In a low-rise building overlooking a busy intersection in Beijing, Ji Rong Wen, a middle-aged scientist with thin-rimmed glasses and a mop of black hair, excitedly describes a project that could advance one of the hottest areas of artificial intelligence. Wen leads a team at the Beijing Academy of Artificial Intelligence (BAAI), a government-sponsored research lab that's testing a powerful new language algorithm--something similar to GPT-3, a program revealed in June by researchers at OpenAI that digests large amounts of text and can generate remarkably coherent, free-flowing language. "This is a big project," Wen says with a big grin. "It takes a lot of computing infrastructure and money." Wen, a professor at Renmin University in Beijing recruited to work part-time at BAAI, hopes to create an algorithm that is even cleverer than GPT-3. He plans to combine machine learning with databases of facts, and to feed the algorithm images and video as well as text, in hope of creating a richer understanding of the physical world--that the words cat and fur don't just often appear in the same sentence, but are associated with one another visually.

The ticket kiosks at Shanghai's frenetic subway station have a mind of their own. Walk up to one and state your destination, and it'll automatically recommend a route before issuing a ticket. In the interest of reducing the rush-hour stampede, the system is set up to let you find information and buy tickets without pushing a button or talking to a person. More impressive still, all this happens successfully in the middle of a crowded, noisy station. Each kiosk has to figure out who is speaking to it; zero in on that person's voice within the crowd; transcribe the incoming speech; parse its meaning; and compare the person's face against a massive database of photos--all within a few seconds.