Stanford University.
OK, so we will pick up on
the one topic that was not
covered from two days ago
because you guys needed
to go play around with these
cellular automata first.
So I will work
with the assumption
that everybody
here has now spent
48 hours playing with those.
But presumably because
of the sleep deprivation,
you've forgotten
much of it by now.
So we will cover some of it.
OK, back to that issue
of fractals and butterfly
effects and that whole
business that, by the time
you look at chaotic
systems that are
determinist but
aperiodical, that, when
they seem to be lines
crossing, getting back
into the same spot,
look closely enough
and they're not going
to actually be touching.
And the centerpiece
of why that matters
was that whole
business of, these
both appear to be the same.
And take them out
a decimal place
and they're actually different.
And a gazillion
decimal places out.
And the entire sort of rationale
for thinking in that way
is the notion that a very
small difference here
can make a difference
one step to the left.
And a million decimal places
out there, a small difference,
will make a difference
one before that.
In a scale-free way, a fractal,
this here, a difference here,
a million decimal places
out is just as likely
to have consequences for
one over as this one for one
over fractal, scale
free, all of that.
But the critical thing
that is encompassed in this
is the notion that
tiny little differences
can have consequences
that magnify and magnify
and amplify into a
butterfly effect.
So cellular automata
are a great way
of seeing this principle along
with a number of others that
are relevant to all of this.
OK, so we start off
with the very first one.
And this is the one
that you no doubt first
discovered is a pattern,
which made you deeply happy.
And if you follow
the rules, it was
starting there-- was this--
which way is this facing,
starting at the bottom.
OK, this is starting
at the bottom.
And what you see is
these very simple rules.
And out of it emerges a
whole complex pattern.
And we'll be seeing
shortly the features
of this that perfectly match
what the requirements are
for emergent complexity.
But we'll see as the elements
are lots of constituents,
lots of building blocks.
The building blocks
being very simple.
They're binary.
Either they are
filled or not filled.
Extremely simple rules as to
how the next generation gets
formed.
And in terms of the extremely
simple rules, none of the rules
have anything to do with other
than the next generation.
It is all local rules built
around what the neighborhood is
like for each one of these.
So you put it
together, and out come
these very structured
patterns like these.
And this is great.
This is very exciting.
Except this isn't
what you usually get.
In most of these cellular
automata systems, where
you start off with an initial
condition and a simple set
of local neighbor rules for
how you get reproduction
into the next
generations, in most cases
the patterns stop after a while.
In the vast majority,
they stop, they
hit a wall, they go extinct.
Aha.
Two terms that
I've already stuck
in here that are
biological metaphors
start to seem less
metaphorical after a while.
First off, the notion that
going from here to here to here
to here represents
each next generation.
And the notion that,
as we were just now,
that the vast majority of
these cellular automata systems
go extinct.
They fail after a while.
So it's a very small subset.
What you then also
see is-- in some ways
the critical point in
this whole business--
is the relatively small
number of starting states
that succeed produce a
remarkably small number
of mature states that all look
very similar to each other.
In other words, you can
start with a whole bunch
of different conditions,
and you will wind up
with a bunch of smaller number
of stereotypical patterns.
Half of the cellular
automata that
wind up taking
off look something
like this with this pattern.
What are we seeing?
Convergence.
Convergence.
The notion that you can start
with different forms and they
will converge over time.
What's this?
Well, you just proved you
look at the mature form
and you can't know
the starting state.
The other thing is, starting
at the beginning just looking
at this line,
there is no way you
can tell what it's going to look
like 20 generations from now.
You've got to march through it.
In other words,
the starting state
gives you no predictive
power about the mature state.
This is a nonlinear system.
The cellular
automata encapsulates
this, this business that
most of these go extinct.
Only a relatively few number
of mature forms exist.
It shows convergence.
Very different starting
states can converge
into the same sort of patterns.
And minor differences
in the starting state
can extend into very
different consequences.
It shows, in other
words, butterfly effects.
OK, so appreciating this a bit.
So what we did was then
go to example number
two, where we changed
the starting state
just a little bit here.
We shifted around
some of the boxes.
And what you see is something
that looks roughly the same,
but it's not exactly the same.
But it's the same
general feel to it.
So that's great.
But then we started an
exercise of starting off
with the initial boxes.
This goes that way.
The initial boxes evenly spaced
with one space between them
and applied the
rules from there.
And this is what you get.
Totally boring, static,
inorganic, inanimate.
This is what it does
for the rest of time.
What this exercise then
did, going to number four,
is what if we now
spaced two boxes
between each one of these?
And here we have an extinction.
This is one of those
where it hits a wall,
and all the next
lines are empty.
OK, how about three boxes in
between the starting states?
OK, another form of extinction.
OK, how about four boxes
between the starting states?
And suddenly, something
very dynamic takes off.
Applying the same rules,
and all you've done
is change the spacing
between the starting states.
And look at, for one thing, how
close this was to going extinct
up there on top, how
asymmetrical the pattern is
that comes out.
And this particular one will
stay asymmetrical forever.
And the ways in
which that generated
something very unexpected.
There is no way you
could sit there a priori
and say, hm, one box in between
generates something that
looks inanimate.
Two boxes, not going to work.
Three boxes, yeah.
Somewhere around four
boxes in between.
That's when dynamic
systems suddenly take off.
There is no way
to have known that
before without marching through
this and actually seeing.
Starting state tells you
nothing about the mature state.
Then we space it even further.
And what we get is
something similar again.
This one is symmetrical.
It is somewhat different
from the previous one,
but it's the same
sorts of patterns
that come up over and over.
So what we've seen here
is, by starting state,
minor differences,
big divergence
between going extinct versus
being a viable pattern.
Minor differences in starting
state, big divergence
between symmetrical and
asymmetrical patterns.
Tiny differences,
butterfly effects.
OK, next.
Looking at the consequences here
of introducing some asymmetry
from the very beginning.
The one on the left up on top
has four boxes and four boxes.
It has eight boxes.
The one on top has eight boxes
on the left, and the one on top
on the right, just adding in
one extra box on the side,
so it's four and five.
Adding a little asymmetry,
and what you see
is a very different pattern.
And one of the things
you tend to see
in these pseudo-animate living
pattern systems is starting
states of asymmetry produce
more dynamic systems,
more dynamic patterns than
even symmetrical ones.
That's one of the only rules
that comes out of there.
So we're seeing now minor
little differences producing
major different consequences.
Divergencies, butterfly effects.
Now showing this
in a different way.
And what we've got here are
four different starting state
conditions.
The one on the far
left is, in fact,
the one from the previous
one, the four and four.
Four different starting
state conditions
where they're not enormously
related to each other.
The first one against the other
three, but the other three
have minor differences.
And the whole thing
is, two of these
are identical after the
first 20 generations or so.
This one and this one.
The two of them are
identical, and for the rest
of the universe
they will produce
the same identical pattern.
And looking at the mature
state, you show up on the scene
somewhere halfway down, and
you could never ever know
what the starting state was.
Did it start like this or
did it start like this?
A convergency here.
And in this case, it's
another one of those rules.
Knowing the starting
state doesn't allow
you to predict the mature form.
Knowing the mature
form, you don't
know which particular starting
state brought it about.
And the only way
to figure it out
is to stepwise go
through the whole process
because you can't just
iterate by a blueprint.
There is no blueprint.
Finally, the last
one was giving you,
instead of different
starting boxes
in each case with the
same reproduction rule,
the last one was the
same starting pattern
of boxes with
different, slightly
different, reproductive rules.
And what you see here are
totally different outcomes,
depending on which variant.
We have the beloved
one on the top left.
And you see here, by slightly
changing the nearest neighbor
rules, if and only
if there is one
neighbor with this
property, if and only
if there's two neighbors.
And working through
that way, and you
see remarkably divergent
outcomes for one thing.
You see that the
majority of them
produce something very
boring, either boring extinct
or boring repetitive in
a very undynamic way.
Only a small subset
produce lively, animated,
living systems.
So we're seeing a whole
bunch of biological metaphors
here over and over and over,
which is the starting states.
You don't know the
mature, the mature,
you don't know the
starting state.
The generations, very simple
rules for generations,
going one to the next.
What we see also is
the vast majority
go extinct and produce,
either go extinct
or some repetitive, very boring,
crystallized type structure.
A small subset, a
tiny subset, produce,
instead, dynamic patterns.
And knowing what the
starting state is is not
going to give you any
predictability whatsoever of,
is this going to produce
a dynamic pattern or not?
Nor does it allow you to look at
a bunch of the starting states
and say, those two
are going to produce
the same mature pattern.
And these are all
properties of the evolution
of different living systems.
So you begin to see,
OK, cellular automata.
Do you see some of
these principles?
The simplest level out
there in the natural world
is looking at all sorts of
shells, seashells and tortoise
shells by the seashore
and whatevers.
And they all have
patterning on them
that is derived from that
first cellular automata
rule, producing patterns that
look a whole lot like these.
And go online and look for them
because I didn't get around
to it in time, but producing
all sorts of patterns in nature.
Very common ones.
What does that tell you?
Very simple rules for generating
the same complex patterns
and different starting
states, cellular automata
and properties here.
Another thing in a
living biological system
that begins to suggest this.
OK, so I do this
research in East Africa.
And every now and
then over the years
I've gone to this mountain
called Mount Kenya, which
is on the equator.
It's about 17,000 feet.
It's got glaciers up on top.
So this is an equatorial
glacial mountain.
And you go up to about
the 15,000-foot zone,
and there's like--
it's more land.
Almost everything is dead
up there from the cold.
And there's
basically only, like,
four or five different
types of plants up there.
Oh, already a very small
number that survive
in that environment.
And each one of them is
very bizarre and distinctive
looking.
