[MUSIC PLAYING]
LAURENCE MORONEY: Welcome to "Coffee with a Googler."
I'm here today to talk with Christine Robson, who's
a research PM for machine learning.
We're going to talk about what's new and exciting in the field
of machine learning, including Project Magenta, which
is all about machine learning for generating music.
We'll also take a look at some of the things
that are happening in Youtube as well as
advances in image recognition.
So that video, that was an artist
who did a cover of a track that was generated by a computer.
Is that right?
CHRISTINE ROBSON: Yes, exactly.
So the piano that you were hearing, that's us.
That's the machine learned music.
That's the generative model, the TensorFlow model,
coming up with a piano track.
And then we put this out there as part of our open sourcing
efforts to put all this generative music
models that we're doing online.
And these artists, they listened to the music.
They were interested in it.
They were inspired by it.
And they decided to sort of jam along with our music track.
[MUSIC PLAYING]
LAURENCE MORONEY: Were you expecting somebody to do that?
CHRISTINE ROBSON: No, we weren't expecting that at all.
It was really very exciting.
We got all excited in the office.
We're all passing the video around, taking a look.
It was really pretty exciting.
But this is what we hope will happen, right,
that we can use machine learning for these really
creative purposes and really get a partnership going where
artists and creators want to work with machine learning
tools to be more creative and to have fun.
LAURENCE MORONEY: Right.
I remember you said something once-- it's not
about replacing music.
It's almost like inspiring musicians.
CHRISTINE ROBSON: Yeah, exactly.
And something-- you know, Doug, who's the lead on this project,
he talks about this all the time,
that it's not that we're trying to make the best music out
there.
I mean, I'm not even a musician.
It's that we're trying to make tools that the best
musicians can use, tools that spur this kind of creativity.
And the Magenta Project, that's the project that
was open source that we generated this music from.
The Magenta Project's really about making those tools
for creators.
Starting with music, but we want to have tools for creators
to do art generation, to do videos,
to really tackle all of these questions
of how can you be creative and work
with a machine-learned model to make yourself more creative?
LAURENCE MORONEY: And now, for this piece of music,
it was-- I recognized the first bars was like "Twinkle Twinkle
Little Star."
CHRISTINE ROBSON: Yeah.
LAURENCE MORONEY: And it took over from there?
CHRISTINE ROBSON: Exactly.
So we give it this quick bar, like [SINGING].
And then the model is trained over a huge swath
of music-- everything from Bach to the Beatles.
And it's just trying to predict what the next note should be.
But it uses a type of machine learning
called a recurrent neural network.
So it looks backwards in time over the music that's
been played already and tries to predict sequentially
what the next note should be based on of all
of the music it's seen before.
So you give it just a couple of notes to get it started,
and then it just takes off.
LAURENCE MORONEY: Wow.
CHRISTINE ROBSON: Yeah.
LAURENCE MORONEY: That's pretty cool.
CHRISTINE ROBSON: It is really cool.
LAURENCE MORONEY: And this was built with Magenta?
CHRISTINE ROBSON: Yes.
LAURENCE MORONEY: And I know you're
the product lead for Magenta.
Could you-- like if somebody wanted
to get started with Magenta and maybe
start inspiring other people in some other kind of art form,
or maybe with music, how would they go about doing this?
CHRISTINE ROBSON: So everything from Magenta is online.
We have a GitHub.
It's part of the TensorFlow project.
So if you go to the tensorflow/magenta on GitHub,
you can go and you can download all of the tools.
We encourage everyone to download, to contribute,
to play with what we've got there
and see if they can do something creative too.
I mean, that's why we've put it out there.
LAURENCE MORONEY: Nice.
And it's not just music, right?
There are other art forms too?
CHRISTINE ROBSON: Well right now,
we've been focusing the project on music.
But the tools are very general.
The tools are really about generative machine learning,
this idea of generating music, generating art, the tools
to create rather than just making predictions
with machine learning.
LAURENCE MORONEY: OK, cool.
And machine learning, of course, it's not just incubation.
