- What is a robot?
Can we redefine what a robot is?
Now, the vast majority of the robots
in storybooks and deployed in the industry
today are inspired by the human form.
We have humanoid robots, we have robot arms, we have boxes on wheels.
But nature comes in a much wider range of shapes.
And so does our build environment.
And so an interesting question is, can we turn any shape into a robot?
My name is Daniela Rus. I'm a roboticist.
I'm a professor of electrical engineering and computer science at MIT.
I'm also the director of the Computer Science and Artificial
Intelligence Laboratory at MIT.
CSAIL has been at the core of advancing
technology, of advancing AI, machine learning, robotics
and what they can accomplish since the very beginning,
CSAIL researchers were among
the pioneers who gathered together to think about
what the future of academic studies might look like.
And that's when in 1956 they coined the term artificial intelligence
to denote the study of machines with humanlike characteristics
in how they move in the world, how they learn, how they play games,
and how they reason and how they make decisions.
And so our researchers have been on a path to advance
the intelligence of machines, what machines can do ever since.
And as such, at CSAIL, we cover a broad range of activities
about AI, machine learning and robotics.
In AI, we are working on understanding
what today's technologies can do and making them better.
We are working on bringing explainability to deep learning.
We are working on a range of applications for deep learning,
but we are also working on creating
new models for machine learning that are more powerful
than the current models that are being deployed today.
We are also working on the hardware side and on the brains
that enable machines to move in the physical world.
So we are advancing robotics
by considering how to build new robot bodies,
how to build new robot brains, and how to bring the machine
and the humans in harmony, how to create better human machine interactions
for machines that adapt to people rather than the other way around.
And within each of these areas, we have a lot of faculty
and students working to advance what is possible.
You can think of our work as going from science fiction.
- Oh it’s the phone, I'll get it.
- Morning, George dear.
- And then reality.
And this is important because while today
we all have smartphones and we have computers
that help us broadly with number crunching work, in the future,
AI, machine learning and robots will help us with cognitive and physical work.
But this means we have to advance the science and engineering
of autonomy and the science and engineering of intelligence
to create the kind of machines that will be friendly to people,
that will be assistive and supportive for people, that will augment people
with the tasks that they need help with.
In my own research, I'm very excited about building soft robots.
So soft robots are unique because they will usher in an era
where machines and people will be able to work side by side.
If you think about the current use of today's robots
in industrial settings, well, we have these machines that are
masterpieces of engineering that can do so much more than humans can.
But these machines stay isolated on the factory floor
because they're big and heavy and dangerous to be around.
What we want is machines that are safer
and that can work side by side with people.
And I'd like to observe that in nature we have organisms that move in
much more fluid ways, much more compliant ways, much more adaptive ways
than the grand industrial robots.
And so in my lab, we are working hard to bring this vision
of a soft robot, of a robot that is made out of materials
that are much more like skin than the industrial robots that in turn
would be more fluid, more compliant, easier and safer to be around.
So we are trying to get our robots to be more capable.
That means creating better brains for the robots.
And part of that work involves
figuring out new ways in which the robots learn.
And to this end, we are working on a new class of machine
learning algorithms that is inspired by what the biologists have discovered
when they study the brains of small creatures.
So we have a new class of algorithms
that are very powerful
and then we are also developing some task-specific applications.
For instance, we have robots that will do your recycling,
and we have robots that will do your grocery packing,
and we have robots that will follow you like a guardian angel
and support you with situation awareness
as you move in the world.
We have robots that swim,
we have robots on the ground, we have robots in the air.
We're looking at all kinds of machines
that could come in to help with a wide range of tasks.
And the third piece is thinking about how
the machines can adapt to humans.
For example, we're looking at whether it's possible to instrument
the body of a person using muscle monitoring sensors.
And from those signals, whether we could understand
the intent of the human and then connect that to a machine
who can come in to help a human, much like a colleague or a teammate,
would come to help you
when you're trying to lift a heavy box and you’re not making much progress.
And so this is how we envision the future of machines.
Machines that adapt to people, machines that support people
with a wide range of cognitive and physical tasks.
I would like for us to get to the point
where people see robots as very capable tools.
People often ask me,
“When is my job going to disappear because of your work?”
And then people also ask me, “When is my car going
to be fully self-driving because of your work?”
And so what I tell you and what you should know
is that neither scenario is here and will not be here for a long time.
Of course, as a technologist and in fact as a very optimistic technologist,
I'm very much in the second group.
I'm very much
trying to believe in a world with machines that support us
in a multitude of ways beyond what is possible today.
But that doesn't mean that
I am not realistic and responsible about the work we do.
I think as we go about our work, we really have to be thoughtful
and considerate about how our work gets deployed,
about the consequences on jobs and on on the planet in general.
And there are a few things I can say about that.
First,
robots, AI, machine learning.
They are tools.
They are extraordinarily capable tools.
But like any other tool we have, they're not inherently good or bad.
They are what we choose to do with them.
And I personally believe that we can choose to do a lot of great things,
but that requires that we actually pause to think about what could go wrong.
And once we have those answers
to anticipate and put in place the guardrails
that ensure that the use of technology is for the greater good.
The third thing I want to emphasize is that when we talk about using
AI, machine learning and robots to help people in the workplace,
we don't talk about automating professions.
We talk about automating some tasks
that we do in each of our professions.
Because in any profession
that you can imagine, people spend time applying expertise.
We spend time engaging with stakeholders and communicating with others.
We spend time managing.
We spend time doing various data tasks, we spend time doing predictable
physical work, and we spend time doing unpredictable physical work.
The state of technology is such that today we can automate
some of the data tasks and some of the physical work
tasks, namely the predictable physical work tasks.
But there is much more left that machines are not yet good for.
The idea is that by offloading to machines the routine aspects of our work,
we can give people more time to focus on
on what they enjoy doing, on what their expertise is.
So that’s the vision.