Google Brain’s founder Andrew Ng said that he thinks artificial general intelligence is overrated.
“AGI has been overhyped,” he said in a talk at Y Combinator published on Thursday. “For a long time, there’ll be a lot of things that humans can do that AI cannot.”
AGI refers to a stage when AI systems possess human-level cognitive abilities and can learn and apply knowledge just like people.
Ng, who runs several AI-focused businesses, made the remarks in response to a question about whether he thinks it is more important for humans to develop AI tools or learn how to use them better.
“Some of us will build tools sometimes, but there are a lot of other tools others will build that we can just use,” he said. “People that know how to use AI to get computers to do what you want it to do will be much more powerful.”
He added that we don’t have to worry about people “running out of things to do,” but we should be mindful that people using AI will have advantages over those who don’t.
Ng joins a series of top AI researchers who say that, given the state of the technology, fears of AGI are overblown.
Related stories
Business Insider tells the innovative stories you want to know
Business Insider tells the innovative stories you want to know
Meta’s chief AI scientist, Yann LeCun, said that large language models are “astonishing” but limited.
“They’re not a road towards what people call AGI,” he said in an interview last year. “I hate the term. They’re useful, there’s no question. But they are not a path towards human-level intelligence.”
Google DeepMind chief Demis Hassabis has said that AGI is both overhyped and underestimated.
“AGI, AI itself, is a little bit overhyped in the short term,” he said at a conference in London last week. “Despite that, it’s still underestimated, how big, enormous a change it’s going to be in a more like 10-year timeframe.”
Microsoft’s CEO, Satya Nadella, has called the push toward AGI “benchmark hacking.” The term refers to when AI researchers and labs design AI models to perform well on industry benchmarks, rather than in real life, in the race to become the best-performing model.
