessarily easy to derive and you don't have time for that logical detection a deduction you may be able to recognize the situation is dangerous and this fear kicks in and you all of a sudden perceive the facts that are important for that and i think that's generally is the role of emotions is it allows you to focus what's relevant uh for your situation and maybe if fear of death plays the same kind of role uh but if it consumes you and it's something that you think in normal life when you don't have to then it's not healthy and then it's not productive yeah but it's fascinating to think how to uh incorporate emotion into a computational agent it almost seems like a silly statement to make but it perhaps seems silly because we have such a poor understanding of the mechanism of emotion of fear of uh i think at the core of it is another word that we know nothing about but say a lot which is consciousness do you ever in your work or like maybe on a coffee break think about what the heck is this thing consciousness and is it at all useful in our thinking about ai systems yes it is an important question you can build representations and functions i think into these agents that act like emotions and consciousness perhaps so i mentioned emotions being something that allow you to focus and pay attention filter out what's important yeah you can have that kind of a filter mechanism and you can it puts you in a different state your computation is in a different state certain things don't really get through and others are heightened now you label that box emotion i don't know if that means it's an emotion but it acts very much like we understand what emotions are and we actually did some work like that um modeling hyenas who were trying to steal a kill from lions which happens in africa i mean hyenas are quite intelligent but not really intelligent and they they have this behavior that's more complex than anything else they do they can band together if there's about 30 of them or so uh they can uh coordinate their effort so that they push the lions away from a kill even though the lions are so strong that they could kill a lion kill a hyena by by striking with a paw but when they work together and precisely time this attack the lions will leave and they get the kill and probably there are some states like emotions that the hyenas go through the first they they call for reinforcements they really want that kill but there's not enough of them so they vocalize and there's more peop more people more hyenas that come around and then they have two emotions they're very afraid of the lion so they want to stay away but they also have a strong affiliation between each other and then this is the balance of the two emotions and and also yes they also want the kill so it's both rebelled and attractive and then but then this affiliation eventually is so strong that when they move they move together they act as a unit and they they can perform that function so there's an interesting behavior that seems to depend on these emotions strongly and makes it possible um important reactions and i think a cr a critical aspect of that the way you're describing is emotion there is a mechanism of social communication of a social interaction maybe that maybe humans won't even be that intelligent or most things we think of as intelligent wouldn't be that intelligent without the social component of interaction maybe most much of our intelligence is essentially in our growth of social interaction and maybe for the creation of intelligent agents we have to be creating yes fundamentally social systems yes i i strongly believe that's true and uh yes the uh communication is multifaceted i mean they they vocalize and call for friends but they also rub against each other and they push and they do all kinds of gestures and so on so they known act alone and i don't think people act alone uh very much either at least normal most of the time and social systems are so strong for humans that i think we build everything on top of these kind of structures and one interesting theory around that bigger this theory for instance for language but language origins is that where did language come from and and it's a plausible theory that first came social systems that you have different roles in a society and then those roles are exchangeable that you know i scratch your back you scratch my back you can exchange roles and once you have the brain structures that allow you to understand actions in terms of roles that can be changed that's the basis for language for grammar and now you can start using symbols to refer to uh objects in the world and you have this flexible structure so there's a social structure that's fundament fundamental for language to develop now again then you have language you can you can refer to things that are not here right now and that allows you to then build all the all the good stuff about uh planning for instance and building things and so on so yeah i think that very strongly uh humans are social and that gives us ability to structure the world but also as a society we can do so much more because we don't one person does not have to do everything you can have different roles and together achieve a lot more and that's also something we see in computational simulations today i mean we have multi-agent systems that can perform tasks this fascinating uh demonstration marco dorico i think it was um these robots little robots that had to navigate through an environment and there were there were things that are dangerous like maybe a a big chasm or some kind of groove a hole and they could not get across it but if they grab each other with their gripper they formed a robot that was much longer on the team and this way they could get across that yeah so this is a great example of how together we can achieve things we couldn't otherwise like the hyenas you know alone they couldn't but as a team they could uh and i think humans do that all the time we're really good at that yeah and the way you describe the the system of hyenas it almost sounds algorithmic like the the problem with humans is they're so complex it's hard to think of them as algorithms but with hyenas there's a it's simple enough to where it feels like um at least hopeful that it's possible to create computational systems that mimic that yeah that's exactly why why we looked at that as opposed to humans um like i said they are intelligent but they are not quite as intelligent intelligent as say baboons which would learn a lot and would be much more flexible that hyenas are relatively rigid in what they can do and therefore you could look at this behavior like this is a breakthrough in evolution about to happen yes that they've discovered something about social structures communication about cooperation and and it might then spill over to other things too yeah in thousands of years in the future yeah i think the problem with baboons and humans is probably too much is going on inside the head we won't be able to measure it if we're observing the system with hyenas is probably easier to observe the actual decision making and the various motivations that are involved yeah they are visible and we can even um quantify possibly their emotional state because they leave droppings behind and there are chemicals there that can be associated with uh with neurotransmitters and we can separate what emotions they might have experienced in the last 24 hours yeah what to use the most beautiful speaking of hyenas uh what do you use the most beautiful uh nature inspired algorithm in your work that you've come across something maybe early on in your work or maybe today i i think that evolution computation is the most amazing method so what fascinates me most is that with computers is that you can you can get more out than you put in i mean you can write a piece of code and your machine does what you told it i mean this happened to me in my freshman year i it did something very simple and i was just amazed i was blown away that it would it would get the number and it would compute the result and i didn't have to do it myself very simple but if you push that a little further you can have machines that learn and they might learn patterns and already say deep learning neural networks they can learn to recognize objects sounds um patterns that humans have trouble with and sometimes they do it better than humans and that's so fascinating and now if you take that one more step you get something like evolution algorithms that discover things they create things they come up with solutions that you did not think of and that just blows me away it's so great that we can build systems algorithms that can be in some sense smarter than we are that they can discover solutions that we might miss a lot of times it is because we have as humans we have certain biases we expect the solutions to be a certain way and you don't put those biases into the algorithm so they are more free to explore and evolution is just absolutely fantastic explorer and that's what what really is fascinating yeah i think uh i get made fun of a bit because i currently don't have any kids but you mentioned programs i mean um do you have kids yeah so maybe you could speak to this but there's a magic to the creation creative process like i uh with spot the boston dynamic spot but really any robot that i've ever worked on it just feels like the similar kind of joy i imagine i would have as a father not the same perhaps level but like the same kind of wonderment like exactly this which is like you know what you had to do initially uh to get this thing going let's speak on the computer science side like what the program looks like but something about it uh doing more than what the program was written on paper is like that somehow connects to the magic of this entire universe like that's that's like i i feel like i found god every time i like it's like uh because you're you've really created something that's living yeah even if it's it has a life of its own has the intelligence of its own it's beyond what you actually thought yeah and that is i think it's exactly spot on that's exactly what it's about uh you created something and has a ability to uh live its life and and do good things and um you just gave it a starting point so in that sense i think it's that may be part of the joy actually uh you see but you mentioned creativity in this context uh especially in the context of evolutionary computation so you know we don't often think of algorithms as creative so how do you think about creativity yeah algorithms absolutely can be creative um they can come up with solutions that you don't think about i mean creativity can be defined a couple of requirements has to be new it has to be useful and it has to be surprising and those certainly are true with say evolution computation discovering solutions so maybe an example for instance we did this collaboration with mit media lab kelp harvest lab where they had a hydroponic food computer they called it environment that was completely computer controlled nutrients water light temperature everything is controlled now um what do you do if you can control everything farmers know a lot about how to do how to make plants grow in their own batch of land but if you can control everything it's too much and it turns out that we don't actually know very much about it so we built a system evolution