Nvidia Corp. on Wednesday won a series of deals in which some of China's biggest technology companies are using its chips to make product recommendations and to develop self-driving vehicles.
Nvidia said ride-hailing service Didi Chuxing has adopted its chips both for developing self-driving vehicles on the road as well as in its back-end data centers.
The Santa Clara, Calif.-based chip supplier officially announced the deals at an event on Wednesday in the Chinese city of Suzhou northwest of Shanghai.
Nvidia also said Didi would use its chips in computing systems in autonomous cars on the road that help drive the vehicles, as well as in back-end data centers where data gathered by test vehicles is used to train algorithms for self-driving cars.
At its event, Nvidia also planned to announce tools that will let participating automakers from Germany, China and North America learn from each others' training data without having to share data directly -- a system called "federated" learning.
"This is a way to aggregate different types of data sets form different companies," Danny Shapiro, senior director of automotive at Nvidia, said at the briefing. "The key thing here is that each (carmaker) or each region can maintain and protect their own data. It's owned wholly. It's not shared."
Nvidia told reporters that e-commerce giant Alibaba Group Holding and search engine provider Baidu Inc. have started using its chips to run systems that make recommendations to users with the aim of increasing the number of times users click on those recommendations.
Nvidia got its start supplying chips to improve videogame graphics on personal computers, but in recent years much of its growth has come from the use of its chips for artificial intelligence, self-driving vehicles and other new areas.
Making recommendations -- whether on a restaurant or product that a user might like -- has long been a challenging artificial intelligence problem. In a Wednesday press release, Alibaba officials said click-through rates improved by about 10 percent using Nvidia's chips.
"The problem of recommendation is related to the explosion of choice," Paresh Kharya, Nvidia's director of product management for accelerated computing, told reporters at a briefing ahead of Nvidia's event. "To recommend the right product to the right user at the right time, you need to model the user's preferences and it takes a lot of different variables to model user preferences.