#25 - Baidu's AI Lab Director on Advancing Speech Recognition and Simulation

**Craig Cannon** (0:00)
Hey, this is Craig Cannon, and you're listening to Y Combinator's podcast. This episode is with Adam Coates. Adam's the director of Baidu's Silicon Valley AI Lab, and what they focus on is developing AI technologies that will impact at least 100 million people. We spent a good chunk of this episode talking about Adam's work in speech-to-text and text-to-speech. So if you want to learn more about those projects, you can check out research.baidu.com. And as always, if you want to read the transcript or watch the video, you can check out blog.ycombinator.com. All right, here we go.
Today, we have Adam Coates here for an interview. Adam, you run the AI lab at Baidu in Silicon Valley.
Could you just give us a quick intro and explain what Baidu is for people who don't know?

**Adam Coates** (0:41)
So Baidu is actually the largest search engine in China. So it turns out the internet ecosystem in China is this incredibly dynamic environment.
And so Baidu, I think, sort of turned out to be an early technology leader and really established itself in PC search, but then also has sort of remade itself in the mobile revolution and increasingly today is becoming an AI company, recognizing the value of AI for a whole bunch of different applications, not just search.

**Craig Cannon** (1:11)
And so, yeah, what do you do exactly?

**Adam Coates** (1:13)
So I'm the director of the Silicon Valley AI Lab, which is one of four labs within Baidu research. So especially as Baidu is becoming an AI company, the need for a team to sort of be on the bleeding edge and understand all of the current research, be able to do a lot of basic research ourselves, but also figure out how we can translate that into business and product impact for the company. That's increasingly critical. So that's what Baidu research is here for.
In the AI lab in particular, we kind of founded recognizing how extreme this problem was about to get. So I think the deep learning research and AI research right now is flying forward so rapidly that the need for teams to be able to both understand that research but also quickly translate it into something that businesses and products can use is more critical than ever. So we founded the AI lab to try to close that gap and help the company move faster.

**Craig Cannon** (2:14)
And so then how do you break up your time in between doing basic research around AI and actually implementing it, bringing it forward to a product?

**Adam Coates** (2:23)
There's no hard and fast rule to this. I think one of the things that we try to repeat to ourselves every day is that we're mission oriented.
So the mission of the AI lab is precisely to create AI technologies that can have a significant impact on at least 100 million people. We chose this to keep bringing ourselves back to the final goal that we want all the research we do to ultimately ends up in the hands of users.
And so sometimes that means that we spot something that needs to happen in the world to really change technology for the better and to help Baidu, but no one knows how to solve it. And there's a basic research problem there that someone has to tackle. And so we'll sort of go back to our visionary stance and think about the long term and invest in research. And then as we have success there, we shift back to the other foot and take responsibility for carrying all of that to a real application and making sure we don't just solve the 90% that you might put in, say, your research paper, but we also solve the last mile. We get to the 99.9%.

**Craig Cannon** (3:38)
So maybe the best way to do this then is to just explain something that started with research here and how that's been brought on to a full on product that exists.

**Adam Coates** (3:47)
So I'll give you an example. We've spent a ton of time on speech recognition. So speech recognition a few years ago was one of these technologies that always felt pretty good, but not good enough.
And so traditionally, speech recognition systems have been heavily optimized for things like mobile search. So if you hold your phone up close to your mouth and you say a short query...

**Craig Cannon** (4:12)
And talk in a non-human voice.

**Adam Coates** (4:14)
Exactly. The systems could figure it out, and they're getting quite good. I think the speech engine that we've built at Baidu called Deep Speech is actually super human for these short queries.
Because you have no context, people can have thick accents. So that speech engine actually started out as a basic research project. We looked at this problem, we said, gosh, what would happen if speech recognition were human level for every product you ever used?