Two things have happened, Li explains. Generative AI has caused the public to wake up to AI technology, she says, because it’s behind concrete tools, such as ChatGPT, that people can try out for themselves. And as a result, businesses have realized that AI technology such as text generation can make them money, and they have started rolling these technologies out in more products for the real world. “Because of that, it impacts our world in a more profound way,” Li says.
Li is one of the tech leaders we interviewed for the latest issue of MIT Technology Review, dedicated to the biggest questions and hardest problems facing the world. We asked big thinkers in their fields to weigh in on the underserved issues at the intersection of technology and society. Read what other tech luminaries and AI heavyweights, such as Bill Gates, Yoshua Bengio, Andrew Ng, Joelle Pineau, Emily Bender, and Meredith Broussard, had to say here.
In her newly published memoir, The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI, Li recounts how she went from an immigrant living in poverty to the AI heavyweight she is today. It’s a touching look into the sacrifices immigrants have to make to achieve their dreams, and an insider’s telling of how artificial-intelligence research rose to prominence.
When we spoke, Li told me she has her eyes set firmly on the future of AI and the hard problems that lie ahead for the field.
Here are some highlights from our conversation.
Why she disagrees with some of the AI “godfathers” about catastrophic AI risks: Other AI heavyweights, such as Geoffrey Hinton, Yann LeCun, and Yoshua Bengio, have been jousting in public about the risks of AI systems and how to govern the technology safely. Hinton, in particular, has been vocal about his concerns that AI could pose an existential risk to humanity. Li is less convinced. “I absolutely respect that. I think, intellectually, we should talk about all this. But if you ask me as an AI leader… I feel there are other risks that are what I would call catastrophic risks to society that are more pressing and urgent,” she says. Li highlights practical, “rubber meets the road” problems such as misinformation, workforce disruption, bias, and privacy infringements.
Hard problems: Another major AI risk Li is concerned about is the increasingly concentrated power and dominance of the tech industry at the expense of investment in science and technology research in the public sector. “AI is so expensive—hundreds of millions of dollars for one large model, making it impossible for academia. Where does that leave science for public good? Or diverse voices beyond the customer? America needs a moon-shot moment in AI and to significantly invest in public-sector research and compute capabilities, including a National AI Research Resource and labs similar to CERN. I firmly believe AI will help the human condition, but not without a coordinated effort to ensure America’s leadership in AI,” she told us.
The flaws of ImageNet: ImageNet, which Li created, has been criticized for being biased and containing unsafe or harmful photos, which in turn lead to biases and harmful outcomes in AI systems. Li admits the database is not perfect. “It takes people to call out the imperfections of ImageNet and to call out fairness issues. This is why we need diverse voices,” she says. “It takes a village to make technology better.”
