Good morning.
My name is Daniela Rus.
I'm a roboticist and I'm privileged to be
the director of CSAIL where our mission
is to invent the future of computing
and make the world better through computing.
Now, you can't understand artificial intelligence at MIT
without knowing a little bit about where we came from.
So picture this, it is 1956, and the young Marvin Minsky
decides to gather his friends and then off they
go to the woods of New Hampshire.
And when they emerge from the woods they announce
to the world that they have invented a new field of study,
artificial intelligence, the science and engineering
of understanding how machines exhibit human level
intelligence in how they move, , how they speak,
how they see how they play games, and even how they learn.
Now, the founders of AI believe that machines could one day
exhibit human level intelligence and we at MIT
have been pursuing this objective ever since.
And with the MIT intelligence quest initiative,
we're taking our efforts to a whole new level.
We continue to dream about creating increasingly more
autonomous and more capable machines that are smart,
and obedient, and support us with cognitive and physical
tasks.
Now, to make this possible, we need to solve the challenge,
how to make intelligent machines.
Specifically, machines are made of a body and a brain,
and we need the tools to make the body and the understanding
of how to create the brain.
Now the body may be inspired by nature,
for example, a humanoid robot, but it may also
be matched to do the best possible task.
So for example, we could engineer the Roomba robots
to clean your floors, or the body
could also be optimized for computation for example,
in the case of a GPU machine.
The brain is a software, the implemented algorithms
that give the machine the intended capabilities,
for example, to walk in the case of the humanoid robot,
to sweep the floors in the case of the Roomba,
or to crunch numbers in the case of a GPU.
The function of the machine, the function of a brain
may be inspired by human cognition,
or it may be developed as an engineer's computation
engine that optimizes the task.
Right now, since we know very little about the brain,
the vast majority of the algorithms
are driven by mathematics and physics.
As we learn more about the human brain,
we will be able to develop more nature inspired algorithms.
And within the MIT intelligence core,
we are excited to advance both the engineering algorithms
and the nature inspired algorithms
for the future intelligent machines.
In the process, we hope to create
new hypotheses that will help our colleagues
in cognitive neuroscience develop new experiments.
Now, as a field, AI is very broad.
AI researchers have explored many different strategies
than problems over the 60 plus years of the existence
of the field and these are some of the sub-disciplines that
have emerged.
As you can see the field expanded greatly,
but it still seems far from developing
a computational account of true intelligence.
However, the solutions are becoming
part of what we would say is a tool
kit that enables us to create increasingly more
capable machines.
I think of these themes as the brain, the collection
of fundamental implemented algorithms
that give us support with cognitive intelligence
by developing AI at rest and with physical intelligence
by developing AI in motion.
And now I would like to give you just a couple of examples that
show you where we are in this quest and where we hope to go.
So progress is happening in three fields simultaneously.
With artificial intelligence we hope
to give machines intelligence.
We hope to enable machines to see, hear,
communicate like humans.
With robots we put computation in motion
and give machines the ability to move.
And with machine learning we hope to aim, to learn from,
and make predictions using data.
So let me give you just a couple of examples of AI machine
learning and robotics at work, synergistically,
and all together.
I will point to a number of connections
between the brain and physical machines
and there are so many more examples
than I can give you today.
So this is the MIT CSAIL autonomous car.
Its body is a Toyota car extended
with sensors like laser scanners and cameras
for perceiving the world and also controlled actuators
like actuated steering, brakes, and acceleration
for moving in the world.
Now, the video shows how this car learned from single images
to steer like a human.
And as you can see, this car is driving very smoothly
on a road it has never seen before.
In fact, I would say that the car
drives way better than my first drive, which I still remember.
Now, the same sensor and actuator package,
and the same suite of algorithms can be ported to a wheelchair
to turn that wheelchair into an autonomous
device, as you can see here in the MIT CSAIL autonomous
wheelchair.
And the same technology for autonomous driving
can be mapped on wearable devices
to help provide safe navigation and better situation awareness
for blind and visually impaired users.
Now, a wearable laser belt and camera combination
can map the world locally and then describe it
to the blind user, for example, describing a fabulous window
display on a Braille buckle or triggering
the presence of a moving obstacle, or a friend,
or even something as small as a cat.
So we can use this technology to replace the walking-stick
and provide blind and visually impaired
people with a greater experience in their surrounding world,
to experience the world in much richer ways.
But at the same time, these advances we hope
are also inspiring cognitive neuroscientist's
to advance the understanding of blindness,
and even identify solutions to reverse it.
While advances in robotics are providing feedback
to cognitive neuroscientist's, advances in neuroscience
are also providing feedback to roboticists
and here's an example.
So recently, cognitive neuroscientist
introduced a sensor called the EEG caps.
So EEG caps have 48 sensors that monitor your brain activity.
And in a recent work, my group was
able to show that with such an EEG cap
we can detect one particular signal that all of you
make when you notice that something is wrong,
it is called the error potential, and it's localized,
it can be detected in about 100 milliseconds
and then it can be used to control a robot.
For example, in this video you see
the use of the sensor to correct the robot's mistakes.
Here the robot is asked to sort paint can in a bin labeled
paint, and wire spool in a bin labeled wire.
The human observes and when the robot randomly
chooses a direction that is correct nothing happens,
but when the robot randomly chooses the direction that
is incorrect the human triggers the error potential,
which can be detected in real time
and transmitted to the robot to correct its mistake.
So today, we can only correct simple tasks
like these binary choices that robots make,
but imagine a future where the same suite of capabilities
can be used to control the body of a robot
just like we control our own bodies.
That would be an amazing future, because we
would have a very intuitive and direct interface to a machine.
It will be a future where machines and people will
be working together, collaborating together
with machines supporting us with cognitive tasks
and physical tasks.
Autonomous driving will impact our work profoundly.
It is not a matter of if, it is a matter of when.
And when we get the support of machines for mobility
we will ensure that our parents and grandparents have a higher
quality of life in retirement and all of us
will be able to go anywhere, any time.
Machines supporting us will enable a profound change
in our lives just like smartphones
have enabled a profound change in our lives
through computation.
Thank you.