The following is a conversation with Risto Michalainen, a computer scientist at 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. As usual, I'll do a few minutes of as now. I try to make these interesting, but I give you timestamps. So if you skip, please still check out the sponsors by clicking the links in the description. It is the best way to support this podcast.
I'm fortunate to be able to be very selective with the sponsors we take on. So hopefully if you buy their stuff, which you should, you'll find value in it just as I have. This episode is sponsored by Jordan Harbinger Show. Go to jordanharbinger.com slash lex. You don't even need to buy stuff. It's free.
and it's beautiful. Subscribe to it, listen, you won't regret it. He's a great interviewer and I especially like his Feedback Fridays episodes where his combination of fearlessness and thoughtfulness is especially on display. touching topics of sex, corruption, mental disorders, hate, love, and everything between. All my favorite topics.
Jordan has in many ways inspired me to keep pushing myself in these kinds of difficult conversations that I have on weird topics that sort of challenge me, that are risky, that I can get in trouble with and sort of. walk through that fire, walk through it with grace. But at the same time, accepting the responsibility of the mistakes I make, and of course, I will make many mistakes. That is life.
Again, go to jordanharbinger.com slash lex. It's how he knows I sent you. That's jordanharbinger.com slash lex. If you love this podcast, if you love podcasts, listen to it. It's great. This show is also sponsored by Grammarly, a writing assistant tool that checks spelling, grammar, sentence structure, and readability. Grammarly Premium, the version you pay for, and the one
they hope you sign up for, offers a bunch of extra features. My favorite is the clarity check, which helps detect rambling chaos that many of us can descend into, and I certainly do. If Grammarly could only process audio and detect mumbling, it would probably go into red alert mode. But that's talking. When I write, let's say the thing I aspire to when I write is simplicity.
I think simplicity is beautiful. I think it's powerful in being able to communicate ideas. Striving for simplicity forces you to think deeply about a topic. It's not just that simplicity is a good metric for effective communication. It's also just on a personal level, a great way to challenge yourself to think and to understand a topic.
Anyway, Grammarly is available on basically any platform and major sites and apps like Gmail and Twitter and so on. Get 20% off Grammarly Premium by signing up at grammarly.com. That's 20% off at grammarly.com slash lex. Grammar, by the way, has two Ms. In case this was something you were wondering about. This show is also sponsored by...
one of my favorite sponsors, Belcampo Farms, whose mission is to deliver meat you can feel good about. Meat that is good for you, good for the animals, and good for the planet. Belcampo animals graze on open pastures and seasonal grasses resulting in meat that has higher nutrients and healthy fats. And, friends, it's delicious. Belcampo has been the best meat I've ever had at home.
In fact, just for the fun of it, for the experience of planning a visit out there, maybe I'll videotape some parts of it, maybe not. It is going to be more expensive than store-bought meat. but I honestly think both on the ethical side and the deliciousness side, it's worth it. Of course, under financial constraints, we have to make trade-offs in this life, but I think this is a worthy investment.
Anyway, you can order Belcampo's sustainably raised meats to be delivered straight to your door using code Lex at Belcampo.com slash Lex for 20% off for first-time customers. That's code Lex at Belcampo.com slash Lex. If you order from them, post on Instagram, tag me. Hopefully I'll see it and it'll give me a good smile.
And in general, for other sponsors and just any cool things going on in your life, if you tag me, I really appreciate it. It often puts a smile on my face. The internet, I think in most cases, at least for me, can be an amazing place. This episode is also brought to you by Indeed, a hiring website. I've used them as part of many hiring efforts I've done.
for the teams I've led in the past. The main task is to quickly go from a huge number of initial applicants to a short list of great candidates. I'm going through the hiring process currently. I need a lot of help in more ways than one. I certainly need help with the video editor and all those kinds of things. There's a huge number of people that applied. I'm still very early in that process. So if you're thinking about applying, you can still apply and I'll still get to you.
When I look for people to work with, I'm really looking, hopefully, for somebody I can work with for a very long time, somebody who will help inspire me, somebody whose passion drives me to be more passionate and vice versa. I mean, what else is life about if not working on a team with awesome people doing awesome things? Anyway.
Right now, get a free $75 credit at Indeed.com slash Lex. That's their best offer anywhere. Get it at Indeed.com slash Lex. Terms and conditions apply. Indeed.com slash Lex. This is the Lux Friedman Podcast, and here is my conversation with Risto Mikulainen. 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 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 a million times and actually answer that question. How much variation do we see when we simulate it?
You know, that's a little bit beyond what we can do today. 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 towards the end. But there are things that need to be discovered, and they probably will be over and over again, like manipulation of objects, opposable thumbs. And also some way to communicate, maybe orally, like where you have speech. It might be some other kind of sound.
and decision-making, but also vision. Eye 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 get the job done. The really interesting question is, would we have primates? Would we have humans or something that resembles humans? And would that be an apex of evolution after a while? We don't know where we're going from here, but...
We certainly see a lot of tool use 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 are running the simulation and you observe... humans emerge like these like tool makers they start a fire and all this 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 really busy as the creator of this evolutionary system, so you don't have much time to observe, 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 what we would do.
roughly what we want, intelligent agents that communicate, cooperate, manipulate, and we would build detections and visualizations of those processes. Yeah, and there's a lot of... We'd 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 at the beginning. Well, that seems really difficult to build a detector of intelligent or intelligent communication.