optimization system together with a surrogate model of how plants grow and let this system explore recipes on its own and initially now we were focusing on light uh how strong what wavelengths how long the light was on um and we put some boundaries which we thought were reasonable for instance that there was um at least six hours of darkness like night because that's what we have in the world and very quickly um the system evolution pushed all the recipes to that limit uh we were trying to grow basil um and we had initially have some 200 300 recipes exploration as well as known recipes but but now we are going beyond that and everything was like pushed at that limit so we look at it and say well you know we can easily just change it let's have it your way and it turns out uh the system discovered that bazel does not need to sleep uh 24 hours lights on and it will thrive it will be bigger it will be tastier and this was a big surprise not just to us but also the biologists in the team that anticipated that this is some constraints that that are in the world for a reason it turns out that evolution did not have the same bias and therefore it discovered something that was creative it was surprising it was useful and it was new that's fascinating to think about like the things we think that are fundamental to living systems on earth today whether they're actually fundamental or they somehow shape uh fit the constraints of the system and all we'll have to do is just remove the constraints do you ever think about um i don't know how much you know about bringing computer interfaces in your link the the idea there is you know our brains are very limited and if we just allow we plug in we provide a mechanism for a computer to speak with the brain so you're thereby expanding the computational power of the brain the possibilities there sort of from a very high level philosophical perspective is limitless but i wonder how limitless it is are the constraints we have like features that are fundamental to our intelligence or is this just like this weird constraint in terms of our brain size and skull and uh lifespan and the senses it's just the weird little like quirk of evolution and if we just open that up like add much more senses add much more computational power the uh intelligence will be will expand exponentially do you have a do you have a sense about constraints the relationship of evolution computation to the constraints of the environment um well at first i'd like to comment on on that like changing the inputs uh to human brain uh yes and flexibility of of the brain i think there's a lot of that uh there are experiments that are done in animals like megan kasir um the mit is switching the um auditory and visual information and going going to the wrong part of the cortex and the animal was still able to hear and perceive the visual environment and there are kids that are born with severe disorders and sometimes they have to remove half of the brain like one half and they still grow up they have the functions migrate to the other parts there's a lot of flexibility like that so i think it's quite possible to hook up the brain with different kinds of sensors for instance and something that we don't even quite understand or have today on different kind of wavelengths or or whatever they are um and then the brain can learn to make sense of it and that i think is um this good hope that these prosthetic devices for instance work not because we make them so good and so easy to use but the brain adapts to them and can learn to take advantage of them um and so in that sense if there's a trouble a problem i think that brain can be used to correct it now going beyond what we have today can you get smarter that's really much harder to do uh giving the brain more more input probably might overwhelm it it would have to learn to filter it and focus um and in order to use the information effectively and augmenting intelligence with some kind of external devices like that might be difficult uh i think but replacing what's lost i think is quite possible right so our intuition allows us to sort of imagine that we can replace what's been lost but expansion beyond what we have i mean we are already one of the most if not the most intelligent things on this earth right so it's hard to imagine um if the brain can hold up with an order of magnitude greater set of information thrown at it if it can do if you can reason through that part of me this is the russian thing i think is uh i tend to think that the limitations is where the the superpower is that you know immortality and uh huge increase in bandwidth of uh information by connecting computers with the brain is not going to produce greater intelligence it might produce lesser intelligence so i don't know there's something about the scarcity being essential to uh um fitness or performance but that could be just because we're so uh limited no exactly you make do with what you have but you can uh you don't have to pipe it directly to the brain i mean we already have devices like phones where we can look up information at any point yeah and that can make us more productive you don't have to argue about i don't know what happened in that baseball game or whatever it is because you can look it up right away and i think in that sense we can learn to utilize tools and that's what we we have been doing for a long long time um so and we are already the brain is already drinking from the water fire hose like vision there's way more information in the vision that we actually process so brain is already good at identifying what matters yeah and that we can switch that from vision to some other wavelength or some other kind of modality but i think that the same processing principles probably still apply uh but but also indeed this uh ability to uh have information more accessible and more relevant i think can enhance what we do i mean kids today at school they learn about dna i mean things that we discovered just a couple of years ago and it's already common knowledge and we are building on it and we don't see a problem where um where there's too much information that we can absorb and learn maybe people become a little bit more narrow in what they know they are in one field but this information that we have accumulated it is passed on and people are picking up on it and they are building on it so it's not like we have reached the point of saturation um we have still this process that allows us to be selective and decide what's interesting um i think still works even even with the more information we have today yeah it's fascinating to think about like wikipedia becoming a sensor like uh so the fire hose of information from wikipedia so it's like you integrate it directly into the brain to where you're thinking like you're observing the world with all of wikipedia directly piping into your brain so like when i see a light i immediately have like the history of who invented electricity like integrated very quickly into so just the way you think about the world might be very interesting if you can integrate that kind of information what are your thoughts if i could ask uh on uh early steps on that on the neurolink side i don't know if you got a chance to see but uh there's a monkey playing pong yeah through the brain computer interface and uh the dream there is sort of you're already replacing the thumbs essentially that you would use to play video game the dream is to be able to increase further the the interface by which you interact with the computer are you impressed by this are you worried about this what are your thoughts as a human i think it's wonderful i think it's great that we could we could do something like that i mean you can there are devices that read your eeg for instance and and you and humans can learn um to control things using using just their thoughts in that sense and i i don't think it's that different i mean those signals would go to limbs they would go to thumbs uh now the same signals go through a sensor to some computing system it still probably has to be built on human terms uh not to overwhelm them but but utilize what's there and sense the right kind of um patterns that are easy to generate but oh that i think is really quite possible and and wonderful and could be very much more efficient is there so you mentioned surprising being a characteristic of uh creativity is there something you already mentioned a few examples but is there something that jumps out at you as was particularly surprising from the various evolutionary computation systems you've worked on the solutions that were come up along the way not necessarily the final solutions but maybe things have even discarded is there something that just jumps to mind it it happens all the time i mean evolution is so creative uh so good at discovering uh solutions you don't anticipate a lot of times they are taking advantage of something that you didn't think was there like a bug in the software for instance a lot of there's a great paper uh the community put it together about uh surprising anecdotes about evolution computation a lot of them are indeed in some software environment there was an a loophole or a bug and the system uh utilizes that by the way for people who want to read it's kind of fun to read it's called the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities and there's just a bunch of stories from all the seminal figures in this community uh you have a story in there uh that released to you at least on the tic-tac-toe memory bomb so can you can you uh i guess uh describe that situation if you think that's yeah that was that's a quite a bit smaller scale than our um basil doesn't need to sleep surprised but but it was actually done by students in my class um in a neural net evolution computation class uh there was an assignment uh it was perhaps a final project where people built game playing uh ai it was an ai class uh and this one and and it was for tic-tac-toe or five in a row in a large board uh and uh this one team evolved a neural network to make these moves uh and um they set it up the evolution they didn't really know what would come out but it turned out that they did really well evolution actually won the tournament and most of the time when it won it went because the other teams crashed and then when we look at it like what was going on was that evolution discovered that if it makes a move that's really really far away like millions of squares away the other teams the other programs just expanded memory in order to take that into account until they ran out of memory and crashed and then you win a tournament by crushing all your opponents i think that's quite a profound example which it probably applies to most games from even a game theoretic perspective that sometimes to win you don't have to be better within the rules of the game you have to come up with ways to break your opponent's uh brain as a human like not through violence but through some hack where the brain just is not um you're basically uh how would you put it you're the you're going outside the constraints of where the brain is able to to function expectations of your opponent i mean yeah this was even kasparov pointed that out that when the blue was playing against kasparov that it was not playing the same way as kasparov expected uh and this has to do with you know being not having the