Hi, I'm Jeff.
I lead AI Research and Health at Google.
I joined Google more than 20 years ago,
when we were all wedged into a tiny office space,
above what's now a T-Mobile store in downtown Palo Alto.
I've seen a lot of computing transformations in that time,
and in the last decade, we've seen AI be able to do tremendous things.
But we're still doing it all wrong in many ways.
That's what I want to talk to you about today.
But first, let's talk about what AI can do.
So in the last decade, we've seen tremendous progress
in how AI can help computers see, understand language,
understand speech better than ever before.
Things that we couldn't do before, now we can do.
If you think about computer vision alone,
just in the last 10 years,
computers have effectively developed the ability to see;
10 years ago, they couldn't see, now they can see.
You can imagine this has had a transformative effect
on what we can do with computers.
So let's look at a couple of the great applications
enabled by these capabilities.
We can better predict flooding, keep everyone safe,
using machine learning.
We can translate over 100 languages so we all can communicate better,
and better predict and diagnose disease,
where everyone gets the treatment that they need.
So let's look at two key components
that underlie the progress in AI systems today.
The first is neural networks,
a breakthrough approach to solving some of these difficult problems
that has really shone in the last 15 years.
But they're not a new idea.
And the second is computational power.
It actually takes a lot of computational power
to make neural networks able to really sing,
and in the last 15 years, we’ve been able to halve that,
and that's partly what's enabled all this progress.
But at the same time, I think we're doing several things wrong,
and that's what I want to talk to you about
at the end of the talk.
First, a bit of a history lesson.
So for decades,
almost since the very beginning of computing,
people have wanted to be able to build computers
that could see, understand language, understand speech.
The earliest approaches to this, generally,
people were trying to hand-code all the algorithms
that you need to accomplish those difficult tasks,
and it just turned out to not work very well.
But in the last 15 years, a single approach
unexpectedly advanced all these different problem spaces all at once:
neural networks.
So neural networks are not a new idea.
They're kind of loosely based
on some of the properties that are in real neural systems.
And many of the ideas behind neural networks
have been around since the 1960s and 70s.
A neural network is what it sounds like,
a series of interconnected artificial neurons
that loosely emulate the properties of your real neurons.
An individual neuron in one of these systems
has a set of inputs,
each with an associated weight,
and the output of a neuron
is a function of those inputs multiplied by those weights.
So pretty simple,
and lots and lots of these work together to learn complicated things.
So how do we actually learn in a neural network?
It turns out the learning process
consists of repeatedly making tiny little adjustments
to the weight values,
strengthening the influence of some things,
weakening the influence of others.
By driving the overall system towards desired behaviors,
these systems can be trained to do really complicated things,
like translate from one language to another,
detect what kind of objects are in a photo,
all kinds of complicated things.
I first got interested in neural networks
when I took a class on them as an undergraduate in 1990.
At that time,
neural networks showed impressive results on tiny problems,
but they really couldn't scale to do real-world important tasks.
But I was super excited.
(Laughter)
I felt maybe we just needed more compute power.
And the University of Minnesota had a 32-processor machine.
I thought, "With more compute power,
boy, we could really make neural networks really sing."
So I decided to do a senior thesis on parallel training of neural networks,
the idea of using processors in a computer or in a computer system
to all work toward the same task,
that of training neural networks.
32 processors, wow,
we’ve got to be able to do great things with this.
But I was wrong.
Turns out we needed about a million times as much computational power
as we had in 1990
before we could actually get neural networks to do impressive things.
But starting around 2005,
thanks to the computing progress of Moore's law,
we actually started to have that much computing power,
and researchers in a few universities around the world started to see success
in using neural networks for a wide variety of different kinds of tasks.
I and a few others at Google heard about some of these successes,
and we decided to start a project to train very large neural networks.
One system that we trained,
we trained with 10 million randomly selected frames
from YouTube videos.
The system developed the capability
to recognize all kinds of different objects.
And it being YouTube, of course,
it developed the ability to recognize cats.
YouTube is full of cats.
(Laughter)
But what made that so remarkable
is that the system was never told what a cat was.
So using just patterns in data,
the system honed in on the concept of a cat all on its own.
All of this occurred at the beginning of a decade-long string of successes,
of using neural networks for a huge variety of tasks,
at Google and elsewhere.
Many of the things you use every day,
things like better speech recognition for your phone,
improved understanding of queries and documents
for better search quality,
better understanding of geographic information to improve maps,
and so on.
Around that time,
we also got excited about how we could build hardware that was better tailored
to the kinds of computations neural networks wanted to do.
Neural network computations have two special properties.
The first is they're very tolerant of reduced precision.
Couple of significant digits, you don't need six or seven.
And the second is that all the algorithms are generally composed
of different sequences of matrix and vector operations.
So if you can build a computer
that is really good at low-precision matrix and vector operations
but can't do much else,
that's going to be great for neural-network computation,
even though you can't use it for a lot of other things.