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 uh and colonies obviously dolphins is the most intelligent 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
It could be some other 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 thing your detector is detecting. Humans have been here for a very short time and we're just...
creating a lot of pollution, but so is the other creatures. I don't know. 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... 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 the first...
measurement would be to detect how much of an effect you can have in your environment. So if you 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 hives and things like that. But the complexity of the human... uh 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 their environment, which humans are about to do, which is not a very smart thing.
But we'll get over it, I believe. And we can get over it by doing some construction that actually is benign and maybe even enhances the... resilience of nature so you mentioned that this simulation that we run over and over might start slowly 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 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 molecules? And they are very close. With a little bit of help, you can make that happen.
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 took a long time. For us to recreate it probably won't take that long. And the next steps from there, I think also with some hand-holding, I think we can make that happen. But 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 if you put your computer scientist hat on, is there 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 that is a way of defining it. And that means that it's some kind of an object or a program that has limited...
sensory and effective capabilities interacting with the world, and then also a mechanism for making decisions. So with limited abilities like that, can it survive? Survival is the simplest goal, but you could also give it other goals. Can it multiply? Can it solve problems that you give it? And that is quite a bit less than human intelligence. Animals would be intelligent, of course, with that definition.
And you might have even some other forms of life. So intelligence in that sense is a survival skill. given resources that you have and using your resources so that you will stay around. Do you think death, mortality is fundamental to an agent? So there's, I don't know if you're familiar, there's a philosopher named Ernest Becker who wrote 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 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 it count 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 the second decision, the second level of definition that intelligence means something that you do something that stays behind you. That's more than... your existence. You create something that is useful for others, is useful in the future, not just for yourself. And I think that's a nice definition of intelligence.
Next level. And it's also nice because it doesn't require that they are humans or biological. They could be artificial agents that are intelligence. They could achieve those kind of goals. So particular agent, 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 butterfly with the flap of a wing, and then you can trace a lot of nuclear wars and all the conflicts of human history somehow connected to that one butterfly that created all the chaos. So maybe that's not... Maybe that's a very poetic way to think. That's something we humans in a human-centric way want to hope we have this.
Like that is the secondary effect of our intelligence. We've had that 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, like The Wonderful Life, what if you're not here? Will somebody else do this? Is it something that...
You actually contributed because you had something unique to contribute. That's a pretty high bar, though. Uniqueness. Yeah. So, you know, you have to be Mozart or something to actually reach that level. Nobody would have. developed that. But other people might have solved this equation if you didn't do it. 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 then that could change the way things move forward for a while. So I don't think we have to... be Mozart's 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
Do computational agents a fear of mortality? Like, does that make any sense? So there's a very trivial thing where it's like, You could just code in a parameter, which is how long life ends, but more of a fear of mortality, like awareness of 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 fear that's not healthy, that paralyzes you, that you can't do anything. And somewhere in between there, not caring at all and getting paralyzed because of fear, is a normal response, which is a little bit more than just logic and it's emotion.
So now the question is, what good are emotions? I mean, they are quite complex and there are multiple dimensions of emotions and they... probably do serve a 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 if it's not logically necessarily easy to derive and you don't have time for that logical 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. It allows you to focus. what's relevant for your situation. And maybe fear of death plays the same kind of role. 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 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... 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. 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 it puts you in a different state. Your computation is in a different state. Certain things don't really get through and others...
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, modeling hyenas. who were trying to steal a kill from lions, which happens in Africa. I mean, Hyannas are quite intelligent, but not really intelligent. And 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, they can 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 hyena by striking with a paw. But when they work together and precisely time this attack, the lions will leave and they get the kill.
Probably there are some states, like emotions, that the hyenas go through. The first, they call for reinforcements. They really want that kill, but there's not enough of them, so they vocalize, and there's 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 also, yes, they also want to kill. So it's both repelled and attractive. But then this...
So affiliation eventually is so strong that when they move, they move together. They act as a unit and they can perform that function. So there's an interesting... behavior that seems to depend on these emotions strongly and makes it possible um coordinate the actions and i think a critical aspect of that the way you're describing is emotion there is this is a mechanism
of social communication, of social interaction. 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 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 fundamentally social systems. Yes, I strongly believe that's true. And yes, the...