same biases uh and that's that's really one of the strengths of of the ai approach yeah can you at a high level say what are the basic mechanisms of evolutionary computation algorithms that use something that could be called an evolutionary approach like how does it work uh what are the connections to the it's what are the echoes of the connection to is biological a lot of these algorithms really do take motivation from biology but they are carry catches you try to essentialize it and take the elements that you believe matter so in evolution computation it is the creation of variation and then the selection upon that so the creation of variation you have to have some mechanism that allow you to create new individuals that are very different from what you already have that's the creativity part and then you have to have some way of measuring how well they are doing uh and using the uh that measure to select uh who goes to the next generation and you continue so first you also you have to have some kind of digital representation of an individual that can be then modified so i guess humans i mean biological systems have dna and all those kinds of things and so you have to have similar kind of encodings in a computer program yes and that is a big question how do you encode these individuals so there's a genotype which is that encoding and then a decoding mechanism just gives you the phenotype which is the actual individual that then performs the task and in an environment can be evaluated how good it is so even that mapping is a big question and how do you do it but typically the representations are either they are strings of numbers or they are some kind of trees those are something we know very well in computer science and we try to do that but they and you know dna in some sense is also a sequence um and it's a string um so it's not that far from it but dna also has many other aspects that we don't take into account necessarily like this folding and and interactions that are other than just the sequence itself and lots of that is not yet captured and we don't know whether they are really crucial um evolution biological evolution has produced wonderful things but if you look at them it's not necessarily the case that every piece is irreplaceable and essential there's a lot of baggage because you have to construct it and it has to go through various stages and we still have appendiciti appendix and we have tailbones and things like that that are not really that useful if you try to explain them now it would make no sense very hard but if you think of us as productive evolution you can see where they came from they were useful at one point perhaps and and no longer are but they're still there so um that process is complex uh and your representation should support it uh and that is quite difficult if if we are limited with strings or trees and then we are pretty much limited what can be constructed and one thing that we are still missing in evolution computation in particular is what we saw in biology major transitions so that you go from for instance single cell to multi-cell organisms and eventually societies there are transitions of level of selection and level of what a unit is and that's something we haven't captured in evolution computation yet does that require a dramatic expansion of the representation is that what that that is most likely it does but it's quite we don't even understand it in biology very well where it's coming from so it would be really good to look at major transitions in biology try to characterize them a little bit more in detail what the processes are how how does a so like a unit a cell is no longer evaluated alone it's evaluated as part of a community organism right even though it could reproduce now it can't alone and it has has to have its environment so there's a there's a push to another level at least the selection and how do you make that jump to this yes how do you make the jump that's part of the algorithm yeah yeah so we haven't really seen that in computation um yet and there are certainly attempts to have open-ended evolution things that could add more complexity and start selecting at a higher level but it is still not um quite the same as going from single to multi to society for instance in in biology so so there essentially would be as opposed to having one agent those agent all of a sudden spontaneously decide to then be together and then your entire system would then be treating them as one agent something like that some kind of weird merger building but also so you mentioned i think you mentioned selection so basically there's an agent and they don't get to live on if they don't do well so there's some kind of measure of what doing well is and isn't and uh does the mutation come into play at all in the process and what the world does it serve yeah so in again back to what the computational mechanisms of evolution computation are so um the way to create variation uh you can take multiple individuals too usually but but you could do more and you exchanged the part of the representation you do some kind of recombination it could be crossover for instance um in biology you do have dna strings that that are cut and put together again we could do something like that um and it seems to be that in biology crossover is really the workhorse in in biological evolution in computation we tend to rely more on mutation and that is making random changes into parts of the chromosome you try to be intelligent and target certain areas of it and make the mutations also follow some principle like you collect statistics of performance and correlations and try to make mutations you believe are going to be helpful that's where evolution computation has moved in the last 20 years i mean evolution competition has been around for 50 years but a lot of the recent um success comes from mutation comes from comes from using statistics it's like the rest of machine learning based on statistics we use similar tools to guide evolutionary computation and in that sense it has diverged a bit from biological evolution and that's one of the things i think we could look at again having a weaker selection more crossover large populations more time and maybe a different kind of creativity would come out of it we are very impatient in evolution competition today we want answers right now right quickly and every if no somebody doesn't perform kill it yeah uh and biological evolution doesn't work quite that way uh and and it's more patient yes much more patient so i guess we need to add some kind of mating some kind of like dating mechanisms like marriage may be in there so to uh in into our algorithms to improve the the the the combination mechanism as opposed to all mutation doing all of the work yeah and many ways of being successful you know usually in every competition we have one goal you know play this game really well uh and compared to others but in biology there are many ways of being successful you can build niches you can be stronger faster larger or smarter or you know eat this or eat that or you know so so there are many ways to solve the same problem of survival and that then breeds creativity um and um it allows more exploration and eventually you get solutions that are perhaps more creative rather than trying to go from initial population directly or more or less directly to your maximum fitness which you measure that's just one metric so in a broad sense before we talk about newer evolution do you see evolutionary computation as more effective than deep learning in certain contexts machine learning broadly speaking maybe even supervised machine learning i don't know if you want to draw any kind of lines and distinctions and borders where they rub up against each other kind of thing or one is more effective than the other in the current state of things yes of course they are very different and they address different kinds of problems and the deep learning has been really successful in domains where we have a lot of data and that means not just data about situations but also what the right answers were so labeled examples or they might be predictions may be weather prediction where the data itself becomes labels what happened what the weather was today and what will be tomorrow so they are very effective deep learning methods on that kind of tasks but there are other kinds of tasks where we don't really know what the right answer is uh game playing for instance but many robotics tasks and actions in the world decision making um and actual practical applications like treatments and healthcare or investment in stock market many tasks are like that we will we don't know and we'll never know what the optimal answers were and there you need different kinds of approaches reinforcement learning is one of those uh reinforcement learning comes from biology as well agents learn during their lifetime they eat berries and sometimes they get sick and then they don't and get stronger and then that's how you learn and evolution is also a mechanism like that at a different time scale because you have a population not an individual during its lifetime but an entire population as a whole can discover um what works and there you can afford individuals that don't work out they will you know everybody dies and you have a next generation and it will be better than the previous one so that's that's the big difference between these methods they apply to different kinds of problems um and um in particular there's often a comparison that's kind of interesting and important between reinforcement learning and evolution and computation and initially um reinforcement learning was about individual learning during the lifetime and evolution is more engineering you don't care about the lifetime you don't care about all the individuals that are tested you only care about the final result the last one the best candidate that evolution produced in that sense they also apply to different kinds of problems and now that boundary is starting to blur a bit you can use evolution as an online method and reinforcement learning to create engineering solutions but that's still roughly the distinction and from the point of view what algorithm you want to use if you have something where there is a cost for every trial reinforcement learning might be your choice now if you have a domain where you can use a surrogate perhaps so you don't have much of a cost for trial and you want to have surprises you want to explore more broadly then this population-based method is perhaps a better choice because you you can try things out that you wouldn't afford when you're doing reinforcement there's very few things as entertaining as watching either evolution competition or reinforcement learning teaching a simulated robot to walk i maybe there's a higher level question that could be asked here but do you find this whole space in of applications in the robotics interesting for evolution computation yeah yeah very much um and indeed that's the fascinating videos of that and that's actually one of the examples where you can contrast the difference so between reinforcement learning evolution yes so if you have a reinforcement learning agent it tries to be conservative because it wants to walk as long as possible and be stable but if you have evolutionary computation it can afford these agents that go haywire they fall flat