It's in use in the real world already,
like things like YouTube are using it for recommendations.
CHRISTINE ROBSON: When you think about a machine-learned model
and you think about all of the possible data they
can go into it and all of the places
where the signals are going to come from,
if you can capture more signals than you might
be able to think of as a human, you can do better
than a rule-based system.
And there's also this notion in-- it's a very hard concept
to explain.
There's this notion of embeddings in computer science
and machine learning research, where
you put a way to capture all this information together.
You capture relationships which aren't easy to describe
by simple, human labels.
Like maybe the relationship between these two things
is really easy to explain.
You can imagine writing a rule for it,
like I really like cat videos.
So maybe you'd say, this cat video
is similar to this other cat video.
They both contain cats.
They're both from Japan, something like this.
But then there might be other factors
that are really hard for a human to suss out.
Like maybe what I really like about this
is sort of an abstract feel about the video,
the way in which I like watching the cat
jump in and out of the box.
And that kind of abstract feel translates
to this totally other also kind of cute video involving
pandas on slides.
And these two things are related,
but it's hard to put your tongue on exactly why.
But they're obviously related, and they're
related for everybody who looks at these YouTube videos.
And that's something that a machine-learned model
can capture really easily, especially
with these embeddings that we use,
and that it's really hard for a human to write a rule for.
LAURENCE MORONEY: Right, got it.
So it's like if you like x, you might like y.
And you may not realize it, but when
you have all these variables coming out of an LM model
that it can actually show you that,
and you can actually learn that about yourself.
CHRISTINE ROBSON: Yes, exactly.
LAURENCE MORONEY: It's like an emergent type thing.
CHRISTINE ROBSON: It does feel emergent sometimes,
these notions of ideas that you wouldn't have thought really
mattered for your preferences-- like things
that I might not have realized I wanted to see in a YouTube
video, like how I want to spend my time,
that I really like this particular niche
kind of cat video.
LAURENCE MORONEY: I've discovered lots of music
that I like in that way, where it's
like I'm watching one piece of music,
and then it suddenly goes onto something else
on the playlist of somebody I've never
even heard of in a style that I probably don't even like.
But hey, that's not bad, that type of thing.
CHRISTINE ROBSON: Yeah.
And we actually do that.
We do machine learning for like music list curation.
So actually, the lead on the Magenta Project,
Doug, his role at Google before we started this project
was in the music recommendation system.
He's a musician.
He's got a real passion, both for machine learning
and for music.
And so we spent a lot of time working
on how to predict what music you're going to really enjoy
listening to based on what music you already like,
and looking not just at a very simple thing, like which people
like the same kind of music, but actually analyzing
the structure of the music and understanding how
different musical pieces are related to each other in sort
of subtle ways and fundamental ways,
and being able to really do very curated playlists for you
to find things that you're going to be interested in.
LAURENCE MORONEY: So I understand that YouTube,
it's done so well with this kind of stuff.
It's even won some awards.
Is that right?
CHRISTINE ROBSON: Yeah, so you don't
think when you're a computer scientist that you're
going to be working in a field that gets an Emmy.
But actually, YouTube won an Emmy for personalized video
recommendations.
LAURENCE MORONEY: Oh, wow.
CHRISTINE ROBSON: It was a technical Emmy.
LAURENCE MORONEY: Hey, an Emmy's an Emmy.
CHRISTINE ROBSON: I know.
It's true.
And this was awarded because we do
such a good job of personalizing video recommendations.
When I see that perfect cat video,
that's because we're doing such a good job
prioritizing what you want to watch
and prioritizing how to find the perfect video for you
in this moment.
LAURENCE MORONEY: So going beyond
like recommendation engines and that kind of thing,
one of the things I keep reading about
is how LM is now used for image recognition
and for attributing images.
And I know you've been involved in that a little bit.
Could you tell us about it?
CHRISTINE ROBSON: Yeah, so if you
haven't used the Google Photos app,
you should use the Google Photos app.
It's my new favorite thing.