Communication is multifaceted. I mean, 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 don't act alone. And I don't think people act alone very much either, at least normal most of the time. And social systems are so strong for humans that I think we...
built everything on top of these kind of structures. And one interesting theory around that, Bickerton's theory, for instance, for language, where language origins is, that where did language come from? 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. I scratch your back, you scratch my back, we 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 objects in the world. And you have this flexible structure. So there's a social structure. That's fundamental for language to develop. Now, again, then you have language. You can refer to things that are not here right now. And that allows you to then...
build all the good stuff about planning, for instance, and building things and so on. So yeah, I think that very strongly humans are social and that gives us ability. to structure the world. But also as a society, we can do so much more because 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 demonstration, Marco Dorico, I think it was, these robots, little robots that had to navigate through an environment, and they were... things that are dangerous, like maybe a big...
chasm or some kind of groove, a hole, and they could not get across it. But if they grabbed each other with their gripper, they formed a robot that was much longer under the team, and this way they could get across that. 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. And I think humans do that all the time. We're really good at that. Yeah, and the way you described 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
at least hopeful that it's possible to create computational systems that mimic that. Yeah. That's exactly why we looked at that. As opposed to humans. Like I said, they are intelligent, but they are not quite as intelligent as, say...
baboons, which would learn a lot and would be much more flexible, the 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 it might then spill over to other things too 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 where we won't be able to measure it if we're observing the system. With hyenas, it's probably... easier to observe the actual decision making and the various motivations that are involved. Yeah, they are visible. And we can even quantify possibly their emotional state because
They leave droppings behind. And there are chemicals there that can be associated with neurotransmitters. And we can separate what emotions they might have experienced in the last 24 hours. What to you is the most beautiful, speaking of hyenas, what to you is the most beautiful nature-inspired algorithm in your work that you've come across something maybe earlier on in your work or maybe today?
I think evolutionary computation is the most amazing method. So what fascinates me most with computers is that 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. It did something very simple and I was just amazed. I was blown away that 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, 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 evolutionary 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 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
Do you have kids? Yeah. So maybe you could speak to this. But there's a magic to the creative process. With Spot, the Boston Dynamics 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 that exactly this which is like you know what you had to do initially
To get this thing going, let's speak on the computer science side, like what the program looks like. But something about it... 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 intelligence of its own
beyond what you actually thought. And that is, I think it's exactly spot on. That's exactly what it's about. You created something that has a ability to. live its life and do good things. And you just gave it a starting point. So in that sense, I think that may be part of the joy, actually. But you mentioned creativity in this context, especially in the context of evolutionary computation. So we don't often think of algorithms as creative. So how do you think about creativity? Yeah.
algorithms absolutely can be creative. They can... come up with solutions that you don't think about. I mean, creativity can be defined. A couple of requirements have to, 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, Caleb 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... What do you do if you can control everything? Farmers know a lot about how to make plants grow in their own patch 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... We were focusing on light, how strong, what wavelengths, how long the light was on. And we put some boundaries, which we thought were reasonable. For instance, that there was at least six hours of darkness, like night, because that's what we have in the world. And very quickly, the system evolution pushed all the recipes to that limit.
We were trying to grow basil, and we initially had some... 200, 300 recipes, exploration as well as known recipes. 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... the system discovered that basil does not need to sleep. 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 there's some constraints 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 fit the constraints of the system and all we'll have to do is just remove the constraints
Do you ever think about, I don't know how much you know about brain-computer interfaces and Neuralink. 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 lifespan and senses is 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 intelligence will expand exponentially. Do you have a sense about constraints?
the relationship of evolutionary computation to the constraints of the environment um well at first i'd like to comment on on that like changing the inputs to human brain. Yes, that would be great. And flexibility of the brain. I think there's a lot of that. There are experiments that are done in animals like Mikangasur, but they might be switching the...
auditory and visual information 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... 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, a different kind of wavelengths or whatever they are. And then the brain can learn to make sense of it. And that, I think, is 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.
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. Giving the brain more input probably might overwhelm it. have to learn to filter it and focus in order to use the information effectively. And augmenting...
intelligence with some kind of external devices like that might be difficult, 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're already one of the most if not the most intelligent things on this earth right so it's hard to imagine if the brain can hold up with an order of magnitude greater set of information thrown at it.
if it can do if it 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 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 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. 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 have been doing for a long, long time. And the brain is already drinking from the fire hose.
Like vision, there's way more information in the vision that we actually process. So brain is already good at it. identifying what matters. And 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. But also, indeed, this... 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 were discovered just a couple of years ago, it's already common knowledge. And we are building on it. And we don't see a problem where... where there's too much information that we can't 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. We have still this process that allows us to be selective and decide what's interesting. I think still works, even with the more information we have today. Yeah, it's fascinating to think about Wikipedia becoming a sensor. 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, on the...
Early steps on the Neuralink side, I don't know if you got a chance to see, but there's a monkey playing Pong through the brain-computer interface. And the dream there is... sort of you're already replacing the thumbs essentially that you would use to play a video game the dream is to be able to increase further the um
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 do something like that. I mean, you can... There are devices that read your EEG, for instance, and humans can learn to control things using just their thoughts in that sense.
I don't think it's that different. I mean, those signals would go to limbs. They would go to thumbs. Now the same signals go through a sensor to some computing system. It still probably has to be built on human terms. not to overwhelm them, but utilize what's there and sense the right kind of patterns that are easy to generate. But, oh, that I think is really quite possible and wonderful and could be very much more efficient.
So you mentioned surprising being a characteristic of creativity. You already mentioned a few examples. 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 that were even discarded. Is there something that just jumps to mind? It happens all the time. I mean, evolution is so creative, so good at discovering.
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. There's a great paper, the community put it together. about surprising anecdotes about evolutionary computation.
A lot of them are indeed in some software environment. There was a loophole or a bug and the system utilizes that. By the way, for people who want to read it, 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. You have a story in there.