on their face and they could take a step and then they jump and then again fall flat yeah and eventually what comes out of that is something like a falling that's controlled yeah and you take another step another step and you no longer fall instead you run you go fast so that's a way of discovering something that's hard to discover step by step incrementally because you can afford these evolutionists dead ends although they are not entirely dead ends in the sense that they can serve as stepping stones when you take two of those put them together you get something that works even better and that is a great example of of this kind of discovery yeah learning to walk is a is fascinating i talked quite a bit to russ tedron because mit there's a there's a community of folks who who just roboticists who love the elegance and beauty of uh movement right and uh walking bipedal robotics is um beautiful but also exceptionally dangerous in the sense that like you're constantly falling essentially if you want to do elegant movement and uh the discovery of that is uh i mean it it's such a good example of um that the discovery of a good solution sometimes requires a leap of faith and patience and all those kinds of things i wonder what other spaces where you have to discover those kinds of things in yeah yeah yeah and another interesting direction is um learning um for for uh virtual creatures learning to walk uh we did a study in in simulation obviously that um you create those creatures not just their controller but also their body so you have cylinders you have muscles you have joints and sensors and you're creating creatures that look quite different some of them have multiple legs some of them have no legs at all and then the goal was to get them to move the walk to run uh and what was interesting is is that when you evolve the controller together with the body you get movements that look natural because they're optimized for that physical setup and and these creatures you start believing them that they're alive because they walk in a way that you would expect somebody with that kind of a setup to walk yeah there's a there's something subjective also about that right i've been thinking a lot about that especially in the human robot interaction context you know i mentioned spot the boston dynamics robot there is something about human robot communication let's say let's put in another context something about human and uh dog context like like a living dog where there's uh there's a there's a dance of communication first of all the eyes you both look at the same thing and you dogs communicate with their eyes as well like if if the if you and a dog want to uh like deal with a particular object you will look at the person the dog will look at you and then look at the object and look back at you all those kinds of things but there's also just a elegance of movement i mean there's the of course the tail and all those kinds of mechanisms of communication it all seems natural and often joyful and for robots to communicate that is it's really difficult how to figure that out because it's it's almost seems impossible to hard code in you can hard code it for a demo purpose with so you know something like that but it's essentially choreographed like if you watch some of the boston dynamics videos where they're dancing all of that is choreographed by human beings but to learn how to with your movement demonstrate a naturalness and elegance that's fascinating of course in the physical space that's very difficult to do to learn the kind of at scale that you're referring to but the hope is that you could do that in stimulation and then transfer into the physical space if you're able to model the robots efficiently naturally yeah and and sometimes i think that that requires a theory of mind on the yes on the on the side of the robot that that they as they understand what you're doing because they themselves are doing something similar and uh that's a big question too uh we talked about how intelligence in general and and the social aspect of of intelligence and i think that's what is required that we humans understand other humans because we assume that they are similar to us um we have one simulation we did a while ago ken stanley um did that um two robots that were uh competing um simulation like you said they were foraging for food to gain energy and then when they were really strong they would bounce into the other robot and win if they were stronger and we watched evolution discover more and more complex behaviors they first went to the nearest food and then they started to plot a trajectory so they get more get more but then they started to take pay attention what the other robot was doing and in the end there was a behavior where one of the robots the most sophisticated one you know sensed where the food pieces were and identified that the other robot was close to uh two of a very far distance uh and there was one more food near by so it faked that's now i'm using anthropomorphized terms but it made a move towards those other pieces in order for the other robot to actually go and get them because it knew that the other the last remaining piece of food was close and the other robot would have to travel a long way lose its energy and then lose the whole competition so there was like emergence of something like a theory of mind knowing what the other robot would do guided towards bad behavior in order to win so we can get things like that happen uh in in simulation as well but that's a complete natural emergence of a theory of mind but i feel like if you add a little bit of a place for a theory of mind to emerge like easier then you can go really far i mean some of these things with evolution you know you add a little bit of design in there it'll really help and i think i tend to think that a very simple theory of mind will go a really long way for cooperation between agents and certainly for human robot interaction like it doesn't have to be super complicated um i've gotten a chance to in the autonomous vehicle space to watch vehicles interact with pedestrians or pedestrians interacting with vehicles in general i mean you would think that there's a very complicated theory of mind thing going on but i have a sense it's not well understood yet but i have a sense it's pretty dumb like it's pretty simple there's a social contract there where between humans a human driver and a human crossing the road where um the the human crossing the road trusts that the human in the car is not going to murder them and there's something about again back to that mortality thing there's some dance of ethics and morality that's built in that you're mapping your own morality onto the the person in the car and even if they're driving at a speed where you think if they don't stop they're going to kill you you trust that if you step in front of them they're going to hit the brakes and there's that weird dance that we do that i think is a pretty simple model but of course it's very difficult to introspect what it is and autonomous robots in the human robot interaction context have to have to build that current robots are much less than what you're describing they're currently just afraid of everything they're they're more they're not the kind that fall and discover how to run they're more like please don't touch anything don't hurt anything stay as far away from humans as possible treat humans as ballistic objects that you can't uh that you do uh with a large spatial envelope make sure you do not collide with that's how like you mentioned elon musk thinks about autonomous vehicles i tend to think autonomous vehicles need to have a beautiful dance between human and machine where it's not just a collision avoidance problem but a weird dance yeah i think that you these systems need to be able to predict what will happen what the other agent is going to do and then have a structure of what the goals are and whether those predictions actually meet the goals and and you can go probably pretty far with that relatively simple setup already but to call it a theory of mind i don't think you need to i mean it it doesn't matter whether a pedestrian has a mind it's an object and we can predict what we'll do and then we can predict what the states will be in the future and whether they are desirable states stay away from those that are undesirable and go towards those that are desirable so it's a relatively simple functional approach to that where do we really need the theory of mind maybe maybe when you start interacting and you're trying to get the other agent to do something and jointly so that you can jointly collaboratively achieve something then then you then it becomes more complex well i mean even with the pedestrians you have to have a sense of where their attention actual attention in terms of their gaze is but also like a tent i mean there's this vision science people talk about this all time just because i'm looking at it doesn't mean i'm paying attention to it so figuring out what is the person looking at what is the sensory information they've taken in and the theory of mind piece comes in is what are they actually attending to cognitively and also what are they thinking about like what is the computation they're performing and you have you have probably maybe a few options you know for the pedestrian crossing it doesn't have to be it's like a variable with a few discrete states but you have to have a good estimation which of the states that brain is in for the pedestrian case and the same is for attending with a robot if you're collaborating to pick up an object you have to figure out is the human like uh like there's a few discrete states that the human could be and you have to you have to predict that by observing the human and that seems like a machine learning problem to figure out uh what's how the human is uh what's the human up to it's not as simple as sort of planning just because they move their arm means the arm will continue moving in this direction you have to you have to really have a model of what they're thinking about and what's the motivation behind the moment and here we are talking about uh relatively simple physical actions yeah but you can take that the higher levels also like to predict what the people are going to do you need to know what uh what their goals are uh what are they trying to are they exercising are they starting to get somewhere but even even higher level i mean you are predicting what people will do in their career what their life themes are do they want to be famous rich or do good and that takes a lot more information but it allows you to then predict their their actions what choices they might make so how does uh evolution computation apply to the world of neural networks because i've seen quite a bit of work from you and others on the in the world of neural evolution so maybe first can you say what is this field yeah a new evolution is a combination of of uh neural networks and evolution computation in many different forms but the early versions were simply using evolution the way um as a way to construct the neural network instead of say stochastic gradient descent or back propagation because evolution can evolve these parameters weight values in a neural network just like any other string of numbers you can you can do that and that's useful because some cases you don't have those