And if you build such things, people will find amazing uses for them.
This is the first one we built, TPU v1.
"TPU" stands for Tensor Processing Unit.
These have been used for many years behind every Google search,
for translation,
in the DeepMind AlphaGo matches,
so Lee Sedol and Ke Jie maybe didn't realize,
but they were competing against racks of TPU cards.
And we've built a bunch of subsequent versions of TPUs
that are even better and more exciting.
But despite all these successes,
I think we're still doing many things wrong,
and I'll tell you about three key things we're doing wrong,
and how we'll fix them.
The first is that most neural networks today
are trained to do one thing, and one thing only.
You train it for a particular task that you might care deeply about,
but it's a pretty heavyweight activity.
You need to curate a data set,
you need to decide what network architecture you'll use
for this problem,
you need to initialize the weights with random values,
apply lots of computation to make adjustments to the weights.
And at the end, if you’re lucky, you end up with a model
that is really good at that task you care about.
But if you do this over and over,
you end up with thousands of separate models,
each perhaps very capable,
but separate for all the different tasks you care about.
But think about how people learn.
In the last year, many of us have picked up a bunch of new skills.
I've been honing my gardening skills,
experimenting with vertical hydroponic gardening.
To do that, I didn't need to relearn everything I already knew about plants.
I was able to know how to put a plant in a hole,
how to pour water, that plants need sun,
and leverage that in learning this new skill.
Computers can work the same way, but they don’t today.
If you train a neural network from scratch,
it's effectively like forgetting your entire education
every time you try to do something new.
That’s crazy, right?
So instead, I think we can and should be training
multitask models that can do thousands or millions of different tasks.
Each part of that model would specialize in different kinds of things.
And then, if we have a model that can do a thousand things,
and the thousand and first thing comes along,
we can leverage the expertise we already have
in the related kinds of things
so that we can more quickly be able to do this new task,
just like you, if you're confronted with some new problem,
you quickly identify the 17 things you already know
that are going to be helpful in solving that problem.
Second problem is that most of our models today
deal with only a single modality of data --
with images, or text or speech,
but not all of these all at once.
But think about how you go about the world.
You're continuously using all your senses
to learn from, react to,
figure out what actions you want to take in the world.
Makes a lot more sense to do that,
and we can build models in the same way.
We can build models that take in these different modalities of input data,
text, images, speech,
but then fuse them together,
so that regardless of whether the model sees the word "leopard,"
sees a video of a leopard or hears someone say the word "leopard,"
the same response is triggered inside the model:
the concept of a leopard
can deal with different kinds of input data,
even nonhuman inputs, like genetic sequences,
3D clouds of points, as well as images, text and video.
The third problem is that today's models are dense.
There's a single model,
the model is fully activated for every task,
for every example that we want to accomplish,
whether that's a really simple or a really complicated thing.
This, too, is unlike how our own brains work.
Different parts of our brains are good at different things,
and we're continuously calling upon the pieces of them
that are relevant for the task at hand.
For example, nervously watching a garbage truck
back up towards your car,
the part of your brain that thinks about Shakespearean sonnets
is probably inactive.
(Laughter)
AI models can work the same way.
Instead of a dense model,
we can have one that is sparsely activated.
So for particular different tasks, we call upon different parts of the model.
During training, the model can also learn which parts are good at which things,
to continuously identify what parts it wants to call upon
in order to accomplish a new task.
The advantage of this is we can have a very high-capacity model,
but it's very efficient,
because we're only calling upon the parts that we need
for any given task.
So fixing these three things, I think,
will lead to a more powerful AI system:
instead of thousands of separate models,
train a handful of general-purpose models
that can do thousands or millions of things.
Instead of dealing with single modalities,
deal with all modalities,
and be able to fuse them together.
And instead of dense models, use sparse, high-capacity models,
where we call upon the relevant bits as we need them.
We've been building a system that enables these kinds of approaches,
and we’ve been calling the system “Pathways.”
So the idea is this model will be able to do
thousands or millions of different tasks,
and then, we can incrementally add new tasks,
and it can deal with all modalities at once,
and then incrementally learn new tasks as needed
and call upon the relevant bits of the model
for different examples or tasks.
And we're pretty excited about this,
we think this is going to be a step forward
in how we build AI systems.
But I also wanted to touch on responsible AI.
We clearly need to make sure that this vision of powerful AI systems
benefits everyone.
These kinds of models raise important new questions
about how do we build them with fairness,
interpretability, privacy and security,
for all users in mind.
For example, if we're going to train these models
on thousands or millions of tasks,
we'll need to be able to train them on large amounts of data.
And we need to make sure that data is thoughtfully collected
and is representative of different communities and situations
all around the world.
And data concerns are only one aspect of responsible AI.
We have a lot of work to do here.
So in 2018, Google published this set of AI principles
by which we think about developing these kinds of technology.
