Last October Ma announced that his company would spend $15 billion over the next three years on a research institute called the DAMO Academy (“discovery, adventure, momentum, and outlook”), dedicated to fundamental technologies. The Chinese name for the institute, 达摩, references Dharma, a legendary Indian monk said to have brought Buddhism to China in the fifth century.

China has long since shaken off its reputation for simply copying Western innovations. According to the Organization for Economic Cooperation and Development (OECD), R&D spending in China grew tenfold between 2000 and 2016, rising from $40.8 billion to $412 billion in today’s dollars. The US still spends more—$464 billion in 2016—but its total has increased by only one-third since 2000.

Alibaba is already China’s biggest R&D spender, forking out $2.6 billion in 2017. DAMO will effectively triple its research budget, to more than $7 billion. That most likely means Alibaba will overtake IBM, Facebook, and Ford and will narrow the gap with the world’s leaders, Amazon and Alphabet, which spent $16.1 billion and $13.9 billion respectively on R&D in 2017.

DAMO will include a portfolio of research groups working on fundamental and emerging technologies including blockchain, computer security, fintech, and quantum computing. But AI is the biggest focus, and it seems like the one with the greatest potential.

DAMO clearly takes inspiration from the great commercial research labs of the 20th century. Liu mentions, for instance, AT&T’s Bell Labs, which conducted fundamental research on materials, electronics, and software, producing breakthroughs including the transistor, the laser, and the charge-coupled device for digital imaging, as well as the UNIX operating system and the programming languages C and C++. Liu says Alibaba is also inspired by the way the US’s Defense Advanced Research Projects Agency (DARPA) funds different teams competing on the same project.

Alibaba is clearly learning from the likes of Alphabet and Amazon, too. Like them, it has released a cloud machine-learning platform. The first from a Chinese company, it was launched in 2015 and upgraded significantly last year. The tools it offers are similar to those on Google Cloud and Amazon Web Services, including off-the-shelf solutions for things like voice recognition and image classification.

Developing these tools was a major technical undertaking for Alibaba. It signals both how ambitious the company is to shape the future of AI and how big a role cloud computing will play.

Another such signal is that Alibaba’s cloud supports several other companies’ deep-learning frameworks, including Google’s TensorFlow and Amazon’s MXNet. Deep learning—a technique for training machines to recognize things by feeding lots of data into a many-layered neural network—is the most important approach in AI right now, used for everything from controlling autonomous vehicles to transcribing speech. Tech companies build their own deep-learning frameworks in part to get users onto their cloud platforms, because those frameworks typically run best on their infrastructure. By supporting its competitors’ frameworks, Alibaba gives developers a reason to use its platform instead.

And that’s not all: Liu hints that Alibaba may be working on its own deep-learning framework, something that could help it get even more engineers hooked on its cloud. When asked if Alibaba might release some of the code it has developed, she answers: “When it’s mature.”

Smart answers

There have been other glimpses of Alibaba’s progress in AI lately. Last month a research team at the company released an AI program capable of reading a piece of text, and answering simple questions about that text, more accurately than anything ever built before.

The text was in English, not Chinese, because the program was trained on the Stanford Question Answering Dataset (SQuAD), a benchmark used to test computerized question-and-answer systems. Alibaba’s program uses several novel machine-learning techniques, and it notched a higher score than entries from Microsoft, Samsung, and others. Remarkably, it scored better than the average human being (although this is a bit deceptive; it doesn’t mean the program actually understands what it is reading).

More remarkable, though, is how fast Alibaba rose up the leaderboard. The company only submitted its first entry to SQuAD in September 2017. “Quite a few of the top 10 teams represent top Chinese institutions, reflecting the ongoing democratization of AI,” says Pranav Samir Rajpurkar, a PhD student at Stanford who runs the SQuAD contest.

Alibaba has already used the program to improve the automated customer support on its online marketplace, says Si Luo, a member of the team. And it hopes to deploy language understanding across its platforms and technologies.

Alibaba’s AI researchers are working on other cutting-edge projects, such as generative adversarial networks, or GANs. In this exciting new machine-learning approach, developed by a Google researcher, two neural networks are pitted against one another; one tries to generate data that seems as if it comes from a real data set, and the other tries to distinguish real examples from fake ones. The technique lets computers learn more efficiently from unlabeled data, and it can be used to create realistic-looking synthetic images and video (see “The GANfather: The man who’s given machines the gift of imagination”).

Wang He | Getty

Gathering clouds

One advantage China’s tech companies have over their Western counterparts is the government’s commitment to AI. Smart cities that use the kind of technology found in Shanghai’s metro kiosks are likely to be in the country’s future. One of Alibaba’s cloud AI tools is a suite called City Brain, designed for tasks like managing traffic data and analyzing footage from city video cameras.

There are such experiments in the West too, such as Alphabet’s Sidewalk project, which plans to transform a suburb of Toronto with autonomous vehicles, delivery robots, and AI-based management systems. But China will most likely want to do things on a larger scale, which will give its companies an edge in the global marketplace for AI.

The Chinese authorities’ interest in using technology for social control also helps. There are plans for a “social credit system” that would track and score citizens’ everyday behavior with a view to perks or punishment. Face recognition software from Chinese companies like SenseTime is being used to find criminals in surveillance footage, and to track suspected dissidents.