Welcome, Yuanqing. I'm really glad you could join us today.
Sure.
Today you are the head of IT research and when the Chinese government,
the government of China, was looking for someone to start
up and build a National Deep Learning Research Lab,
they tapped you to help start this thing.
So, you know, arguably, I think maybe you're
the number one deep learning person in the entire country of China.
I'd like to ask you a lot questions about your work,
but before that, I want to hear about your personal story.
So how did you end up getting to do this work that you do?
Yeah, so, actually, before my Ph.D. program,
my major was in Optics,
it's more like in Physics.
I think I had a fairly good kind of background,
a very good background, on maths.
After I came to the US,
then I was thinking,
what kind of major can I take for my Ph.D. program?
I was thinking, well,
I guess I could go for Optics or go for something else.
Back to like early 2000,
I think nanotechnology was very very hot.
But I was thinking that probably I should look at something even more exciting.
And that leaves a good chance that I was taking
some classes at UPenn and I got to know Dan Lee.
So later, he become my Ph.D. adviser.
I was thinking, machine learning was a great thing to do.
And I got really excited and I changed my major.
So, I did my Ph.D at UPenn.
I majored in machine learning.
I was there for five years and it was kind of
very exciting and I
learned a lot of things from scratch and lots of algorithms, even like PCAs.
I didn't know all those before.
I feel like I was learning new things every day.
So it was a very, very exciting experience for me.
This was one of those things of a lot of starts.
Although, you know, you just did a lot of
work and it was an underappreciated for its time.
Right, that's right. Yes. So I think NEC was exciting place,
and I was there at the beginning as a researcher.
Again, I also like to feel like, whoa.
I learned lots of things.
Actually, later at NEC,
I started working on computer visions.
I actually started working on computer vision very late, relatively late.
And the first thing I did was
I participated in ImageNet Challenge. That's the first year of ImageNet Challenge.
I was kind of managing a team to work on a project.
It was lucky, we were quite lucky that we were quite strong,
and in the end,
we actually got the number one place.
Overwhelming, number one place in the contest.
So you are the first ever in the world ImageNet Competition winner?
Right. Yeah, and I was the person that did a presentation at that workshop.
It was a really nice experience for me,
and that actually get me into Lisa [inaudible] computer vision tasks.
I had been working on Liza [inaudible] probably since then.
When New York Times head paper came out,
and also later on AlexNet came out,
it really blow my mind.
I thought, wow, deep learning is so powerful.
Since then, I put a lot of effort to work on those.
So, as a head of China's National Lab,
National Research Lab on deep learning,
there must be a lot of exciting activities going on there.
So, for the global audience, who are watching this,
what should they know about what's happening with this National Lab?
The mission of this National Engineering Lab is to
build a really large deep learning platform,
and hopefully be the biggest one,
or at least the biggest one in China.
And this platform would offer people deep learning framework like Pelo Pelo.
And we offer people really large scale computing resource,
and we also offer people really large, really big kind of data,
and if people are able to develop
a research or develop good technology on these platform,
we also offer them big applications.
So for example, the technology can be proved into some big applications in Baidu
so the level of technology could get integrate and improve it.
So, we believe that combining those resources altogether,
I think, is going to be a really powerful platform.
You can also get one example on each.
For example, right now, if we publish a paper,
and someone want to reproduce it,
the best thing to do is to provide a code to somewhere,
and you could download the code to
your computer and that you also try and find that the data sets somewhere.
And you probably also need to get good [inaudible] for your computing resources to run smoothly.
So this will easily take you some effort at least.
National Lab things will become much easier.
So if someone's using these platform to write the paper,
to do that work and write a paper,
and the lab who have the code on these platform
and the computing structure is already set up for this code, and data's allowed too.
So basically, you just need a common line to lift the database up.
So, this is a huge relief for loss of reproducibility issues in combination with science.
So you easily, just few seconds,
you can start learning something that you see in a paper.
Yeah. So this is extremely powerful.
So this is just one example that we are working on to make sure that we
are providing a really powerful platform to the community and to the industry.