There's one of them
that looks like
a little, like, rosebud
thing, except it's
about 5 feet across.
And then there's
another one that
has sort of a sprouty
thing like this and then
a big central cactus-looking
thing that isn't really cactus.
So there's a few of
these really distinctive,
bizarre-looking plants.
And in some way or
other, that's what
it takes to survive up there.
So I have this friend who
does research up in the Andes.
And he does botany
stuff up there.
And he goes into this one range
there that is on the equator
and high enough that
there's glaciers up there.
Ah, a glacial equatorial
mountain on the other side
of the globe.
So one day I'm sitting
around and looking
at some of his pictures there.
And suddenly I look and say,
that's the exact same plant.
That's the big rosebud plant
that is in Mount Kenya.
And oh, my god, that's
the tall sprouty one.
And say, it's the
exact same plant.
How can that plant
be over there?
And we go rummage around in
his botany taxonomy stuff,
and they are completely
unrelated plants.
They are taxonomically of
no connection whatsoever.
But what they've done is
converged onto the same shape.
And in some mysterious
way, if you're
going to be a plant growing
on the equator at about
15,000 feet, there's only about
four or five different ways
of appearing.
There is massive convergence.
And there's only
four or five ways
that you can survive an
environment like that.
You get organisms in
very dry environments,
and there's, like,
only four or five ways
that you can go about being
an organism that's super
efficient at retaining water.
And those are the only
ones you see amongst them.
Desert animals, and
completely unrelated ones,
have converged onto some
of the same solutions.
There's only a
very finite number
of ways to do legs
and locomotion.
Two is good, four, weirdo
things that fly of six,
creepy things have eight.
You don't find seven.
You don't find three.
You find some of the solutions
here are immensely different
starting states
and have converged.
What we see is in these living
systems over and over stuff
that look like
cellular automata,
where slight differences magnify
enormously butterfly effects,
where you are modeling living
systems in a very real way.
Most of them go extinct.
Divergence, convergence.
And where each one of these
you get the smaller number,
reflecting the fact that there's
only a limited number of ways
of doing rain forest,
temperate zone rain forest
in the Pacific Northwest.
There's only a
limited number of ways
of doing tundra in Miami Beach.
There's only a limited
number of ways.
In all of these,
this convergence,
and always reflecting that
these are cellular automata.
OK.
So hopefully you are now
feeling desperately regretful
that you didn't spend the
last few days doing this
because these are
so heartwarming.
If you want to read a book
that nobody in their right mind
should read, it's a book by this
guy named Steve Wolfram, who's
one of the gods of
computers and math
and was one of the sort of
people who first developed
cellular automata.
And by all reports
probably one of the largest
egos on the planet.
And he published a
book, self-published it,
a few years ago, which
he can because he
is grotesquely wealthy, from
some of his computer programs.
And just showing what a
low-key sort of humble guy
he is, he called the book
A New Kind of Science,
just showing that
he wasn't just going
from some little piddly new
way of viewing the world,
but here was his
new type of science.
And the book is
about 1,200 pages.
And I suspect not
even his mother
has read the thing it
is so impenetrable.
And it sold a gazillion copies.
And almost all of them are
sitting in people's garages
now, weighting down drain pipes
because no one can actually
read this thing.
But an awful lot of
what the book is about
are patterns in nature coded
for by very simple local rules.
And the simple fact
of that, you've
got a lot of very smart people
doing this cellular automata
stuff.
And they can't
come up with rules
where you could look at
something beforehand a priori
and know this one
is going to survive,
this one is going to
go extinct, those two
are going to turn
the same, these two
that differ by a
slight smidgen are
going to turn out to be
enormously different.
There's no rules for it.
And the book has all
sorts of cool pictures
of a cellular automata
looking things out in nature.
And go buy it for
somebody's birthday
and see if they're not grateful
for the rest of their lives.
But his whole argument
there is, these
show ways in which you can code
for a lot of the complexity
in the natural world with
small numbers of simple rules.
This whole business
of emergence.
This sets us up
now for beginning
to look at some of
the ways in which we
hit a wall the other day, ways
in which the reductive model
of understanding the universe
stops working after a while.
One version being the
problem of not having
enough numbers of things.
Not having enough
neurons to do grandmother
neurons beyond Jennifer
Aniston, that whole business
that you simply
don't have enough
neurons to do that beyond just
the rare ones now and then.
And what do you get instead?
What has the solution
turned out to be?
This field that people focus
on now called neural networks.
And the point of neural networks
is that information, again,
is not coded in a single
molecule, single synapse,
single neuron, one neuron.
This neuron knows one
thing and one thing only,
which is when there's
a dot there, instead,
information is
coded in networks,
in patterns of
neural activation.
And just to give you
an example, and this is
one that's in the Zebra book.
And anyone who is in
Biocore I do this one,
and I do it because
I at one point
learned the name of three
impressionist painters,
except they're not
coming to mind right now.
OK, so you've got two layers.
Here's what a neural
network would look like.
A two-layer one.
These neurons on the bottom
are boring, simple, Hubel and
Wiesel-type neurons
from the other day,
where each neuron knows one
thing and one thing only.
This one knows how to
recognize Gauguin paintings,
this one recognizes Van
Gogh, and this one Monet.
OK.
Each one of them is-- obviously
there is no Hubel and Wiesel
neuron on Earth
that's like that.
But just for our purposes.
They now project up
to this next layer.
Note this neuron projects
to one, two, and three.
This to two, three, and four.
This to three, four, and five.
So what does this
neuron know about?
This one knows how
to recognize Gauguin.
It's only getting
information from this neuron.
It's another one of those
Hubel and Wiesel type,
I know one fact
and one fact only.
This one here is
another one of those.
What does this neuron
know about in the middle?
That's the neuron that
knows how to recognize
Impressionist paintings.
That's the one that
says, I can't tell you
who the artist is, but it's
one of those Impressionists.
It's not one of
those Dutch masters.
It's an Impressionist painting.
And this one does it because
it is getting information that
is not available to these guys.
It is getting information
at the intersection
of all these specific examples.
These ones, number
two and four, those
are ones that recognize
Impressionist paintings also,
but they're not as accurate
at it as number three
because they've got less
examples to work off of.
This is how a
network would work.
And what that suddenly
begins to explain
is something about the human
brain versus a computer.
Computers are amazing at doing
sequential analytical stuff.
Like, you get calculator
things inside Cheerio boxes
that can do more things
than the human brain can
do computationally.
But what we can do is
parallel processing.
What we can do is patterns,
resemblances, similarities,
metaphorical similarities,
physical similarities.
And that's why you need
networks like these.
You don't need neurons that
know one fact and one fact only.
You need neurons
where each one of them
is at the intersection of a
whole bunch of other inputs.
OK, example.
So now suppose
you've got a network.
There's one neuron
which fires, and there's
a whole bunch of neurons sort
of sending projections into it.
And this is a neuron
for remembering
the name of that guy.
What was the name of that guy?
That guy, he was that
Impressionist painter.
So suddenly your
Impressionist painter network
is activating and
firing at this neuron.
So it's sitting there.
So this is-- now you've got
your whole Impressionist network
that's activated.
What was the name of that guy?
He was an Impressionist painter.
He painted women dancers
a lot of the time.
So people who painted dancers.
But it wasn't Degas.
OK, so your "it's not Degas"
circuit going in there.
And what was that guy's name?
God, I had that seventh
grade art teacher
who loved this guy's work.
If I could remember her name,
I would remember his name.
Oh, remember the time
I was at the museum
and there was that really cute
person who seemed to like it
and I had to pretend
I liked this guy also
and it didn't work
out nonetheless?
And going through.
And oh, what's the name?
There's that stupid pun about
the guy, he's really short.
And something about the
tracks being too loose.
Ah, Toulous-Lautrec.
And suddenly it pops
out there, and you've
got enough of these
inputs coming in there.
And this is tip of
the tongue wiring.
This is how you may not
be able to just remember
the guy's name.
Wait, he was the short guy
with a beard who hung out
in bars and Parisian bars.
And here was that
time in seventh grade.
And enough of these inputs,
and suddenly out pops
the information.
And what this begins
to tell you is,
this is ways of
getting similarities.
These are ways of getting
things that vaguely remind you.
This is a world where
humans can now do stuff
like have a piece of
music that reminds
them of a certain
artist because they
both have similar coloration.
And that's something
that makes sense to us.
That's something that can
work because, what you then
begin to see is, every one
of these neurons, this one,
for example,
Impressionist neurons.
This one may also be at the
intersection of another network
that's going this way,
a network of French guys
from the last century.
And it may be part of
another network of people
whose names are
hard to pronounce
so you're anxious about
saying them in a lecture.
Or the intersection--
and each one of these
is going to be an intersection
of a whole bunch of these.
All of these networks,
what does that do?
That's what you can do
that a computer can't.
You see similarities,
similes, metaphors.
And somewhere in there you get
something really important,
which is the ones, the networks,
that have wider expanses that
connect to a broader
number of neurons
in a very simple,
artificial, idiotic way.
That's kind of what
creativity would have to be,
networks that are
spreading far wider
than in some other individual.
It is literally
making connections
that neurons in another
individual does not.
And suddenly you have
a world where everyone
knows this one is a face.
And it was only a limited
number of people who ever
decided that this one's a face.
And in some level Picasso had
a different network, a broader
one, as to what could
constitute a face.
A broader network
in some way is going
to have to be wiring
that is more divergent.
And at the intersection
of a bunch of networks
that are acting in
a convergent way.
So what's some of the
evidence that it actually
does work this way?
You go and you stick electrodes
into neurons in the cortex,
and what you see, if the
world was entirely made up of,
like Hubel and Wiesel, one
piece of knowledge only, what
you would see is you would find
neurons that each one responds
to one single thing.