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, surprise. But it was actually done by students in my class, in a neural nets evolution computation class. There was an assignment. It was perhaps a final project where people built.
game-playing AI. It was an AI class. And it was for tic-tac-toe or five in a row in a large board. And this one team evolved. a neural network to make these moves. And 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 won because...
other teams crashed. And then when we look at it, 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... 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 crossing all your opponents. I think that's quite a profound example.
which 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 opponents. if it's a human, like not through violence, but through some hack where the brain just is not...
You're basically, how would you put it? You're going outside the constraints of where the brain is able to function. Expectations of your opponent. This was even Kasparov pointed out that when Deep Blue was... playing against Kasparov, that it was not playing the same way as Kasparov expected. And this has to do with not having the same biases. And that's really one of the strengths 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 his 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 and using the...
that measure to select 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. 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 which gives you the phenotype, which is the actual individual that then performs the task 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 that 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 there's folding 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. 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 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 no longer are, but they're still there. So that process is complex.
your representation should support it. And that is quite difficult if we are limited with strings or trees. then we are pretty much limited to 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 evolutionary 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 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. It has to have that environment. So there's a push to another level.
at least the selection and how do you make that jump yes how do you make the jump as 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 quite the same as going from single to multi to society, for instance, in biology. So there essentially would be...
As opposed to having one agent, those agents 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, 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 does mutation come into play at all in the process, and what in the world does it serve? Yeah, so... And again, back to what the computational mechanisms of evolution computation are. So the way to create variation, you can take multiple individuals, two usually, but you could do more. And you exchange the...
part of the representation. You do some kind of recombination. It could be crossover, for instance. In biology, you do have DNA strings that are cut and put together again. We could do something like that. And it seems to be that in biology, crossover is really the workhorse in biological evolution. In computation, we tend to rely more on mutation.
And that is making random changes into parts of the chromosome. You can 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...
Success comes from mutation. Yes, 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 evolutionary computation today. We want answers right now, right, quickly. And if somebody doesn't perform, kill it. And biological evolution doesn't work quite that way. 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,
into our algorithms to improve the combination, as opposed to mutation doing all of the work. Yeah, and many ways of being successful. Usually in evolution computation, we have one goal. You know, play... this game really well. 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 there are many ways to solve the same.
problem of survival. And that then breeds creativity. And 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 as just one metric. So in a broad sense. Before we talk about neuroevolution, do you see evolutionary computation as more effective than deep learning in a certain context? 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 where 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. It might be weather prediction where the data itself. becomes labels. What happened, what the weather was today and what it 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. Game playing, for instance, but many robotics tasks and actions in the world, decision making. And actual practical applications like treatments and healthcare or investment in stock market. Many tasks are like that. 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. 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 timescale because you have a population, not an individual during this lifetime, but an entire population as a whole can discover.
what works. And there you can afford individuals that don't work out. Everybody dies and you have a next generation and it will be better than the previous one. So that's the big... difference between these methods, they apply to different kinds of problems. And in particular, there's often a comparison that's kind of interesting and important between reinforcement learning and evolutionary computation.
And initially, reinforcement learning was about individual learning during their 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. from the point of view of 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 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 in computation or reinforcement learning teaching a simulated robot to walk. Maybe there's a... Higher level question that could be asked here, but do you find this whole space of applications in the robotics interesting?
for evolution and computation? Yeah, yeah, very much. And indeed, there are fascinating videos of that. And that's actually one of the examples where you can contrast the difference. Between reinforcement learning and evolution. Between reinforcement learning and evolution, yes. 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.
fall flat on their face and they could take a step and then they jump and then again fall flat. And eventually what comes out of that is something like a falling that's controlled and you take another step and 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
evolutionist 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 this kind of discovery. Yeah, learning to walk is fascinating. I talked quite a bit to Russ Tedrick, who's at MIT. There's a community of folks who just, roboticists, who love the elegance and beauty of... Right. And walking, bipedal robotics is 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's such a good example of 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, another interesting direction is learning for virtual creatures, learning to walk. We did a study in simulation, obviously, that... 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, to walk, to run. And what was interesting 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 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. I mentioned Spot, the Boston Dynamics robot.
There is something about human robot communication. Let's say, let's put it in another context, something about human and dog context, like a living dog. where there's a 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 the 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,
It's really difficult how to figure that out because it almost seems impossible to hard code in. You can hard code it for a demo purpose, 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 scale that you're referring to, but The hope is that you could do that in simulation and then transfer it into the physical space if you're able to model the robot sufficiently naturally. Yeah, and sometimes I think that it requires a theory of mind on the side of the robot.
that they understand what you're doing because they themselves are doing something similar. And that's a big question too. We talked about... intelligence in general and the social aspect 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. We have one simulation we did a while ago. Ken Stanley did that. Two robots that were competing.
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.
But then they started to pay attention to 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 two of a very far distance. And there was one more food nearby. So it faked. 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 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. to guide it towards bad behavior in order to win. So we can get things like that happen in simulation as well. But that's a complete natural emergence of a theory of mind.
I feel like if you add a little bit of a place for a theory of mind to emerge 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 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. It doesn't have to be super complicated. I've gotten a chance 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 between humans, a human driver and a human crossing the road where...
