>> STRICKLAND: Hey there.
I'm Henry Strickland, our speaker is Virgil
Griffith he's talking about Polyworld using
evolution to design artificial intelligence
and having had to take artificial intelligence
classes in--in college, I'd be very happy
to let evolution do it instead of me debugging
all those list programs they gave me.
So, Virgil, as a young lad read a little too
much of Douglas Hofstadter and he therefore
dedicated his life to cognitive science and
causing trouble.
After some under graduate at University of
Alabama, he went to Indiana, where he teamed
up with Larry Jaeger.
Some of the older Googlers might know Larry
Jaeger form Apple Computer.
He had a project called Polyworld long time
ago and it still leaves on and Virgil's been
working on it and adding features and things
to it.
Virgil has done internships at the Santa Fe
Institute and at the Keck Institute and now
is his first year as a grad student at Caltech.
All right.
>> GRIFFITH: Thank you Stu.
Hi.
I'm Virgil, I'm a--I'm first year grad student
at Caltech.
You can reach me, that's my--that's my web
site for those of you wondering, the .gr stands
for Griffith, people get confused about that.
I'm not Greek.
And that's my email address.
So--so, in short, yes, I'll be talking to
you about basically trying to use evolutionary
algorithms as a shortcut to creating artificial
intelligence.
Simply because artificial intelligence is
well, hard and--and evolution is fairly easy--well
this was easy to set up.
And the hope is that--that us--we can take
advantage having lots of the CPU cycles and
we let evolution to do a lot of the designing
for us.
So--so, that's the--that's the general gist
and well--well, let's move on with it.
So, there we go.
So, what I interested in--feel like asking,
what is artificial life anyway?
They just go, you know, this is ill-defined.
Well, in short, artificial life is--is--artificial
life is like a super set of biology.
So, all biology is artificial life but to
be more precise, artificial life is all as
it is today.
So, as it says in the circle, and also what
like potentially could be.
So, all these, all their possible evolutionary
paths that are--that evolution could have
taken will also ultimately create artificial
life and will be [INDISTINCT] with these areas
because we'll be hoping to explore AI.
Please I'll say it once.
So, I was--so, just to begin, let's show real
quick.
So, this is a brief intro to evolution.
Evolution is an algorithm.
It's really straight forward actually.
Here's how it goes.
You had a population and you have--and some
things stick around more than others.
So, and--but some, yeah, that must--must be
the case.
So, that's for selection.
And then, you had these things--there are
some heredity.
And then, you rinse-repeat.
And regardless of substrate, you always get
evolution with this.
Very straight forward.
You have a population of things, you--you
only have only one that you have hill climbing
and that--that's crap, you got to have a bunch
and some reproduce more than others straight
forward and then there's heredity.
And with--with occasional errors.
Done.
It's all you got to do.
So, no matter--just--yeah, it's great.
So, okay, get that on the table.
So moving on, I'm showing you a nice--nice--good
example of using evolution to design body
plans so, this is--before we get to AI.
And this was not my work, this is by Carl
Sims in 1994, it's very [INDISTINCT] so I'm
showing it to you.
So, basically in this case--so, he's doing--using
evolution to design bodies--design body--body
morphologies to do a different task in the
world.
In this case, the population is a--do we have
a laser pointer or anything like that?
I can just point.
Well, anyway, okay so, the population is a
whole bunch of these nodes and connections
joining them.
And you can mix and match nodes so, it's like
they say, "Hey.
I'm going to put this [INDISTINCT] over here
and vice versa."
And you kind of see, can--how they make these
morphologies you know, about how--you know,
how this makes a tree and vice versa.
It's actually kind of cute when you look at
it.
So, they're actually worth understanding so.
All right, sweet.
Okay.
So, in this case the--yeah, usually there's
joints between parts.
So, yeah.
So, this population is a graph of nodes and
edges and the--and the selection is to go
different with certain tasks so, walking,
jumping, something like that.
And the--and the mutation is grafting nodes
here and there.
And we're just going to let it go and see
what happens and here we go.
So--no, okay.
>> This demonstration shows.
>> GRIFFITH: Trying to...
>> Virtual creatures that were evolved to
perform specific tasks in simulated physical.
>> GRIFFITH: And that one.
All right, start it again.
>> This demonstration shows virtual creatures
that were evolved to perform specific tasks
in simulated physical environments.
Swimming speed was used to determine survival.
Most of the creatures are results from independent
evolutions.
Some developed strategies--is their evolved.
Multiple--these creatures in simulated together--friction.
Some simple solutions was just two parts were
found.
Some seemed like they could use some assistance
while others were fairly efficient such as
this rowing like behavior.
Here is an odd cousin of the previous.
A mutation caused him to tumble.
Some creatures evolve to incorporate contact
sensors in their control systems.
Here is another inch worm like creature that
tends to go in circles.
This was actually a creature first evolved
for its ability to swim in water then later
put on land and evolved further.
A successful side winding ability resulted.
Here is one with a hopping style.
The protrusions on its arms seem to help prevent
it from tipping over.
This was the fastest with a successful galloping
like stride.
This group was evolved for their jumping ability.
This group was evolved for their ability to
adaptively follow a red light source.
The resulting creatures are now being interacted
with.
A user is moving the light source around as
the creature behaves.
This one seems to flail randomly but somehow
still manages to approach the light.
Perhaps it is mean to move the goal away just
it is arrives.
Here is one that has propeller like fins which
are tilted depending on the direction of the
light.
It can adaptively swim up or down very well.
>> Just a pause.
This is one is especially nice because it
looks like something that a human would design.
Some kind of motor thing and if it weren't
for this little part just hanging off here,
you'd swear it was design and this case is
a case where evolution has--has toned across--they're
are very good designs, extremely efficient
and it looks, you know, very much something
that we would build ourselves.
So, like basic seeing designs like this should
like--should comfort so yes, this--this can
work.
Sure, is there a question?
>> [INDISTINCT] recently this [INDISTINCT]
>> GRIFFITH: You mean the cross network?
X, this work was recently redone for the Artificial
Life Ten Conference.
I know that I used to, to evolve catapult
designs.
So I don't--I don't know if they've actual
recreated all of this but--but I do know at
least large sections of this have been recreated
and I know that for a fact because I worked
in the lab.
So, so that's all I got.
>> I'd like to read this book.
>> GRIFFITH: Okay.
All right, now I still-what's the next one
we got here?
Oh, so I've set some before where they--where
they're moving kind--kind of weirdly specially
the one where this--have like the big hanging
mass.
The sole fitting this function in this case
was to--was to move your center of mass forward
or just--just move it period.
So in this case like--like evolution is very--like
it loves to cheat all the time to--to find
some way to do this.
So in this case what I was doing is was have
this big long tentacle thing and it was just
moving its tentacle thing around.
Thus its--thus its center of mass was moving.
So just another thing to keep in mind is that--is
that if you ever have any--you have to--when
you design your evolutionary simulations you
have to always know all the weird ways it
could cheat and we'll get back to that later.