All these grandmother neurons.
And instead, what
you see by the time
you get to the interesting
part of the cortex
past the first three
layers of the visual cortex
and the first three
layers of the auditory.
Once you get into
the 90% that's called
the associational
cortex-- and it's
called that because
nobody really
knows what it does--
then what you see
are neurons that are
multimodal in their responses.
All sorts of things
stimulate them.
And here we have a neuron
that's being stimulated
by a type of painting, by
the knowledge of French guys,
by something phonetic,
by all sorts--
and they're multiresponsive.
So that's what you
wind up seeing.
The majority of
cortical neurons,
when you record from
them with an electrode,
they're not grandmother neurons.
They're at the intersection
of a bunch of nets.
More evidence for this.
This was-- one of the grand
poobahs of neuroscience
around the 1940s or so,
a guy named Karl Lashley.
And obviously a
very different time
in terms of thinking about
specification of brain
function.
And what he did was a
very systematic attempt
to be able to show
where in the brain
individual facts were stored.
And the term for it at the time,
this jargony term, was engrams.
He was searching for the
engram for different facts.
And what he would
show was, he would
destroy parts of the cortex
in an experimental animal.
And he couldn't make the
information disappear.
He would have to
destroy broader areas.
And some of the knowledge,
some of the memory
was still in there.
And he concluded in
this famous paper
in the search for the
engram that, according
to all the science
he knew, there
could be no such
thing as memory.
And the reason why
was he was working
with a model of being
able to-- there's
a single neuron where,
if I could ablate it,
I should be able to
now show in that rat
that it's just lost the name
of its kindergarten teacher.
And instead, you see
networks going on.
You see the same thing
clinically in something
like people with
Alzheimer's disease.
Early on in Alzheimer's, you
will lose, in these networks,
you'll lose a neuron here or
you'll lose a neuron there
when you're just
beginning to lose neurons.
And what you see is,
clinically, in people
with Alzheimer's, early on, it's
not that they forget things.
It's not that memory is gone.
It's just harder to get to.
And you do this with
all sorts of testing,
neuropsychological
testing, where
you try to give the person
cues to pull it out.
Example.
You're giving somebody,
potentially with Alzheimer's,
a classic orientation test.
You ask them, OK, do you know
the name of the president?
OK, they manage to get that.
Do you know the name
of the last president?
No idea.
So now you give them
a little bit of cuing.
OK, let me help
you a little bit.
It's a one-syllable word.
Still not there, even
though you've now
activated the one-syllable word
network, obviously artificial.
Still can't say.
OK, let's make it a
little bit easier.
It's things you could find
in a park, in a city park.
So you're activating that.
No, still not coming out.
And then you give even more
explicit priming there.
You give them a forced choice
paradigm, is what it's called.
OK, so is it President Tree or
President Shrub or President
Bench or President Bush?
Bush, Bush, the kid
with the father also.
It's still in there.
It was still in there.
It just takes more
work to pull it out.
What you're seeing
there is not the death
of individual memories.
You're seeing a weakening
of a network, a network that
is now taking stronger priming
to pull it out of there.
And just to show how subtle
network stuff can be,
here's something that would
work with a lot of individuals
with early stage dementias.
What you do is another
type of priming.
So you're eventually
going to ask
them the name of the
previous president.
And they first come in and
you say, oh, great to see you.
Come on in.
What a beautiful day.
I walked here by
way of the park.
The bushes were so beautiful
this morning in the park.
Some of them had flowers,
some of them didn't.
But bushes are so nice
to look at when you're
walking through a
park because bushes
are one of my favorite
forms of [INAUDIBLE].
And then five
minutes later, they
are more likely to
remember the name Bush
out of a whole different realm
of more subtle networks you're
tapping into.
So all of this is the
beginning of a way
of solving the problem we
had the other day of not
enough neurons for them
to be grandmother neurons.
More solutions.
We then went to our
next realm of trouble,
which was the problem of,
there's not enough genes.
There's not enough genes
in that specific realm
of explaining bifurcations.
And there can't be a gene
that specifies, OK, this
is where you bifurcate if you
were this particular blood
vessel and a different gene
for this particular bronchial
and a different
gene for this branch
of a dendrite and a single--
it can't work that way.
There are not enough genes.
What this introduces
is the idea of there
being fractal genes,
genes whose instructions
are ones that are scale free.
What do I mean by this?
OK, here's what a
fractal gene might do.
So we've got a tube.
And this is a tube that's going
to be part of a blood vessel
or a dendrite or a
lung or whatever.
We've got a tube.
And the fractal rule here is,
grow this tube in distance,
grow it until it is five
times longer than it is wide.
The width, the opening,
and that's the simple rule.
And the rule is, when it's grown
five times longer, bifurcate.
So what's going to
happen at that point
is just gone five times longer.
And it bifurcates at that point.
And what you've got is now,
because this is split in two,
the cross-section is
going to be shorter.
But you apply the same rule.
Now with the shorter
cross-section,
you have the same rule.
Grow five times the length
of that cross-section
until you split.
And what you wind
up getting is, this
is one simple fractal rule
that will generate the tree
patterns.
That the branchings get
shorter and shorter,
the distances between
the branch points
get shorter and shorter
because the cross-sections are
getting-- one simple rule
and you could generate
a circulatory system,
a pulmonary system,
and a dendritic tree by
giving a fractal instruction,
in this case, one
that is scale free.
That is, independent of
what the unit is here.
And this could work
within the single neuron
or within an entire
circulatory system.
So all of that's great.
That's totally hypothetical.
Ooh, fractal genes.
Well, you know by now
that's got to translate
into a protein in
some way or other.
How might this actually
look in a real system?
So suppose-- OK, so a
gene coding for a protein.
This is one copy of the
protein, this is another,
this is another.
They bind to each other in a
way so that they form a tube.
And they bind to
each other in a way
that's just pure mechanical
reality of, these are not
bits of information,
these are actual proteins.
So it's going up
in the tube there.
And suppose that the forces
are, as the tube goes up
it gets more and more unstable.
And when the tube
is high enough,
it gets unstable
enough that these bonds
between the proteins begin to
weaken, and it begins to split.
The splitting
there is a function
of the length of these.
So it's split.
And now the next one
has half the number
of proteins in this one, and
thus it's that much weaker.
So you only have to go
a shorter distance now
before it begins to split.
This doesn't exist.
It is no way it's like this.
But what you could
begin to see is,
here's how you could turn a
scale-free set of instructions
potentially into what
it would actually
look like with mortar and
bricks in terms of proteins.
How it might actually work.
Now the notion of fractal
genetics, of fractal genes,
and fractal instructions begins
to solve another problem,
and this is that space problem
of how much stuff can you
jam into a space.
Here's the challenge here in
terms of how dense things are.
In the body-- amazing factoid--
there is no cell in your body
that is more than five cells
away from a blood vessel.
OK, you could see why you
would want to do that.
But that is not an
easy thing to pull off.
How do you do that with
the circulatory system?
And amazing other
factoid to factor in
with that is, the circulatory
system comprises less than 5%
of your body mass.
How can this be?
You've got this system
that's everywhere.
But it's taking up
almost no space.
It's within five cells
of every cell out there,
yet it's less than
5% of the body.
And, OK, forget it.
I'm not going to put that up.
But what this begins to--
OK, you convinced me.
So let's do this.
So what you begin
to do is transition
to a world of fractal geometry.
You've got all your Euclidean
world of nice, smiley, strange
things there.
You've got this
whole world of shapes
that are constrained by classic
Cartesian geometry and all
of that.
And what fractal geometry
generates are objects that
simply cannot exist.
Here up on top,
eventually, you will
see the first example of this.
And this is out
of the Chaos book.
And this is this cantor set.
What you do is you
start with a line.
Start with a line, and you
cut out the middle third.
Now for those
remaining two ones,
you cut out the middle third.
For those remaining four,
you cut out the middle third.
And there it is.
And you just keep doing this
over and over and over again.
And what do you do when you
take it out to infinity?
What have you generated?
A set of an infinitely
large number of objects,
lines, that take up an
infinitely small amount
of space.
It's not possible for
that to work, yet,
as you go more and
more in that direction,
you get this
impossible phenomenon
of something approaching having
an infinite number of places
that something appears while
taking up almost an infinitely
small amount of space.
And what this winds up being is,
it's not quite a line anymore
at the bottom, but it's
kind of more than a dot.
It's somewhere between
one and two dimensions.
It's a fractal.
Its dimensional
state is somewhere
one point something or other.
It is somewhere between
dots and a line,
and it does this
impossible thing,
which is it's everywhere
without taking up any space.
Or you could then push
it to the same thing
in the next dimension.
And this is this Koch snowflake.
And it's the same sort of rule.
You start with the
triangle there.
And the rule is, you
take the middle third
and you put a little
triangle out of it.
And then take the
middle third of that
and put a little triangle out.
And a middle third.
And you just keep doing it
forever and ever and ever.
And you wind up with
something that is impossible,
which is an object that has an
infinite amount of perimeter,
an infinite amount of surface
area, within a finite space.
That's impossible.
But it begins to approach this.
And what you see
here, this is a way
of just iterating over
and over and over to jam
a huge amount of surface
area into a tiny space.
And thus it's somewhere but
different, sort of like a line,
but it's sort of
like a plane by then.
And it's got a
fractal form somewhere
between two and three.
It's got a fractal quality of
two point something or other.
It's an impossible
object that is solving
this problem of being--
in another version,
having surface area everywhere
without taking up any space
and being within a finite area.
Next, finally,
this Menger sponge,
which is the same exact concept.
Again, you start with the
box up there, or the ring,
and you take out
the middle third
of each of those segments.
And then you take
out the middle third
of each of those segments.
