Risto Miikkulainen: Neuroevolution and Evolutionary Computation
嘉宾：Risto Miikkulainen
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本篇目录13 章节
§01Transcript · 00:00–08:53§02Transcript · 08:53–18:40§03Transcript · 18:40–27:32§04Transcript · 27:32–37:00§05Transcript · 37:00–45:33§06Transcript · 45:33–54:46§07Transcript · 54:46–1:04:03§08Transcript · 1:04:03–1:13:07§09Transcript · 1:13:07–1:22:02§10Transcript · 1:22:02–1:31:37§11Transcript · 1:31:37–1:40:36§12Transcript · 1:40:36–1:50:28§13Transcript · 1:50:28–1:56:18§01Transcript · 00:00–08:53
The following is a conversation with Risto Michaelainen,
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 Harbin's 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 genetic algorithms
to cellular automata to neural networks
have always captivated my imagination,
not only for their surprising power
in the face of long odds,
but because they always opened up doors
to new ways of thinking about computation.
It does seem that in the long arc of computing history,
running toward biology, not running away from it
is what leads to long term progress.
This is the Lex Friedman podcast,
and here is my conversation with Risto Michaelainen.
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 a computer scientist,
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 when you have speech,
it might be some other kind of sounds,
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, constructing our environment.
So I think that we will get that.
We get some evolution producing,
some agents that can do that,
manipulate the environment and build.
What do you think is special about humans?
Like if you were running the simulation
and you observe humans emerge,
like these tool makers,
they start a fire and all this 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?
Cause you're like really busy
as the creator of this evolutionary system.
So you don't have much time to observe,
like detect if any cool stuff came up, right?
How would you detect humans?
Well, you are running the simulation.
So you also put in visualization
and measurement techniques there.
So if you are looking for certain things like communication,
you'll have detectors to find out whether that's happening,
even if it's a large 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.
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.
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.
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.
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?
Yeah, well, I think that the first measurement
would be to detect how much of an effect
you can have in your environment.
So if you look 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.
And that's something pretty unique.
I mean, there are certainly birds build nests
but they don't build quite cities.
Termites build mounds and ice and things like that.
But the complexity of the human construction cities,
I think would stand out even to an external observer.
Of course, that's what a human would say.
Yeah, and you know, you can certainly say
that sharks are really smart
because they've been around so long
and they haven't destroyed 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 the simulation that we run over and over
might start, it's a slow start.
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 zero,
which is the springing up,
like the origin of life on earth?
And second, how unlikely is the,
anything interesting happening beyond that?
So 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?
And they are very close.
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, 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 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 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.
§02Transcript · 08:53–18:40
But to get something as complex as the brain,
we don't quite know the conditions for that.
And how do you 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.
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.
There are, animals would be intelligent, of course,
with that definition.
And you might have even some other forms of life, even.
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 one 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 force behind
all of the creative efforts of human nature.
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 wanna 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 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 the nicest definition of intelligence
within 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.
They could achieve those kind of goals.
So 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 a,
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 one butterfly
that created all of the chaos.
So maybe that's not, maybe that's a very poetic way
to think that that's something we humans
in a human centric way wanna 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 effects.
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 compute?
That contribute, that's a pretty high bar though.
Uniqueness, yeah.
So, you have to be Mozart or something to actually
reach that level that nobody would have developed that,
but other people might have solved this equation
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.
And it's a positive effect, of course,
you want it to be a positive effect.
Do you think it's possible to engineer
into 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 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 have 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 like 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 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.
So I mentioned emotions being something
that allow you to focus and pay attention,
filter out what's important.
Yeah, you can have that kind of a filter mechanism
and it puts you in a different state.
Your computation is in a different state.
Certain things don't really get through
and others are heightened.
Now you label that box emotion.
I don't know if that means it's an emotion,
but it acts very much like we understand
what emotions are.
And we actually did some work like that,
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.
§03Transcript · 18:40–27:32
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 the kill.
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, coordinate the actions.
And I think a critical aspect of that,
the way you're describing is emotion there
is a mechanism of social communication,
of a 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.
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 build everything
on top of these kinds of structures.
And one interesting theory around that,
bigotness theory, for instance, for language,
but 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.
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 Dorego,
I think it was, these 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 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 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.