I love that I can pick up my phone
and ask it to find me photos of my son,
ask it to find me photos of my son playing with my cat,
and that it has this great model where
it knows-- it can do face detection
and find my son for me.
It can do detection on my cat and find my cat for me.
And it does this because we've fed
this model all of the images we've seen from the web of all
of the different kinds of cats.
And it has this model built up where it sort of
knows what a cat looks like.
And so it can label it very effectively.
LAURENCE MORONEY: So one of things about image recognition
is I remember you had-- I read something from you even
talking about a cat.
And it could recognize a cat's face,
even though the cat didn't have ears.
And the ears are one of the most distinctive parts of a cat,
right?
CHRISTINE ROBSON: Yeah, that's my cat.
So a few years ago, my cat, who lives
in California of course and suntans all the time,
she got some skin cancer in her ears.
She's fine now.
But the vet had to remove her ears to clear the cancer.
And so she looks a little bit funny now.
She's still really cute, but she's got no ears.
And she looks less like a cat than you might imagine.
But our image recognition software is fine.
It can still recognize her as a cat.
And so if I ask my phone for photos of my son and my cat,
it still recognizes that she's a cat and can find her.
And it's this flexibility that you
get from training a model with a lot of different data.
There's not probably a lot of examples
of cats with no ears on YouTube, but we can sort of extrapolate
when we use these really powerful, deep, neural network
models to sort of-- we know basically
what the cat should look like.
And so we can extrapolate past things like the fact
that she doesn't have ears anymore.
LAURENCE MORONEY: Aww.
I'm glad she's doing well, though.
CHRISTINE ROBSON: Yes.
LAURENCE MORONEY: So a couple of months
ago, we had Joshua Gordon on the show.
And there was one thing that he said to me that I
thought was really fascinating.
And he was talking about three, four, five years
from now, the skills of machine learning
and the techniques of machine learning
will be as important to developers
as like JavaScript or SQL are today.
And I think he was right.
And I would love to inspire people to go out and do more
into this, like learn more about machine learning,
do things such as creating music or art,
or who knows what other kind of things they'll come up with.
And I know you've got a wealth of resources.
And what kind of things would you
ask developers to take a look at?
CHRISTINE ROBSON: Well, of course
I'm very passionate about this because machine learning is
my passion.
But I do think that Josh is right.
We've talked about this a lot.
But thinking about machine learning
is so different than thinking about other traditional kinds
of software engineering.
I often make the analogy that the difference
between functional programming and object-oriented
programming, that paradigm shift is
much smaller than the shift of thinking
about machine learning versus thinking
about traditional programming methods.
So the shift is very big and very important,
and that's why we want everybody to be together
on it from the beginning.
That's why we're putting so much effort into open sourcing
our tools.
We're really open sourcing the best of everything
we've got to try and get everybody together around this.
LAURENCE MORONEY: So developers in the outside world
kind of already get the best Google, right?
CHRISTINE ROBSON: Yes.
LAURENCE MORONEY: --which is just mind-blowing
when you think about it.
CHRISTINE ROBSON: Yeah.
And I mean, this is why we've put so much work
into open sourcing TensorFlow and why we really
want to put this all out there.
It's so important to think about what you can do
with machine learning models.
Because I really think this is going
to be transformative for the kinds of things we can do.
I mean everything from YouTube, where
you're predicting the next video or other angles
you can think of that affect your sort of day-to-day life,
to these more esoteric, creative things, like music generation,
things where you really want to have the best
tools at your disposal, and you really
want to be able to be as creative as possible.
And machine learning is going to enable all of these things.
So we want everybody to be really fluent.
LAURENCE MORONEY: Yeah.
I just thoroughly recommend it for anybody.
Any developers out there, just go take a look at the sites
that we've linked below.
There's some fabulous stuff out there,
and you're getting ready for the future.
So thank you so much, Christine.
This has been awesome.
CHRISTINE ROBSON: Thank you.
It was a real pleasure chatting with you.
LAURENCE MORONEY: Had a lot of fun.