Good morning.

Welcome to day three of Google I/O,

and what should be a fun conversation about machine

learning and artificial intelligence.

My name is Tom Simonite.

I'm San Francisco bureau chief for MIT Technology Review.

And like all of you, I've been hearing a lot recently

about the growing power of machine learning.

We've seen some striking results come out

of academic and industrial research labs,

and they've moved very quickly into the hands of developers,

who have been using them to make new products and services

and companies.

I'm joined by three people this morning

who can tell us about how this new technology

and the capabilities it brings are coming out into the world.

They are Aparna Chennapragada, who

is the director of product management

and worked on the Google Now mobile assistant,

Jeff Dean, who leads the Google Brain research group here

in Mountain View, and John Giannandrea,

who is head of search and machine intelligence at Google.

Thanks for joining me, all of you.

We're going to talk for about 30 minutes,

and then there will be time for questions from the floor.

John, why don't we start with you?

You could set the scene for us.

Artificial intelligence and machine learning

are not brand new concepts.

They've been around for a long time,

but we're suddenly hearing a lot more about them.

Large companies and small companies

are investing more in this technology,

and there's a lot of excitement.

You can even get a large number of people

to come to a talk about this thing early in the morning.

So what's going on?

Tell these people why they're here.

JOHN GIANNANDREA: What's going on?

Yeah, thanks, Tom.

I mean, I think in the last few years,

we've seen extraordinary results in fields that hadn't really

moved the needle for many years, like speech recognition

and image understanding.

The error rates are just falling dramatically,

mostly because of advances in deep neural networks,

so-called deep learning.

I think these techniques are not new.

People have been using neural networks for many, many years.

But a combination of events over the last few years

has made them much more effective,

and caused us to invest a lot in getting them

into the hands of developers.

People talk about it in terms of AI winters,

and things like this.

I think we're kind of an AI spring right now.

We're just seeing remarkable progress

across a huge number of fields.

TOM SIMONITE: OK.

And now, how long have you worked

in artificial intelligence, John?

JOHN GIANNANDREA: Well, we started

investing heavily in this at Google about four years ago.

I mean, we've been working in these fields,

like speech recognition, for over a decade.

But we kind of got serious about our investments

about four years ago, and getting organized

to do things that ultimately resulted

in the release of things like TensorFlow, which

Jeff's team's worked on.

TOM SIMONITE: OK.

And we'll talk more about that later, I'm sure.

Aparna, give us a perspective from the view of someone

who builds products.

So John says this technology has suddenly

become more powerful and accurate and useful.

Does that open up new horizons for you,

when you're thinking about what you can build?

APARNA CHENNAPRAGADA: Yeah, absolutely.

I think for me, these are great as a technology.

But as a means to an end, they're

powerful tool kits to help solve real problems, right?

And for us, as building products, and for you guys,

too, there's two ways that machine learning

changes the game.

One is that it can turbo charge existing use cases-- that

is, existing problems like speech recognition--

by dramatically changing some technical components

that power the product.

If you're building a voice enabled assistant, the word

error rate that John was talking about, as soon as it dropped,

we actually saw the usage go up.

So the product gets more usable as machine learning improves

the underlying engine.

Same thing with translation.

As translation gets better, Google Translate,

it scales to 100-plus languages.

And photos is a great example.

You've heard Sundar talk about it, too,

that as soon as you have better image understanding,

the photo labeling gets better, and therefore, I

can organize my photos.

So it's a means to an end.

That's one way, certainly, that we have seen.

But I think the second way that's, personally, far more

exciting to see is where it can unlock new product use cases.

So turbocharging existing use cases is one thing,

but where can you kind of see problems

that really weren't thought of as AI or data problems?

And thanks to mobile, here-- 3 billion phones-- a lot

of the real world problems are turning into AI problems,

right?

Transportation, health, and so on.

That's pretty exciting, too.

TOM SIMONITE: OK.

And so is one consequence of this

that we can make computers less annoying, do you think?

I mean, that would be nice.

We'd all had these experiences where

you have a very clear idea of what it is you're trying to do,

but it feels like the software is doing

everything it can to stop you.

Maybe that's a form of artificial intelligence, too.

I don't know.

But can you make more seamless experiences

that just make life easier?