targets that you need to um back propagate from and it might be an agent that's running a maze or a robot playing a game or something you don't again you don't know what the right answer says you don't have backup but this way you can still evolve in your own hand and neural networks are really good at this task because they um they recognize patterns and they and generalize interpolate between known situations so you want to have a neural network in such a task even if you don't have the supervised targets so that's a reason and that's a solution and also more recently now when we have all this deep learning literature it turns out that we can use evolution to optimize many aspects of those designs the deep learning architectures have become so complex that there's little hope for as little humans to understand their complexity and what actually makes a good design uh and now we can use evolution to give that design for you and it might be mean um optimizing hyper parameters like the depth of layers and so on uh or the topology of the network um how many layers how they're connected but also other aspects like what activation functions you use where in the network during the learning process or what loss function you use you could generalize that generate that even data augmentation all the different aspects of the design of deep learning experiments could be optimized that way so that's an inter interaction between two mechanisms but there's also when we get more into cognitive science and the topics that we've been talking about you could have learning mechanisms at two level time scales so you do have an evolution that gives you baby neural networks that then learn during their lifetime and you have this interaction of two time scales and i think that can potentially be really powerful now in biology we are not born with all our faculties we have to learn we have a developmental period in humans it's really long and most animals have something and and probably the reason is that evolution and dna is not detailed enough or plentiful enough to describe them we can't describe how to set the brain up but we can evolution can decide on a starting point and then have a learning algorithm that will construct the final product and this interaction of you know intelligent um well evolution that has produced a good starting point for the specific purpose of learning from it with the interaction of uh with the environment that can be a really powerful mechanism for constructing brains and construction behaviors i like how you walk back from intelligence so optimize starting point maybe uh yeah uh okay there's a lot of fascinating things to ask here and this is basically this dance between neural networks and evolutionary computations could go into the category of automated machine learning so where you're optimizing whether it's hyper parameters of the topology or hyper parameters taken broadly but the topology thing is really interesting i mean that's not really done that effectively or throughout the history of machine learning has not been done usually there's a fixed architecture maybe there's a few components you're playing with but to grow a neural network essentially the way you grow in their organisms really fascinating space how how hard it is it do you think to grow in your network and maybe what kind of neural networks are more amenable to this kind of idea than others i've seen quite a bit of work on recurrent neural networks is there some architectures that are friendlier than others and is is this just a fun small scale set of experiments or do you have hope that we can be able to grow powerful neural networks i i think we can uh and most of the work up to now is taking architectures that already exist that humans have designed and tried to optimize them further and and you can totally do that a few years ago we did an experiment we took a winner of the uh image captioning competition um and um the architecture and just broke it into pieces and took the pieces and and that was our search effects see if you can do better and we indeed could fifteen percent better performance by just searching around the network design that humans had come up with oriovenials and others uh so but that's starting from a point of point that humans have produced but we could do something more general it doesn't have to be that kind of network the the hard part is just a couple of challenges one of them is to define the search space what are your elements uh and how you put them together and the space is just really really big uh so you have to somehow constrain it and have some hunch of what will work uh because otherwise everything is possible and another challenge is that in order to evaluate how good your design is you have to train it i mean you have to actually try it out and that's currently very expensive right i mean deep learning networks may they take days to train well imagine having a population of 100 and have to run it for 100 generations it's not yet quite feasible computationally um it will be but but also there's a large carbon footprint and all that i mean we're using a lot of computation for doing it so intelligent methods and intelligence i mean we have to do some science in order to figure out what the right representations are and right operators are and how do we evaluate them without having to fully train them and that is where the current research is and we're making progress on all those fronts um so so yes there are certain architectures that are more amenable to that uh approach but also i think we can create our own architecture and whole representations that are even better do you think it's possible to do like uh like a tiny baby network that grows into something that can do state of the art and like even the simple data set like mnist and just like it uh just grows into a you know gigantic monster that's the world's greatest handwriting recognition system yeah there are approaches like that esteban rail and cochlear for instance have worked on evolving a smaller network and then systematically expanding it to a larger one uh your elements are already there and scaling it up will just give you more power so again evolution gives you that starting point yes and then there's a mechanism that gives you the final result and a very powerful approach um but you know you could you could also um simulate the actual growth process and like i said before evolving a starting point and then evolving it uh or training the network there's not that much work that's been done on that yet uh we need some kind of a simulated simulation environment so there are interactions uh at will uh the supervised environment doesn't really it's not as easily uh usable here sorry the interaction between neural networks yeah the neural networks that you are creating interacting the world uh and learning from these uh sequences of interactions perhaps communication with others [Laughter] that's awesome we would like to get there but just the task of simulating something is at that level is very hard it's very difficult i love the idea i mean one of the powerful things about evolution on earth is the predators and prey emerged and like there's just like there's bigger fish and smaller fish and it's fascinating to think that you could have neural networks competing against each other one yellow network being able to destroy another one there's like wars of neural networks competing to solve the mnist problem i don't know yeah yeah oh totally yeah yeah yeah and and we actually simulated also that uh prayer the prey and it was interesting what happened there but budget but minnie roger poland did this and um kay holcomb was a zoologist so we had again um we had simulated hyenas simulated zebras nice uh and initially you know the hyenas just tried to hunt them and when they actually stumbled upon the zebra they ate it and we're happy um and and then the zebras learned to escape uh and the hyenas learned to team up and actually two of them approached in different directions and now the zebras their next step they generated a behavior where they split in different directions just like actually gazelles do in in when they are being hunted they confuse the predator by going in different directions that emerged and then more hyenas joined and and kind of circled them uh and and then when they circled them they could actually hurt the zebras together and and eat multiple uh zebras so there was a like an arms race of predators and prey and they gradually develop more complex behaviors some of which we actually do see in nature uh and and this kind of co-evolution uh that's competitive evolution it's a fascinating topic because there's a a promise or possibility that you will discover something uh new that you don't already know you didn't build it in it came from this arms race it's hard to keep the arms race going it's hard to have reits enough simulation that that supports all of these complex behaviors but at least for several steps we've already seen it in the spread of the prey scenario yeah first of all it's fascinating to think about this context in terms of uh evolving architectures so i've studied tesla autopilot for a long time it's one particular implementation of an ai system that's operating in the real world i find it fascinating because of the scale at which it's used out in the real world and uh i'm not sure if you're familiar with that system much but you know andre kapathi leads that team on the machine learning side and there's a multitask network multi-headed network where there's a core but it's trained on particular tasks and there's a bunch of different heads that are trained on that is there some lessons from evolutionary computation or neural evolution that could be applied to this kind of multi-headed beast that's operating in the real world yes it's a very good problem for new revolution and the reason is that when you have multiple tasks they support each other so let's say you're learning to classify x-ray images uh different pathologies so you have one task is to classify this disease and another one this disease another on this one and when you're learning from one disease that forces certain kinds of internal representations and embeddings and they can serve as a helpful starting point for the other tasks so you are combining the wisdom of multiple tasks into these representations and it turns out that you can do better in each of these tasks when you're learning simultaneously other tasks than you would by one task alone which is a fascinating idea in itself yeah yes and and people do that all the time i mean you use knowledge of domains that you know in new domains uh and and certainly neural networks can do that when your evolution comes in is that um what's the best way to combine these tasks now there's architectural design that allow you to decide where and how the the embeddings the internal representations are combined and how much you combine uh them uh and uh there's quite a bit of research on that and and my team eliot madison has worked on that um in particular like what is a good internal representation that supports multiple tasks uh and we're getting to understand how that's constructed and what's in it uh so that it is in a space that supports multiple different heads like you said um and and that i think is fundamentally how biological intelligence works as well