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JOSH GORDON: Hey, everyone.
My name is Josh Gordon.
I'm here for "The Developer Show" with Sherol and Doug.
So Sherol, could you tell me a little bit about the Magenta
Project and your role on the team?
SHEROL CHEN: Yeah, so the Magenta project is really
focused on how machine learning has
a role in art and expressivity, and how it can extend
the human creative process.
So that starts with generative models research,
but then goes into creating tools
that developers can use to create these entry
points into using these models for art and creativity.
JOSH GORDON: So one of the things
that I think the Magenta project has done an extraordinarily
good job of is making reproduceable software
and demos that other developers can try.
So could you tell me about a couple of your favorite demos
that you've released over the past few years?
DOUGLAS ECK: Yeah, we've done a lot of demos.
I think the goal of these demos has
been to engage the developer community
and to bring artists, and musicians, and creative coders
to work together with us on these machine learning
problems.
I particularly like a project that Sherol
talked about today, Performance RNN, which
learns how to generate something that's
half a composition and half a performance,
because all of the nuanced velocities
and dynamics of a piano performance are there.
And I really love the new Music VAE
stuff that's coming out, where we're
able to explore an embedding space,
and some really cool JavaScript-based tools
developed around TensorFlow.js that
turned things on for everybody.
And I particularly love the idea that we have other developers
that are not Googlers taking our JavaScript interface
and playing with things themselves.
JOSH GORDON: So I actually have one question for you, Doug.
So you talk a lot about generative models for Magenta
in the context of art and music.
What do you think the future might
hold for generative models?
DOUGLAS ECK: I'm perhaps biased because I
work on this every day.
But what I see is that the overall field
of generative models is going to sweep through
in the next decade and become a major, major part of machine
learning research.
I think all you have to do is look at what we have with
Translate, end-to-end, sequence-to-sequence-based
translation, and think, OK, we can start to get some beautiful
nuanced translations when we pass between, for example,
English and Japanese.
What can we do if we start to generate from these models
and try to train these models to help us write,
help us do visual art, help us communicate in different ways?
Think of Smart Reply.
You get that one relevant response.
Now imagine Smart Reply, but its helping
you craft whole paragraphs.
It's still your message.
It's still you communicating.
But you're seeing machine learning
help you solve that problem.
I think the future is very, very bright for generative models.
And very excited to be part of it.
JOSH GORDON: Sherol, another thing
that I really, really like about Magenta
is how easy it is to use the demos that you've released.
Can you tell me a little bit about your Docker images,
and the notebooks, and the different demos
that are available on the website for developers to try?
SHEROL CHEN: For developers, of course, its all in GitHub,
open source.
And you can contribute to the projects as well.
But there's also CoLabs.
And I think CoLabs recently released just an amazing entry
point into training and running Magenta models.
And outside of that, we have just the demos that get built
out of it, anyone can use.
So developers, artists-- you could have no programming
experience and be able to still explore
these generative spaces.
DOUGLAS ECK: You know, Sherol, I'd add to this
that the fact that a JavaScript developer was
able to play around with some of our tools
has led us to go full in on developing Magenta.js
that's heavily integrated with TensorFlow.js
and also is able to pull in from P5
and other commonly used tools for visualization.
And I love the idea that working with our developers,
even creative coders-- someone who doesn't think of themselves
as a developer, but they like to hack--
I want to see people continue to take our ideas
and twist them around a little bit, make something
that we didn't quite expect.
And I think we're getting much closer as we
get simpler and simpler tools and ways to use our research
in the open source community.
JOSH GORDON: And one last question.
Where is the best place for developers
to go to learn more about Magenta and get started?
SHEROL CHEN: All right.
This is where you go.
You go to G.co/Magenta.
And you'll find everything there--
blog posts, references to publications.
The demos themselves will be linked there.
And finally, last but not least, the Magenta discussion group,
where we want to really cultivate
open community for people to share things they do.
Whether they're using Magenta or not,
we want to really tap into the creative space
because we know creativity is an important part
of the human existence.
And we want to see more of that and how
technology can promote that.
JOSH GORDON: That's a great answer, Sherol.
And I like the DevRel enthusiasm.
So again, Doug and Sherol, thank you very much.
I'm Josh Gordon for "The Developer Show."
This has been great.
Thanks very much for watching.
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