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.
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.
Yeah.
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 early on in your work or maybe today?
I think evolution computation is the most amazing method.
So what fascinates me most is that 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 really is fascinating.
Yeah, I think 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.
Like 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 there's 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 like, I feel like I found God.
Every time I like, it's like,
because you've really created something that's living.
Yeah.
Even if it's a simple program.
It has a life of its own, it has the intelligence of its own.
It's beyond what you actually thought.
Yeah.
And that is, I think it's exactly spot on.
That's exactly what it's about.
You created something and it 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 it's,
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 has to be new.
§04Transcript · 27:32–37:00
It has to be useful and it has to be surprising.
And those certainly are true with, say,
evolutionary computation discovering solutions.
So maybe an example, for instance,
we did this collaboration with MIT Media Lab,
Caleb Harbus 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, evolutionary 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 pushed to that limit.
So we look at it and say, well, 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 are 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 the things we think
that are fundamental to living systems on Earth today,
whether they're actually fundamental
or they somehow fit the constraints of the system.
And all we 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 in your link.
The idea there is 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,
from a very high level philosophical perspective,
is limitless.
But I wonder how limitless it is.
Are the constraints we have features
that are fundamental to our intelligence?
Or is this just this weird constraint
in terms of our brain size and skull
and lifespan and senses?
It's just the weird little 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 evolution and computation
to the constraints of the environment?
Well, at first I'd like to comment on that,
like changing the inputs to human brain.
And flexibility of the brain.
I think there's a lot of that.
There are experiments that are done in animals
like Mikangazuru at MIT,
switching the auditory and visual information
and going to the wrong part of the cortex.
And the animal was still able to hear
and perceive the visual environment.
And there are kids that are born with severe disorders
and sometimes they have to remove half of the brain,
like one half, and they still grow up.
They have the functions migrate to the other parts.
There's a lot of flexibility like that.
So I think it's quite possible to hook up the brain
with different kinds of sensors, for instance,
and something that we don't even quite understand
or have today on different kinds 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 the 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.
It would have to learn to filter it and focus
and 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.
But 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 I tend to think that the limitations
is where the superpower is,
that immortality and a 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 limited.
No, exactly, you make do with what you have,
but you don't have to be a genius
but 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 we are already, the brain is already drinking
the water, firehose, like vision.
There's way more information in vision
that we actually process.
So brain is already good at identifying what matters.
And that we can switch that from vision
to some other wavelength or some other kind of modality.
But I think that the same processing principles
probably still apply.
But also indeed this ability to 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.
And it's already common knowledge
and we are building on it.
And we don't see a problem where
there's too much information that we can absorb and learn.
Maybe people become a little bit more narrow
in what they know, they are in one field.
But this information that we have accumulated,
it is passed on and people are picking up on it
and they are building on it.
So it's not like we have reached the point of saturation.
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
like Wikipedia becoming a sensor.
Like, so the fire hose of information from Wikipedia.
So it's like you integrated 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 early steps
on the Neuralink side?
I don't know if you got a chance to see,
but there was 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 video game.
The dream is to be able to increase further
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, there are devices that read your EEG for instance,
and humans can learn to control things
§05Transcript · 37:00–45:33
using just their thoughts in that sense.
And 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.
Is there, so you mentioned surprising
being a characteristic of creativity.
Is there something, you already mentioned a few examples,
but is there something that jumps out at you
as was particularly surprising
from the various evolutionary computation systems
you've worked on, the solutions that were
come up along the way?
Not necessarily the final solutions,
but maybe things that would 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, for instance.
A lot of, 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, I guess, describe that situation
if you think that's still?
Yeah, that's a quite a bit smaller scale
than our basic 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 this one, 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 the other teams crashed.
And then when we look at it, like what was going on
was that evolution discovered that if it makes a move
that's really, really far away,
like millions of squares away,
the other teams, the other programs has expanded memory
in order to take that into account
until they run out of memory and crashed.
And then you win a tournament
by crashing 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 opponent's brain,
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.
I mean, this was even Kasparov pointed that 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.
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?
What are the connections to the,
what are the echoes of the connection to his biological?
A lot of these algorithms really do take motivation
from biology, but they are caricatures.
You try to essentialize it
and take the elements that you believe matter.