So here's some more.
>> This final group of creatures was evolved
through for their ability to compete for control
of a green cube.
The creature closest to the cube at the end
of the simulation is the winner.
Here a strategy first arose for simply tumbling
towards the cube.
Then one learned to block out his opponent.
But then later one learned to overcome the
obstacle by climbing over it.
Some pinned down their opponents.
Some covered the cube with protective arms.
Others simply unfolded onto the cube.
The success of this strategy is often highly
dependent on the opponent.
Here's a Hockey playing creature, which takes
the cube away and wins by a large margin.
Here are two similar Hockey strategies battling
it out with the appropriate gestures.
This crab like creature walks well but often
continues past the cube and instead seems
to prefer beating up on his opponent.
Against the arm, the crab seems to simply
walk away.
A successful strategy is this two armed technique
that swipes quickly in from the side and moves
the cube over to his second arm.
These are the final rounds of competition
amongst the overall best.
Finally, the seeker arm goes against the sideswiper
but the cube is just out of reach.
>> GRIFFITH: Okay, so this is a fun movie
that I would like to show.
Number one, it's pretty and the second is
because, you know, designing body types--well,
that's kind of hard like doing those solutions
yet I kind of think about them for a little
bit.
Now this is not designing AI but it does show
ho--how like--how, how evolution can sow across
very inventive solutions.
And so this is meant to be like inspiring
and say, "Oh, you know, maybe you can do something
else better with this."
So that's what we have next.
So next is using artificial life to evolve
artificial intelligence.
So here's a--well, hear this--this idea.
So the first question is how we do a population
for--like, like what, what's--what thing do
we mutate and tinker with to--force it to
be intelligent and there's a lot of answers
to this question.
So Marionettes had--the Greeks had Marionettes
and so--yeah, they--they strings so they're
all deeply connected in this clearly the way
you think about intelligence.
And then Descartes says it's Hydraulics, so
the mind it's like the Sewer system, here
we have little compartments here and then
lots of pretty art from that time of all about
it.
And Pulleys and Gears such Industrial Revolution--yeah,
we have done this before.
Telephone switchboard--yeah, we, we--we've
heard--we've even heard this analogies.
But Boolean logic--yeah, that didn't go so
well.
But I'm pleased that we finally solved it.
And the answer is, not digital computers but
it's neural networks.
Praise the Lord.
So, so I guess--I mean given the history should
partake neural networks was kind of a grain--a
grain of salt.
But, you know, definitely there's some reason
to think--think neural networks are a reasonable
way for representing intelligence.
I mean, after all, we, we, we really are--like
we're modeling the brain much, much closer
than say digital computer or Boolean logic.
So, so even though there had been many--been
many attempts, it's like what is the proper
frame to--to capture intelligence.
You know, the hi--history is not really on
our side.
But I still think there's--there's a good
reason for it.
So just--just go with me on this one.
So now, the nervous systems--now, this ends
very nice is that, if you look at the neuron--see
a human neuron, like an individual one versus--versus
say--say some other mammalian creature--even
reptiles, you often can' t tell the difference
between them.
It takes like a real expert to do it.
So like the--like an individual neuron level,
we're all pretty much the same.
It's all in--it shows in the connections.
And evolution and it--like from us all the
way down to like sea slugs.
You see--you still see nervous systems that
are roughly the same.
So this very nice because roughly this says,
"Because hey, if we could just get our basic
model right.
You know--you know--was say a sea slug, it
could perhaps ride this model all the way
up to the top."
And if evolution did it once, why couldn't
it do it again?
So yeah--so now, we'll talk about sort of
the way--so now we have our nervous system,
the important parts about it.
So in this case--so this case, we, we do not--do
know some behaviors are innate.
There must be--must be some things that are--that
are, I mean, inherited.
We also have many things that are learned.
So the--so the nervous systems must change
with the organism's lifetime.
This just--this is just sort of basic principles,
seems reasonable, we're going to go with that--so
not too hard.
And with all this in mind--I'm [INDISTINCT]
to you, Polyworld.
Tad-dah!
This is the simulator.
Not to be confused with Polyworld, we--we--we
got a thread about this.
So just so you know, this is not us, we're
the other one.
It's with two L's, we're with one.
And we do--we do pre-date them but not that
really matters.
Okay, so what is Polyworld?
Poly--Polyworld is an attempt to--well, before--since
we evolved, artificial intelligence the same
way natural [INDISTINCT], which is simply
put the evolution of neuro systems in--in
a complex, rich ecology and they compete with
one another.
So and we're--yeah, so the, the hope is that,
we, we view with the model to make very simple
and then through competition and through making
the world, world richer.
It can gradually like get better and better
and better.
Sure.
>> [INDISTINCT] what causes the natural world
very rapidly if they happen with enormous
perils.
How are you going to beat their time schedule?
>> GRIFFITH: That's hard.
I mean, I--let's see.
Well, how would you do that?
I guess, in short, the, the answer would be
number one, we can place the ideas that create,
or even an intelligent designer.
We, we can help it along.
And, and the hope is that, you know, we can
say.
"Oh, that's good.
We want to like really help you."
And it's not something natural evolution had,
had the benefit of.
And furthermore, Moore's law is really nice.
And so I agree with you that is a problem
but, but both of those two, two factors help.
But it's, it's, it's, it's, it's a legitimate
concern.
So yeah--and in short--but Polyworld is a
new software, it's open source.
I'll give you the link at the end.
And, and, you know, there's a kind of a girls--but
most recently, people are using it for doing
behavioral ecology experiments and like--and
like--we experience very simple neural networks.
So if you're side is to use for that to.
So now we know what Polyworld is, what Polyworld
is not.
So Polyworld is not fully open ended.
It's currently just--just designed to be a
flat world.
Well, it's like--yeah, let's have your fight--where--where
critic interacts.
It's not an accurate model of really anything.
But it could be done.
There's--I mean, there's--it's a--there's,
there's no real problem with it.
The--the only reason we hadn't made an accurate
model of especiallly anything is because it's
computationally expensive.
And we don't believe it's, it's specially
important.
So if--so like right now, we're still using
a simple summing and squashing neurons.
If you wanted to, you could like--you could
render all the way down to actual biochemistry
if you're in to that kind of thing.
I'm personally not.
But, you know, you could.
And if you're into ecology, you can do that
to.
So yeah, that's what I got.
So we want some more.
So until we uphold more--so here's usually
what evolves in Polyworld.
So organisms have evolving genes and mate
sexually, straight forward.
They, they do have a body but the most important
thing about them is the neural network brains.
Now, the connections in the net--in the brains
are genetic.
But at birth, all the weights are random.
And--and Hebbian learning, which is the learning
mechanism and, and, and the primary that makes
the human brain.
But simply put well, and that sets all the
weights.
And Hale learning, it's a very simple algorithm.
It works like this.
If two neurons that are connected together,
fire at about the same time.
The connection between them gets stronger.
And then--so that's step one.