That's amazing. That really speeds up deep learning research.
Yeah.
Can you give a sense of how much resources
the Chinese government is putting to back you for this Deep Learning National Lab?
So, I think that for this National Engineering Lab,
I think government can invest some funding here to build up infrastructure.
But I think more importantly,
these are going to be
a flagship in China that are going to lead a lots of deepening the efforts,
including like national project,
and the laws of policies, and things.
So this is actually extremely powerful,
and I think by doing that,
we are really honored to get this lab.
You are somewhat at the heart of deep learning in China.
So, there's a lot of activity in China that
the global audience isn't aware of yet and hasn't seen yet,
so what should people outside China know about deep learning in China?
Yeah. I think in China,
especially in the past few years,
I think deepening empowered a product,
so it's really booming,
ranging from search engines,
to, like, a phrase recognition,
surveillance, to the e-commerce, lots of place.
I think that they are investing big effort into deep learning and
also really make use of technology to make the business much more powerful.
And this actually is very important for developing a high technology in general.
I think for myself,
and also lots of people share this,
we believe that actually it's really important to this, what's often called [inaudible] loop.
For example, when we start out to
think of building some technologies that will have some initial data,
and which we try to do with some initial algorithm, it will launches
our initial product for that service.
Then, after, later, that will get the data for users.
And the others will get more data,
so we would develop a better algorithms.
Because we see more data, we know what would be the better algorithm.
So we have more data and a better algorithm,
we will be able to have better technology for product service,
and then definitely we hope that we will be able attract more users using the product.
The technology is better.
And then we will get more data.
So this is a very good, positive move.
And this is very special,
especially for AI related technologies,
for traditional technology like a laser.
I was working on that before.
So the course of technology is going to be more linear.
But before, this AI technology,
because of this positive loop,
you can imagine that definitely, [inaudible] come with really fast growth of technology.
And [inaudible] is actually super important when we design a research into them,
when we design our ND.
We work on the direction that we're able to get to this quick improvement period.
But if our whole business were not able to fund these positive loop,
if we are not able to fund this strong positive loop,
this will not work out because someone else will
have a strong vision to fund this strong loop
and they will get to this place much more quickly than you are.
So this [inaudible] an important logic for us when we're looking at,
say, hey, you need
a company, what direction should we work on, and what direction we should not work on.
This is definitely a really important factor to look at.
Today, both in China,
in the U.S., and globally,
there are a lot of people wanting to enter deep learning and wanting to enter AI.
What advice would you have for someone that wants to get into this field?
So now, there's definitely people who will start with open source frameworks.
I think that's extremely powerful to many starters.
I think when I was studying my deep learning study,
there was not much open source resources.
I think nowadays, in AI, especially in Deep learning,
it's a very good community,
and there are multiple really good people in the frameworks.
It's always [inaudible] , a cafe.
Now they call it cafe too, right? In China we have a really good Pelo-pelo.
And even in most [inaudible] online,
they have lots of courses to teach you how to use those.
And also, nowadays, there's also
many publically available benchmark and the people will see,
hey, really skillful, really experienced people like,
how well they could do on those benchmark.
So basically, time to get familiar with deep learning.
I think those are really good starting point.
How did you gain that understanding?
Actually, I do it the opposite way.
I learned PCA, I learned LDA,
all of those before I learned deep learning actually.
So, basically, it's also a good way, I feel.
We kind of lay down lots of foundations.
We learned graphic model.
So these are all important.
Although right now, deep learning is beyond [inaudible].
But knowing laws, will actually give you very good intuition
about how deep learning works.
And then one day,
probably leads connection of deep learning into laws like a framework or approach.
I think there are already lots of connections.
And the laws actually make deep learning richer.
I mean, there are richer ways of doing Deep Learning.
Yeah. So, I feel like it's good to start with lots of open source,
and those are extremely powerful resource.
I will also suggest that you also do learn those basic things about machine learning.
So, thank you. That was fascinating.
Even though I've known you for a long time,
there are a lot of details you're thinking that I
didn't realize until now. So, thank you very much.
Thanks so much for having me.