So in evolutionary 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 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 in biological systems
have DNA and all those kinds of things.
And so you have to have similar kind of encodings
in a computer program.
Yes, and that is a big question.
How do you encode these individuals?
So there's a genotype, which is that encoding
and then a decoding mechanism gives you the phenotype,
which is the actual individual that then performs the task
and in an environment can be evaluated how good it is.
So even that mapping is a big question
and how do you do it?
But typically the representations are,
either they are strings of numbers
or they are some kind of trees.
Those are something that we know very well
in computer science and we try to do that.
But they, and DNA in some sense is also a sequence
and it's a string.
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 tail bones
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
and your representation should support it.
And that is quite difficult if we are limited
with strings or trees,
and then we are pretty much limited
what can be constructed.
And one thing that we are still missing
in evolutionary 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 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,
a multi cell organism.
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 a selection.
And how do you make that jump to the next level?
Yes, how do you make the jump?
As part of the algorithm.
Yeah, yeah.
So we haven't really seen that in computation 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,
§06Transcript · 45:33–54:46
those agent all of a sudden spontaneously decide
to then be together.
And then your entire system would then be treating them
as one agent.
Something like that.
Some kind of weird merger building.
But also, so you mentioned,
I think you mentioned selection.
So basically there's an agent and they don't get to live on
if they don't do well.
So there's some kind of measure of what doing well is
and isn't.
And 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 parts of the representation.
You do some kind of recombination.
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, the 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 computation 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 evolution computation.
And in that sense, it has diverged a bit
from biological evolution.
And that's one of the things I think we could look at again,
having a weaker selection, more crossover,
large populations, more time,
and maybe a different kind of creativity
would come out of it.
We are very impatient in evolution 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.
And it's more patient.
Yes, much more patient.
So I guess we need to add some kind of mating,
some kind of like dating mechanisms,
like marriage maybe in there.
So into our algorithms to improve the combination
as opposed to all mutation doing all of the work.
Yeah, and many ways of being successful.
Usually in evolution computation, we have one goal,
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 eat this or eat that.
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,
maybe 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 approach.
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 his lifetime,
but an entire population as a whole
can discover what works.
And there you can afford individuals that don't work out.
They will, you know, everybody dies
and you have a next generation
and they 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.
And from the point of view of what algorithm you wanna 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 learning.
There's very few things as entertaining
as watching either evolutionary 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 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.
Yes, so if you have a reinforcement learning agent,
it tries to be conservative
because it wants to walk as long as possible and be stable.
But if you have evolutionary computation,
it can afford these agents that go haywire.
They fall flat on their face and they could take a step
and then they jump and then again fall flat.
And eventually what comes out of that
is something like a falling that's controlled.
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 movement.
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 the discovery of that is,
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.
§07Transcript · 54:46–1:04:03
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 something subjective also about that, right?
I've been thinking a lot about that,
especially in the human robot interaction context.
You know, I mentioned Spot, the Boston Dynamics robot.
There is something about human robot communication.
Let's say, let's put it in another context,
something about human and dog context,
like a living dog,
where there's a dance of communication.
First of all, the eyes, you both look at the same thing
and the dogs communicate with their eyes as well.
Like if you're a human,
if you and a dog want to 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
and it all seems natural and often joyful.
And for robots to communicate that,
it's really difficult how to figure that out
because it's almost seems impossible to hard code in.
You can hard code it for demo purpose or 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 that 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 I 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
what the other robot was doing.
And in the end, there was a behavior
where one of the robots, the most sophisticated one,
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 anthropomorphizing 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.
But I feel like if you add a little bit of a place
for a theory of mind to emerge like easier,
then you can go really far.
I mean, some of these things with evolution, you know,
you add a little bit of design in there, it'll really help.
And I tend to think that a very simple theory of mind
will go a really long way for cooperation between agents
and certainly for human robot interaction.
Like it doesn't have to be super complicated.
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 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.
They're currently just afraid of everything.
They're more, they're not the kind that fall
and discover how to run.
They're more like, please don't touch anything.
Don't hurt anything.
Stay as far away from humans as possible.
Treat humans as ballistic objects that you can't,
that you do with a large spatial envelope,
make sure you do not collide with.