APARNA CHENNAPRAGADA: Yeah.

And I think in this case, again, one of the things

to think about is, how do you make sure-- especially

as you build products-- how do you

make sure your interface scales with the intelligence?

The UI needs to be proportional to AI.

I cannot believe I said some pseudo formula in front of Jeff

Dean.

But I think that's really important,

to make sure that the UI scales with the AI.

TOM SIMONITE: OK.

And Jeff, for people like Aparna,

building products, to do that, we

need this kind of translation step

which your group is working on.

So Google Brain is a research group.

Works in some very fundamental questions in its field.

But you also build this infrastructure,

which you're kind of inventing from scratch, that makes

it possible to use this stuff.

JEFF DEAN: Yeah.

I mean, I think, obviously, in order

to make progress on these kinds of problems,

it's really important to be able to try lots of experiments

and do that as quickly as you can.

There's a very fundamental difference

between having an experiment take a few hours,

versus something that takes six weeks.

It's just a very different model of doing science.

And so, one of the things we work on

is trying to build really scalable systems that are also

flexible and easy to express new kinds of machine learning

ideas.

So that's how TensorFlow came about.

It's sort of our internal research vehicle,

but also robust enough to take something you've done and done

lots of experiments on, and then, when you get something

that works well, to take that and move it into a production

environment, run things on phones or in data

centers, on RTPUs, that we announced a couple days ago.

And that seamless transition from research

to putting things into real products

is what we're all about.

TOM SIMONITE: OK.

And so, TensorFlow is this very flexible package.

It's very valuable to Google.

You're building a lot of things on top of it.

But you're giving it away for free.

Have you thought this through?

Isn't this something you should be keeping closely held?

JEFF DEAN: Yeah.

There was actually a little bit of debate internally.

But I think we decided to open source it,

and it's got a nice Apache 2.0 license which basically

means you can take it and do pretty much whatever

you want with it.

And the reason we did that is several fold.

One is, we think it's a really good way of making research

ideas and machine learning propagate more quickly

throughout the community.

People can publish something they've done,

and people can pick up that thing

and reproduce those people's results or build on them.

And if you look on GitHub, there's

like 1,500 repositories, now, that mention TensorFlow,

and only five of them are from Google.

And so, it's people doing all kinds of stuff with TensorFlow.

And I think that free exchange of ideas and accelerating

of that is one of the main reasons we did that.

TOM SIMONITE: OK.

And where is this going?

So I imagine, right now, that TensorFlow is mostly

used by people who are quite familiar with machine learning.

But ultimately, the way I hear people

talk about machine learning, it's

just going to be used by everyone everywhere.

So can developers who don't have much

of a background in this stuff pick it up yet?

Is that possible?

JEFF DEAN: Yeah.

So I think, actually, there's a whole set

of ways in which people can take advantage of machine learning.

One is, as a fundamental machine learning researcher,

you want to develop new algorithms.

And that's going to be a relatively small fraction

of people in the world.

But as new algorithms and models are developed

to solve particular problems, those models

can be applied in lots of different kinds of things.

If you look at the use of machine learning

in the diabetic retinopathy stuff

that Sundar mentioned a couple days ago,

that's a very similar problem to a lot of other problems

where you're trying to look at an image

and detect some part of it that's unusual.

We have a similar problem of finding text

in Street View images so that we can read the text.

And that looks pretty similar to a model

to detect diseased parts of an eye, just different training

data, but the same model.

So I think the broader set of models

will be accessible to more and more people.

And then there's even an easier way,

where you don't really need much machine learning knowledge

at all, and that is to use pre-trained APIs.

Essentially, you can use our Cloud Vision API

or our Speech APIs very simply.

You just give us an image, and we give you back good stuff.

And as part of the TensorFlow flow open source,

we also released, for example, an inception model that

does image classification that's the same model as underlies

Google Photos.

TOM SIMONITE: OK.

So will it be possible for someone-- maybe they're

an experienced builder of apps, but don't know much about

machine learning-- they could just

have an idea and kind of use these building blocks to put it

together?

JEFF DEAN: Yeah.

Actually, I think one of the reasons TensorFlow has taken

off, is the tutorials in TensorFlow are actually

quite good at illustrating six or seven important kinds

of models in machine learning, and showing people