When I was a student studying robotics,
a group of us decided to make a present for our professor's birthday.
We wanted to program our robot to cut a slice of cake for him.
We pulled an all-nighter writing the software,
and the next day, disaster.
We programmed this robot to cut a soft, round sponge cake,
but we didn't coordinate well.
And instead, we received a square hard ice cream cake.
The robot flailed wildly and nearly destroyed the cake.
(Laughter)
Our professor was delighted, anyway.
He calmly pushed the stop button
and declared the erratic behavior of the robot
a control singularity.
A robotics technical term.
I was disappointed, but I learned a very important lesson.
The physical world,
with its physics laws and imprecisions,
is a far more demanding space than the digital world.
Today, I lead MIT's Computer Science and AI lab,
the largest research unit at MIT.
This is our building, where I work with brilliant and brave researchers
to invent the future of computing and intelligent machines.
Today in computing,
artificial intelligence and robotics are largely separate fields.
AI has amazed you with its decision-making and learning,
but it remains confined inside computers.
Robots have a physical presence and can execute pre-programmed tasks,
but they're not intelligent.
Well, this separation is starting to change.
AI is about to break free from the 2D computer screen interactions
and enter a vibrant, physical 3D world.
In my lab, we're fusing the digital intelligence of AI
with the mechanical prowess of robots.
Moving AI from the digital world into the physical world
is making machines intelligent
and leading to the next great breakthrough,
what I call physical intelligence.
Physical intelligence is when AI's power to understand text,
images and other online information
is used to make real-world machines smarter.
This means AI can help pre-programmed robots do their tasks better
by using knowledge from data.
With physical intelligence,
AI doesn't just reside in our computers,
but walks, rolls, flies
and interacts with us in surprising ways.
Imagine being surrounded by helpful robots at the supermarket.
The one on the left can help you carry a heavy box.
To make it happen, we need to do a few things.
We need to rethink how machines think.
We need to reorganize how they are designed and how they learn.
So for physical intelligence,
AI has to run on computers that fit on the body of the robot.
For example, our soft robot fish.
Today's AI uses server farms that do not fit.
Today's AI also makes mistakes.
This AI system on a robot car does not detect pedestrians.
For physical intelligence,
we need small brains that do not make mistakes.
We're tackling these challenges using inspiration
from a worm called C. elegans
In sharp contrast to the billions of neurons in the human brain,
C. elegans has a happy life on only 302 neurons,
and biologists understand the math of what each of these neurons do.
So here's the idea.
Can we build AI using inspiration from the math of these neurons?
We have developed, together with my collaborators and students,
a new approach to AI we call “liquid networks.”
And liquid networks results in much more compact
and explainable solutions than today's traditional AI solutions.
Let me show you.
This is our self-driving car.
It's trained using a traditional AI solution,
the kind you find in many applications today.
This is the dashboard of the car.
In the lower right corner, you'll see the map.
In the upper left corner, the camera input stream.
And the big box in the middle with the blinking lights
is the decision-making engine.
It consists of tens of thousands of artificial neurons,
and it decides how the car should steer.
It is impossible to correlate the activity of these neurons
with the behavior of the car.
Moreover, if you look at the lower left side,
you see where in the image this decision-making engine looks
to tell the car what to do.
And you see how noisy it is.
And this car drives by looking at the bushes and the trees
on the side of the road.
That's not how we drive.
People look at the road.
Now contrast this with our liquid network solution,
which consists of only 19 neurons rather than tens of thousands.
And look at its attention map.
It's so clean and focused on the road horizon
and the side of the road.
Because these models are so much smaller,
we actually understand how they make decisions.
So how did we get this performance?
Well, in a traditional AI system,
the computational neuron is the artificial neuron,
and the artificial neuron is essentially an on/off computational unit.
It takes in some numbers, adds them up,
applies some basic math
and passes along the result.
And this is complex
because it happens across thousands of computational units.
In liquid networks,
we have fewer neurons,
but each one does more complex math.
Here's what happens inside our liquid neuron.
We use differential equations to model the neural computation
and the artificial synapse.