And then step two is, all connections decrease
in strength slightly so--and that's it.
It's, it's kind of surprising.
It's kind of surprising that--that it's this
one learning mechanism that accounts most
of our intelligence.
But you know, so it goes.
And their--and their vision of the world is
quite--it inhabits flatland so they see a
little--a little strip of pixels in front
of them.
And so basically, it's evolving--it, it is
evolving a neural network to take their one
dimensional vision and turn it into behaviors
that help them survive.
So--and just to let you know, there's no cheating
in any of this on as you often see in evolution
stimulations.
There's no fitness function.
This is like pure natural selection.
This is as raw as it gets.
If something survive--like, like the only
criterion is really to survive any way you
can.
And this includes exploiting bugs in the code.
And we'll show an example of that.
So okay--yeah, so--yeah, I'll show you that
in a second.
So too-too-doo.
Okay--go back.
Okay, so here's a nice, pretty picture of
Polyworld.
Here's how it goes.
So--where is my thing?
Here we go.
So, these round things are barriers, they
can't cross those.
These moving things here are the critters.
And these green things here are food.
So you see when a critter dies, they become
food.
Now, this is kind of an early stage--stage
of the stimulator.
And so they aren't very smart.
They like going along the edge a lot.
But they get smarter.
I promise.
So--so--so, basically, merely existing in
this world cause you to lose energy.
And if you--and if you--and if your energy
gets to zero, well, you--you seize to exist.
So, so thus like for anything to stick around,
it must go out and find food or go out and
kill something and--or it must mate with other
organisms as well.
If it doesn't, it just not going to stick
around long.
It's, it's, it's pure Darwinian.
So--and you can kind of see how it looks here.
So here's the--there's a top down view.
And in between these little--well, in between
these little squares here you saw, this was
the world rendered from one critter's perception.
But it's a stretched out slightly for our
convenience.
But to know exactly, they see--they see the
middle strip of pixels in that.
So okay--so that's Polyworld.
So now listen to the, the Genetic model because
I always get asked about that.
You have to pay a lot of attention--this is
mostly for reference, for those of you who
are into this kind of thing.
So these are--so the--I think before, there
are body genes, there are brain genes.
And this is the body ones.
So here's usually how it works.
A critter can be big, but when--but when it's
big, it doesn't move really fast, but it can
hold more energy in it.
So, you know, it's kind of a trade off.
And if a critter wants to be a predator, it
can be really strong, so we can do that.
And it can also determine its maximum lifespan.
This come--this actually--this actually form
from the evolution literature.
They--they said that it--it's good that we
have like a hard limit that we can't that--see.
It's good we have a hard limit that if you
just--eventually die of age.
Because even though it's extremely unlikely
for something unfit to live a long time, it's
so utterly bad if something unfit lives a
long time and mates a lot that you--that you
want a really hard limit on the--on how long
you can live.
So this is also kind of motivated, it's kind
of [INDISTINCT] so like for example, you want
to have tons of kids, but give the most no
energy.
So it's the parent can decide how much energy
they want to give them.
Or if you want to have a few kids, and gives
them lots of your energy.
So this is, you know, whichever you want to
use.
So we'll go back to the colors in a little
bit, but--yeah.
So the green--how green a particular critter
is--is determined at birth.
So you could have like the light green critter
and dark green critters, and stuff like that.
And also their mutationry is also specified
genetically.
So--yeah.
No counter points of genetic grade.
Okay.
So this exciting part, so this is the brain
genes.
This is like 95 percent of the genome.
So here's how it works.
So the genetic models specifies which colors
you want to attention to in your environment.
So if you think red is really important in
your environment, you can spend a lot of neurons
to go see it.
Yeah.
Also there are internal groups and these internal
neural groups which were like this, and supposed
how they're connected.
So the genetic model only specifies roughly
how many connections are between each neural
group.
It does not specify at the pure neuron level.
And this is motivated from biology.
So you --so if you see--yeah.
Like--well, it just is, and stuff we were
getting into.
So for those who are neural network buffs,
you can be with all that.
But the main thing that takes home from this
is that the genes loosely specify--loosely
specify the brain, and it does that in sort
of the neural groups level.
That's really the main thing to take from
this.
So, to make this clearer, so here is how a
typical brain looks.
So you have one neural group here, you have
excitatory neurons and inhibitory neutron.
We distinct--we distinct--many neural networks
have been the inhibitory neurons and excitatory
neurons.
They can like, like a single a single neuron
have both excitatory and inhibitory connections.
But when you do that, some biologist puts
up their hands and says that brains don't
work like that.
And you say, "Well, fine."
So there, for you biologists in the room,
they're different, be happy.
All right.
So you have multiple of these things and they
can have different numbers of excitatory--inhibitory
nodes.
And they cling to each other.
So straightforward.
And they connect back, it's nice.
And then you can have multiple neural groups.
And they can all connect to each other however
else they want.
Now, these internal neural groups connect
to some output neurons or behavior neurons.
And here they are.
Now, these are--these are the seven behavior
neurons, and they're defined in the simulation.
And in short, there are things like move forward,
turn left, turn right, eat, mate, fight, blink--I'll
show that in one second, and focus.
So basically every critter has this little
light in front of it.
That it can sort of, they can--that they can
blink with.
The idea is they could use some primitive
signaling mechanism.
As far as I know, they haven't fully--they
haven't taken advantage to this fore signaling.
But you know, you can give room to grow.
They obviously can't evolve from doing it
if you don't give it to m in the the first
place.
So it's in there.
And we also weren't sure what kind of eye
they wanted.
So this--so depending on the activity of this
neuron, they can have sort of a fish eye lens
where they can have like, you know, really
straight.
So, and that's just only because we weren't
sure what kind of eye they might want.
So, you know, evolution can decide.
Sure.
>> [INDISTINCT]
>> GRIFFITH: Oh, no.
This comes next.
Oh, sorry [INDISTINCT].
Okay.
So here--so here are the inputs.
Okay.
So genetically--so if you're going to pay
attention--so this critter wants to pay attention
a lot to green, a little bit to red and not
so much to blue . And so these are basically
the inputs.
And these inputs can connect to any of these
internal groups that they want.
And it also has an energy level.
So this tells you roughly how healthy the
critter is how healthy it is.
And it also has sort of a random firing.
Just because, you know, might want it this
is the free will of the critter.
You can think of it like that.
And I'm surprised that they actually use the
random.
You wouldn't really think so.
But they like random.
I'm not entirely sure why they like random.
But--you know, well, regardless.
We put in there because they might--they might
like it, and behold they do.
So...
>> [INDISTINCT] networks, how does a [INDISTINCT]
networks?
>> GRIFFITH: So like these internal groups
could connect to each other however they want.
>> [INDISTINCT] convergence?
>> GRIFFITH: Yes.
>> Okay.
>> GRIFFITH: Oh, okay.
Yeah, we'll deal with this later, so this
thing's the input units and processing units.
Not so important.
Sure.
>> Have you assigned energy cost to neurons?