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 build that. Current robots are much less than what you're describing.
currently just afraid of everything. 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, that you do with a large spatial envelope.
make sure you do not collide with that's how like 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 the collision avoidance problem, but a weird dance. Yeah, I think 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 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 doesn't matter whether a pedestrian has a mind. It's an object. And we can predict what we will 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 when you start interacting and you're trying to get the other agent to do something so that you can jointly collaboratively achieve something, 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, there's this vision science people talk about this all the 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 probably maybe a few options. 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 there's a few discrete states that the human could be in and you have to.
You have to predict that by observing the human. And that seems like a machine learning problem to figure out what's... how the human is, 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 really have a model of what they're thinking about and what's-
motivation behind the movement of the arm. Yeah, and here we are talking about relatively simple physical actions, but you can take that to higher levels also, like to predict what the people are going to do. You need to know what their... goals are uh what are they trying to are they exercising are they just trying 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 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 evolutionary computation apply to the world of neural networks? Because I've seen quite a bit of work from you and others in the world of neuroevolution. So maybe first, can you say, what is this field? Yeah, neuroevolution...
It's a combination of neural networks and evolution computation in many different forms. But the early versions were simply using evolution the way... as a way to construct a neural network instead of, say, stochastic gradient descent or backpropagation. Because evolution can evolve. these parameters, weight values in a neural network, just like any other string of numbers, you can do that. And that's useful because some cases you don't have those targets that you need to backpropagate from.
And it might be an agent that's running a maze or a robot playing a game or something. Again, you don't know what the right answers are. You don't have backup. But this way, you can still evolve a neural net. And neural networks are really good at these tasks. Because 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 the reason and that's the 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. And now we can use evolution to give that design for you. And it might mean... optimizing hyperparameters, like the depth of layers and so on, or the topology of the network, 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 generate that. Even data augmentation, all the different aspects of the design of a... deep learning experiments could be optimized that way. So that's an 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 timescales. 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 timescales. And I think that can potentially be really powerful. Now, in biology, we are not born with all our... We have to learn. We have a developmental period. In humans, it's really long. And most animals have something. And probably the reason is that evolution, the DNA, is not detailed enough or plentiful enough to...
describe them. We can describe how to set the brain up, but evolution can decide on a starting point and then have a learning algorithm that will construct the final product. And this interaction of intelligent... well, evolution that has produced a good starting point for the specific purpose of learning from it with the interaction with the environment. That can be a really powerful mechanism for constructing brains and constructing behaviors.
I like how you walk back from intelligence. So optimize starting point, maybe. Yeah. Okay. There's a lot of fascinating things to ask here. And this is basically... This dance between neural networks and evolution and computation could go into the category of automated machine learning to where you're optimizing whether it's hyperparameters of the topology or hyperparameters 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 that organism is really fascinating space how how hard is it do you think
to grow neural 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 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 think we can.
Most of the work up to now is taking architectures that already exist, that humans have designed, and try to optimize them further. And you can totally do that. A few years ago, we did an experiment. We took a winner of the... image captioning competition and the architecture and just broke it into pieces and took the pieces and that was our search base. See if you can do better.
And we indeed could. 15% better performance by just searching around the network design that humans had come up with, oreo vinyls and others. 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 hard part is to... There are a couple of challenges. One of them is to define the search space. What are your elements and how you put them together? And the space is just really...
really big. So you have to somehow constrain it and have some hunch of what will work 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. Deep learning networks may 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. It will be, but...
but also there's a large carbon footprint and all that. I mean, we are using a lot of computation for doing it. So intelligent methods, and intelligent, 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.
So, yes, there are certain architectures that are more amenable to that approach. But also, I think we can create our own architecture and all representations that are even better at that. Do you think it's possible to do like... like a tiny baby network that grows into something that can do state-of-the-art and like even the simple data set like amnist
It just grows into a gigantic monster that's the world's greatest handwriting recognition system. Yeah, there are approaches like that. Esteban Real and Cochlear, for instance, I worked on. evolving a smaller network and then systematically expanding it to a larger one. your elements are already there and scaling it up will just give you more power. So again, evolution gives you that starting point and then there's a mechanism that gives you the final result and a very powerful approach.
But, you know, you could also simulate the actual growth process. And like I said before, evolving a starting point and then evolving or training the network. There's not that much work that's been done on that yet. We need some kind of a simulation environment so there are interactions at will. The supervised environment doesn't.
It's not as easily usable here. Sorry, the interaction between neural networks? Yeah, the neural networks that you're creating, interacting the world, and learning from these... sequences of interactions perhaps communication with others 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 there's bigger fish and smaller fish. And it's fascinating to think that you could have neural networks competing against each other and one neural network being able to destroy another one. There's like wars of neural networks competing to solve a... The M-ness problem, I don't know. Yeah, yeah. Oh, totally, yeah, yeah, yeah. And we actually simulated also that prayer to pray, and it was interesting what happened there. But, I mean, Rudolf Bolland did this, and...