And these differential equations
are what biologists have mapped for the neural structure of the worms.
We also wire the neurons differently to increase the information flow.
Well, these changes yield phenomenal results.
Traditional AI systems are frozen after training.
That means they cannot continue to improve
when we deploy them in a physical world in the wild.
We just wait for the next release.
Because of what's happening inside the liquid neuron,
liquid networks continue to adapt after training
based on the inputs that they see.
Let me show you.
We trained traditional AI and liquid networks
using summertime videos like these ones,
and the task was to find things in the woods.
All the models learned how to do the task in the summer.
Then we tried to use the models on drones in the fall.
The traditional AI solution gets confused by the background.
Look at the attention map, cannot do the task.
Liquid networks do not get confused by the background
and very successfully execute the task.
So this is it.
This is the step forward:
AI that adapts after training.
Liquid networks are important
because they give us a new way of getting machines to think
that is rooted into physics models,
a new technology for AI.
We can run them on smartphones, on robots,
on enterprise computers,
and even on new types of machines
that we can now begin to imagine and design.
The second aspect of physical intelligence.
So by now you've probably generated images using text-to-image systems.
We can also do text-to-robot,
but not using today's AI solutions because they work on statistics
and do not understand physics.
In my lab,
we developed an approach that guides the design process
by checking and simulating the physical constraints for the machine.
We start with a language prompt,
"Make me a robot that can walk forward,"
and our system generates the designs including shape, materials, actuators,
sensors, the program to control it
and the fabrication files to make it.
And then the designs get refined in simulation
until they meet the specifications.
So in a few hours we can go from idea
to controllable physical machine.
We can also do image-to-robot.
This photo can be transformed into a cuddly robotic bunny.
To do so, our algorithm computes a 3D representation of the photo
that gets sliced and folded, printed.
Then we fold the printed layers, we string some motors and sensors.
We write some code, and we get the bunny you see in this video.
We can use this approach to make anything almost,
from an image, from a photo.
So the ability to transform text into images
and to transform images into robots is important,
because we are drastically reducing the amount of time
and the resources needed to prototype and test new products,
and this is allowing for a much faster innovation cycle.
And now we are ready to even make the leap
to get these machines to learn.
The third aspect of physical intelligence.
These machines can learn from humans how to do tasks.
You can think of it as human-to-robot.
In my lab, we created a kitchen environment
where we instrument people with sensors,
and we collect a lot of data about how people do kitchen tasks.
We need physical data
because videos do not capture the dynamics of the task.
So we collect muscle, pose, even gaze information
about how people do tasks.
And then we train AI using this data
to teach robots how to do the same tasks.
And the end result is machines that move with grace and agility,
as well as adapt and learn.
Physical intelligence.
We can use this approach to teach robots
how to do a wide range of tasks:
food preparation, cleaning and so much more.
The ability to turn images and text into functional machines,
coupled with using liquid networks
to create powerful brains for these machines
that can learn from humans, is incredibly exciting.
Because this means we can make almost anything we imagine.
Today's AI has a ceiling.
It requires server farms.
It's not sustainable.
It makes inexplicable mistakes.
Let's not settle for the current offering.
When AI moves into the physical world,
the opportunities for benefits and for breakthroughs is extraordinary.
You can get personal assistants that optimize your routines
and anticipate your needs,
bespoke machines that help you at work
and robots that delight you in your spare time.
The promise of physical intelligence is to transcend our human limitations
with capabilities that extend our reach,
amplify our strengths
and refine our precision
and grant us ways to interact with the world
we've only dreamed of.
We are the only species so advanced, so aware,
so capable of building these extraordinary tools.
Yet, developing physical intelligence
is teaching us that we have so much more to learn
about technology and about ourselves.
We need human guiding hands over AI sooner rather than later.
After all, we remain responsible for this planet
and everything living on it.
I remain convinced that we have the power
to use physical intelligence to ensure a better future for humanity
and for the planet.
And I'd like to invite you to help us in this quest.
Some of you will help develop physical intelligence.
Some of you will use it.
And some of you will invent the future.
Thank you.
(Applause)