>> GRIFFITH: Yes.
And roughly, the reason we did the...
>> Repeat the questions.
>> GRIFFITH: Huh?
>> Repeat the question.
>> GRIFFITH: Oh, I'm' sorry, I was asked whether
or not there's an energy penalty for--for
having a large number of neurons or for neurons
being activated, the answer is yes to both,
that problem was you didn't do this, they
grew huge brains that like 99% did nothing,
so you're just like well, like computation
is just silly, so if you're going to have
a big brain, it better well do something.
So yes they get a cost for having--for just
having a size--certain sized brain, or for
neurons being activated, so like doing anything,
cost you something.
Okay, so good question, we didn't do that
initially, and that's what happened.
So this is rough--roughly the same picture
I showed you before, and this is made using--using
dot, it's really nice.
Oh sorry, graph this, so this just shows your
Polyworld brain map, saying no, really, I'm
not joshing you, that's how they work, and
these are the inputs here, they connect to
excitatory neurons and inhibitory neurons,
and these are sort of the behavior neurons,
up here, you know, there's fight, turn, light,
blink, et cetera.
So it just kind of shows you what their brains
typically look like, when they are not idealized,
so, that's all you get from that.
So, okay, so as far as the previous concern,
everything is about getting energy so they
get energy, they die, and that's bad.
So, here's how they get energy, they can eat
food pellets or they can eat other critters,
straightforward.
And they lose energy by doing anything, like
merely existing loses energy, so if they don't
do one of these things, they're gone.
These especially, like mating cost energy,
and being big and strong costs energy and
just for having a brain costs energy, so,
mention that.
Okay, so now I'm going to show you some behavioral
samples, of how the output neurons, well this
is what it looks like, when they turn these
things on.
So here's eating, this neuron right here,
and you see it slurps it up, Ta-dah.
So, I'm going to show you some more of these,
more into the emergent stuff.
So, what's going to happen here is that, one
critter, so okay--oh I'm sorry, I should mention
this, the color of every critter, is an Archibee
triplet, so, the redder a critter is, is how
aggressive it is at this moment, the bluer
a critter is, is how much it wants to mate
with--mate with--just mate, at this moment,
and the green is genetic, as specified before.
The reason we decided this is because, well
you know, you want to know when someone wants
to kill you, you want to know someone wants
to mate with you, very straight forward.
And for green, the idea is that you might
want to do kin selection.
It's like for example, say hey, I'm in light
green now and I want to be nice to you because
you're a light green.
Sure.
So it's--we've seen a few cases where they
have done some tribalism based on the green,
but usually you have to kind of like trick
it into doing it, but it does happen.
So, based--the important thing is here is
that these are both kind of red so they're
going to do battle, so let's watch this one.
So here we go, he runs into it, and it gets
eaten and it turned into a food pellet and
this one slurped up the body.
That's how eating works.
Oh, in this case, so the bigness of a critter
is proportional to its strength, so basically,
even though this critter was stronger, it
just had like a lower amount of energy, and
it got eaten by the weaker one.
Okay, so here's how mating works, so this
goes--I'm going to come in here, and mate
with this one, and a little child will pop-out.
Okay, so now we see what happened here, okay
so, they made a child but they were so, they
expended so much energy given the child--they
put so much energy into the child that they
immediately died afterward and the child ate
their carcasses.
So here, we can see that again, for those
of you with kids.
What is he going to--with the loop?
Let's do it, there we go, nope, okay, mating,
let's see it again.
Okay, so their coming on, make the child,
and they--they both die, and the child doesn't
really care, slurp, okay.
Next we have is the lighting, this is the
blinky, I'll just show you this.
This is--because it's me coming here and he's
going to blink at you, so here it comes in
oh, I'm sorry, it turns his blinky off, so
right now the blinky is on cause you see that's
his normal color and that's the blinky and
now it's turned it off.
So, so they could shine lights at each other.
Okay, so, now I'll show you some--I'll show
you some of the emergent behaviors.
So this is one of the--so we call these things
species just because it's kind of natural,
technically they can still mate with each
other but behaviorally they're so different
that, it's seemed trees would call them that,
so these are joggers, and all they do, they
just go forward all the time.
This case the world is--is--is--is-is a toroidal
world so you can't go off the edge.
We have other worlds where you can go off
the edge.
And they move in circles a lot.
So, by this case, usually the first thing
you see in a simulation, just always go straight.
It's very easy to code and the food is--is--is--is--is
randomly distributed.
Why not?
I mean you know, you're--it's--it's quick
and simple.
So that works.
Okay, so this is a really nice one.
I talked to you before about how evolution
takes advantage of absolutely anything, like
including your bugs.
So, this is a very nice bug.
Now, what this was--this was initially done,
it had not occurred to me that--that having
a child cost energy.
You know, because you--you just do it, it's
pretty easy.
So, you know, that's my male bias.
But, well, I'm sure it happens.
When we initially--there initially was no
cost for having children.
Guess what happens so you'll see them, I think
they're over there and we will zoom in a little
bit.
So, you see, they're all in a cluster over
there.
And we're going to zoom.
There we go, okay.
So, you see that--that they have this whole
orgy going on here and they are--they are
popping out kids, like--like looked like,
and then immediately eating them.
And with this is--this is because--because
eating--eating the children becomes a free
source of energy.
So, you have two so as far as the critters
are concerned, you have two choices: you go
out and get food or you can mate and have
a piece of food appear right next to you.
The solution is clear, and--and this was like
really boggling, like why are they doing that?
Because, this would be immensely successful,
we take over everything.
And I could--took a lot to figure that out.
But yeah, so we--now, we cost--so, now like
it costs energy to have kids.
So, now we don't eat them.
It's not as--not as--not as not as prevalently.
So, okay.
So, just--just to let you know that evolution
will take advantage of your bugs.
That's a really good way to test.
So, okay.
So, moving on from the indolent cannibals.
Okay.
So, now I'm going to show you some--so now
I'm going to show you some actually intelligent
behavior, at least well, primitive intelligent
behavior, that has emerged form this.
So, this is just to show you that yes, this
is actually doing something, all right.
Okay.
So, we're going to actually get to see them
act--they actually use their visions.
So, [INDISTINCT] come by and this--and the
critter lurched forward.
And see that--okay.
Here, well's--okay, there's more of them.
Yeah, see--see, it jump forward.
So, really all this was saying is that hey,
they actually are using their eyes for something
and they're using their eyes to control their
behavior.
So, simple enough, not--not very big claim.
But you'll see is that we're actually getting
something right, like keep in mind when these
critters start, they have completely random
brains.
And I assure you, they're crap.
They don't do that.
So, I'll show you examples if you'd like.
Okay, so now I'll show you some more ones.
Here's fleeing attack.
Or in short running away from red things.
So, usually like the first thing--usually
the first things the critters learn is number
one, move that helps to find food.
Number two, move towards green things because
green--because food is the only green things.
Well, solely green things and that internal
getaway--turn toward the blue things because
they want to mate with you.