Kay Holcomb was a zoologist. So we had, again, we had simulated hyenas and simulated zebras. Nice. And initially, you know, the... Hyenas just tried to hunt them. And when they actually stumbled upon the zebra, they ate it and were happy. And then the zebras learned to... escape 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...
a behavior where they split in different directions, just like actually gazelles do when they are being hunted. They confuse the predator by going in different directions. That emerged. And then more hyenas joined. And then when they circled them, they could actually herd the zebras together and eat multiple 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. And this kind of co-evolution that's competitive co-evolution, it's a fascinating... topic because there's a promise or possibility that you will discover something 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...
reads enough simulation that supports all of these complex behaviors. But at least for several steps, we've already seen it in this predator-prey scenario. First of all, it's fascinating to think about this context in terms of... 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 I'm not sure if you're familiar with that system much, but 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 neuroevolution 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 neuroevolution uh 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 to different pathologies. So you have one task is to classify... this disease and another one, this disease, another one, 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 are learning simultaneously other tasks than you would by one task alone. Which is a fascinating idea in itself, yeah. Yes, and people do that all the time. time. I mean, you use knowledge of domains that you know in new domains, and certainly neural networks can do that. Where new revolution comes in is that, what's the best way to combine these tasks?
Now there's architectural design that allows you to decide where and how the embeddings, the internal representations are combined and how much you combine them. And there's quite a bit of research on that. My team, Elliot Mayerson, has worked on that in particular. What is a good internal representation that supports multiple tasks? And we're getting to understand...
how that's constructed and what's in it, so that it is in a space that supports multiple different heads, like you said. And that, I think, is fundamentally how biological intelligence works as well. 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 the structure of...
of the world. And that helps you in all kinds of 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 also it turns out, and that's, again, the 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 if it's very uh very disparate fields um I mean, on that small topic, let me ask you, because you've also, on the competition neuroscience side, you worked on both language and vision.
What's the connection between the two? What's more, maybe there's a bunch of ways to ask this, but what's more difficult to build from an engineering perspective and 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 multitask idea that you're speaking to that they they need to be deeply integrated yeah Absolutely, the latter. Learning both at the same time, I think, is...
a fascinating direction in the future. So you have data sets where there's visual component as well as verbal descriptions, for instance. And that way you can learn a deeper representation, a more useful representation for both. But it's still an interesting question of... Which one is easier? 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.
The visual world, the 3D, what are the objects doing and predicting what will happen, the relationships. That's what makes vision difficult. And language, obviously, it's what is being said, what the meaning is. And the meaning doesn't stop at... who did what to whom, there are goals and plans and themes. And eventually you have to understand the entire human society and history in order to understand the sentence very much.
fully. There are plenty of examples of those kinds of short sentences when you bring in all the world knowledge to understand it. And that's the big challenge. Now, we are far from that, but even just bringing in the visual world 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 had ImageNet for a long time, and now we have...
all these text collections, but having both together and then learning a semantic understanding of what is happening. I think that will be the next step in the next few years. Yeah, you're starting to see that with all the work with Transformers. The community, the AI community started to dip their toe into this idea of having 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 do still wonder what's more fundamental. or their vision is, 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 are taking pixels as a 2D image, that we don't sufficiently think about vision as language. You know, maybe Chomsky is right all along. That vision is fundamental to, 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, linguistic representation. Yeah.
Well, earlier we talked about the social structures, and that may be what's underlying the language. That's the more fundamental part, and then language has been added on top of that. Language emerges from the social interaction. Yeah, that's a very good guess. We are visual animals. A lot of the brain is dedicated to vision. And also, when we think about various abstract concepts, we usually reduce that to vision.
and images. And that's, you know, we go to a whiteboard, you draw pictures of very abstract concepts. So we tend to resort to that quite a bit. And that's a fundamental representation. It's probably... possible that it predated language even. I mean, animals, a lot of, they don't talk, but they certainly do have vision. And language is interesting development in...
From mastication, from eating, you develop an organ that actually can produce sound and manipulate them. Maybe that was an accident. Maybe that was something that was available and then allowed us to... to do the 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 communicated. And I think that it comes from those representations. Now, 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 is supposed to be very much just vision. Well, if you are thinking of semantic concepts, if 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. And when you are understanding a scene, you're understanding relationships.
Well, that'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... Once it closed our eyes, there's still a world in there somehow operating and somehow possibly the visual system somehow integrated into all of it. I tend to enjoy thinking about aliens and thinking about...
The sad thing to me about extraterrestrial intelligent life, that if it was... if it visited us here on earth or if we came on mars and or maybe another 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 were too self-obsessed to see it. Not self-obsessed, but our tools, our frameworks of thinking would... not detect it as a good movie arrival and so on where steven 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... 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
and a common methodology of communication. I think from a computational perspective, the way to ask that is you have very fundamentally different creatures, agents that are created. Would they be able to find a common language? Yes, I do think about that. I mean, I think a lot of people who are in computing, they...
And AI in particular, they got into it because they were fascinated with science fiction and all of these options. I mean, Star Trek generated all kinds of devices that we have now. They envisioned it first and it's a great motivator. to think about things like that. And again, being a computational scientist 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 that 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 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 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. 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 of where those languages can be. Because we can set up their environment differently. It doesn't need to be gravity. You can have all kinds of... Societies can be different. They may have no predators. Everybody is a predator. All kinds of situations. And then see what the space possibly is, where those languages are.
and what the difficulties are. That would be really good, actually, to do that before the aliens come here. Yes, it's good practice. On the similar connection...