And if you are doing
this with what starts off
as a three-dimensional cube,
eventually you get something
that cannot exist, which is an
object that has an infinitely
large amount of surface
area while having no volume.
That's what it produces
at the extreme.
And we got something here
that's somewhere between two
different dimensions,
a fractal again.
And what you see is, this is
how the body solves the packing
problem.
Because all you need to do is
make the circulatory system,
the circulatory system
some version of this,
some version of splitting
the ends of the capillaries
over and over and over or making
the lungs, with their surface
area for exchanging oxygen,
looking something like this.
And this is how you
generate a system that
is everywhere and taking
up virtually no space.
Obviously, it's not
taken out to infinity.
But this is how you can have
a circulatory system that's
five cells away from
every cell in the body,
yet takes up less
than 5% of the body.
This is a fractal solution.
All you do here
to generate these
is taking some of
these qualities
over and over and
over and over, and you
can begin to produce absolutely
bizarre, impossible things
in terms of surface area
and perimeter and volume
and all of that.
This is how you can
use a fractal system
to solve the packing problem.
Now of course, as soon
as you're coming up
with the notion of something
like fractal genes,
you, of course, have to consider
the possibility of there
being fractal mutations.
What would a fractal
mutation look like?
And again, most people,
most geneticists
and molecular
people, do not think
about this in these terms.
But there are people who do
who actually talk about things
like fractal gene mutations.
What would it look like?
Suppose you've got a mutation,
and it produces a protein
that's slightly different.
And as a result,
its got bonds here
that are slightly weaker
between different proteins.
So on a mechanical level,
what have we just defined?
This is a tube that's going
to grow these proteins where
it's a shorter distance
before it begins to split.
Because these bonds between
them are not as strong.
There is a mutation
now where, instead
of growing five times
the cross-section, maybe
you're growing 4.9
times the cross-section.
And thanks to that mutation,
the entire branching system
is going to be compacted a bit.
It's not going to
reach the target cells.
And these would be
catastrophic mutations where
the pulmonary system
doesn't develop,
the circulatory system
doesn't develop.
And what you would
see in those cases is,
the mutation is something
that has consequences
that are scale free.
Another hint when you see some
fractal gene mutations are
a small number of
diseases that they're
about spatial
relationships in the body.
For example, there's a disease
called Kallmann syndrome, where
you get stuff that's wrong with
midline structures in the body.
Something is wrong
with the septum
between the nose, the nostrils.
Something is wrong
in the hypothalamus.
Something is wrong in
the septum of the heart.
This is not three
different mutations.
This is some sort of
fractal mutation messing up
how that embryo did
symmetry, how the embryo does
midline structures.
So you begin to see
ways here in which
you can solve this and, within
the biological metaphor,
where you could begin to get
solutions for these problems
and also mutations that
can put you up the creek.
OK.
So that is another realm
for beginning to solve this.
Another domain.
And here we begin to move
into the realm of emergence,
emergent complexity.
Which we will first look at a
couple of crude passes at it.
First, emergence driven
by biophysical properties.
And do not freak
out if you don't
know what I mean because I have
no idea what I mean by that.
So I will explain in
a more accessible way.
And this was something that
was explained endlessly
by a guy who used
to be in the bio
department, a developmental
botanist named Paul Green, who
died about 10 years ago
way too young from cancer.
He was a really good guy.
He would give this
famous lecture
where he would start
off and he would
describe some sort of disk.
And the point is that the
disk, the material inside
was of a softer material than
the material on the perimeter.
And he'd be putting
up math at this point
that I didn't understand.
But it was sort of
a disk like that.
And then he would show that what
happens if you heat the system.
What happens if you put
heat on a disk like this?
And what he would
wind up showing,
going through agonizing
amounts of math,
is that, when you heat a
system, the only solution
for this system that's
trying to respond to the heat
but in different ways on the
perimeter versus the inside
is to come up with a double
saddle, a double saddle shape.
And the math proved this.
And I had no idea what he was
talking about when you come up
with a double saddle shape.
And then what he says
is, so that's how
you get a potato chip.
You take a slice
of potato, where
there is more resistance
on the perimeter
and less on the inside,
and you heat it.
And the only solution
to the problem
is to come up with a double
saddle potato chip shape.
And if you change the
outside, the force of it,
if you take one of
those great organic,
"give you the runs"
type potato chips,
where it's going to have the
skin left on the outside,
it's going to be a somewhat
different-shaped double saddle.
Because there's
only one solution
mathematically to that.
And then you sit
there, and you deal
with a very simple,
important fact,
which is, that slice of
potato knows no biophysics.
That slice of potato didn't fit.
There's no gene that
instructs potatoes
to respond to heat in this way.
This was the inevitable outcome
of the biophysical properties
of a slice of potato.
And what he then shows
is, in plant systems
after plant systems,
they develop
where two shoots
come out this way
and a little higher
up two shoots
this way and two this
way and two this way.
They're all double saddles.
And this winds up being
a mathematical solution
to a packing problem there.
When plants are
growing their stems,
there is no gene specifying it.
You don't need
genetic instructions.
It is an emergent property
of the physical constraints
of the system.
Another example here that's
sort of proto-emergent, somewhat
simpler versions,
this phenomenon
of wisdom of the crowd.
And this is one that
was first identified
by Francis Galton, who was
some relative of Darwin
and started eugenics and
was bad news in that regard
but famous statistician.
And being an Englishman
somewhere in the 19th century,
he spent huge amounts of time
going to state fairs and county
fairs or whatever.
And he was at this
fair one day where
they had some oxen up there.
And they were having a contest
that, if you could guess
the exact weight of
the oxen, you would
get to milk it or something.
I don't know what
the prize would be.
And there were
hundreds of farmers
around filling out
little pieces of paper
where they were guessing.
And what he
discovered at the end
was that nobody got
the answer right.
Good.
So the owners of
this get off easy
without having to give up
any of their oxen milk.
But he then did
something interesting.
He collected all the
little slips of paper,
and he averaged all of them.
And it came out to the correct
weight within an ounce.
In other words, no
individual in that group
had enough knowledge to be
able to truly accurately tell
what this thing was.
But put them
together in a crowd,
and out comes the right answer.
Another version of this.
And this one is deeply
important in terms
of Western
intellectual tradition.
Back to-- is that program Who
Wants to Marry a Millionaire?,
does that still exist?
[INAUDIBLE]
In reruns?
In-- OK, so it was this one.
They give you questions,
and if you answer them
they give you money
and it's great.
And at various points, if you're
stumped you've got three things
you could do.
One is, they could eliminate--
you've got four choices.
They can eliminate
two of them to make
it a little bit easier for you.
Another is, you have this
expert who you can call up.
And the third option is
to ask the audience what
they think is the right answer.
And all the audience there
has these little buttons,
so they can choose A, B, C, or
D of the multiple choice there.
And what the logic is supposed
to be is, cut it down to two.
Your chances are better
if you have to guess.
Talk to your wise expert, who's
sitting by on the phone there.
And they're going to be wise
and be able to hopefully answer
this question.
Or ask a whole bunch of people.
And they would all vote.
And any smart
contestant would choose
whatever the audience chose.
Because, when the audience
was asked, 91% of the time
they got the right answer.
They got the majority of people
voting for the right answer.
And this is more
wisdom of the crowd.
And this was a much
better hit rate
than whoever the expert was on
the other side of the phone.
One person could be
extremely expert,
but they're not
going to be as expert
as a whole bunch of somewhat
decent experts thrown together.
This is the notion behind a
field called prediction markets
where what you do is you are
trying to predict some event.
For example, the
Pentagon is very
interested in using
prediction markets
to try to predict where the
next terrorist attack might be.
And what you do is you get
a whole bunch of experts,
and you ask each of them
to think about whatever
the parameters are and take a
guess as to how long it will
be before the next one occurs.
And what you do is,
you average them up
and assume there is a wisdom
of the crowd thing going on.
And that will give you
lots of information.
Great case of this
a few years ago.
There was some
submarine or something
that sunk somewhere out in
the Pacific, in the ocean.
And nobody knew where
it was, but they kind of
knew where the last
sighting, the last recording,
was from it.
But they had a whole
bunch of naval experts.
And they had all of
them sort of bone
up on the knowledge of what was
the water temperature and wind
speeds and where they
were on the last sighting
and what was on TV
that day or whatever.
They got all the
information, and each one
made a guess as to where
it would be on the map.
And you put them all together.
And they had guesses
covering hundreds
of square miles of ocean floor.
And they put it all
together, and they came up
within 300 yards of
the right location.
So what we have over and over
here is this business of,
put a lot of somewhat decent
experts together on a problem,
and they will be more accurate
than almost any one single
amazing expert at it.
Under a few conditions.
The collection of these partial
experts can't be biased.
Or if they are,
they all have to be
biased in a random
scattering of directions.
And they need to really
do be somewhat expert.
If you get a whole
bunch of people
off the subway in
New York and ask
them to guess the
weight of the oxen,
they are not going to wisdom
of the crowd their way
into being able to milk
the thing afterwards.
You've got to have people who
have some experience with it.
And you wind up seeing
wisdom of the crowd
stuff going on in all
sorts of living systems.
For example, here
is an ant colony.
And here's a dead ant.
And they're trying to
get the dead ant back
to the ant colony.
And when you look
at these things,
they know how to get it, or
they get some dead beetle
or something to eat, and
a whole bunch of ants
push it over back
to their colony.
Oh.
Does each one of them
know exactly where
they should be pushing?
No.
What you have
instead is, each ant
has somewhat of the
right idea as to where
they should be going.
And there are more ants that
have a reasonably accurate
notion, a smaller number
that are somewhat off,
and a really small number
that are way out of whack
because in general ants
are kind of experts
at finding ant colonies.
