Gary Marcus: Why He Became AI’s Biggest Critic

Maybe you’ve read about Gary Marcus’s testimony before the Senate in May of 2023, when he sat next to Sam Altman and called for strict regulation of Altman’s company, OpenAI, as well as the other tech companies that were suddenly all-in on generative AI. Maybe you’ve caught some of his arguments on Twitter with Geoffrey Hinton and Yann LeCun, two of the so-called “godfathers of AI.” One way or another, most people who are paying attention to artificial intelligence today know Gary Marcus’s name, and know that he is not happy with the current state of AI.

He lays out his concerns in full in his new book, Taming Silicon Valley: How We Can Ensure That AI Works for Us, which was published today by MIT Press. Marcus goes through the immediate dangers posed by generative AI, which include things like mass-produced disinformation, the easy creation of deepfake pornography, and the theft of creative intellectual property to train new models (he doesn’t include an AI apocalypse as a danger, he’s not a doomer). He also takes issue with how Silicon Valley has manipulated public opinion and government policy, and explains his ideas for regulating AI companies.

Marcus studied cognitive science under the legendary Steven Pinker, was a professor at New York University for many years, and co-founded two AI companies, Geometric Intelligence and Robust.AI. He spoke with IEEE Spectrum about his path to this point.

What was your first introduction to AI?

Gary MarcusBen Wong

Gary Marcus: Well, I started coding when I was eight years old. One of the reasons I was able to skip the last two years of high school was because I wrote a Latin-to-English translator in the programming language Logo on my Commodore 64. So I was already, by the time I was 16, in college and working on AI and cognitive science.

So you were already interested in AI, but you studied cognitive science both in undergrad and for your Ph.D. at MIT.

Marcus: Part of why I went into cognitive science is I thought maybe if I understood how people think, it might lead to new approaches to AI. I suspect we need to take a broad view of how the human mind works if we’re to build really advanced AI. As a scientist and a philosopher, I would say it’s still unknown how we will build artificial general intelligence or even just trustworthy general AI. But we have not been able to do that with these big statistical models, and we have given them a huge chance. There’s basically been $75 billion spent on generative AI, another $100 billion on driverless cars. And neither of them has really yielded stable AI that we can trust. We don’t know for sure what we need to do, but we have very good reason to think that merely scaling things up will not work. The current approach keeps coming up against the same problems over and over again.

What do you see as the main problems it keeps coming up against?

Marcus: Number one is hallucinations. These systems smear together a lot of words, and they come…