uh you don't build a representation just for one task you try to build something that's general not only so that you can do better in one task or multiple tasks but also future tasks and future challenges so you learn to learn the structure of of the world um and and that helps you uh in all kinds of future future challenges and so you're trying to design a representation that will support an arbitrary set of tasks in a particular sort of class of problem yeah and and also it turns out and that's again a surprise that elliot found was that those tasks don't have to be very related you know you can learn to do better vision by learning language or better language by learning about dna structure no somehow the world yeah it rhymes the world rhymes even it's very uh very desperate fields um i mean on that small topic let me ask you because you've also on the competition your science side you worked on both language and vision what's what's the connection between the two uh what's more maybe there's a bunch of ways to ask this but what's more difficult to build from an engineering perspective an evolutionary perspective the human language system or the human vision system or the equivalent of in the ai space language and vision or is it the the best is the multi-task idea that you're speaking to that they they need to be deeply integrated yeah absolutely learning both at the same time i i think is a fascinating direction in that in the future so you have data sets where there's visual component as well as verbal descriptions for instance and and that way you can learn a deeper representation a more useful representation for both uh but it's still an interesting question of um which one is easier eventually i mean recognizing objects or even understanding sentences that's relatively possible but where it becomes where the challenges are is to understand the world like the visual world the 3d uh what are the objects doing and predicting what will happen uh the relationships that's what makes vision difficult and language obviously it's it's what's the mean what what is being said what the meaning is and the meaning doesn't stop at who did what to whom um there are goals and plans and themes and you eventually have to understand the entire uh human society and history in order to understand the sentence very much fully that there are plenty of examples of those kind of short sentences when you bring in all the world knowledge uh to understand it uh and that's the big challenge now we are far from that but even just bringing in the visual world uh together with the sentence will give you already a lot deeper understanding of what's happening and i think that that's where we're going very soon i mean we've we've had imagenet for a long time and now we have all these uh text collections but having both together uh and then learning a semantic understanding of what is happening i think that that will be the next step in the next few years yeah you're starting to see that with all the work with transformers was the the community the ai community started to dip their toe into this idea idea of having uh language models that are now doing stuff with images with vision and then connecting the two i mean right now it's like these little explorations we're literally dipping the toe in but like maybe at some point we'll just like dive into the pool and it'll just be all seen as the same thing i i do still wonder what's more fundamental well their vision is um whether we don't think about vision correctly maybe the fact because we're humans and we see things as beautiful and so on that and because we have cameras that taking pixels is a 2d image that we don't sufficiently think about vision as language you know maybe maybe chomsky is right all along that vision is fundamental to uh sorry that language is fundamental to everything to even cognition to even consciousness like the base layer is all language not necessarily like english but some weird abstract representation uh the linguistic representation yeah well earlier we talked about the social structures and that may be what's underlying the language and that's the more fundamental part and then language has been added on top of that language emerges from the social interaction probably yeah that's a very good guess um via visual animals though a lot of the brain is dedicated to vision and and also when we think about various abstract concepts uh we usually reduce that to vision uh and and images and that's you know go to a whiteboard you draw pictures of very abstract concepts so we tend to tend to resort to that quite a bit and that's a fundamental representation it's probably possible that it predated um you know language even i mean animals a lot of they don't talk but they certainly do have vision uh and and language is interesting development in um from for mastication from eating you develop an organ that actually can produce sound to manipulate them maybe that was an accident maybe that was something that was available and and then allowed us to to do that communication or maybe it was gestures sign language could have been the original proto-language we don't quite know but they're the language is more fundamental than the medium in which it's uh communicated and i think that it comes from those representations now in in current world they are so strongly integrated it's really hard to say which one is fundamental you look at the brain structures and even visual cortex which supposed to be very much just vision well if you are thinking of semantic concepts you're thinking of language visual cortex lights up it's still useful even for language computations so there are common structures underlying them so utilize what you need yeah and and when you are understanding a scene you're understanding relationships well it's not so far from understanding relationships between words and concepts so i think that that's how they are integrated yeah and there's dreams and wants to close our eyes there's still a world in there somehow operating and somehow possibly the visual visual system somehow integrated into all of it i tend to enjoy thinking about aliens and thinking about uh the sad thing to me about extraterrestrial intelligent life that if it was if it visit us here on earth or if we came on mars and or maybe another other solar system another galaxy one day that uh us humans would not be able to detect it or communicate with it or appreciate like it'd be right in front of our nose and we're too self-obsessed to see it not self-obsessed but our our our our tools our frameworks of thinking would not detect it as a good movie arrival and so on where stephen wolfram and his son i think were part of developing this alien language of how aliens would communicate with humans do you ever think about that kind of stuff where if humans and aliens would be able to communicate with each other like if we uh met each other at some okay we could do seti which is communicating from across a very big distance but also just us you know if you did a podcast with an alien do you think we'd be able to find a common language uh and a common methodology of communication i think from a computational perspective the way to ask that is is you have very fundamentally different creatures agents that are created would they be able to find a common language yes that's i do think about that i mean i think a lot of people who are in computing they uh and ai in particular they got into it because they were fascinated with science fiction and and all of these options i mean star trek generated all kinds of devices that we have now they they envisioned it's true first and and it's a great motivator um to think about things like that um and i so one and again being a computational scientist and and trying to build intelligent agents what i would like to do is have a simulation where the agents actually evolve communication not just communication we've done that people have done that many times they communicate they signal and so on but actually develop a language and language means grammar it means all these social structures and on top of that grammatical structures and we do it in under various conditions and actually try to identify what conditions are necessary for it to come out and then we can start asking that kind of questions are those languages that emerge in that those different simulated environments are they understandable to us can we somehow make a translation we can make it a concrete question so machine translation of evolved languages and so like languages that evolve come up with can we translate like i have a google translate for the evolved languages yes and if we do that enough we have perhaps an idea what an alien language might be like the space or where those languages can be because we can set up their environment differently there doesn't need to be gravity you know you can you can have all kinds of societies can be different they may have no predators they may have all everybody is a predator all kinds of situations and and then see what the space possibly is where those languages are and what the difficulties are they'll be really good actually to do that before the aliens come here yes it's good practice yeah uh on the similar connection you know you can think of ai systems as aliens is there uh ways to evolve a communication scheme for there's a field you can call like explainable ai for ai systems to be able to communicate so you have a but you evolve a bunch of agents but for some of them to be able to talk to you yeah also so to evolve a way for agents to be able to communicate about their world to us humans do you think that there's possible mechanisms for doing that we can certainly try and if we um if it's an evidence competition system for instance you reward those solutions that are actually functional that that communication makes sense it allows us to together again achieve common goals i think it's possible but even from that um paper that you mentioned the the anecdotes it's quite likely also that the uh the agents learn to you know lie and fake and do all kinds of things like that yes i mean we see that in in even very low level like bacterial evolution there are there are cheaters um and who's to say that what they say is actually what they think it um but but that's what i'm saying that there would have to be some common goal so that we can evaluate whether that communication is at least useful um you know they may be saying things just to make us feel good or or get us to do what we want whatever not turn them off or something but but uh so we would have to understand their internal representation is much better to really make sure that that translation is political um but it can be useful and i think that it's possible to do that there are examples where visualizations um are automatically created so that we can look into the what the system uh and the language is not that far from it i mean it is a way of communicating and logging what you're doing in some inter interpretable way um i think a fascinating topic yeah to do that yeah you're making me realize that it's a good scientific question whether lying is an effective mechanism for integrating yourself and succeeding in a social network in a social in a world that is social i tend to believe that honesty and love are evolutionary advantages in us in a in an environment