They're pretty informed.
And what you do is you
put them all together
and you do this
vector geometry stuff.
And it moves perfectly
in that direction.
And no single ant knows
exactly where the colony is.
You've got a wisdom of the
crowd thing here going on.
OK.
Where are we?
Five-minute break.
If you have a chance, could
you email me that website
so we could post it
in the CourseWorks?
That's great.
OK, picking up.
So now we are ready to
take some of those building
blocks, wisdom of the crowd
stuff, biophysical potato
chips, and begin to
see it more formally
in this field of
emergent complexity.
What is that about?
What we've already alluded to.
It's systems where you have
a very small number of rules
for how very large numbers of
simple participants interact.
What's that about?
Here's what emergence is about.
You take an ant and you
put it on a table top
and you watch what
it's doing and it
makes no sense whatsoever.
You take 10 ants and do it and
none of them make any sense.
You put 100 and they're
all scattering around.
And somewhere around, I don't
know, 1,000 ants or so, they
suddenly start making sense.
And you put in 10,000 or
100,000 or whatever it is,
and suddenly, instead of some
little thing wandering around
aimlessly, you
suddenly have a colony
that can grow fungi and regulate
the temperature of the colony
and all these things.
And suddenly, out of
these ants emerges
an incredibly complex, adapted
system, an adaptive one.
And the critical point
there is, no single ant
knows what the temperature
should be in the colony.
Or if this is time
to go out foraging
in this direction instead
of that direction.
It all emerges out of the
nature of ant interactions.
You've got very simple
constituent parts.
An ant, much like
one box that's filled
in the cellular automata.
You've got very
simple rules for how
they interact with each other.
Ants have, I don't know,
maybe 3 and 1/2 rules.
Don't tell Deborah Gordon
in the department, who's
an ant obsessive.
But that I may be
inadvertently dissing the ants.
But they have a small number of
rules as to how they interact.
If you bump into an ant and you
do this with the pheromones,
you go this way, and
if you go that way.
And I'm just making it up.
They have a small
number of rules.
And as long as you've got a lot
of ants doing this, out of this
can emerge hugely complex
adaptive patterns.
And this is what an
emergent system is about.
Simple players, huge
numbers of them,
simple nearest neighbor rules.
And you throw them all together,
and out comes patterning.
And there is no single ant that
knows what the blueprint is,
and there's no blueprint.
There is no plan
anywhere that says
what the mature form of the
colony should look like.
There are no instructions.
It is bottom-up organization
rather than top down.
And you see all sorts
of versions, then,
of emergent complexity
built around, again,
lots of elements of things
with a small number of very
simple rules about how neighbors
interact with each other.
We need that board.
OK.
Here we have two, four,
six, eight different cities
or eight different
places where ant
can find good food or
eight different something
or others, eight
different locales.
And you were trying to
do something efficient.
You need to go to each one
of them to sell your product
or to see if there's
good food there or not.
You need to go to
all eight of them,
and you want to do it as
efficiently as possible.
You want to find the way to
have the shortest possible path
to go to all of these places.
And this is the classic
traveling salesman problem.
And nobody at this
point can solve it.
There's no formal
mathematical solution.
And by the time you get
to, like, eight locales,
there's, like, hundreds of
billions of different ways
you can do it.
So how can-- you can't come
up with the perfect solution.
But you could come up with maybe
kind of a good, decent one.
There's two ways
you could do it.
First is to have an unbelievably
good computer that just,
by sheer force, cranks out a
bazillion different outcomes
and in each case measures
how much you're doing it.
And you can get something
close to an optimal answer.
The other way of doing
it is to have yourself
some virtual ants in
something that is now
called swarm intelligence.
Here's what you do.
You need to have two
generations of ants.
The first generation,
you stick them all down,
different numbers of
them, and they all
start off in these
different cities,
these different locales.
And their rule is, each one
of them goes to another city.
Each one of them goes
to another destination.
But here's the follow-me rule.
The ants are leaving a pheromone
trail, pheromone trail,
and they stick
their rear end down.
What is it?
Head, thorax, abdomen.
And they stick
their abdomen down.
And they've got a
gland at the bottom
there, which
releases a pheromone
and makes a track, a scent
track, of the pheromone there.
And a very simple rule,
they have a finite amount
of pheromone in there to expend
on the entire path they're
making.
In other words, the
shorter the path,
the thicker the pheromone
trail is going to be.
Now what you do is
deal with the fact
that the pheromones
dissipate after a while.
They evaporate.
And thus, the thicker
the path, the longer
it's going to be there.
You now take a second
generation of virtual ants,
and you throw them in there.
And what their rule is,
they wander around randomly.
And any time they hit
a pheromone trail,
they join the trail
one way or the other,
and they lay down a
pheromone trail of their own
with their abdomen.
They reinforce the
markings on this trail.
And let 10,000
virtual ants do that
for a couple of hundred
thousand rounds of generations,
and they solve the traveling
salesman problem for you.
Because it winds up
being, the short paths,
the more efficient ways
of connecting locales,
will leave larger,
thicker trails,
which are more likely to
last longer and thus increase
the odds that an ant
wandering around randomly
will bump into it
and reinforce it.
And what you see
is, initially, there
will be every possible path.
And as you run
this over and over,
it will begin to fade
out, and out will
emerge the more efficient ones.
You can optimize the
outcome doing it this way,
just asking virtual
ants to do it for you.
And this is exactly how ants
do it out in the real world.
When they're foraging
in different places,
there is a first wave
of them that comes out,
and they go to locales
leaving scent trails.
And then there are the
wanderers that come in,
and when they hit a
trail they join it.
There are now telecommunications
companies that use swarm
intelligence to figure out
what's the shortest length
of cable they need to use to
connect up eight different
states' worth of
telecommunication towers,
whatever they're called.
And they can sit
there and do math
till the end of
the universe trying
to figure out the cheapest
way to wire them up.
Or they can use
swarm intelligence.
And that's what a lot of
them do at this point.
It works.
What are the features of it?
This is not wisdom of the crowd.
This is not that every ant knows
a solution to the traveling
salesman problem, except none of
them have the perfect solution.
But put them all
together, and they all
get to vote on outcomes.
The ant don't know from
traveling salesman problems.
The ant knows nothing about
trying to optimize this.
All the ant knows is one
of two different rules.
If I'm walking from one
of these to one of these,
the longer I walk, the
thinner the pheromone trail.
Or rule number two, if I
stumble into one of these,
I join it and put down
my markings there.
Two simple rules,
one very simple type
of sort of unit of
information in there, an ant.
And all you need to do is make
sure there's enough of them,
and they solve the
problem for you.
This winds up explaining
another thing.
How do bees pick a
new nesting site?
A bee's nest, a bee's--
hornet's nest, a bee's nest.
Every now and then the
bees need to leave and pick
a new place to live.
And how do they figure
out the good place?
And there's all sorts of
criteria of nutrients.
And so all sorts of bees go
out there, and what they do
is they look for food sources.
And they look for a place
that will have a lot of food.
Maybe that's a place to
go and move the colony.
So we know already, the bee will
go out and find its food there,
its food source.
It will come back in.
And here's the colony
cut in cross-section.
And what you wind up having
is this ring of bees.
Here is the entry.
And you have the
bee dancing going on
that we've heard about in
the middle of the dance floor
there.
And we've already heard it's
this pattern of this figure
eight while shaking
the rear end.
And we know what
the information is,
which is the angle tells the
direction to go out there.
And the extent to
which it's wiggling
its rear end is how long
you're supposed to fly for.
But the final variable is,
the better the resource,
the longer you do the dance.
So you've got bees coming in
from all over the place that
have found good
resources, that have
found so-so ones, all of that.
And so there's bees doing
all this dancing stuff here
of different durations.
And the ones who have
found the good solution
to where do we want to
live are dancing longer.
The ones who have found
the most efficient path
are leaving a message longer.
So now you bring in
your second generation.
And the rule is
among bees, if they
happen to bump into a bee
that is doing a dance,
the bee responds and goes
where it tells you to go.
So a bee may randomly sort of
bump into one of these guys
and then off it goes.
Actually, I'm sure it's
more complicated than this,
but it's along the
lines of there's now
random interactions.
If one of the peripheral
bees encounters, bumps into,
one of these bees
that has information,
it joins in in that bee's group.
And it then goes and
finds the food resource
and comes back with
the information.
So thus, by definition, if you
have found a great food source,
you're going to
be dancing longer,
which increases the odds of
other bees randomly bumping
into you, which causes them to
go and find the same great food
source and come back
and dance longer.
And the ones with lousy ones
are coming in and dancing
very briefly, and thus
there is hardly any odds
of somebody bumping into them.
And what you begin to
do is, you suddenly
optimize where the
hive is supposed to go.
Again, it's not
wisdom of the crowd.
It is an emergent feature of
one generation with information
based on some very simple
rules, and one information
that generates
some random element
and out comes an ideal solution.
More versions of this.
Another domain where some
very simple rules out of it
emerges something very
complex and adaptive.
OK, so the themes here
are two generations,
the more adaptive the
signal, the stronger it is
and the longer it lasts.
And then the
randomization element.
Another theme that comes through
in a lot of emergence, which
is to have your elements
in there, your ants,
your bees, your
traveling salesmen,
whatever the constituents are.
And now what the rules
are are simple rules
of attraction and repulsion.
Which is to say,
some of the elements
are attracted to each other,
and some of the elements
are repulsed by each other.
Some are pulled together,
some are pushed apart
like, for example, magnets.
Magnets are polarized
in the sense
that magnets only have
two ways of interacting
with each other, simple
nearest neighbor rules.
They're either
attracting or repelling,
depending on the orientation.
So here's what you do now.
You take a system and
something very simple.
