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.