As a kid, I was really inspired by the explorers.
I grew up in Seattle, and Lewis and Clark were kind of heroes locally,
and I wanted to be an explorer when I grew up.
As an electrical engineer, I would always look
for new things that we can do that just weren't ever possible.
And machine learning and research is an exploration,
it feels like an intellectual exploration.
We've definitely seen a big uptake in the last five years
in what machines are able to do,
compared to say the previous decade or two.
With the advent of a lot more data
and a lot more computing power, we really can think bigger
and sort of change the game about what kind of models we can envision.
The real world is actually very messy;
hard, logical rules are not the way to solve real-world problems.
So machine learning is all about learning from examples.
Rather than writing 500,000 lines of code,
we instead have the machine learn from observations about the world.
We look through a bunch of these examples,
in the machine-learning algorithm,
maybe millions, maybe billions, even trillions,
to identify the patterns and generalize from there.
In the task of image recognition, we've been able to train
models who take the pixels of an image,
and from those pixels, learn high-level features.
It starts to learn that if you see a cake and you see a kid,
it's maybe a birthday party.
If you a cake and lots of kids, it's very likely a birthday party.
That's essentially teaching the machines to do
the perceptions that we humans are so natural and so good at.
You realize just how amazing humans are,
just how amazing your four year old is who can recognize faces.
Machine learning has really been the
beginning of a bigger revolution in the field of speech recognition.
To teach speech recognition, I'd interact with a noisy room.
We used real-world sounds
and we mix it in to the examples that we already have.
Is it cold outside? Is it cold outside? Is it cold outside?
Now, no matter what the noise in the environment,
our speech-recognition systems can understand what you're saying,
they can separate out one speaker from another.
With machine learning, we have now an algorithm
that learns how to simulate a human linguist.
A lot of the language that we see today, it's very informal…
Blah blah blah blah blah, and they say OK.
… interspersed with emojis and stickers…
Now, with Google, we're getting to the point
where you can have a much more natural conversation…
Any good Mexican places around here? Here you go.
The Assistant product that we're building at Google
uses the best of our machine-learning techniques and speech recognition,
image understanding, natural-language understanding.
That's a promising direction for
developing systems that can really navigate the mess of the real world.
We wanted to make this an open-source project so that
everyone outside of Google could use the same system we're using inside Google.
There are lots of people who have made very, very creative uses of it,
without knowing a single bit of machine learning.
So if they have the ideas, they don't need to do the heavy lifting
that we've already done.
I saw a cool example, where somebody used to have
a cat going around their house all the time,
so they trained the model to identify whenever the cat was there,
and they would turn the sprinklers on, to scare the cat away.
This elderly couple in Japan, who ran a cucumber farm,
and one of the big tasks is to sort the cucumbers
into prickly ones, less prickly ones, straight ones, curved ones,
and it's actually a complicated task.
So the wife would spend many hours a day sorting cucumbers.
So the son picked up a computer-vision model
and was able to build a system to categorize the cucumbers
and sort them automatically.
All the time wasted sorting cucumbers is going to be used in much better ways.
387 million people with diabetes are at risk for diabetic retinopathy.
It causes blindness.
The way that you can find signs of diabetic retinopathy
is by taking pictures of the back of the eye.
But there's simply not enough doctors
and it takes hours for an interpretation.
So we trained an algorithm that can read the images, right then and there.
The algorithm can help the doctors to get more people screened for the disease.
The more you see
machine learning and the kinds of things it can do, the more you see opportunity
for it to improve people's lives.
You can use machine learning to save power at significant scales,
even track the spread of diseases and epidemics.
We could use a computer-vision model for everyone who is visually impaired.
We could make speech recognizer for everyone on the planet
and drastically improve the experience
of millions and billions of people.
I don't see any area of science, or even human endeavor,
that learn systems can't help with.
If you'd asked me a few years ago if a computer would be able to do this
anytime soon, I would have said "I don't really think so."
It's very, very empowering to imagine what's going to come.
We're thinking thoughts and doing things that, you know, no man has ever done
and sort of setting forth and setting foot in really new intellectual territory here.
The promise of AI and machine learning is that we can actually produce
solutions to previously unsolved problems that will really help people.