You've got some simulated
SimCity sort of thing
where you're letting the
system run to design a city.
You want to do your urban
planning in your city
that you're going
to construct there.
And what you do is,
you can sit there
and you can study millions of
laws about zoning and economics
and all of that to decide
something very simple.
Where are you going to put
the commercial districts?
And where are the residential
districts going to be?
Or you can have just a small
number of simple rules.
Which is, for example,
if a market appears
in some place, what it
attracts is a Starbucks.
And what it also attracts
is a clothing store
or some such thing.
So a bunch of rules.
But then you have
repulsion rules,
which is, if you
have a Starbucks,
it will repulse any
other Starbucks.
So the nearest other Starbucks
can be this far away.
If you have a
competitor's market,
it can't get any
closer than this.
That sort of thing, these simple
attraction/repulsion rules.
And what you wind
up getting when
you run these simulations are
commercial districts in a city
where you get clusters of
commercial sort of places that
are balanced by
attraction and repulsion
where you have thoroughfares
connecting them.
And the more elements there
are in the two neighborhood
commercial centers, the bigger
the connection is going to be,
the bigger the street is, the
more lanes, the more powerful
the signal coming through there.
And you throw it in.
And out pops an
urban plan that looks
exactly like the sort
of ones that the best
urban planners come up with.
And all you need to do instead
is run these simulations
with some very simple
attraction and repulsion rules.
So you do that, and it winds
up producing stuff that looks
like cities.
You do that with a
bunch of neurons.
You take a Petri dish, and
you throw in a whole bunch
of individual neurons.
And they have very simple rules.
They secrete factors which
attract some types of neurons.
And they secrete factors which
repel other types of neurons.
And all of them are having
some very simple rules.
When I encounter this,
I grow projections
towards where it's coming from.
If I encounter that,
I grow projections
in the opposite direction.
Simple attraction and repulsion.
And what you do
is, at this point,
you throw in a whole bunch
of neurons, each one where
you throw into a Petri dish.
And at the beginning they're
all scattered evenly all
over the place.
And you come back, and you
come back two days later,
and it looks just like this.
You have clusters of
neurons sending projections,
and you have all these empty
residential areas in between.
And if you just mark this
in a schematic way, looking
from above you're not
going to be able to tell,
is this the commercial
districts in a big city?
Or are these neurons
growing in a dish?
And you get areas of nuclei
of cell bodies and areas
of projections, and it winds
up looking exactly like that.
And amazingly, there was a paper
in Science earlier this year.
And it was looking at
one of these versions,
again, in this case
attraction and repulsion
rules with ants' colonies
setting up foraging paths.
And they explicitly
compared one colony
to the efficiency of the
distribution of the train
stations in the
Tokyo subway system.
And what they showed was
very similar solutions,
but the ants had gotten
a more optimal one.
And the subway system had
people sitting there salaried
to figure out the
best way to do it.
All the ants had were
very simple rules
of, if it's someone from the
other colony I stay this way,
if it's someone from
mine, simple attraction
and repulsion.
And out comes something that
looks like this as well.
So here you see that happening
with a remarkably small number
of rules.
Now you put it into a really
interesting context, which
is something we bumped into back
when first introducing proteins
and DNA sequence equals shape
equals function, all of that.
Molecules have charges on them.
Some of them were positively
charged, some of them
were negatively.
Whoa.
Attraction and repulsion.
Positively charged
molecules are attracted
to negatively charged ones.
Same charged ones repulse.
Here we have a system with very
simple attraction and repulsion
rules.
And that's the logic behind,
when one thinks about it,
one of the all-time important
experiments, something
that was done in the 1950s by a
pair of scientists, University
of Chicago, Urey and Miller.
Here's what they did.
They took, like, big
vats of organic soup
stuff that just had all sorts
of simple molecules in there.
Little fragments of carbon,
carbon, little fragments
of-- all sorts of inorganic
molecules in there,
little ones in there, floating
around in this organic soup.
And what they did was they would
pass electricity through it.
And they did this
vast numbers of times.
And eventually
what they saw was,
they would come back and check,
and these random distribution
of these things, of
these little fragments,
had begun to form amino acids.
Whoa.
Metaphor.
The organic soup, just the
evenly distributed sort
of world of potentially
organic molecules
in a world in which electricity
passes through, lightning.
What had these guys
just come up with?
Some in your, like,
kitchen sink experiment
of the origins of life.
And what people have done
subsequently is show,
you don't need the catalyst.
There's a whole
world of researchers
who study origin of life.
And the basic notion is, you
put in enough simple molecules
in there that have attraction
and repulsion rules,
and you get perturbations
and spatial distributions
of certain ways,
and they will begin
to form rational
structures after a while.
Here's another version of this.
And I used to do this in
class, except I can never
pull this one off, and
it just became chaotic.
Kid's toy, you've
got these magnets.
You either have-- you
have magnets like that.
And then you have
little metal balls that
can go onto the magnet here.
And you've got vast
numbers of them.
And you can piece them together.
Whoa.
This is starting to look
kind of familiar here.
So we have these constituents
with very simple rules, which
is the magnets repel each other.
They bind.
These things.
And here's what you would do.
Here's what I would
attempt to do.
First off, I would get
somebody to show me
how to get the video thing
on here to project it.
But you would put up a whole
bunch of these magnets in rows,
not too close to each
other, nice and symmetrical.
And what you do then is you take
a handful of the metal balls
and fling them in there.
And if you do that
400 or 500 times,
eventually they
will bounce around.
And amid all the
pieces flying, you're
going to get a pyramidal
structure like this.
One of those just like that,
it's three dimensional,
you know that.
You are going to get one of
those that will simply pop out
of this because that's the
nature of potato chips solving
their math problem
with double saddles.
That's the nature of throwing
a whole bunch of elements
with simple attraction
and repulsion rules.
And given enough chances, throw
in enough perturbations there,
and structures will
begin to emerge.
And it's the same
exact principle there,
these same ones over and over.
So we've got some very
simple versions where
you get emergent complexity.
One is this version
of a first generation
has directed searches
and the intensity
of the signal that
it leaves afterward
is a function of
how good of a search
they've done, random wanderers.
Then you have the
attraction/repulsion world
of putting these together,
lots of elements.
And you begin to get
structures out of it.
Next version of
this, or next domain
of where you begin to see
the fact that these rules are
underlying an awful
lot of things.
Suppose here you were
studying earthquakes.
And apparently there's just,
like, little earthquakes going
on 20 times an hour or so
all down on the Richter
scale of, you know, one
quarter or who knows what.
But you get enough of these,
you get a huge database,
and you can begin to graph
the frequency of Richter 1.0
earthquakes and how often do you
get the Richter 2.0 and Richter
3.0 and all of that.
And you graph it.
And it's going to look
something like this,
a distribution like
that, which is obviously
there's a huge number of
number one categories.
And it drops off until the
extremely rare at this end.
There's a distribution,
which mathematically
can be described, something
called a power law
distribution, with a
certain angle to it.
OK, so here's the
relationship between how often
do you get little teensy
earthquakes and the big ones.
Now instead, what you do
is something much more
different from that, which
is, you look at 50,000 people,
and you look at
their phone calls
over the course of the year.
And you keep track
of how far the phone
call was, how distant the
person is that they called.
And now you map the distance,
the very shortest calls,
the very longest,
and the frequency.
And it's the exact same curve.
It's the same power
law distribution.
Next version of it.
This was a study that was
done, which was-- I don't quite
know how these guys did it.
I always get lost in
the math on these.
But in this one,
what they did was
they took a whole bunch
of marked dollar bills,
and they started in the middle
of-- I don't know where,
I think it was at
Columbia, something--
and they were
somehow able to keep
track of how far the bills
had traveled a week later.
And asking, OK,
how many bills had
traveled no more than a mile?
How many five miles?
And it was the exact same curve.
And people now have been
showing this same power law
distribution.
Here are some of the things
that have been shown.
The number of
links that websites
have to other websites.
The number that
have only one link.
Power law distribution.
Proteins.
The number of proteins showing
certain degrees of complexity
and the numbers dropping
off with the same power law.
Here's one which is the
number of emails somebody
sends over the
course of the year.
This is the one that
was done at Columbia.
They got access to
everybody's email records.
I don't understand how
they could have done this.
But it was a couple of million
over the course of the year.
And what they showed was the
frequency, how many people
were making this small of
a number of emails over--
and the same power law.
Then there's this
totally crazy one,
which is, OK, do you guys know
the Kevin Bacon, six degrees
of separation thing there?
OK.
Someone went and did
a study about this
that they got, like,
every actor that they
could find who was in a
film in the last two years.
And they got all of
their filmographies.
And they generated their Kevin
Bacon degrees of freedom,
degrees of--
Separation.
Sing it out.
OK.
And they figured it out, the
number for each individual.
And then they graphed it.
How many people were six
degrees of separation away,
how many were five, so on.
And it's the same pattern.
And this one keeps popping
up, this power law business.
And what you see intrinsic
in that is, it's a fractal.
Because some of the
time you're talking
about what's happening with
the tectonic plates on Earth,
and some of the time you're
talking about phone calls,
and some of the time you're
talking about how molecules
interact with each other.
There's something
emergent that goes
on there, which is
an outcome of some
of these simple
attraction/repulsion rules,
an outcome of simple
pioneer generation
and then random movement ones.
And out come
structures like these.
This winds up being applicable
in a very interesting domain
biologically.
OK, so now we go back to the
traveling salesman problem.
And we're having now a
cellular version of it
in terms of networks.
You've got a whole
bunch of nodes here.
And the choice that
each node has to make,
in effect, is how
many connections it
will make in the network
to other nodes and how far
should those connections be.
Should it only connect
with ones way out there?