where there's a network of intelligent agents but it's also very possible that dishonesty and manipulation and uh even you know violence all those kinds of things might be more beneficial that's the old open question about uh good versus evil but i tend to there's some i mean i don't know if it's a hopeful maybe i'm delusional but it feels like karma is a thing which is like if long term the agents that are just kind to others sometimes for no reason will do better in a society that's not highly constrained on resources it's like people start getting weird and evil towards each other and bad when the resources are very low relative to the needs of the the populace especially at the basic level like survival shelter uh food all those kinds of things but um i i tend to believe that uh once you have those things established then well not to believe i i guess i hope that ai systems would be honest but it's fun it's scary to think about the touring test you know ai systems that will eventually pass the touring test will be ones that are exceptionally good at lying that's a terrifying concept yeah i mean i i don't know first of all so from uh from somebody who studied language and obviously are not just the world expert in ai but somebody who dreams about the future of the field do you hope do you think there will be human level or superhuman level intelligences in the future that we eventually build well definitely hope that we can we can get there one i think um important perspective is that we are building ai to help us uh that it is a it is tool like cars or or or language or communication uh ai will help us be more productive uh and that is always a condition it's not something that we build and let run and it becomes an entity of its own that doesn't care about us now of course really far in the future maybe that might be possible but not in the foreseeable future when we are building it uh and therefore we are always in a position of limiting what it can or cannot do uh and the um your point about lying is very interesting um even even in these highness societies for instance uh when a number of these hyenas band together and they they still they take a risk and steal the kill they're always hyenas that hang back and don't participate in that uh risky behavior but they walk in later and and join the party after the after the kill and there are even some that may be ineffective and cause others to have harm so and like i said even bacteria cheat and we see it in biology there's always some element an opportunity if you have a i think that is this because if you have a society in order for society to be effective you have to have this cooperation and you have to have trust uh and and if you have enough of agents who are able to trust each other you can achieve a lot more but if you have trust you also have opportunity for cheaters and liars and i don't think that's ever going to go away there will be hopefully a minority so that they don't get in the way and we studied in these high-end simulations like what the proportion needs to be before it is no longer functional and you can point out that you can tolerate a few cheaters and a few liars and the society can still function and that's probably going to happen um when we build these systems that autonomously learn um the really successful ones are honest because that's the best way of getting things done um but there probably are also intelligent agents that find that they can achieve their goals by by bending the rules of cheating so there could be a huge benefit to uh as opposed to having fixed ai systems say we build an agi system and deploying millions of them it'd be that are exactly the same uh there might be a huge benefit to um introducing sort of from like an evolution computation perspective a lot of variation yeah sort of uh like diversity in all its forms is beneficial even if some people are or some robots are so like it's it's beneficial to have that because i uh because you can't always at pre-order i know what's good what's bad but uh there's that that's a fascinating absolutely diversity is the bread and butter i mean if you're running away you see diversity is the one fundamental thing you have to have and absolutely it also it's not always good diversity right it may be something that can be destructive we had in these heinous simulations we have hyenas that just are suicidal they just run and get killed but they form the basis of those who actually are really fast but stop before they get killed and eventually turn into this mob uh so there might be something useful there if it's recombined with something else right so i think that as long as we can tolerate some of that it may turn into something better you may change the rules because it's so much more efficient to do something that was actually against the rules before yes uh and we've seen society change uh over time quite a bit along those lines that there were rules in society that we don't believe are fair anymore even though they were you know considered proper behavior before yes um so things are changing and i think that in that sense i think it's um it's a good idea to be able to tolerate some of that some of that cheating because eventually we might turn into something better so yeah i think this is a message to the trolls and the of the internet that you two have a beautiful purpose in this uh human ecosystem so we i appreciate you guys watering quantities yeah moderate quantities uh so there's a whole field of artificial life i don't know if you're connected to this field if you uh pay attention is do you think about this kind of thing uh is there a impressive demonstration to you of artificial life do you think of the agents that you work with in the evolutionary competition at perspective as life and where do you think this is headed like is there interesting systems that we'll be creating more and more that uh make us redefine maybe rethink about the nature of life different levels of definition and goals there then i mean at some level artificial life can be considered multi-agent systems that build a society that again achieves a goal and it might be robots that go into a building and clean it up or or after an earthquake or something you can think of that as an artificial life problem in some sense um or you can really think of it artificial life as a simulation of life and a tool to understand what life is and how life evolved in on earth and like i said in artificial life conference there are branches of that conference sessions of people who really worry about molecular designs and and the start of life like the like i said primordial soup where eventually you get something self-replicating and they're really trying to build that um so it's a whole range of of uh of topics um and i think that artificial life is a great tool uh to understand life and there are questions like sustainability um species we're losing species uh how bad is it is it natural uh is there a tipping point um and where are we going i mean like the hyena evolution we may have understood that there's a pivotal point in their evolution they discovered cooperation and coordination you know artificialized simulations can identify that and maybe encourage things like that um so and and also societies can be seen as a form of life itself i mean we're not talking about biological evolution we have all evolution of societies maybe some of the same phenomena emerging in that uh domain and unders and having artificial life simulations and understanding could help us build better societies yeah and thinking from a meme perspective of of uh from richard dawkins that maybe the organisms ideas of the organisms not the humans in these societies that from it's almost like reframing what is exactly evolving maybe the interesting the humans aren't the interesting thing is the contents of our minds is the interesting thing and that's what's multiplying and that's actually multiplying and evolving in a much faster time scale and that maybe has more power on the trajectory of life on earth than this biological evolution yes the evolution of these ideas yes and it's fascinating like i said before that we can keep up somehow biologically yeah we have we belong to a point where we can keep up with this meme evolution literature you know internet um we understand dna and we understand fundamental particles we didn't start that way i mean thousand years ago and we haven't evolved biologically very much but somehow our minds are able to uh extend um and and therefore ai can be seen also as one such step that we created and it's our tool uh and it's part of that meme evolution that that we create even if our biological evolution does not progress as fast and us humans might only be able to understand so much we're keeping up so far or we think we're keeping up so far but we might need ai systems to understand maybe like the physics of the universe is operating like a string theory maybe it's operating in much higher dimensions maybe we're totally because of our cognitive limitations are not able to truly internalize the way this world works and so our limit we're running up against the limitation of our own minds and we have to create these next level organisms like ai systems that would be able to understand much deeper like really understand what it means to live in a uh multi-dimensional world that's outside of the four dimensions the three of space and one of them yeah translation and and generally we can deal with the world even if you don't understand all the details we can use computers yes even though we don't most of us don't know all the structures underneath or drive a car i mean there are many components especially new cars that you don't quite fully know but you have the interface you have an abstraction of it that allows you to operate it and utilize it and i think that that's that's perfectly adequate and we can build on it and ai can be play a similar role i have to ask uh about beautiful artificial life systems or evolutionary computation systems uh cellular automata to me like i remember it was as a game changer for me early on in life when i saw conway's game of life who recently passed away unfortunately it's beautiful how much complexity can emerge from such simple rules i i just don't somehow that simplicity is such a powerful illustration and also humbling because it feels like i personally from my perspective understand almost nothing about uh this world because because like my intuition fails completely how complexity can emerge from such simplicity like my intuition fails i think is the biggest problem i have do you find systems like that beautiful is there do you do you think about cellular automata because cellular tama don't really have um and many other artificial life systems don't necessarily have an objective maybe maybe that's a wrong way to say it it's almost like it's just evolving and creating and there's not even a good definition of what it means to create something complex and interesting and surprising all those words that you said um is there some some of those systems you find uh beautiful yeah yeah and uh similarly evolution does not have a goal uh it is responding to uh current situation uh and so survival then if it creates more complexity and therefore we have something that we perceive as progress but that's not what evolution is inherently