Good morning.
I'm Jim DiCaralo.
I'm privileged to lead a science community here
at MIT, the brain and cognitive sciences
community whose mission is to understand the brain
and how that gives rise to the mind.
Now, the science quest to understand human intelligence
is one of the most exciting frontiers of our field.
It's really a quest to understand ourselves.
And it's aligned with a technology quest of developing
intelligent systems.
This is a unique time in human history.
It's a unique chapter in MIT'S legacy
as we see tremendous new opportunity for synergy.
And today, some of my colleagues and I
would like to tell you about that synergy
in our vision of the future.
Now, all the fields of artificial intelligence
and machine learning have led to many technological advances
that continue to transform and improve our lives.
None of these systems are truly intelligent,
as you'll hear about in a moment.
We're still very far from real AI.
However, as brain and cognitive scientists,
we know that real AI is possible because we
study a real intelligence machine every day.
The machine behind your eyes can navigate new situations,
infer what others believe, use language to communicate,
write poetry and music to express how it feels,
create math to build bridges, devices, and life saving
medicines.
In short, it's built civilization from scratch.
The human brain can do what AI systems cannot yet do.
If harnessed, the science of human intelligence
provides an incredible opportunity
to replenish the well of AI algorithms.
Now, the human brain, shown here, is a 20 watt machine.
To some, the idea that the brain is a machine is itself
an astonishing hypothesis as illustrated by this quote
from the late Francis Crick, you, your joys, your sorrows,
your memories, your ambitions, your sense of personal identity
and free will, are in fact, no more
than the behavior of a vast assembly of nerve cells
and their associated molecules.
As scientists, we have the opportunity and, indeed,
the obligation to reverse engineer this brain machine.
But this is not a simple task, as can be illustrated
by thinking about this far less advanced, less
intelligent machine but that one that we know more about.
In a similar analogous quote, your remarkable favorite app,
all its performance an amazing user interface
is in fact, no more than the behavior of a vast assembly
of transistors.
So many of you in the audience know that this is true,
that that device and its amazing interface
is due to those transistors, it's
building blocks shown on the left.
But you can't understand how that amazing performance
on that app comes from without understand the complexity that
lives between.
Similarly, as brain cognitive scientists,
we study the amazing intelligence and cognition
of humans.
And we know the building blocks of that system.
They're called neurons.
And there are connections called synapses.
You have billions of neurons in each of your brain.
We know something about what intervenes between those
neurons as they're put together into circuits
that give rise to algorithms that
are underlying our cognition.
But the challenge of our science is
to discover which arrangements of neural building blocks
can explain and underlie our intelligence behavior.
So how do we propose to do this?
Well, as we make measurements and discoveries as scientists,
shown here on the left, we must, and we
are building algorithms that offer the potential of showing
how those neurons are connected to give rise
to the intelligent behavior, shown here in the center.
You could refer to those as models,
or our intelligent algorithms, are hypotheses
about brain function.
And here's where we see great synergy with similar efforts
from our machine colleagues engineers that
work in software hardware robotics,
as they are great at synthesizing, and creating,
and are also interested in building things
that can explain intelligence.
So we're united around the mission
of intelligence algorithms.
And we aim to leverage, in the core,
the complementary strengths of both sides of this equation
and to build algorithms at human relevant scales.
These algorithms will, in turn, inform our science
through these reverse green arrows
that you see here, about hypotheses of a brain
function, where intelligence comes from,
and inform our engineering as possible new technologies,
shown by the blue arrows.
We believe this approach, over time,
will again, replenish the will of AI algorithms
that can impact the greater MIT and, indeed, the greater
world, through the work of the MIT IQ bridge
that you'll hear later today.
Why do we think this is a good bet?
We think this is a good bet based on past success.
Let me give you just one recent breakthrough example.
You probably all recognize this person.
He's our leader, Rafael Reif.
But how did you do that so quickly and effortlessly?
This object recognition problem was a longstanding problem
in computer science and brain and cognitive sciences.
For decades, neuroscientists like myself and others
accumulated knowledge about how the brain solves this problem.