What does it want to do?
That's nonsense.
In terms of optimizing
a system, what
do you want your distribution
of connections of nodes
in a network to be?
What is it you want to optimize?
You want to get
a system that has
very stable, solid interactions
amongst clusters of nodes
but nevertheless
occasionally has
the capacity to make
long-distance connections
there.
And what you wind up
seeing is, if you generate
a power law distribution
in terms of, OK, all
of my projections are going
to be within this distance
and within this same
power law distribution
so that the vast majority
of the nodes in the network
are having very
local connections.
But still there is a possibility
now and then of very long ones.
You get a system that is
the most optimal for solving
problems most cheaply, cheaply,
and whatever the term is there.
And this solves it for you.
And then you look at
brain development.
So you've got neurons
forming in the cortex,
in the fetal cortex,
and you've got neurons.
You've got all these nodes.
And they have to figure out
how to wire up with each other
and how to wire up in a
way that is most efficient.
What's most efficient
in order to be
able to do the sorts of things
the cortex specializes in?
And you now begin to look at
the distribution of projections.
And it's a power
law relationship.
Most neurons in the
cortex are having
the vast majority of their
projections very local.
But then you have
ones now and then
that have moderate ones,
even rarer ones, that
have extremely long ones.
And you look, and this is
how the cortex is wired up.
It follows a power
law distribution.
And what this
allows you to do is
have clusters of stable,
functional interactions.
But every now and then, you
can talk to somebody way
over at the other end of the
cortex to see what's happening.
Interesting finding.
Autism.
Autism, people have been looking
for what's up biologically.
And the initial
assumptions would
be, there's not
going to be enough
neurons in some
part of the brain
or maybe too many in another.
What appears to
be the case so far
is there's relatively
normal number
of neurons in the cortex.
But then some people started
studying the projection
profiles of neurons in
the cortex of individuals
with autism post-mortem.
Very rare to get these.
And you see a power
law distribution.
But it's a different one.
It's a steeper one.
What does that mean?
In the cortex of
autistic individuals,
way more of the connections
are little local ones.
There's far fewer of
the long-distance ones.
There are way more local ones.
What does that produce?
Little pockets, little
modules of function
that are isolated
from other ones.
And that in some ways is
what's going on functionally
in someone with autism.
There is a lack of
integration of a whole bunch
of these different
functions there.
And that's what happens when you
have maybe a mutation or maybe
some epigenetic something
or other prenatally that
changes the shape of the
power law distribution.
Interesting.
There's a gender difference
in the power law distribution
of wiring in the cortex.
Which is, in the
typical female brain,
if this is the power
law distribution.
And in the male brain
it's a little steeper.
Male brains are more
modular in their wiring.
What's the biggest
part of the brain?
OK, we're running
out of space here.
There it is.
There's the brain
in cross-section.
And you've got cortex
here and cortex there.
And famously, here's
all the cell bodies.
And when projections are
going from one hemisphere
to the other, it goes across
this huge bundle of axons
called the corpus callosum.
The corpus callosum is
thicker in women than in men,
on the average.
It is thicker in
females than in males
because the power
law pattern is such
that there are more
long-distance connections
in female networks, and thus
it's a thicker corpus callosum.
The same thing is playing
out with connections
like this, and connections.
But this is the big honker one.
You get a thinner
corpus callosum in men.
You get an even thinner corpus
callosum in people with autism.
Again, that hyper male notion
there of Baron Cohen's.
What you have here is a
perfectly normal number
of neurons, probably even
perfectly normal number
of connections
between the neurons.
But they're more
local, they're more
isolated in the autistic cortex.
There's less
integration of function.
It's more isolated
islands of function there.
OK.
More examples of
where you can get
sort of patterns coming out.
Another version of it, which
is bottom-up quality control.
You start a website, you
are selling some product,
you are selling
books or whatever,
and you're asking people
to rate the books.
And you have a board of experts
that read all your books,
and they're editors and they're
wise and they're learned.
And they write your book
reviews and recommend
which ones should be
bought and which ones not.
And you get this very
successful business
going so that you're selling
more and more different
kinds of books.
And as a result,
you need to hire
more and more of these
experts to read the books
and produce their ratings.
And eventually that just
becomes too top heavy.
And what do you do?
The whole world that
we completely take for
granted now, you have bottom-up,
bottom-up evaluations.
Everybody rates things.
And that's the world where you
punch in a book into Amazon
or you look at something in
Netflix and when you return it,
it will give you, people
who liked this movie tend
to like these things as well.
There are no critics,
professional critics,
sitting there doing
top-down evaluations.
This is another
realm of expressing
attraction and repulsion rules.
I liked this.
I didn't like this.
And all you need to
do, then, is throw
in elements of
randomization, and you've
got bottom-up quality control.
And that's a completely
different way
of doing these things.
What's the greatest example
out there of bottom-up systems
with quality control?
Wikipedia.
Wikipedia does not have
gray-bearded silverback elders
there writing up the
Wikipedia knowledge
and sending it on
down to everyone else.
It is a bottom-up
self-correcting system.
It is very easy to make
fun of some of the stuff
that winds up in
Wikipedia, which is, like,
wildly, insanely wrong.
But when you get into areas
that are fairly hard nosed.
Very interesting study
about five years ago
that Nature commissioned, which
was getting a bunch of experts
to look at Wikipedia and to look
at the Encyclopedia Britannica
and look at the
hard-nosed facts in there
about the physical
sciences, the life sciences.
And what you got
was, Wikipedia was
in hailing distance of the
Encyclopedia Britannica's
level of accuracy.
And that was five years ago.
And it has five years of
self-organized correction
since then.
This is amazing.
The Encyclopedia Britannica
is like written-- there's,
like, 30, like, elderly, stuffed
British scholars that they,
like, have locked
in a room for years
who produced the encyclopedia.
And these are the law
givers and the knowledge--
And you just let a
whole bunch of people
loose with somewhat
differing opinions
about whether Madonna
was born in 1994 or 1987
or whatever it is.
And you throw them
all together and you
do wisdom of the crowd stuff.
And out comes a self-correcting,
accurate, adaptive system
with no blueprint, just with
some very simple local rules.
Very simple ones, which is
looking for similar patterns
shared between
different individuals,
and self-correcting.
Where you get even
more efficient versions
of that is with a
lot of websites,
where not only does everybody
get to put in their opinion,
but people whose
opinions are better
rated have more of a voice
in evaluating somebody else.
You're putting in
weighted wisdom
of the crowd-type
functions in there,
and out comes
incredible accuracy.
These are great.
There is one drawback with
those systems, though, which
is, with ones like
Netflix, where it tells you
you're going to like this if you
like this, that sort of thing.
It's a system that is very
biased towards conformity.
It's not good at spotting
outliers and sort of taste
and such.
What you really want to
do in those systems is,
here are the movies-- of the
movies that are out right now,
here are the ones that have
10% of the people think
it's the greatest
movie they've ever seen
and 10% think it's
the worst movie.
That's an interesting
movie to see.
That's when you want
to be able to get
a way of bottom-up information
about the extremes.
Movies that generate
controversy.
Everybody's going to
love whatever it is,
and that doesn't
take a whole lot.
This is a way to break the
potential for conformity
in these bottom-up systems.
Nonetheless, overall it
winds up solving a problem
without professional
critics, without a blueprint,
without top-down control.
So how do you wire
some of these up?
Back to the cortex.
And the adult cortex has
these power law distributions,
and they're great
because they optimize.
They've got lots of stable,
local communication,
but there's still
the ability to do
creative long-distance
connections.
So that's great.
But how do you get that?
How does the nervous
system wire up this way?
And it does swarm intelligence.
The developing cortex does a
swarm intelligence solution.
When the cortex is
first developing,
what you will have is a
first generation, a pioneer
generation, a pioneer
generation of cells.
The cortex surface, all of
that, that there is a pioneer
generation of cells
that basically grow
processes up like these.
And these are called
radial glial cells.
What they are, they're the
ants with the first generation
of setting down the trail here.
They're the first
bees coming in.
And what you then
have, the neurons
are the second-generation
random wanderers.
And what they do
is they come in.
And as they begin
to develop, they
have rules that, when
they hit a radial glia,
they grow up along it.
They migrate along it,
they throw up connections.
And you do that with
enough of the cortex, which
is hundreds of millions of
billions of neurons in there,
and you get optimal
power law distributions.
All you need are some
very simple local rules.
And out of that emerges
an optimally wired cortex.
And it's the same simple
emergent stuff going on.
OK.
So how do we begin to really
apply this stuff to humans?
Because it winds up being
very pertinent and making
sense of some of the most
interesting complex things
about us.
So what's the difference between
humans and every other species?
Nothing all that exciting.
From a neurobiological
standpoint,
you've got this real challenge,
which is, you look at a neuron
from a fruit fly
under a microscope
and you look at one
from us and it's
going to look kind of the same.
Looking at a single
neuron, you can't tell
which species it came from.
We have the same kind
of neurotransmitters
that a worm uses in
its nervous system.
We've got the same
kind of ion channels,
the same sort of excitability,
the same action potentials.
You know, minor
details are different.
We have not become
humans by inventing
new types of brain cells and new
types of chemical messengers.
We have the same basic
off-the-rack neuron
that a fly does.
Oh.
We have very similar
basic building blocks.
What's the
difference, of course,
is we've got 100 million
of them for every neuron
that you find in a fly brain.
And out of that comes
emergent properties.
Great story.
Garry KAS-pah-rof,
kas-PAH-rof, I never
remember which
syllable to emphasize.
Grandmaster Russian, chess
grandmaster in the '90s.
And apparently he's rated as one
of the strongest of all times.
And he was the
person who wound up
participating in this
really major event, which
was this tournament with
this chess-playing computer
that IBM had built called Deep
Blue or Big Blue or Old Yeller.