And get away from red things because they
want to kill you.
So, here's them wanting to get away from red
things.
So, we see a red thing coming up here and
it's going to run away form it.
And, run away.
So, this is very nice.
So, they--they--and this came out completely
naturally.
No--no--no--no supervision at all.
Just--just--just playing do as the creator
and letting it go.
So, here's some more.
So here are some foraging patterns.
So, usually they--they--they like to kind
of act out on their own, become a lone forager.
But some of them they swarm, so you'll find
like a whole bunch of very weak critters and
they mostly just go in--just go in circles
all the time.
And they--and so, like they say hey, like,
say there will be dark greens, okay, I want
to turn--I want to follow dark green things
and I want to turn in circles a lot.
And if you do that, the swarm just sort of
gradually moves, because the ones that are
near food, they live.
And so the swarm just kind of gradually moves
towards the direction where food is.
And that's--and that works.
Slowly but it does work.
Okay, well that's--let's see, [INDISTINCT]
this one.
Oh, this is kind of fun, you can see--actually
you can--can't see them engaging in a purposeful
behavior.
Like you saw at the very begin of the stimulation,
they all just kind of sat there.
We just [INDISTINCT] no, they're actually
moving around, actually turning towards green
things, actually displaying kind of you know,
pseudo purposeful behavior.
So, that's a--steps in the right direction.
All right, so here is what we've seen so far.
First of all they make a lot of different
kinds of brains.
They're actually--they are using their eyes
for something, that's good.
And they're actually doing useful things with
them, also good.
So, all right.
So now I'll show you some--show you some more
science-y things we've tried to look it--we've
tried to analyze the behavior to determine
if we're actually getting anywhere and trying
to quantify it.
So this is a nice one from the animal foraging
literature.
So this is actually pretty straight forward.
This is what you do, you have a world.
You have a food patch on one end and a food
patch on the other.
And you say "Okay, well how are the critters
going to allocate themselves?"
So the very beginning they kind of uniformly
dispersed, middle some like "Oh, like you
know some hang out in here, some hang out
in there, some in no man's land.
And then the late they go "Oh being in no
man's land is bad" I don't want to go there,
so they hang out in the two food patches.
So, so, so this--they're foraging that's good
and they are doing it correctly.
And even better if you actually look at--they
actually form their optimal foraging pattern.
So there's this distribution you commonly
see in the foraging literature called the
ideal free distribution and lo and behold,
they hit it perfectly.
So, all right, good for the critters in optimal
foraging.
So now I'll show you some Predator-Prey Cycles,
these are kind of neat.
So the colors don't come out that gray but
it will be okay.
So in this case we're looking at predator-Prey
Cycles between the critters and the food.
So in this case the red is the critters.
This is for a particular food patch, the ones
you saw before.
So the red is numbers of critters in that
food--is the percent of critters in that food
patch.
And the green is the percent of food in that
food patch.
So in short, what you see, let's pick I'll
say this one here okay.
There you see that the--that the critters
lag the food.
So first the food grew up high and then shortly
afterward the critter said "Oh I want to go
in this food patch" and then they over harvested
and the food goes down.
And the critters leave and go to the other
food patch.
And then the food was back up again and moved
up and go back to the food patch.
And this oscillates forever.
Yes?
>> [INDISTINCT] distribution there is no food
growing in the middle...
>> GRIFFITH: Right.
>> Does the food in this graph strictly other
critters?
>> GRIFFITH: The food in this patch?
No, no and this--this case this was two food
patches close to each other and they would
just go back and forth between the two food
patches is what they would do.
And depending on where the food--where more
food was at that time.
And they would oscillate always following
the food.
So, yeah.
And this is nice because this is--this is
a very similar pattern to what we see in like--in
Predator-Prey cycles.
You know the standard [INDISTINCT] thing so,
also nice.
And this is again like we didn't program any
of this.
Like we just simply designed a simple world
with food and neural nets and said go.
And we get all this--it just comes right out.
So, okay, so now we look at the brains cause
that's what we're really concerned about.
So the main thing to keep in mind here is
really kind of the connection matrix.
This other stuff here being a scientist, like
that.
So anyway, this is a random brain from the
very beginning--at the very beginning of evolution.
All things are randomly wired together.
And so there--there's one connection matrix.
And this is one from the vision cortex of
the cat.
Now and it should be random slides of it.
And actually one from a Polyworld critter
after evolution.
Ta-daa!
Now let me take away from this.
It's not a cat but it's certainly not random.
And so they seem that evolution has gone from
this to this with doing nothing but just sitting
there and letting see a few cycles turn on
it.
So, again I'm not claiming the poly organisms
are cats but I am saying that evolution is
doing something very useful and it's putting
tons of structure in there that you do not
put in so all right.
And this is kind of inspiring and you would
go wow and maybe we actually could get a cat
with this.
So here we go.
So now I'm going to show you some more quantitative
plot, more than just looking at pictures.
Oh sorry, so I always get this question a
lot from philosophers in the room.
They always say, "Oh is not alive" well okay,
fortunately there's [INDISTINCT] that a really
good definition of life.
It's the Farmer Belin--the artificial revolution,
published from the Santa Fe Institute.
And basically it says it has these measured
criteria to determine if something is alive.
And not so coincidentally Polyworld explicitly
designed to satisfy all these criterions.
So in short yet kind of space-time, it does
reproduce, it does have creature storage,
it does eat and it has interactive environment
and it does evolve.
So in short, to that--well it fits the definition
of life that most people used.
So in your face.
Okay.
So then you will you say, I'm not sure if
it's intelligent.
Well it's a--sure?
>> [INDISTINCT] it certainly has metabolism
and it has functional interaction.
>> GRIFFITH: Right.
>> [INDISTINCT]
>> GRIFFITH: Yeah.
No, no, no, I'm saying here is quite satisfies
all these.
>> [INDISTINCT]
>> GRIFFITH: It doesn't have information storage,
it doesn't have that.
>> Well if you have a coal left over from
a fire you can initiate another fire.
Would--is that information?
>> GRIFFITH: I suspect--I mean, I don't really
care if fire's alive or not.
Fire probably can satisfy three or four of
these.
I mean, I'm not really attached--I'm indifferent
to fire.
But I suspect if you look at the kind of structure
of coal or something.
You probably wouldn't find--it might be I
wouldn't have much information there.
I'm not sure exactly how you'd like at it,
I'm sure it's something you could do but even
if fire is alive, okay sure why not.
Okay Belin would say, well it is it really
intelligent cause we just see them just moving
around.
Well there's no real way to quantify intelligence
unfortunately.
And I even [INDISTINCT] can do this.
But however we see this on simulation means
we have access to a lot more things that biologists
don't.
And sure we can use information theory and
complexity theory to try and analyze the critters
behaviors and their brains.
And this is most of our research right now.
So yes we analyze their brains over time.
So, so here's a nice one so there are like
three or four measures of Neural complexity
out there.
And so far I've implemented two of them and
the critters all kind of follow this pattern.
