(calming instrumental music)
- Well I think actually there are some basic principles
to how human thinking works.
My last book, "How to Create a Mind",
which came out in 2012,
which resulted in me being recruited to Google,
I practiced those ideas, talked about
how human neocortex works,
it's outgrowth of a thesis
I have had actually for 50 years
because I wrote a paper
when I was 14 or 15 in 1962,
and won a science contest to Westinghouse Talent Search.
Now the intel inside this talent got to be present content
and I described human thinking as consisting
of modules of neurons,
each module can recognize a pattern,
and the basis of human thinking is pattern recognition.
And those patterns are actually sequential.
And they're in one direction.
And I gave a lot of evidence for this.
For example, try to recite the alphabet.
Now, most of you can do that fast.
Okay well, recite it backwards.
You probably can't do that,
unless you learned that as a new sequence.
It's a pretty trivial transformation,
and yet we can't do it.
So we have different hints as to how the human brain works.
In recent years, it's been
an explosion of neuroscience evidence.
For example, the European brain reverse engineering project
has identified modules of about 100 neurons each,
and since the neocortex has 30 billion neurons,
that means 300 million modules,
and they all are pretty much the same.
They have the same structure,
the same organization within them.
And there's no plasticity, no change,
within that module for your entire life.
Despite the idea that your brain
is constantly rewiring itself.
There is plasticity, constant rewiring between the modules,
and each module is recognizing a pattern.
We can see the axons coming in from other modules
that are feeding the sequential input
that represents the pattern, that this module will learn.
So it's a hierarchy of patterns.
This pattern is based on a hierarchy
of patterns and the modules below it.
And each one of those has input
from modules below it, and it's a very elaborate hierarchy.
And biology, biological evolution evolved
this hierarchical structure in the brain,
so that it can understand and learn
the hierarchical structure of the world,
because the world is organized hierarchically.
The neocortex emerged 200 million years ago in mammals;
only mammals have a neocortex.
And it was a thin structure, the neocortex
means new rind, and there was about.
In the first mammals, which were rodents,
it was about the size of a postage stamp,
and just as thin as a postage stamp,
and it wrapped around the walnut-sized brains
of these early mammals,
but it was capable of a new type of thinking.
You could invent new behaviors,
non-mammalian animals, like reptiles,
that didn't have a neocortex, couldn't do that.
They have fixed behaviors.
Didn't help them that much actually
because the environment changed very slowly
and could take 50 thousand years for there
to be an environmental change that would require
a new behavior, and over the 13 thousand years,
these non-mammalian animals could evolve
using normal Darwinian evolution, a new fixed behavior.
But then something happened 65 million years ago.
It was a sudden catastrophic change to the environment;
we call it the crustacean extinction event,
and that's when mammals overtook their ecological niche.
That's when the neocortex actually showed its capability.
And then biological evolution then grew it.
Mammals now, instead of being just little rodents,
got bigger, their brains got bigger, at an even faster pace,
taking up a larger fraction of their body.
And the neocortex got bigger even faster than that
and developed these curvatures and folds.
If you look at a primate brain,
it's got these characteristic curvatures,
so it now takes up 80% of the brain.
Then something else happened, two million years ago.
If your remember, two million years ago,
we were walking around; we didn't have these big foreheads.
So humanoids came along with a big forehead.
And that houses the frontal cortex.
And up until recently, it was said
"Well, the frontal cortex does
"such qualitatively different things,
"it must be organized differently.
"It must have a different method, a different algorithm."
I make the case, and I think the neuroscientists
coming around to this view,
it really was just an additional quantity of neocortex.
Well, so what did we do with that additional quantity?
Well, we were already doing a very good job
of being primates, so we put it at the top
of the neocortical hierarchy.
So this hierarchy that I mentioned now got bigger.
As you go up the hierarchy,
things get more general, more intelligent, more abstract.
The very bottom, I can tell that that's a straight line.
At the top, I can tell
that's funny, that's ironic, she's pretty.
So that additional hierarchy
that we got two million years ago
was the enabling factor for us to invent
language, and art, and science, and music.
Every human culture we every discovered has music.
No primate or any other animal has music.
That came from this additional neocortex.
And I make the case in my book, "How to Create a Mind",
what the algorithm is, of each of these modules.
They all have the same algorithm.
So a lot of people like to say,
"Oh, the brain is so complex,
"it's the most complex thing in the universe";
that may be true, but it has a regular repeating structure.
Each of these 300 million modules is basically the same.
Now they self-organize into these hierarchies,
and each module discovers a pattern,
and learns it, remembers it, and can recognize it,
even in a different context, so it's very good at metaphor.
And I describe my thesis on how this works
as we continue doing more brain reverse engineering,
we will refine that model,
but I've been working with this model,
and we find that it can in fact master things
like language, not yet at human levels,
but doing still some impressive things.
We look beyond, for example, for things like jeopardy,
which itself was pretty sophisticated.
So there is kind of a master algorithm,
at least I have a proposal for one.
These deep neural nets,
which there's tremendous excitement about,
which is a little bit different from the model I have,
but they have done remarkable things.
I mean, they won the Go Championship,
and they can recognize images as I mentioned,
better than humans, and can drive cars.
And that's actually pretty simple.
You can read about deep neural nets,
the algorithm is again, a repeating structure
that's not that complicated.
So the mathematics of thinking,
I think is being understood,
but I would not claim that we understand it fully.
But we're getting more and more hints
as we learn more and more about the human brain.
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