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.