That's how, like you mentioned,
Elon Musk thinks about autonomous vehicles.
I tend to think autonomous vehicles need to have
a beautiful dance between human and machine,
where it's not just 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 the 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
and jointly, 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 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.
You have to predict that by observing the human.
And that seems like a machine learning problem
to figure out what's the human up to.
It's not as simple as sort of planning
just because they move their arm
§08Transcript · 1:04:03–1:13:07
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 the motivation behind the movement of the arm.
Here we are talking about relatively simple physical actions,
but you can take that the higher levels also
like to predict what the people are going to do,
you need to know what their goals are.
What are they trying to, are they exercising?
Are they just starting to get somewhere?
But even higher level, I mean,
you are predicting what people will do in their career,
what their life themes are.
Do they want to be famous, rich, or do good?
And that takes a lot more information,
but it allows you to then predict their actions,
what choices they might make.
So how does evolution and computation apply
to the world of neural networks?
I've seen quite a bit of work from you and others
in the world of neural evolution.
So maybe first, can you say, what is this field?
Yeah, neural evolution is a combination of neural networks
and evolution computation in many different forms,
but the early versions were simply using evolution
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.
You don't, again, you don't know what the right answers are,
you don't have backprop,
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 generalize, interpolate between known situations.
So you want to have a neural network in such a task,
even if you don't have a supervised targets.
So that's a reason and that's a solution.
And also more recently,
now when we have all this deep learning literature,
it turns out that we can use evolution
to optimize many aspects of those designs.
The deep learning architectures have become so complex
that there's little hope for us 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 generalize that.
You could generate that, even data augmentation,
all the different aspects of the design
of deep learning experiments could be optimized that way.
So that's an 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 faculties.
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 of DNA
is not detailed enough or plentiful enough to describe them.
We can describe how to set the brain up,
but we can, evolution can decide on a starting point
and then have a learning algorithm
that will construct the final product.
And this interaction of 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 an organism is really fascinating space.
How hard is it, do you think, to grow a 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.
And 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.
So, but that's starting from a 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, there are a couple of challenges.
One of them is to define the search base.
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 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, right?
I mean, deep learning networks may take days to train
while imagine you having a population of a hundred
and have to run it for a hundred generations.
It's not yet quite feasible computationally.
It will be, 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.
And do you think it's possible to do like a tiny baby network
that grows into something that can do state of the art
on like even the simple data set like MNIST,
and just like 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 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 the interactions at will,
the supervised environment doesn't really,
it's not as easily usable here.
Sorry, the interaction between neural networks?
Yeah, the neural networks that you're creating,
interacting with 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 like there's just like,
there's bigger fish and smaller fish
and it's fascinating to think
§09Transcript · 1:13:07–1:22:02
that you could have neural networks competing
against each other in one neural network
being able to destroy another one.
There's like wars of neural networks competing
to solve the MNIST problem, I don't know.
Yeah, yeah.
Oh, totally, yeah, yeah, yeah.
And we actually simulated also that prey
and it was interesting what happened there,
Padmini Rajagopalan did this
and Kay Holkamp was a zoologist.
So we had, again,
we had simulated hyenas, simulated zebras.
Nice.
And initially, 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 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 kind of circled them.
And then when they circled them,
they could actually herd the zebras together
and eat multiple zebras.
So there was like an arms race of predators and prey.
And they gradually developed more complex behaviors,
some of which we actually do see in nature.
And this kind of coevolution,
that's competitive coevolution,
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 rich 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, yeah.
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, you know, Andre Kapathy leads that team
on the machine learning side.
And there's a multitask network, multiheaded 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 multiheaded beast
that's operating in the real world?
Yes, it's a very good problem for neuroevolution.
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.
I mean, you use knowledge of domains that you know
in new domains, and certainly neural network can do that.
When neuroevolution comes in is that,
what's the best way to combine these tasks?
Now there's architectural design that allow 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.
And my team, Elliot Meyerson has worked on that
in particular, like 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 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, a surprise that Elliot found
was that those tasks don't have to be very related.
You know, you can learn to do better vision
by learning language or better language
by learning about DNA structure.
No, somehow the world.
Yeah, it rhymes.
The world rhymes, even if it's very disparate fields.
