TIMOTHY JORDAN: Hi, everyone.
Timothy Jordan here for one last interview--
this one with Francoise, a principal scientist
here at Google.
FRANCOISE BEAUFAYS: Hi, Timothy.
TIMOTHY JORDAN: [LAUGHS] How are you?
FRANCOISE BEAUFAYS: Very good, and you?
TIMOTHY JORDAN: I'm doing so good.
The festival has been amazing, and we've
had the opportunity to talk to a lot of people
about what they do.
And that's why I'm excited for this moment
here because you do some really cool stuff.
FRANCOISE BEAUFAYS: All right!
Yeah, it is cool!
I agree!
TIMOTHY JORDAN: So why don't we give everybody
just an introduction to the work that you do around machine
learning and the input.
FRANCOISE BEAUFAYS: Yeah, so I work on speech recognition,
and I work on keyboard input.
So speech recognition, you all know what it's about.
Sundar has been talking about our progress in Voice and how
we've lowered our word-error rate quite a bit--
Google Home and all that.
And I've been working on speech for the last 12 years
at Google.
And then, more recently, maybe the last two-three years,
I've been working on keyboard input.
And when people think of their keyboard,
they think it's something very simple,
where they just take the mobile keyboard on their phone
and they just tap the letters of what they want to spell.
But in reality, if you want a good keyboard that
really understands you and corrects your mistakes
and predicts the next word that you're going to type,
it's pretty complicated.
And there's a lot of machine learning
that goes into keyboards as well.
TIMOTHY JORDAN: Yeah, that's one of the things when
you were talking about this earlier,
is that it's a common misconception
that machine learning wouldn't apply to the keyboard
because you're just hitting buttons.
But that's actually not true at all.
FRANCOISE BEAUFAYS: No, it's not true at all.
And a keyboard is very interesting, because it's
such a complex product in itself,
because you can do a lot of things with a keyboard.
You can tap on the keys, you can swipe what you want to write,
and then you can even click on the microphone,
and then you can speak to your keyboard, too, right?
So the two modalities come together there.
But doing a good job at correcting your typos,
yet letting you type new words that the keyboard maybe
doesn't know about, and then learn
those words and those phrases for you,
and then being able to predict the next word
or the next phrases that you're going to type so that you don't
have to type them, you can just say, oh, yeah,
that's what I meant, click--
that takes a lot of machine learning.
TIMOTHY JORDAN: Awesome.
One of the other aspects of your work
that I find really interesting is
I end up talking to a lot of machine learning researchers,
and they're just blazing new ground.
It's really interesting, but it's
like very theory and theoretical.
But what you do-- is you have problems,
and you've just found that machine learning is a good way
to solve them.
FRANCOISE BEAUFAYS: Right.
So the way that I like to think about these problems
is, I have a technology to build, and it has many facets,
and there are many things that we need to optimize.
It can be the machine learning.
It could be the UI.
It could be the infrastructure that's
behind it that nobody sees.
And so what we're really trying to do
is to understand the pinpoints for the user--
where are the places where our users are wasting time
and are getting frustrated?
What are the places that they enjoy instead?
And then trying to understand those problems,
then we look at what are the right solutions
for those problems.
And sometimes, the solution is just sweeping a parameter.
And sometimes, it takes like a really complex piece
of machine learning.
So we're just picking the technology
that matches the problem that we have to solve,
and we improve the technology one step at a time.
TIMOTHY JORDAN: It's really interesting
to kind of have that perspective of, its very
pragmatic about machine learning.
FRANCOISE BEAUFAYS: Right, right.
Exactly, exactly.
And I think it's a good approach because, once you
know exactly what--
the hardest part is really figuring out
what is the problem that you're trying to solve.
And I think a lot of developers are struggling exactly
with that same problem.
It's, what is it that I'm trying to solve?
How am I formalizing that?
And then, what is the best tool to solve that problem?