Oh sorry for this kind of complexity this
is the [INDISTINCT] complexity.
I'll get you the paper on it.
In short this metric of Neuro complexity and
Schwartz says, if all neurons fire independently
that's not complex.
And so yeah and if they all fire in unison,
that's not complex either.
So in short you want this kind of middle ground
between everything behaving randomly and everything
behaving uniformly that's what Neuro complexity
is.
But in short, if you look at any of these
they encourage all on.
They go up for a little bit and then they
kind of plateau.
And they're like "hmm" And both metrics do
that.
So well that's what I got.
And right now we're trying to figure out how
to make that go up more and try to explain
why it plateaus.
So I'm changing some other stuff now.
So now that we know that neuro complexity
does indeed go up.
We want to know if evolution is actually helping
this--helping the complexity go up or if it
was just kind of going up accidentally.
So there are two kinds of views of the evolution
of complexity.
The first one is this one, this is a more
natural one.
And it says that "Hey, you know evolution
actually favors more complex from bacteria."
You know just big bacteria and then eventually
to us.
And evolution really wants that.
And the other one kind of says, you know what
evolution really doesn't give a crap about
complexity.
Some things just kind of increase by accident
on complexity and some doesn't really care.
And the idea of this one is that if this is
just mirrored if this diffuses outwards.
You know on the spectrum of complexity you
know just doesn't care about it at all, you
know you will eventually get complex things
and it's ready to start with this and you
could get to that.
And so this is evolution actually favoring
complexity versus evolution not giving a rip.
This is actually a debated question and we
can use part one to answer this.
>> [INDISTINCT]
>> GRIFFITH: Right.
>> [INDISTINCT] simple environment.
>> GRIFFITH: Yes I do.
The question is whether or not the complexity
of organisms is predominantly a product of
their environment.
And the reason that we're not seeing a big
increase in complexity is because the environment
is so simple.
And I think that's exactly it.
So--and--so what we are looking at that now
for ways we can make the environment more
complicated to encourage more interactions
and things like that.
But that's about four or five slides from
now so we'll get to it.
This is the two ones this kind of experiment.
Here's what you see so basically [INDISTINCT]
polyworld to make all matings random.
So in short even if you mate with someone,
you don't actually get their gene.
You get some random persons genes.
It's sneaky so--and this is the dash line.
This is where evolution turned off and oh
sorry.
This is complexity here, and this is time
and the dark line here is with evolution on.
Now this is very depressing, because you're
like oh well with evolution turned off you
get a higher complexity.
You're like, well you're doing nothing.
And I was very sad when I first saw this graph.
But I always look at this thing here.
This always appears like I've run this thing--I
don't know, I believe it's ten times now.
In short, there's always this hump here and
I'm sorry and this is also a T test right
here will get that in a second.
But in short--the idea I came out with is
that there's always this hump here and this--and
the solution that I came was, well evolution
does fairly increase in complexity but only
up to a point.
After you solve the world, we don't care if
you're complicated anymore.
In fact it actually costs you something to
be complicated.
And so as to the result we're going to keep
you roughly right there.
While the diffusive one just kind of goes
up on its own.
It's completely--it doesn't give out complexity
at all.
And it continues to go on up.
Sure.
>> Yeah.
Isn't this [INDISTINCT] evolution just where
the fitness function is how long will you
survive instead of how much you made because
if you randomly select a creature, creatures
who live a long time are going to be around
more to get selected at random?
So, if you just survive a long time and you're
alive when other people are mating then your
genes will get passed on more?
>> Let--let me think about this.
>> [INDISTINCT] what did you do to select
this--selection--the selection of the random
genes from all the creatures who are alive
at that time, that's my question.
>> Yeah.
I'm thinking, how was it done?
I think it was--I think it was from all the
critters who were alive at that time.
So, the idea was--no, I'm sorry.
No, actually--no, this is [INDISTINCT] that
a very good--that's very good question but
that was controlled for.
So, in short, I'll--well, I'll give the more
of the detail.
Basically, this was that we ran this black
line first and then, we said, okay, you know,
I--and then we--then we, and then we said
okay like critter--critter one lived exactly
as many times of creature two, live exactly
this number of time steps.
So, we did random--so, we did random mating
combined with enforcing that--that each creature
lived exactly the same amount of time.
So, but--but good question, clever.
>> [INDISTINCT] for several thousand years
sort of pruning out the dead code?
>> Sorry, what does that mean, I don't quite
understand.
>> The complexity goes down because some of
it is discovered to be unnecessary?
>> Yes.
I think--yes, correct.
And that basically fits with my--with my current
belief.
I'm not exactly sure--sure why it plateaus
and why it gets--why it stays there while
the past has go up.
But I think--I think it's pretty reasonable.
So the idea is that, I mean, because you always
see that like in the complexity is useful
at the beginning but you want to be more complex
than your environment makes you be so--so,
the idea is that we don't make that more complicated
and we'll see that if it goes up more.
But yeah that's--I agree exactly.
If you want to see this here, this is a T
test Pleistocene to what extent based on the
degree of confidence to which the dash line
and the solid line are thought to come from
the same population and they say, if it's
above this critical here, which basically
says, "Yes, we're pretty sure that humans
have different populations."
So, we see that--okay, right here, we're sure
they came from different populations now,
but actually [INDISTINCT] kind of crosses
about right here.
It just--it just--it just kind of--it mostly
kind of sits there.
So, there's a--so there's some math to make
us think that as well.
Okay.
That's just what I got.
So, now it's a Neural complexity--another
one for genetic complexity and this came from
my professor at Caltech, Professor Adame and
it's really nice that you correlate math over
quite well.
So, it [INDISTINCT] complexity of the genes.
[INDISTINCT] actually was it--it was 7,000
when they cross before ? Yeah, about 7,000.
Okay, how about this one?
7,000 we see is roughly similar.
Okay.
So, the way this one works the dash lines
again are the passive runs and the solid lines
are the--are the--are the--with the evolution
turned on.
And so, in--we basically, see that on the
passive runs the genetic--genetic complexity
basically went down to crap while on the--on
the active ronds the [INDISTINCT] did not
go to crap, and in fact it stays quite high.
So, roughly what this says--roughly what this--what
this measures look for, it looks like the
amount are not of disorder in the genome so,
basically, if every gene was equally probable
or--sorry, if every gene is equally present
in the population then--then it goes to here.
But if there some genes that are more favored
than others then--then, I get this measure
gets higher.
I can see the equation for it but that's roughly
how it goes, roughly it measures the amount
of disorder in the population of genes and
roughly this says, okay with evolution turned
on, there's less disorder in the genes, so.
That's good and nice.
It's also can be that we see, the genetic
complexity and the Neural complexity being
roughly correlated, yes?
>> [INDISTINCT] when you say evolution is
off, your [INDISTINCT] turned off the sharing
of genetic information for mating.
>> Yeah.
>> [INDISTINCT] for mating, where do you [INDISTINCT]
>> Okay.