said to do uh and yeah that's that's really fascinating how how a simple set of rules or simple uh mappings can um from from how from such simple mapping complexity can emerge so it's a question of emergence and self-organization uh and um the game of life is one of the simplest ones and very visual and therefore it drives home the point that it's possible that non-linear interactions uh and and this kind of complexity can emerge emerge from them and biology and evolution is along the same lines we have simple representations dna if you really think of it it's not that complex um it's a long sequence of them there's lots of them but it's a very simple representation and similar evolutionary computation whatever string or tree representation we have any operations you know the amount of code that's required to manipulate those is really really little and of course came alive even less so how complexity emerges from such simple principles that's that's absolutely fascinating um the challenge is to be able to control it and guide it and direct it so that it becomes useful and like game of life is fascinating to look at and and evolution all the forms that come out is fascinating but can we actually make it useful for us and efficient because if you actually think about each of the cells in the game of life as a living organism there's a lot of death that has to happen to create anything interesting yeah and so i guess the questions for us humans that are mortal and then life ends quickly we want to kind of hurry up and make sure we make sure we take evolution uh uh the trajectory that is a little bit more efficient than uh the alternatives and that that's one something we talked about earlier that evolution computation is very uh impatient yeah we had we have a goal we want it right away whereas this biology has a lot of time and and deep deep time and weak pressure and large populations uh one great example of of this is the novelty search uh so evolutionary computation where you don't actually specify a fitness goal something that is your actual thing that you want but you just reward solutions that are different from what you've seen before yeah nothing else yeah and you know what you actually discover things that are interesting and useful that way um guess danny and joe lemon did this one study where they actually tried to evolve walking behavior on robots and that's actually we talked about earlier where your robot actually failed in all kinds of ways and eventually discovered something that was a very efficient walk uh and and it was because they if they rewarded things that were different that you were able to discover something uh and i think that this is crucial um because in order to be really different from what you already have you have to utilize what is there in a domain to create something really different so you have encoded the uh fundamentals of your world and then you make changes to those fundamentals you get further away so that's probably what's happening in these systems of emergence uh that the fundamentals are there and when you follow those fundamentals you get into points and some of those are actually interesting and useful now even in that robotic walker simulation there was a large set of garbage but among them there were some of these you know gems and then those are the ones that somehow you have to outside recognize and make useful but these kind of productive systems if you code them the right kind of principles i think that they that encode the structure of the of the domain then you will get to these solutions and the discoveries it feels like that might also be a good way to live life so let me ask do you have advice for young people today about how to live life or how to succeed in their career or forget career just succeed in life form an evolutionary computation perspective yes yes definitely explore diversity exploration yeah and i mean individuals take classes in music history philosophy yeah you know math engineering uh see connections between them travel you know learn a language i mean all this diversity is fascinating and we have it at our fingerprint fingertips today it's possible you have to make a bit of an effort because it's not easy but the rewards are wonderful um yeah there's something interesting about an objective function of new experiences so try to figure out i mean uh what what is the maximally new experience that could have today and that so like that novelty optimizing for novelty for some period of time might be a very interesting way to sort of uh expand the the sets of experiences you had and uh then ground from that perspective um like what you what would be the most fulfilling trajectory through life of course the flip side of that this is where i come from again maybe russian i don't know but the the choice has a choice is a has a detrimental effect i think from at least from my mind where scarcity is has a empowering effect so if i sort of if i have very little of something and only one of that something i will appreciate it deeply until i came to texas recently and i've been picking out on delicious incredible meat i've been fasting a lot so i need to do that again but when you fast for a few days that the first taste of of a food is is incredible so the downside of exploration is that uh somehow maybe maybe you can correct me but somehow you don't get to experience deeply any one of the particular moments but that could be a psychology thing that could be just a very human peculiar flaw yeah i didn't mean that you superficially explore i mean you can explore deeply yeah so you don't have to explore 100 things but maybe a few topics where you can take a deep enough time a dive that you gain an understanding um you yourself have to decide at some point that this is deep enough and i i unders i i've obtained what i can from this topic uh and now it's time to move on and that might take years um people sometimes switch careers and they may stay on some career for a decade and switch to another one you can do it you're not pretty determined to stay where you are but you know in order to achieve um something you know 10 000 hours makes you need 10 000 hours to become an expert on something uh so you don't have to become an expert but to even develop an understanding and gain the experience that you can use later you probably have to spend like i said it's not easy you got to spend some effort on it now also at some point then when you have this diversity and you have these experiences exploration you may want to um you may find something that you can't stay away from uh like for as it was computers it was ai it was you know that you i just have to do it you know and i uh you know and then we'll it will take decades maybe and you are pursuing it because you figured out that this is really exciting and you can bring in your experiences and there's nothing wrong with that either but you asked what's the advice for young people that's the expiration part and then beyond that if after that expiration you actually can focus and and build a career and you know even there you can switch multiple times but but i think the diversity exploration is fundamental to having a successful career as is concentration and spending an effort where it matters and and but you're in better position to make that choice when you have done your homework so exploration precedes commitment but both are beautiful uh so again from an evolutionary computation perspective we look at all the agents that had to die in order to come up with different solutions in simulation what do you think from that individual agent's perspective is the meaning of it all so far as humans you're just one agent who's going to be dead unfortunately one day too soon what do you think is the why of why that agent came to be and uh eventually will be no more is there meaning to it all yeah in evolution there is meaning everything is a potential direction everything is a potential stepping stone um not all of them are going to work out some of them are foundations for further um improvement and even those that are perhaps going to die out uh where potential energies potential solutions in biology we see a lot of species die off naturally and you know like the dinosaurs i mean they have a really good solution for a while but then it didn't turn out to be not such a good solution in the long term uh when there's an environmental change you have to have diversity some other solutions become better it doesn't mean that that there was an attempt it didn't quite work out or last uh but they're still dinosaurs and mountains at least they're relatives uh and they may one day again be useful who knows so from an individual's perspective you've got to think of a bigger picture that it is a huge engine that is innovative and these elements are all part of it potentially innovations on their own and also as as raw material perhaps or um stepping stones for other things that could come after but it still feels from an individual perspective that i i matter a lot but even if i'm just a little cog in the giant machine well is that just a silly human notion in uh individualistic society and though she'll let go of that do you find beauty in being part of the giant machine yeah i think it's meaningful um i think it adds purpose to your life that you are part of something bigger [Laughter] that said are you uh do you ponder your individual agent's mortality do you do you think about death do you fear death well certainly more now than when i was a youngster and did skydiving and paragliding and you know all these things you've become wiser um there is a reason for this uh life arc that younger folks are more fearless in many ways it's part of the exploration you know they are the they are the individuals who think hmm i wonder what's over those mountains or what if i go really far in that ocean what would i find i mean older folks i don't necessarily think that way but younger do and it's kind of counterintuitive so yeah this is uh and biologically it's like you know you have limited amount of time what can you do with it that matters so you try to you have done your exploration you committed to a certain direction and you become an expert perhaps in it what can i do that matters uh with with the limited resources that i have that's what how you i think a lot of people myself included start thinking later on in their career and uh like you said leave a bit of a trace and a bit of an impact even though after the agent is gone yeah that's the goal well this was a fascinating conversation i don't think there's a better way to end it uh thank you so much so first of all i'm very inspired of how vibrant the community at ut austin in austin is it's really exciting for me uh to see it and this whole field seems like profound philosophically but also the path forward for the artificial intelligence community so thank you so much for explaining so many cool things to me today and for wasting all of your valuable time with me oh it was a pleasure thanks i appreciate it thanks for listening to this conversation with and thank you to the jordan harbinger show grammarly belcampo and indeed check them out in the description to support this podcast and now let me leave you with some words from carl sagan extinction is the rule survival is the exception thank you for listening i hope to see you next time you