What was it called?
Deep Blue, Deep Blue, Deep Blue.
And they played
against each other.
And apparently what happened
was, in the first game,
Kasparov won perhaps.
And the computer was able
to modify its strategy
and then proceeded to
mop the floor with him.
And this was a landmark
event in computer science.
This was the first
time that a computer
had beaten a chess grandmaster.
Amazing event.
Not surprisingly,
afterward Kasparov
is all bummed out and depressed.
And his friends were trying
to make him feel better.
And they go to him and they say,
look, all you got done in by
is quantity.
All you got done
in by is the fact
that that computer could
do a whole lot more
computations than you could
in a set amount of time.
I'm told, apparently
chess grandmaster types
can see five, six moves ahead.
And they can intuit where
the interesting ones were.
And Deep Blue could calculate
every single possible outcome,
like, seven, eight
moves in advance.
And every time, it would
simply pick the one
that was the best outcome.
It was like generating solutions
to the traveling salesman
problem.
Kasparov didn't have a
chance because the computer
could simply generate
enough solutions
to pick the right one.
So all of them
are saying to him,
you should not be depressed
because all that computer had
going for it was quantity.
And what he said
in response was,
yeah, but with enough
quantity you invent quality.
And that's the exact
equivalent of one ant
makes no sense and 10,000 do.
That's the exact equivalent,
with enough of these elements
here, you optimize.
We do not have
fancy neurons that
are different than
in any other species.
We've just got more of them.
And simple nearest neighbor
rules, and you throw a million
of them together and
you get a fruit fly.
And you throw 100
billion of them together
and you get poetry and you get
symphonies and you get theology
and you get all of that.
And it's the same
building blocks.
With enough quantity,
you invent quality.
And this is the punch
line that came out
of really important
work a few years ago.
OK, we're now, what, 10
years, I think, into having
the human genome sequenced.
And about five years ago they
sequenced the chimp genome.
Soundbite.
Everybody learned
from whenever back
when is that the human and
chimp share 98% of its DNA.
So finally you had these two
gigantic rolls of print-out.
And here is the
entire human genome
and here's the entire chimp one.
And somebody could
finally sit there
and compare them and compare
them and see, indeed,
is it 98% shared?
And that winds up
being the answer,
even though what that number
actually means is debatable.
But that brings up the question,
of course, what's the 2%?
What's the 2% that differs?
And what has come out of that
have been some very interesting
findings.
Some that were
mentioned earlier on,
which is, they are
disproportionately
coding for transcription
factors and splicing enzymes
and, OK, that amplification
of network stuff.
It is preferentially coding for
non-coding regions, differing
but non-- all the
stuff from back,
that lecture for getting
macroevolutionary changes.
That's how you get a
different species coming out.
But how about other
types of genes?
What were some of
the key differences?
Here was one big difference.
We have about 1,000 fewer genes
for our olfactory receptors
than chimps do.
They've been inactivated in us.
They're called
pseudogenes in us.
They don't express.
And that's about half of
the difference in the genome
between humans and chimps.
If you want to turn
a chimp into a human,
you're halfway there if you just
give it a lousy sense of smell.
That's half the
genetic differences?
What other
differences are there?
There were ones having to
do with morphology, bone
development, probably
bipedalism versus being
a partial quadruped.
There are ones having to do
with hair development, which
is why chimps have all the
hair on them and only those,
like, disturbing people with
the hair on their shoulders
have that much hair.
So that's-- there's differences
in some reproductive-related
genes.
You don't want to mate
with them, all of that.
And then you say,
where's the genes
having to do with the brain?
Are there any differences there?
And there turned out
to be very, very few.
And they turned out to
be very, very logical.
The handful that differ
seem to have something
to do with cell division.
Have something to do with how
many rounds of cell division
these cells go through.
And what you have is,
the human versions
go through more rounds.
And calculations have
been done looking
at the average number of
neurons that each progenitor
cell generates, say, during
cortical development.
And if you start with
the number of neurons
that you find in a
rhesus monkey brain
and have it do three or four
more rounds of cell division,
you get a human brain
in terms of the numbers.
Qualitatively, it's
the exact same neurons.
All that differs is quantity.
And you put enough
of these together
and you go from
tools, which are meant
to get little termites
out, into human technology,
the difference between us
and them is one of quantity.
Throw enough neurons in
there, and out begins
emerging all these
distinctive human things.
So what does that do?
That begins to, for
one thing, underline
what the main genetics
are about in terms
of the genetic differences
in the brain between us
and, say, chimps are
genes that free you
from genetic influences.
Because those are not
specifying what sort of cells
you generate in larger
numbers in the brain.
They're not specifying
connections.
They're just specifying
larger quantity.
And all this stuff goes to work
and out comes a human brain
instead of a chimp one.
OK, so what does this
whole subject get us?
The chaos stuff, the
complexity emergent stuff.
What are some of the themes that
come through with all of it?
The first one is this
emphasis on quantity.
You want to get a very,
very fancy system.
You don't necessarily have
to invent a new type of ant
or a new type of 0 or
1 in a binary system
or a new type of neuron.
You could do it with quantity.
You get quality, you get
excellence, you get complexity,
you get adaptive optimization
with huge numbers of elements
with the very simple rules.
What's the next theme
that comes out of it?
One that is totally
counterintuitive.
Once again, like
this whole subject
that shoots reductionism
down the drain,
totally counterintuitive.
The simpler the constituent
parts, the better.
Fancy, complicated ants that
are specialized and have
all sorts of different rules.
They are not going to
generate swarm intelligence
as effectively as do systems
with the simpler elements.
The more simple the building
blocks are, the better.
Something else that
is intrinsic to all
of this, which runs
counter to all sorts
of rational intuitions, which
is more random interactions
make for better, more
adaptive networks.
You want lots of
random noise thrown
in there because that's how you
stumble onto optimal solutions.
Randomness is a good thing.
And remember, right at the time
that we're making new neurons
in the cortex, that's when you
induce the transposable events
in the genome.
That's where you juggle the
DNA producing randomness there.
Randomness is a good thing.
Randomness adds to the
excellence of networks
What else?
Next thing as a theme
that comes out of it
is the power of
gradients of information.
Things that guide you,
you a cell, you an ant,
you a commercial district.
Things that can guide
you towards things,
things that can
repel you, gradients
of attraction and repulsion.
And that's exactly
what's going on.
There is a gradient
in magnets when
they're this close and the power
that they have is dropping off.
As they move,
gradients provide a lot
of the optimization
in these systems.
Very, very important as well is
nearest neighbor interactions.
These are not just a
handful of simple rules
about how you're interacting
with somebody in Chicago.
These are all how you
interact with another ant,
another bee when you bump
into it, a glial cell.
Local interactions
with simple rules.
Something else.
Another one that
runs totally counter
to intuition, which
is generalists
work better in these
systems than specialists do.
Generalists are more
likely to come up
with these adaptive outcomes.
OK, so what does all of
this mean on a larger level?
And what I think is
that this is where
the complexity of human brains
and human behaviors come from,
these emergent properties.
And this is now a generation
or two into people
thinking about this stuff.
And it is incredibly
hard to think about.
And most of the work
I do and my peers do
is reductive stuff
that is very limited.
And like, I don't
understand how to think
about it in this other way.
And the odds are,
you guys are not
going to be good
enough at it either.
You're good enough that you were
a first generation growing up
that knows, if you
want to find out
if you're going to
like a movie or not,
you don't need to have
somebody with expertise
and a label on their forehead
and a blueprint and top down.
You don't need critics anymore.
You have bottom-up systems.
You guys who are first
generation growing up
thinking in that way.
What's a consequence of that?
You are beginning to get
better at this stuff.
And my guess is, it's not
until, like, your grandkids
that you're going to
have people thinking
so much in the emergent
systems that we're finally
going to be able to figure
out what the brain is doing.
And where you see
there is all sorts
of things that can happen.
If there was more
bottom-up communication
in the trenches in
World War I, they
would have stopped the war.
All these emergent
things bottom up.
We've now had
revolutions when Marcos
was overthrown in
the Philippines
back when that was
basically bloodless.
When the Czech
revolution occurred,
it was called the
Velvet Revolution
because there was no violence.
All they had to do was get
enough people in the town
square in the capital
and paralyze the country,
and they took it over.
I will predict that
within our lifetime
there is going to
be a revolution
in some country at
some point where nobody
leaves their living rooms.
All they do is do
something online
with some emergent
bottom-up thing
and they collapse the government
and do it in and no one
will have to leave their
living room because it will be
all emergent things coming up.
The final couple of points here.
First one is, all that chaotic,
strange, attractor stuff, all
of us spend a lot
of time thinking
about how we're not quite
up to the ideal this or not.
We're not at the
ideal appearance.
We're not at the
ideal intelligence.
We're not at the ideal
choice of perfume.
We're not at the ideal anything.
What strange attractors
and chaos shows
you is the notion that
there is an ideal,
that there is an essentialist
optimal, whatever, is a myth.
We are all deviating
from the optima
because the optima is just
an emergent, imaginary thing.
The other final point is,
something that you guys are
going to be much better at
than any previous generation,
which, if you grow
up thinking, when
I want to find out if
a movie is good or not,
I do bottom-up stuff, you
are growing up with a mindset
that you don't need blueprints.
You don't need
top-down blueprints.
And implicit in
that, when you look
at how you can get complex,
adaptive, optimized systems
without blueprints is
the fact that, if you
don't need blueprints,
you don't need somebody
who makes the blueprints.
And it will be a lot
easier to comprehend that
as being the case.
You don't have to have a
source of top-down instruction
if you don't need a blueprint.
OK, so I don't know.
I'm talking about something--
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