You can think of AI systems as aliens. Is there 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 it's an evolution computation 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 paper that you mentioned, the anecdotes, it's quite likely also that the agents learn to...
you know, lie and fake and do all kinds of things like that. I mean, we see that in even very low level, like bacterial evolution. They are... There are cheaters. And who's to say that what they say is actually what they think? 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. You know, they may be saying things just to make us feel good or get us to do what we want, whatever, not turn them off or something. We would have to understand their internal representation is much better to really make sure that that translation is political. But it can be useful. And I think it's possible to do that. There are examples where...
visualizations are automatically created so that we can look into the system 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 interpretable way. I think a fascinating topic, yeah, to do that. 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 world that is social.
I tend to believe that honesty and love are evolutionary advantages in a... in an environment where there's a network of intelligent agents, but it's also very possible that dishonesty and manipulation and... Even, you know, violence, all those kinds of things might be more beneficial. That's the old open question about good versus evil. But I tend to, 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, 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 populace, especially at the basic level, like survival shelter. food, all those kinds of things. But I tend to believe that once you have those things established, then...
Well, not to believe. I guess I hope that AI systems will be honest. But it's fun. It's scary to think about the Turing test. You know, AI systems that will eventually. pass the Turing test will be ones that are exceptionally good at lying. That's a terrifying concept. I mean, I don't know. First of all, from somebody who studied language and obviously,
are not just a world expert in AI, but somebody who dreams about the future of the field. Do you hope, do you think there'll be human level or superhuman level intelligences in the future that... we eventually build? Well, I definitely hope that we can get there. One, I think, important perspective is that we are building AI to help us. That it is a tool like cars or language or communication. AI will help us be more productive.
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 find the future, maybe that might be possible, but not in the foreseeable future when we are building it. And therefore, we are always in a position of limiting what it can or cannot do. Your point about lying is very interesting. Even in these hyena societies, for instance, when a number of these...
Hyenas band together and they take a risk and steal the kill. There are always hyenas that hang back and don't participate in that risky behavior, but they walk in later. join the party 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. I think that this is 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. 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's 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 when we build these systems that autonomously learn.
The really successful ones are honest because that's the best way of getting things done. But there probably are also intelligent agents that find that they can achieve their goals by... by bending the rules or cheating. So that could be a huge benefit to, as opposed to having fixed AI systems. Say we build an AGI system and deploying millions of them that are exactly the same.
there might be a huge benefit to introducing sort of from like an evolution computation perspective, a lot of variation. Sort of like... Diversity in all its forms is beneficial, even if some people are assholes or some robots are assholes. So it's beneficial to have that because you can't always a priori know. what's good, what's bad. But that's fascinating. Absolutely. Diversity is the bread and butter.
I mean, if you're running a competition, you see diversity is the one fundamental thing you have to have. And absolutely. Also, it's not always good diversity. It may be something that can be destructive. We had in these hyenas 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. So there might be something useful there if it's recombined with something else. 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. And we've seen society...
change over time, quite a bit along those lines. There were rules in society that we don't believe are fair anymore, even though they were... considered proper behavior before. So things are changing. And I think that in that sense, I think 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 assholes of the internet that you too have a beautiful purpose in this uh human ecosystem so we i appreciate you very much in moderate quantities yeah in 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 pay attention is do you think about this kind of thing um is there
impressive demonstration to you of artificial life. Do you think of the agents that you work with in the evolutionary computation perspective as life? And where do you think this is headed? Is there interesting systems that we'll be creating more and more that make us redefine, maybe rethink about the nature of life? Different levels of... definition and goals there that 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 after an earthquake or something. You can think of that as an artificial life problem in some sense. 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 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 the start of life. Like I said, primordial soup where eventually you get something self-replicating. And they're really trying to build that. So it's a whole range of topics. And I think that artificial life is a great tool to understand life. And there are questions like sustainability, species, we're losing species. How bad is it? Is it natural? Is there a tipping point? And where are we going? I mean, like the hyena evolution, we may have understood that there's an...
a pivotal point in their evolution. They discovered cooperation and coordination. Artificial life simulations can identify that. maybe encourage things like that. And also, societies can be seen as a form of life itself. I mean, we're not talking about biological evolution, evolution of societies. Maybe some of the same...
phenomena emerge in that domain, and having artificial life simulations and understanding could help us build better societies. Yeah, and thinking from a meme perspective, from... Richard Dawkins, that may be the organisms, ideas of the organisms, not the humans in these societies.
It's almost like reframing what is exactly evolving. Maybe the humans aren't the interesting thing as 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 timescale. And that maybe has more power on the trajectory of life on Earth than does biological evolution. It's the evolution of these ideas. Yes. And it's fascinating, like I said before, that we can keep up somehow biologically. Yeah.
We evolve to a point where we can keep up with this meme evolution, literature, you know, internet. We understand... DNA and we understand fundamental particles. We didn't start that way a thousand years ago and we haven't evolved biologically very much, but somehow our minds are able to extend. And therefore, AI can be seen also as one such step that we created, and it's our tool.