And these have helped guide us in how we do research in this space,
how we use AI in our products.
And I think it's a really helpful and important framing
for how to think about these deep and complex questions
about how we should be using AI in society.
We continue to update these as we learn more.
Many of these kinds of principles are active areas of research --
super important area.
Moving from single-purpose systems that kind of recognize patterns in data
to these kinds of general-purpose intelligent systems
that have a deeper understanding of the world
will really enable us to tackle
some of the greatest problems humanity faces.
For example,
we’ll be able to diagnose more disease;
we'll be able to engineer better medicines
by infusing these models with knowledge of chemistry and physics;
we'll be able to advance educational systems
by providing more individualized tutoring
to help people learn in new and better ways;
we’ll be able to tackle really complicated issues,
like climate change,
and perhaps engineering of clean energy solutions.
So really, all of these kinds of systems
are going to be requiring the multidisciplinary expertise
of people all over the world.
So connecting AI with whatever field you are in,
in order to make progress.
So I've seen a lot of advances in computing,
and how computing, over the past decades,
has really helped millions of people better understand the world around them.
And AI today has the potential to help billions of people.
We truly live in exciting times.
Thank you.
(Applause)
Chris Anderson: Thank you so much.
I want to follow up on a couple things.
This is what I heard.
Most people's traditional picture of AI
is that computers recognize a pattern of information,
and with a bit of machine learning,
they can get really good at that, better than humans.
What you're saying is those patterns
are no longer the atoms that AI is working with,
that it's much richer-layered concepts
that can include all manners of types of things
that go to make up a leopard, for example.
So what could that lead to?
Give me an example of when that AI is working,
what do you picture happening in the world
in the next five or 10 years that excites you?
Jeff Dean: I think the grand challenge in AI
is how do you generalize from a set of tasks
you already know how to do
to new tasks,
as easily and effortlessly as possible.
And the current approach of training separate models for everything
means you need lots of data about that particular problem,
because you're effectively trying to learn everything
about the world and that problem, from nothing.
But if you can build these systems
that already are infused with how to do thousands and millions of tasks,
then you can effectively teach them to do a new thing
with relatively few examples.
So I think that's the real hope,
that you could then have a system where you just give it five examples
of something you care about,
and it learns to do that new task.
CA: You can do a form of self-supervised learning
that is based on remarkably little seeding.
JD: Yeah, as opposed to needing 10,000 or 100,000 examples
to figure everything in the world out.
CA: Aren't there kind of terrifying unintended consequences
possible, from that?
JD: I think it depends on how you apply these systems.
It's very clear that AI can be a powerful system for good,
or if you apply it in ways that are not so great,
it can be a negative consequence.
So I think that's why it's important to have a set of principles
by which you look at potential uses of AI
and really are careful and thoughtful about how you consider applications.
CA: One of the things people worry most about
is that, if AI is so good at learning from the world as it is,
it's going to carry forward into the future
aspects of the world as it is that actually aren't right, right now.
And there's obviously been a huge controversy about that
recently at Google.
Some of those principles of AI development,
you've been challenged that you're not actually holding to them.
Not really interested to hear about comments on a specific case,
but ... are you really committed?
How do we know that you are committed to these principles?
Is that just PR, or is that real, at the heart of your day-to-day?
JD: No, that is absolutely real.
Like, we have literally hundreds of people
working on many of these related research issues,
because many of those things are research topics
in their own right.
How do you take data from the real world,
that is the world as it is, not as we would like it to be,
and how do you then use that to train a machine-learning model
and adapt the data bit of the scene
or augment the data with additional data
so that it can better reflect the values we want the system to have,
not the values that it sees in the world?
CA: But you work for Google,
Google is funding the research.
How do we know that the main values that this AI will build
are for the world,
and not, for example, to maximize the profitability of an ad model?
When you know everything there is to know about human attention,
you're going to know so much
about the little wriggly, weird, dark parts of us.
In your group, are there rules about how you hold off,
church-state wall between a sort of commercial push,
"You must do it for this purpose,"
so that you can inspire your engineers and so forth,
to do this for the world, for all of us.
JD: Yeah, our research group does collaborate
with a number of groups across Google,
including the Ads group, the Search group, the Maps group,
so we do have some collaboration, but also a lot of basic research
that we publish openly.
We've published more than 1,000 papers last year
in different topics, including the ones you discussed,
about fairness, interpretability of the machine-learning models,
things that are super important,
and we need to advance the state of the art in this
in order to continue to make progress
to make sure these models are developed safely and responsibly.
CA: It feels like we're at a time when people are concerned
about the power of the big tech companies,
and it's almost, if there was ever a moment to really show the world
that this is being done to make a better future,
that is actually key to Google's future,
as well as all of ours.
JD: Indeed.
CA: It's very good to hear you come and say that, Jeff.
Thank you so much for coming here to TED.
JD: Thank you.
(Applause)