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 best as the multitask idea
that you're speaking to
that 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 we 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.
Like 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 you eventually 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 that will be the next step
in the next few years.
Yeah, you're starting to see that
with all the work with Transformers,
was the community, the AI community
starting 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 maybe at some point we'll just dive into the pool
and it'll just be all seen as the same thing.
I do still wonder what's more fundamental,
whether 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,
and because we have cameras that are taking pixels
as a 2D image, that we don't sufficiently think
about vision as language.
Maybe Chomsky is right all along,
that vision is fundamental to,
sorry, that language is fundamental to everything,
to even cognition, to even consciousness.
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
§10Transcript · 1:22:02–1:31:37
and that may be what's underlying the language,
and that's the more fundamental part,
and then language has been added on top of that.
Language emerges from the social interaction.
Yeah, that's a very good guess.
We are visual animals, though.
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
to manipulate them.
Maybe that was an accident.
Maybe that was something that was available
and then allowed us to do the communication,
or maybe it was gestures.
Sign language could have been the original proto language.
We don't quite know, but 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,
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 we close 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 visited us here on Earth,
or if we came on Mars or maybe another solar system,
another galaxy one day,
that 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 Stephen Wolfram and his son,
I think were part of developing this alien language
of how aliens would communicate with humans.
Do you ever think about that kind of stuff
where if humans and aliens would be able to communicate
with each other, like if we met each other at some,
okay, we could do SETI, which is communicating
from across a very big distance,
but also just us, 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 I, so one, 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.
And so like languages that evolve come up with,
can we translate, like I have a Google translate
for the evolved languages.
Yes, and if we do that enough,
we have perhaps an idea what an alien language
might be like, the space 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.
They may have all, everybody's a predator.
All kinds of situations.
And then see what the space possibly is
where those languages are and what the difficulties are.
That'd 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 it explainable AI,
for AI systems to be able to communicate.
So you evolve a bunch of agents,
but for some of them to be able to talk to you 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 competition system,
for instance, you reward those solutions
that are actually functional.
That communication makes sense.
It allows us to together again, achieve common goals.
I think that's possible.
But even from that paper that you mentioned,
the anecdotes, it's quite likely also
that the agents learn to lie and fake
and do all kinds of things like that.
I mean, we see that in even very low level,
like bacterial evolution.
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.
They may be saying things just to make us feel good
or get us to do what we want,
but they would not turn them off or something.
But so we would have to understand
their internal representations much better
to really make sure that that translation is critical.
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 that 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.
Yeah, you're making me realize
that it's a good scientific question
whether lying is an effective mechanism
for integrating yourself and succeeding
in a social network, in a world that is social.
I tend to believe that honesty and love
are evolutionary advantages in an environment
where there's a network of intelligent agents.
But it's also very possible that dishonesty
and manipulation and even 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,
they're just kind to others sometimes for no reason
will do better.
In a society that's not highly constrained on resources.
So 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 scary to think about the Turing test,
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, sort of from somebody who studied language
and obviously are not just a world expert in AI,
§11Transcript · 1:31:37–1:40:36
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.
And that is always a condition.
It's not something that we build and let run
and it becomes an entity of its own
that doesn't care about us.
Now, of course, really find the future,
maybe that might be possible,
but not in the foreseeable future when we are building it.
And therefore we always in a position of limiting
what it can or cannot do.
And your point about lying is very interesting.
Even in these hyenas 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 and 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 on opportunity.
If you have a society, I think that is just 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 gonna go away.
There will be hopefully a minority
so that they don't get in the way.
And we studied in these hyena simulations,
like what the proportion needs to be
before it is no longer functional.
And you can point out that you can tolerate
a few cheaters and a few liars
and the society can still function.
And that's probably going to happen
when we build these systems at 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 bending the rules or cheating.
So that could be a huge benefit
as opposed to having fixed AI systems.
Say we build an AGI system and deploying millions of them,
it'd be that are exactly the same.
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 like it's beneficial to have that
because you can't always a priori know
what's good, what's bad.
But that's a fascinating.
Absolutely.
Diversity is the bread and butter.
I mean, if you're running an evolution,
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.
That 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 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 human ecosystem.
So I appreciate you very much.
In moderate quantities, yeah.
In moderate quantities.
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?