TIMOTHY JORDAN: Awesome.
So there's another aspect of your work I want to bring up--
it's the last one, I promise--
which I find really interesting.
And you call it deep internationalization.
Can you tell me what that is?
FRANCOISE BEAUFAYS: Yes, so as you may guess,
I'm not a native US American speaker.
I grew up in Belgium.
Belgium is a country with French, and German,
and Flemish.
I learned Italian when I came here to the United States.
So I care deeply about languages.
And we have found developing those keyboards
that a lot of countries in the world and a lot of languages
are not well represented in mobile keyboards.
What I mean by that is that there are a lot of people
in the world, sometimes tens of millions
of people, who speak a language and would like
to write to their friends in that language,
but that language is not supported on a keyboard.
So what do they do?
Either they go back to another language--
so maybe I can not type in French.
I will go back to English because it's
OK my friends understand English too.
But that's not what I want to do.
I want to speak French with my family.
And if we happen to speak a much more distant language that
is not well represented, we wouldn't
have the luxury of being able to talk
to each other in our own language.
So that brings us into this space
where we think it's not just about bringing
a few tens of languages into our keyboards
and into Google technology in general.
It's about hundreds of languages.
It's about going beyond 1,000 languages.
There are in the world 1,342 languages,
according to linguists, that has more than 100,000 speakers.
So you can imagine.
And if you go into even the deeper end,
it would be in the 6,000-7,000 languages.
And so I really have a passion for bringing as many
of those languages online as possible and progressively
enabling technologies like speech recognition and keyboard
input to more and more languages.
TIMOTHY JORDAN: And the way you design
an app is just a little bit different
if you're trying to design for all of them, right?
FRANCOISE BEAUFAYS: Yes.
So the app itself, we try to keep
things as general as possible because there's
no way you can scale to hundreds of languages
if your infrastructure is not general
and your modeling methodologies are not as general
and scalable as possible.
And that's where machine learning actually is very
helpful to us because before we had machine learning to solve
our problems, we would really [? hurt ?] good things,
and you would see pieces of C++ code that would say,
if language equal German, do this, right?
And that's not what you want.
There is no way to scale that.
With machine learning, you essentially
have the algorithms that are data independent and language
independent.
And then the data is the piece that
contains the knowledge about the language.
So now there's this nice interface
between algorithmic work and data work.
And so once you have the right to algorithms,
it's just a question of bumping data from different languages
into them to create a new capability in those languages.
And if you improve your algorithms,
all the languages improve at the same time.
So it's a really nice synergy as long as you
can establish that boundary.
And with machine learning, that's very easy to do.
TIMOTHY JORDAN: Yeah, and it sounds very sort of,
once you get the right perspective,
things start to fall into place.
FRANCOISE BEAUFAYS: Exactly.
You really want to build from scratch
with this thinking of, how am I going to internationalize
my product, my technology, but not just to five languages
or just to hundreds of languages.
TIMOTHY JORDAN: OK.
FRANCOISE BEAUFAYS: Yeah.
TIMOTHY JORDAN: So I think that's about all the time
that we have, but thank you so much for joining with me
and talking about all this.
Is there anything else you'd like, say,
if you could see some developers on the other end of this camera
that would love to be doing the kind of work
that you're doing now, what's some of the stuff
that you'd tell them to get started on?
FRANCOISE BEAUFAYS: I think the best work
that you do is through when you're
passionate about the thing that you're doing.
So I would think for each developer,
if they are-- and I assume they are-- passionate about what
it is that they're trying to develop, dig deep into it.
Just don't be satisfied with something
that's average quality.
Keep digging and digging to try to understand your product
the best you can and what your users want the best you can.
And then keep developing from there,
and everything will go well.
TIMOTHY JORDAN: Awesome, Francoise.
Thank you so much.
FRANCOISE BEAUFAYS: You're welcome!
Thank you!