When I say evolution is off, I say that the
matings are random.
And--yeah, I just say, the matings are random
and critters are forced to live the same amount
of time.
So, the idea--so, there's controls and the
matings are random and so.
>> [INDISTINCT] made the results is one, it
is in fact [INDISTINCT] Okay.
Whenever evolution is off--when evolution
is on, when two creatures make, there genes
get match together and they make a child,
so, completely normal.
When evolution is off, when two creatures
mate, it takes a completely--it takes a two
random genes from things currently alive so,
and then, it pops out that child.
>> [INDISTINCT] made a copy of one of the
parents or something.
I don't understand the motivation for getting
a random gene from some other creature.
>> I'd like to think...
>> [INDISTINCT] main copy [INDISTINCT]
>> If it's completely random, its random--I
mean I--I think--I know I have to--there you
may be able to do this if you just make--make
a copy of one of the--of the parents.
You may be able to--I'll have think about
it that's why that one would work two but-but
I know if--if that--if--if very creature is
equally favored, no matter what its genes
are, evolution doesn't move like that--that's
the rule.
Like--like--like that--that has--that has
selection with everything being equal--equally
selected for.
So that--that's what motivated it.
Sure?
>> Random selection on the [INDISTINCT] any
have plan of population?
You will have a genetic group, is that right?
>> Yes.
>> So.
>> I think you should see here.
This--this up and down genetic drift due to
decline.
>> I mean sometimes some ideals will be lost
in the population just because of they.
>> Right.
>> Regular see.
There would be--there will be this pair of
mixing of possibilities but its slowly go
to a fixed point, right?
>> Um.
>> Do you--do you see this?
>> Well, you--you certainly all right.
Like I mean because of funny population you--you
will see variations in the pop--in the population.
And I think its--is what you're seeing here.
So in this case like this is two has completely
random mating and it's moving up and down
a little bit.
And I--and this is--this is due to drift but
as you increase population size this gets
less and less and less as exactly as you'd
expect.
So--so yes, you're right.
And--and in we're seeing it.
So it's good.
Okay.
>> [INDISTINCT]
>> Oh, okay we have to quick to them.
Al l right, so it's the next time do really
quick and to pass through this.
So there's a real question of, so for this
passive complexity it could be just be this
passing complete--like why is this leveling
out at all?
So it could be that--that sort of--sort of
upper bound in simulation because simulation
cant support something--something of higher
neural complexity plus we'll--so we journey
with Polyworld to say, okay we will sole--we
put through a fitness function mode.
There's no longer natural selection of.
We were working solely for having a complex
brain and that that's the red one here.
So in short this says, hey, you know the simulation
can support much higher complexity if you--if
you like really forced it do it.
So, in short this phase says, hey there--there's
room to grow for--for evolution.
So, all right.
So basically we have so the next pencil be
making more complex environment and trying
to move--move this curves closer up to the
red.
So, okay its making a draw from there.
So, these are the few directions will take
Polyworld into but predominantly making the
world more--more complex and then come in
with more measures of complexity for studying
it.
So in short more exercise in complexity there's--there's
still like--there's still four or five that
we haven't looked into yet, more complex environment.
So the first thing at right now I want to
add all like day and night cycles.
So--so in this is really easy to do because
it's all an open GL and you could just tweak
the ambient lighting up and down.
And the idea is that these would force them
to--to have a sort of an internal clock, saying,
hey it's dark now.
I--I can't see anything probably shouldn't
go foraging.
Notice having different kind of food types.
So you could have different colors of food
and--and one will give you more energy that
the other.
So it's kind of having specialization.
And the others giving them more--more senses,
right now they only see and if you give like
smell or touch it is they could have more
interaction with the environment and that
would be good.
Yeah, so were done the actual forging we did
that by recently.
Yeah, and we held this to answer question
about evolutionary theory as we did.
Answer more questions of evolutionary theory
like we did before.
And did eventually we can skip up to casual--casual
conditioning experiments.
So this is kind of like--like the direction
you want to go for the next few years.
And I think you have ideas especially for
here.
Let me know or you want to get to decode.
So this is mostly it.
The source codes available, you can get it
now.
It runs on Linux and Mac via Qt, its just
works.
And then we can download it.
Yeah--and at the very end I always get the
questions, oh, you're making Frankenstein
this is a terrible idea.
And I--I was like this snide respond to them.
So and yeah, I have no problem with that responsibility.
It's a--its--its--if the polygon kill us all
well--well it happens.
Okay and I'm done.
>> Questions.
>> So just an idea about directions for--to
test theories and evolution.
Have you thought of a sex selection to see
if their specialization have been given very
little or a lot of contribution to the offspring,
as if their two initials, two genders developed?
>> Well, currently there's no gender.
You could certainly do it.
My--right now there is no gender right now
it could be the one cut-cut the population
like the mating pool in half.
So like right now, these critters currently
run with about 300 agents in a simulation,
I'm sorry, the answer is yes you could do
that.
That'd be really cool but right now we don't
do it because we are concerned about, it might
be hard to find a mate.
>> But I mean, I'm pretty ignorant to this,
under some theories would say that the origin
of the division of genders is that there was
a specialization to niches, the males contribute
very little, they tried to mate a lot, the
females contribute a lot more and so maybe
you can look for, you see if this two niches
develop, even in the absence of explicit gender,
I don't know, that was just an idea.
>> GRIFFITH: You could certainly--it's certainly
possible like if you had two different kinds
of behaviors, and one was favorable one time
and the other was favorable the other times,
you could get that to come out naturally,
but when they can always mate sort of all
the time, it's going to be tricky for that
two not to be enforced over the long term,
but yeah, it's certainly possible and if you
wanted to do gender differences, it's actually
really neat, I mean if you start enforcing
it see if they were starting to use each other,
things like that, so that would be cool.
>> This networks, at least, as I took it to
mean, don't have any state on our cursive
networks?
>> GRIFFITH: Ah, no, they are recurrent networks
so they can connect back if they want, we
actually have a new kind, this recur to something
like squashing neurons, we have a brand new
model that has spiking neurons, I don't know
much about it yet, but I haven't use it much
yet but we do have more fancier models.
>> And do you save state in between cycles?
>> GRIFFITH: Well see, no we don't save state
between cycles, but we do update their vision.
>> Right, right, that seems to me to be necessary
in order to maintain a mental model of where
you are in the world, as opposed to just a
single state, here I am, what am I going to
do, it seems like that's uh...
>> GRIFFITH: That's a fundamental part, yeah,
let's see, I don't think we're saving--we're
saving the state of the network from [INDISTINCT]
to the next, like of the internal nodes.
I'd have to think, well I can answer the question
empirically, and like 10 minutes I went to
the code, so I'll answer it a little bit.
>> I think this is a really good, interesting
presentation but I guess I have a little difficulty
because I'm not that familiar with the area
to have some context for it, could you say
just a few words about sugar world and Tierra
and Neuro Darwinisms so I have some sense
on how this ..
