Risto Miikkulainen is a computer scientist at UT Austin. Please support this podcast by checking out our sponsors:
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OUTLINE:
0:00 - Introduction
1:07 - If we re-ran Earth over 1 million times
4:24 - Would aliens detect humans?
7:02 - Evolution of intelligent life
10:47 - Fear of death
17:03 - Hyenas
20:28 - Language
23:59 - The magic of programming
29:59 - Neuralink
37:31 - Surprising discoveries by AI
41:06 - How evolutionary computation works
52:28 - Learning to walk
55:41 - Robots and a theory of mind
1:04:45 - Neuroevolution
1:15:03 - Tesla Autopilot
1:18:28 - Language and vision
1:24:09 - Aliens communicating with humans
1:29:45 - Would AI learn to lie to humans?
1:36:20 - Artificial life
1:41:12 - Cellular automata
1:46:49 - Advice for young people
1:51:25 - Meaning of life

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The following is a conversation with Risto Michelinan, 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.

This is the Lex Friedman Podcast, and here is my conversation with Visto Michelinen.

If we ran the Earth experiment, this fun little experiment we're on, over and over and over and over a million times, and watched 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 million times and actually answer that question.

How much variation do we see when we simulate it?

And 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 why we have speech, might be some other kinds of sounds and decision-making, but also vision, eye has evolved many times.

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.

We get some evolution producing some agents that can do that, manipulate the environment and build.

What do you think is special about humans?

Like if you were running the simulation and you observe humans emerge, like these like tool makers, they start a fire and all that stuff, start running around, building buildings and then running for president and all those kinds of things.

What would be, how would you detect that?

Because you're like really busy as the creator of this evolutionary system, so you don't have much time to observe, like detect if any cool stuff came up, right?

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.

We know roughly what we want, intelligent agents that communicate, cooperate, manipulate, and we would build detections and visualizations of those processes.

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 to begin with.

Well, that seems really difficult to build a detector of intelligent or intelligent communication.

Sort of if we take an alien perspective, observing earth, are you sure that they would be able to detect humans as the special thing?

Wouldn't they be already curious about other things?

There's way more insects by body mass, I think, than humans by far, and colonies.

Obviously dolphins is the most intelligent 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.

I just learned that sharks have been here 400 million years and that's longer than trees have been here.

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 short time and we're just creating a lot of pollution, but so is the other creatures.

So 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 things?

Do you basically have to have a strict objective function by which you measure the performance of a system, or can you find curiosities and interesting things?

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.

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 construction cities, I think would stand out even to an external observer.

Of course, that's what a human would say.

And 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.

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 the simulation that we run over and over might start slowly.

So do you think how unlikely, first of all, I don't know if you think about this kind of stuff, but How unlikely is step number 0, which is the springing up, like the origin of life on earth?

And second, how unlikely is anything interesting happening beyond that?

Sort of like the start that creates all the rich complexity that we see on Earth today.

Yeah, there are people who are working on exactly that problem from primordial soup.

How do you actually get self-replicating molecules?

With a little bit of help, you can make that happen.

So 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, it 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 handholding, 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 was something that happened really fast.

And if it happens, does it go in the same direction?

Even in computational terms, I think that it's relatively possible to come up with, 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 a favorite ways you like to think about that question of what is intelligence?

Well, my goal is to create agents that are intelligent.

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.

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 like there's, I don't know if you're familiar, there's a philosopher named Ernest Becker who wrote the denial of death and his whole idea.

And there's folks, psychologists, cognitive scientists that work on terror management theory.

And they think that 1 of the special things about humans is that we're able to sort of foresee our death, right?

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.

And that in itself is the most, is the force behind all of the creative efforts of human nature.

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, second level of definition, that intelligence means something and 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 in a 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.

So a particular agent, the ripple effects of their existence on the entirety of the system is significant.

So like they leave a trace where there's like, yeah, like ripple effects.

But see, then you go back to the butterfly with the flap of a wing.

And then you can trace a lot of like nuclear wars and all the conflicts of human history, somehow connected to that 1 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 impact, like that is the secondary effect of our intelligence.

We've had the long-lasting impact on the world, but maybe the entirety of physics in the universe has a very long lasting effect.

Sure, but you can also think of it, what if, like the wonderful life, what if you're not here?

Is it something that you actually contributed because you had something unique to contribute?

Yeah, so 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 to be called intelligence.

But we have this local effect that is changing.

If you weren't there, that would not have happened.

Do you think it's possible to engineer in to computational agents a fear of mortality?

So there's a very trivial thing where it's like, you could just code in a parameter, which is how long the life ends, but more of a fear of mortality, like awareness of the 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 referring to the emotion of fear, something because we 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 if fear of death plays the same kind of role, but if it consumes you and it's something that you think in normal life and 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?

You can build representations and functions, I think, into these agents that act like emotions and consciousness, perhaps.

So I mentioned Emotions being something that allow you to focus and pay attention, filter out what's important.

Yeah, you can have that kind of a filter mechanism.

Your computation is in a different state.

Certain things don't really get through and others are heightened.

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, hyenas 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.

And probably there are some states, like emotions that the hyenas go through.

The first day, 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 2 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 2 emotions.

So it's both repelled and attractive, but then this 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,

And I think a critical aspect of that, the way you're describing is emotion there 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 an outgrowth of social interaction.

And maybe for the creation of intelligent agents, we have to be creating fundamentally social systems.

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.

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 build everything on top of these kind of structures.

And 1 interesting theory around that, Bigotin'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 that 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.

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 1 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 Dorigo, I think it was, these robots, little robots that had to navigate through an environment, and there 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 grab each other with their gripper, they formed a robot that was much longer than a team.

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.

Yeah, and the way you described the system of hyenas, it almost sounds algorithmic.

Like the problem with humans is they're so complex, it's hard to think of them as algorithms.

But with hyenas, 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.

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, that hyenas are relatively rigid in what they can do.

And therefore, you could look at this behavior, like this is a breakthrough in evolution about to happen.

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.

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

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 it's evolutionary computation is the most amazing method.

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.

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 now, if you take that 1 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.

It's so great that we can build systems, algorithms, that can be in some sense smarter than we are, that they can discover solutions that we might miss.

A lot of times it is because we have, as humans, we have certain biases.

We expect the solutions to be a certain way.

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Risto Miikkulainen: Neuroevolution and Evolutionary Computation | Lex Fridman Podcast #177