And that knowledge, this schematized
at the top of this slide, in its deep neural network
that was discovered within the brain and the arrangement
of neurons and processing the image locally.
And we learned a lot about the responses of neurons
along that system as well as the behavior of that system.
Yet, all of that work, decades of work,
was not enough to tell us the visual intelligence algorithms
working in the brain.
Similarly, engineers working in a separate silo
were trying to build computer vision algorithms that could
do vision cast like this one.
And all of those algorithms fell far short
of human capabilities.
Now, true progress came when researchers
used a combination of science and engineering,
specifically some researchers began
to build networks out of brain like components,
so-called deep neural networks with brain
like neural elements, like this example schematized
at the bottom here.
They also use mathematical models
of how to teach those networks proposed
by cognitive scientists.
My lab and others showed there was a remarkable similarity
from the artificial neurons at the bottom network
to the actual neurons in the brain shown at the top.
It was a very tight match.
As well as a very good similarity
between the behavior, the visual behavior
of the human visual system, and the behavior
of these artificial systems.
And in 2012, these brain like deep neural networks
took over computer vision, and the deep learning revolution
and that transformative AI that resulted was launched.
We're not finished even with this vision problem.
There are feedback loops you can see on the slide
at the top that aren't even present
in those feedforward networks.
We're working on those now, as we
think they're important to scene understanding.
But the big lesson I want you to understand
is that when engineers guide their algorithm building
efforts with discoveries and measurements from science, then
you get transformative progress.
This is why we believe in, this is why we're confident in,
this is, indeed, why we need the MIT corps.
But here, I should also remind you,
we're just scratching the surface.
I'm talking about core visual object
recognition shown at the top.
That's just one aspect of visual intelligence.
Imagine what we can do if we can understand
all the other aspects of human visual intelligence.
Things like intuitive physics, intuitive psychology,
language acquisition, and many others
that you'll hear some later today.
Now, this slide shows moonshot research goals
that our faculty have brainstormed thus far.
Bottom line, we have very exciting and ambitious goals,
and we think with a combination of science and engineering
that they are doable.
Why now?
Behavioral measurements are rapidly
increasing in our ability to obtain them both in adults,
but also in developing and learning children,
as you hear about from my colleague, Laura Schultz,
in a moment.
Neural measurements have been preceding
a pace in animal models for the last decade.
Our ability to measure more and more
neurons simultaneously, learn more and more about the brain,
as well as make new measurements from human brains
that you'll hear about from my colleague
Rebecca Saks in a moment.
New algorithms are being hypothesized
based on both theoretical considerations and observations
of human behavior from colleagues all across MIT, not
just in brain cognitive sciences,
but places like robotics, and CSAIL, and others.
You'll hear about a couple of examples of this
from my colleagues Tommy Poggio and Josh Tenenbaum, who
will speak in a minute.
And rapidly advancing computational power
is allowing us to build these algorithms
at relevant scales along with new ways
to implement that hardware in more efficient ways.
And you'll hear a bit about that in session two.
But I want to stress that because
of that kind of recent success with deep networks,
showing that science and engineering works together,
many engineers are now motivated to build algorithms using brain
like components, artificial neural networks,
and to interface those networks those rooms with the brain.
And you'll hear about some of those ideas
from Antonio Torralba and Daniela Rus in a moment.
In short, we're poised for an incredible decade of progress
in intelligence research.
Today, you'll hear a lot about AI.
And I hope I've convinced you that work
in brain and cognitive sciences around intelligence
is a guiding path towards real AI.
But this quest is likely to lead to many other world changing
impacts.
For example, a deep understanding
of human learning in both its strengths and its weaknesses
could have transformative impact on education.
An engineering description of the brain
will help us pinpoint when, where,
and how something can go wrong.
And armed with this, we'll likely
be able to see new ways to ameliorate things
like Alzheimer's, autism, hearing and vision loss,
and many other conditions.
Let me end by reminding you that over time, the MIT IQ
corps could answer one of the biggest basic science questions
of all time.
How does the arrangement of billions of neurons
make you, you?
Thank you.