>> GRIFFITH: Oh, yeah, I've heard of sugar
world, but I haven't--I have never, I've heard
of sugar world, I know that--I'm sorry, I
should back-up, so there are previous simulations,
Tom Ray's Tierra was basically--was the first
thing of evolving code, and it was really
awesome, but there were a few problems with
it is that they see things always got smaller
and smaller and smaller, so that was kind
of a problem in Tom Ray's Tierra, so like
it always became better, if you're genome
got smaller because that way you can reproduce
faster because they were penalized.
They only get a certain number of cycles to
reproduce themselves and if you're very small
you reproduce yourself a lot.
I don't actually know if, as far as I know
Tierra has not been extended to account for
this original defects, but certainly Tierra
is like really great, as far as sugar scape,
I've heard of it, I don't know much about
it, so, but if you send me a paper on it,
I'll certainly read it, and I can go and come
and tell you then.
Sorry, what was the other--oh, neuro Darwinism,
okay, so neuro Darwinism is a theory of neuro
science.
It's probably even true, in short it says
that the way connections are formed in the
brain, is kind of like evolution , it's not
exactly, but roughly it says that neurons
initially kept to a whole bunch of things
and most of them suck and the ones that sucked,
get pruned and they go away.
So, roughly neuro Darwinism is like expand,
prune, expand, prune.
And it says this is how connectivity in the
brain comes about, and it's probably true.
>> So on your final slide, I think it was
the final slide, you said that one of your
goal is to make the environment more complex?
>> GRIFFITH: Yes.
>> And experiment with more features [INDISTINCT]
and so I think it's, maybe a little bit of
problem because your current system is already
very complex and the thousand thing that affect
the way evolution goes in your kind of system
and you know how you construct their production
procedure and so on and so on, so you're not
afraid that if you make the environment more
complex, you will be, possibly you will be
able to see very fancy simulations but, it
maybe more difficult to understand why actually
evolution went this part, not the other way.
>> GRIFFITH: Your bachelors isn't in physics
by any chance?
>> I'm sorry?
>> GRIFFITH: Your bachelors isn't in physics
by any chance?
I mean, physicists always say that.
So I'm wondering what your background is.
>> No, no.
my background is actually, I solve evolutionary
computation for my...
>> GRIFFITH: Oh, okay, all right.
Well, yes, okay.
Well--the concern is roughly, well if you
make it more complex, you always have parameter
help.
You already have parameter help but it could
be even worse.
Like ninth layer parameter help.
And the answer is yeas.
That--that can happen.
And I guess the response is, well, it seems
like a lot of these things don't depend on
the parameter very sensitively.
So be like very, a bunch of parameters we
have right now, you roughly see a lot of the
same stuff.
And the hope is if you choose this even remotely
reasonable values, the good stuff will come
out.
And so, the point is valid.
But we don't think--but we think like the
benefit of having a more complex world far
exceeds the concern of parameter help.
>> I've been thinking about this for a while,
I mean, you showed it to me earlier today,
but I've also been thinking about this general
problem, and I think that we can state without
being too contentious that there are better
strategies in the worlds that you're presenting.
Like if we're really careful and designed
one, we could probably clean the clock of
a number of these evolved systems.
And I think part of that's going to be not
a product of the structures of the brains
but the kind of input that they have available
to them when they drive their behavior.
Put another way, I don't think you should
be adding complexity to your simulated world
in terms of adding lighting effects or for
or the things like that.
I think there need to be more signals that
have to do with kin selection and not--not
just green.
Like, in the natural world, even at the very
cellular level, you--just is a natural by-product
of the way evolution's is going to effect
what kind of presentation you throw up on
your cell walls.
Like you can do kin selection in the environment
pretty easily, like that's assumed.
And so you can--a lot of the complexity we
see in natural systems and how central systems
are and how predation systems interact, seem
to be driven by really complicated gradients
that end up working out down the kin similarity.
Like, I don't want to mate with someone who's
exactly like me and I don't want to mate with
someone who's really, really different from
me either because, if I mate with someone
who's exactly like me, it's not worth the
energy because there's not going to be much
variation.
If I mate with someone who's too different,
the child's not going to be viable.
And like, the complexity in your environment
should flow out of the behavior of the features
that you're competing with.
And you should see speciation resulting from
preferences.
And alternate patterns in like--that it doesn't
seem like there's enough input for the neural
networks that you're evolving which seem to
be really cool to exploit that gradient.
So I think maybe finding some way to allow
them to sense the presence--and go ahead and
cheat.
You know, like look aside--do similarity scores
and provide like, a sense that--similarity
sense.
You know, not based on light at all I mean,
you're looking directly at the genes, because
in any natural evolving system, you'd end
up having pheromones and various other markers
that you would learn to exploit.
But they don't really have that.
All they have is what they present directly
and it would take a very, very long time for
that to evolve.
>> GRIFFITH: I think I get your--so your point
seems to be roughly that the critter should
have more complex interactions with each other
rather than more complex interactions with
the environment.
>> Well, not even necessarily more--I mean,
the actions that they can take are fine.
I just don't think that they can observe the
other critters well enough.
>> GRIFFITH: Okay, yeah.
Well, and so I guess the answer is "I agree."
And if someone's to write it.
If you're a writer, I would gladly put the
patch in.
So, now as to whether that would be--that--as
to whether or not more complexity between--more
complexity between critters would be more
valuable than interaction with the environment,
I guess you could try it and find out.
I mean, I think those would be great.
So, yeah, so there's no contention.
Okay.
>> All right, let's take one more question
in Mountain View and then we'll let the video
tapers go, unless there's a remote office
that had a question that I wasn't fair to.
>> GRIFFITH: Yes, so as a biological creature
myself, I kind of hope that death is not inevitable
and I was curious of what you were noticing
in your simulations if you had turned off
the limited lifespan of a creature.
>> Oh, let's see.
I guess you could just clamp it.
You could do that.
I don't know.
The reason I did that is just because, like,
I saw a paper at a conference that just had
these mating populations.
And it said that--that having a fixed lifespan
or at least a max lifespan was a good thing.
So I said, "Oh, well, just put it in a gene,
done."
So I have no--I've never actually clamped
it and compare the differences.
But you can certainly do it.
I mean it's just a parameter.
So...
>> GRIFFITH: So what I've been thinking is
that if you didn't have a limited lifespan,
what would the results of your simulations
be?
That's what I'm curious about.
>> Well, most critters don't get to their
max lifespan.
Most of them die of energy.
So in this case, like, I think, like, the
average critter lifespan is something like
300, 400 time steps and the maximum lifespan
is something like 700, 800, something like
that.
So most, so very, very few get killed by that.
So, I guess I don't think the maximum lifespan
has much impact on it.
And I just put it in there because I saw a
paper that said this was good.
So, and it was--and I was writing that piece
of code at that time, so.
>> GRIFFITH: Okay.
We'll still be around after the talk is over
if anyone wants to chat more.
>> Okay.
>> GRIFFITH: Thank you.
