After AlphaGo’s historic victory against South Korean grandmaster Lee Sedol in March 2016, Go teacher Jianlun Qian felt a sense of impending crisis. He fretted about the demise of the game brought about by AI.
Now that a more powerful AlphaGo has beaten the world’s number one player, though, Qian feels differently. At DeepMind’s Go summit in Wuzhen last week, Qian, a teacher at the local Go association’s training center, contemplated a very different future in which humans and AI can complement each other.
“I’m indifferent to the results now,” said Qian, who coaches about 50 preschoolers in Wuzhen. “Instead, I think about how to use AI to help us.”
After defeating Ke Jie, AlphaGo played alongside several top Go players in a number of cooperative human-and-machine matches. These games—and Qian’s change of mentality—reflect the summit’s perceived significance for China’s booming AI scene.
Earlier this year, AI became the latest component in the national scientific and technological innovation strategy China has planned until 2030. The Chinese government has pledged to invest about $15 billion in the AI industry by 2018. The Chinese Association for Artificial Intelligence, meanwhile, is pushing for a higher academic status for AI courses such as machine learning and computer vision, which today are often nestled within computer science departments.
Online courses have sprouted up to meet the demand for these lessons. For $131, anyone interested can take a class in deep learning—the AI approach that powers AlphaGo—offered by a Beijing-based company called ChinaHadoop. At Tsinghua University, a machine-learning course is capped at 60 students, but sometimes as many as 120 students show up, says Jie Tang, an associate professor there who studies machine learning and data mining.
The summit will “trigger a new round of thinking and discussion regarding AI,” in China, adds Minlie Huang, an associate professor at Tsinghua University who specializes in deep learning and natural-language processing.
Huang stresses the need to focus more on fundamental research in AI, which requires long-term planning, rather than rushing into whatever AI application is currently popular. He says that AI still has a long way to go in terms of mastering language, for example. Progress on this challenge will make AI more truly intelligent and more useful. “It’s inevitable that [humans and machines] will complement each other,” he says.
The summit may also be a preview of sorts for how AI research is likely to progress. Tang predicts that collaboration between AI researchers in academia and in industry will deepen. There may also be a rise in research on how humans and AI algorithms can collaborate more effectively.
Even for Qian, the Go teacher, the impact of AlphaGo extends beyond the game. “The program was not developed just to play Go,” he says. “It will change many things in life.”
This new data poisoning tool lets artists fight back against generative AI
The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models.
Rogue superintelligence and merging with machines: Inside the mind of OpenAI’s chief scientist
An exclusive conversation with Ilya Sutskever on his fears for the future of AI and why they’ve made him change the focus of his life’s work.
Data analytics reveal real business value
Sophisticated analytics tools mine insights from data, optimizing operational processes across the enterprise.
The Biggest Questions: What is death?
New neuroscience is challenging our understanding of the dying process—bringing opportunities for the living.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.