Is there impressive demonstration to you
of artificial life?
Do you think of the agency you work with
in the evolutionary computation perspective as life?
And where do you think this is headed?
Like, is there interesting systems
that we'll be creating more and more
that make us redefine, maybe rethink
about the nature of life?
Different levels of definition and goals there.
I mean, at some level, artificial life
can be considered multiagent 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 a pivotal point
in their evolution.
They discovered cooperation and coordination.
Artificial life simulations can identify that
and 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
of from Richard Dawkins,
that maybe the organisms, ideas of the organisms,
not the humans in these societies that from,
it's almost like reframing what is exactly evolving.
Maybe the interesting,
the humans aren't the interesting thing
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
is the evolution of these ideas.
Yes, and it's fascinating, like I said before,
that we can keep up somehow biologically.
We evolved to a point where we can keep up
with this meme evolution, literature, 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 created,
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,
look at strength 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 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,
§12Transcript · 1:40:36–1:50:28
like really understand what it means to live
in a multi dimensional world
that's outside of the four dimensions,
the three of space and one of time.
Translation, and generally we can deal with the world,
even if you don't understand all the details,
we can use computers, even though we don't,
most of us don't know all the structure
that's 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 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.
And 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?
Is there, 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 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 set to do.
And yeah, that's really fascinating
how a simple set of rules or simple mappings can,
how 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 similarly with evolutionary computation,
whatever string or tree representation we have
and the operations, the amount of code that's required
to manipulate those, it's really, really little.
And of course, game of life 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 wanna kinda 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 impatient.
We have a goal, we want it right away
whereas this biology has a lot of time and deep time
and weak pressure 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 Joel Lehmann did this one study
where they actually tried to evolve walking behavior
on robots.
And that's actually, we talked about earlier
where your robot actually failed in all kinds of ways
and eventually discovered something
that was a very efficient walk.
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.
That the fundamentals are there.
And when you follow those fundamentals
you get into points
and some of those are actually interesting and useful.
Now, even in that robotic Walker simulation
there was a large set of garbage,
but among them, there were some of these gems.
And then those are the ones
that somehow you have to outside recognize and make useful.
But this kind of productive systems
if you code them the right kind of principles
I think that encode the structure of the domain
then you will get to these solutions and discoveries.
It feels like that might also be a good way to live life.
So let me ask, do you have advice for young people today
about how to live life or how to succeed in their career
or forget career, just succeed in life
from an evolution and computation perspective?
Yes, yes, definitely.
Explore, diversity, exploration and individuals
take classes in music, history, philosophy,
math, engineering, see connections between them,
travel, 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 that sort of that novelty, optimizing for novelty
for some period of time might be very interesting way
to sort of maximally expand the sets of experiences you had
and then ground from that perspective,
like 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 choice 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 100 things,
but maybe a few topics
where you can take a deep enough dive
that you gain an understanding.
You yourself have to decide at some point
that this is deep enough.
And I 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 in order to achieve something,
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 they 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
§13Transcript · 1:50:28–1:56:18
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 I just have to do it.
And I, you know, and then 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, 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.
And, 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.
Yeah.
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 far as humans, you're just one agent
who's going to be dead, unfortunately, one day too soon.
What do you think is the why
of why that agent came to be
and eventually will be no more?
Is there a meaning to it all?
Yeah.
In evolution, there is meaning.
Everything is a potential direction.
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
were potential energies, potential solutions.
In biology, we see a lot of species die off naturally.
And you know, like the dinosaurs,
I mean, they were really good solution for a while,
but then it didn't turned 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.
Doesn't mean that that was an attempt.
It didn't quite work out or last,
but there are still dinosaurs among us,
at least their relatives.
And they may one day again be useful, who knows?
So from an individual's perspective,
you 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 on their own.
And also as raw material perhaps,
or stepping stones for other things that could come after.
But it still feels from an individual perspective
that I matter a lot.
But even if I'm just a little cog in a giant machine,
is that just a silly human notion
in an individualistic society, no, she'll 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 youngster
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.
That's part of the exploration.
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.
So yeah, 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 though after the agent is gone.
Yeah, that's the goal.
Well, this was a fascinating conversation.
I don't think there's a better way to end it.
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 McAlignan.
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.