So, that’s different. What’s similar?
The similarity part is the technology. The core technology is still speech recognition, signal processing, natural language understanding, and the platform. Our platform architecture, in many ways, is very similar to Amazon. In my view, Amazon is doing a very great job. Even though I worked at Microsoft. I’m always gonna be rooting for Microsoft. But honestly, Amazon is leading.
But don’t you think that Amazon’s handicap is on its back end, in that it can’t keep up on the technology side with Google and Microsoft?
I worked on Cortana four and a half years ago. At the time we all were like, “Amazon, yeah, that technology is so far behind.” But one thing I learned is that in this race to AI, it’s actually more about having the right application scenarios and the right ecosystems. Google and Microsoft, technologically, were ahead of Amazon by a wide margin. But look at the AI race today. The Amazon Alexa ecosystem is far ahead of anybody else in the United States. It’s because they got the scenario right. They got the device right. Essentially, Alexa is an AI-first device.
Microsoft and Google made the same mistake. We focused on Cortana on the phone and PC, particularly the phone. The phone, in my view, is going to be, for the foreseeable future, a finger-first, mobile-first device. You need an AI-first device to solidify an emerging base of ecosystems.
It’s become so much clearer, living in China, what AI-first really means. It means you interact with the technology differently from the start. It has to be voice or image recognition, facial recognition, in the first interactions. You can use a screen or touch, but that’s secondary.
At Baidu [headquarters], it’s all face recognition-based. At the vending machine at Baidu, you can buy stuff with voice and a face. And we’re also working on a cafeteria project. Our goal is, when you go to a cafeteria, you walk away with food.
Technically, that’s possible now in a lot of places, but that doesn’t mean people are receptive to it.
It’s not all technology. It’s about the structure of the environment—the culture, the policy regime. This is why AI plus China, to me, is such an interesting opportunity. It’s just different cultures, different policy regimes, and a different environment.
So how about the ethical consequences of the tools that we’re creating? Do people have the same types of conversations at Baidu as they do at Microsoft?
Similar. Protection of privacy is of paramount importance to us. Ultimately, our users trust in our technology. So, this is something we talk quite a bit about. And we are going to continue to seriously invest in capabilities to make sure that you can trust our services, in terms of privacy. For example, we talked about voice interactions. We’re working on technologies that would prevent the unintended activation of smartphones. It’s because we know that people don’t want their conversations to be shipped to the Cloud. I may have very private conversations in my living room. [But sometimes] the speakers think you are trying to wake them up, and then send those bits to the Cloud.
Do you think that Chinese consumers care as much? Do you think that they expect something different, by virtue of the fact that they live under a different political environment?
Our assumption is that people will care about this. Ultimately, we believe people are rational. If there’s a compelling benefit, people will weigh the consequences and then make those choices. I think this is global.
Baidu announced an ambitious self-driving initiative called Apollo this spring, and you’ve announced 50 partnerships so far. Why are you doubling down on autos?
If you want to truly build digital intelligence to be able to acquire knowledge, make decisions, and adapt to the environment, you need to build autonomous systems. In autonomous systems, the car is the first major commercial application that is going to land.
It’s just like the phone ecosystem today. The phone ecosystem is the largest silicon software ecosystem. I believe the same thing will happen for the autonomous system. The car is going to build a larger ecosystem. And the same set of capabilities—hardware, sensors, chip sets, software—will be used to build industry robots, home robots. We want to have hundreds of companies and universities all at work on this, building a very large ecosystem. Then we can build robots, build drones, and build all those autonomous systems. So, to me, autonomy is a key.
You were instrumental in developing Apollo, right?
I am the COO of the company, but I run that business directly. For the last three plus months, I probably spent about about 40 percent of my time on the autonomous driving technology product—talking to customers; talking to partners. Essentially, from where things are today, toward the future of being able to be fully autonomous, the fundamental technological path for the self-driving technology is the speed of iterations.
What does that speed depend on?
Essentially, how much data you can get. Because to be able to drive on the road, you have to drive different kinds of roads in different kinds of conditions—lighting, weather, whether it’s wet, how much physical pressure is on your tires. And with Apollo, we will be able to pull together all the resources, particularly the data resources, in a way that enables everybody to be better off.
We wrote a manifesto of Apollo. Essentially, there are four principles. Each is important. One is open capability. At Baidu, we open up our capability—in code, in services, in data—to all partners. This works particularly well in China, because China is highly, highly fragmented. There’s more than 250 car OEMs [original equipment manufacturers], unlike the United States, which is a heavily concentrated industry. None of the OEMs will have the full capabilities to build out deep R&Ds. With our code base that we released on July 5, [we will make it possible for] one person to assemble a vehicle in three days that can do autonomous driving in limited forms and start on R&Ds.
The second is shared resources. Essentially, with the Apollo design, there are two tiers. You are able to use the Apollo code and capability, and some data sets, with no strings attached. The second tier is enables you to use all the data that Baidu provides—HD maps, the training data—but we ask you to contribute your data. However, there’s a key principle. The more you contribute, the more you should be able to get back.
The third principle is the accelerating pace of innovation. Essentially, because we’re able to put together more data, we are able to achieve more capability in our simulation engines. We enable everybody, collectively, to innovate at a much faster pace.
And the fourth principle is sustained win-win. Baidu is the biggest model. It’s going to focus on delivering high-end services, high-value services, HD maps, [and] security services. We’re competing against nobody. We enable each OEM, whether it’s Bosch, Continental, or Nvidia, to be able to do more.
This is the reason I created a subsidiary in the United States, Apollo US. And, also Apollo Singapore. The Singapore government essentially was like, “Wow, this is…Just come to Singapore. I’m ready to invest.”