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TOMASO POGGIO: This
problem of intelligence,
it's one of those problems that
mankind has been busy with it
for the last 2,000 years or so.
But 50 years ago or
so, that was the start
of artificial intelligence.
It was a conference in
Dartmouth, '62 or so,
with people like John
McCarthy and Marvin Minsky,
who coined the term
artificial intelligence.
And at that time,
progress was made.
Progress has been made,
especially in the last 20
years.
I'll go through it.
But they relied, really,
only on computer science
and common sense.
And in the meantime, there are
all these other disciplines
which have made a
lot of progress,
and that are very likely
to play a key role
in the search for answers to
the problem of intelligence.
So it was obvious that we
needed different expertises.
Not all in computer
science, but in other ones.
And so, this was the
people that we put together
from different labs, from
neuroscience, from computer
science, from cognitive
science, and from a number
of institutions in the US.
Especially MIT and Harvard.
Let me tell you a bit more
about the background here.
This idea of merging brain
research and computer
science in the quest to
understand intelligence.
Part of the reason for this
was progress and convergence
we saw between
different disciplines.
And one of them
was progress in AI.
And this started,
really, with Deep Blue,
I guess it was
called at the time.
The machine IBM that managed
to beat Kasparov at chess
for the world championship.
And then, of course, there
was Watson beating champions
in Jeopardy.
And things like drones able
to land on aircraft carriers.
So that's the most difficult
thing for the pilot to do.
And in the meantime, things had
continued to go pretty fast.
This was the cover of
Nature, probably eight months
ago or so.
DeepMind, which is one of
our industrial partners
in the center, has developed
an artificial intelligence
called DeepQ I
think, that learned
to play better than humans,
49 classical Atari games.
By itself.
And this was two or
three months ago,
a cover of a Nature supplement,
on artificial intelligence
and machine learning.
This is showing a
system by Mobileye,
this is an old video,
that gives vision to cars.
There is a camera
looking outside,
and is able to brake and
accelerate when needed.
[AUDIO OUT]
There have been,
there are, and there
will be a lot of
significant advances in AI.
I think it's a golden age
for intelligent applications.
You know, if people want
to make a lot of money
with useful things,
that's the time.
But this is kind of engineering.
Interesting one,
but engineering.
And we are still very far from
understanding how people can
answer questions about images.
This is one of the main
focus in the center, really.
How does your brain
answer simple questions
about this image?
About what is there?
And what is this?
Who is this person?
What is she doing?
What is she thinking?
Please tell me a story
about this, what's going on?
[INAUDIBLE]
And we would like to know to
have a system that does that.
But also, to know how
our brain does it.
So that's the science part.
It's not enough to
pass the Turing Test.
In this case, to have
a system that does it.
We want to have a system
that does it in the same way
as our brain does it.
And we want to compare
your model, our system,
with measurements on the brain
of people, or monkeys, also
during the same task.
So that's what we call
Turing plus, plus questions.
And part of the
rationale about it
is, this is kind of a more
philosophical discussion.
I personally think
that it's very
difficult to have a definition
of intelligence, in general.
There are many different
forms of intelligence.
What we can ask is
questions about,
what is human intelligence?
Because we can study that.
Right.
You know, it is, I don't
know, the ENIAC computers
in the '50, more or less
intelligent than a person.
You know, it can do
things a person cannot do.
And so on.
There are certain things ants
or bees do, are pretty amazing.
Is this intelligence?
Yeah, in a certain sense is.
So I think, in terms of
a well-defined question,
the real question is
about human intelligence.
And so that's what, from
the scientific part,
we are focused on.
And would like to be
able to answer how
people do understand images.
We start with vision.
We are not limited,
eventually, to vision.
But in the first five
years of the center,
that's the main focus.
And answer the
question about images.
And we want to understand
how the answers are produced
by our brain at the
computational, psychophysical,
and neural level.
It's ambitious.
And I think there are
probably, in terms
of having all these
different levels, levels
of really understanding from the
what, where, the neuroscience,
to the behavior.
We are not yet at
the point in which
we can answer all
those kind of questions
at all these different levels.
But some, we are.
One example is, who is there?
It's essentially
face recognition.
And this is an
interesting problem.
Because we know from work,
originally in the monkeys,
and then with fMRI in humans.
Shown here, parts of
the brains of cortex,
which are involved in
face recognition and face
perception.
And then, it's possible
to identify analog regions
in the monkey.
And record from the different
patches in the monkeys,
each one probably around 100,000
neurons, maybe 200,000 or so.
And look at their
properties when the monkeys
is looking at the face.
And make models of
what's going on.
And, of course, we
want these models
to respect the neural
data, ideally the MRI data.
And do the job of recognizing
faces as well as human do.
So we are getting there.
I'm not saying we
have the answers,
but we have at least
models that can be tested
at all these different levels.
So that's kind of
the ideal situation,
from the point of view of what
we want to do in the center.
Now as I said that,
not all problems
are mature at this level.
There are certain
like telling a story.
We don't know exactly.
We cannot record yet from
neurons in the monkey,
when the monkey is
telling a story.
Because the monkey has not been
able to tell its story, right.
And so there are other
questions that are not
as advanced as this one.
But other type of studies can
be done on them, should be done.
And this is what
we'll hear about.