And it's part of that meme evolution 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... multidimensional world that's outside of the four dimensions, the three of space and one of time. And generally we can deal with the world even if you don't understand all the details. We can use computers even though most of us don't know all the...
structure this 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 play a similar role. I have to ask about beautiful artificial life systems or evolutionary computation systems.
Cellular Automata to me. I remember it was 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 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 this world.
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? Do you think about cellular automata? Because cellular automata don't really have... And many other artificial life systems don't necessarily have an objective. 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. Is there some of those systems that you find beautiful? Yeah, yeah. And similarly, evolution does not have a goal. It is responding to the current situation and survival then. creates more complexity, and therefore we have something that we perceive as progress. But that's not what evolution is inherently said to do.
And yeah, that's really fascinating how a simple set of rules or simple mappings from such simple mappings. complexity can emerge. So it's a question of emergence and self-organization. And 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 nonlinear interactions and this kind of complexity can 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. It's a long sequence of them. There's lots of them, but it's a very simple representation. And similar evolution computation, whatever string or tree representation we have, and the operations, you know, the amount of code. that's required to manipulate those. It's really, really little. And of course, came alive even less. So how complexity emerges from such simple principles, that's absolutely fascinating.
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 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. And so I guess the question is for us humans that are mortal and then life ends quickly. We want to kind of hurry up.
and make sure we take evolution, the trajectory that is a little bit more efficient than... the alternatives and that touches upon something we talked about earlier that evolution competition 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 and uh weak presser and large populations. One great example of this is the novelty search. 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. Nothing else. And you know what? you actually discover things that are interesting and useful that way. Ken Stanley and Joe Lehman did this one study where they actually tried to evolve walking behavior on robots. And that's actually what we talked about earlier, where...
your robot actually failed in all kinds of ways and eventually discovered something that was a very efficient walk. And it was because they rewarded... things that were different, that you were able to discover something. And I think that this is crucial 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 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. 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 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 encode the structure of the domain, then you will get to these solutions and these 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? Forget career, just succeed in life. From an evolutionary computation perspective. Yes. Yes, definitely. Explore. Diversity. Exploration. And individuals take classes in music, history, philosophy, you know, math, engineering. See connections between them. Travel, you know, learn a language.
I mean, all this diversity is fascinating and we have it at our fingertips today. It's possible. You have to make a bit of an effort because it's not easy. But the rewards are wonderful. Yeah, there's something interesting about an objective function of new experiences. So try to figure out, I mean, what is the maximally new experience I could have today?
And so that novelty, optimizing for novelty for some period of time might be a very interesting way to sort of maximally expand the sets of experiences you had and then ground from that perspective. like what you what will be the most fulfilling trajectory through life of course the flip side of that 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, at least from my mind, where scarcity has an 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 pigging 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 a food is incredible. So the downside of exploration is that...
Somehow, 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 a hundred things, but maybe a few topics where you can take a deep enough time dive that you...
gain an understanding. You yourself have to decide at some point that this is deep enough. And I've obtained what I can from this topic. And now it's time to move on. And that might take years. 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 something, you know, 10,000 hours makes, you need 10,000 hours to become an expert on something. So you don't have to become an expert, but 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've 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, you may find something that you can't stay away from. Like for us, it was computers, it was AI, it was, you know, that you, I just have to do it, you know, and I, 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 exploration part. And then beyond that, it... After that exploration, you actually can focus and build a career. And, you know, even there you can switch multiple times. But I think that diversity exploration is fundamental to having a successful career as is.
concentration and spending an effort where it matters. But you are in better position to make the choice when you have done your homework. Explored. So exploration precedes commitment, but both are beautiful. So again, from an evolutionary computation perspective, we'll 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 for us 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 aging came to be and eventually will be no more is there meaning to it all yeah in evolution there is meaning everything is a potential Everything is a potential stepping stone. Not all of them are going to work out. Some of them are foundations for further improvement.
and even those that are perhaps going to die out, where potential lineages, potential solutions. In biology, we see a lot of species die of naturally and, you know, like the dinosaurs. I mean, they were really good solution for a while, but then it didn't turn out to be not such a... good solution in the long term. When there's an environmental change, you have to have diversity. Some other solutions become better. It doesn't mean that the...
That was an attempt. It didn't quite work out or last. But there are still dinosaurs and mountains, at least their relatives. 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 potential innovations uh on their own and also as um 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, is that just a silly human notion in an individualistic society and they should let go of that? Do you find beauty in being part of the giant machine? Yeah, I think it's meaningful. I think it adds purpose to your life that you are part of something bigger. That said, do you ponder your individual agent's mortality? Do you think about death? Do you fear death? Well, certainly more now than when I was a...
and did skydiving and paragliding and all these things. You've become wiser. There is a reason for this life arc that... Younger folks are more fearless in many ways. It's part of the exploration. You know, 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 don't necessarily think that way, but younger do. And it's kind of counterintuitive. But logically, it's like, you have a 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 with the limited resources that I have? That's how I think.
a lot of people, myself included, start thinking later on in their career. And like you said, leave a bit of a trace and a bit of an impact, even 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 and Austin is. It's really exciting for me 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 Risto McCleinan, 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.