Don’t be fooled by all the money pouring into artificial intelligence projects these days. The former head of Uber’s AI Lab, Gary Marcus, warns that the field isn’t moving nearly as fast as many people think.
Speaking at MIT Technology Review’s EmTech Digital conference in San Francisco this week, Marcus said the current obsession with statistical machine learning, combined with the short-term focus of companies that are investing in AI, was limiting progress towards human-level artificial intelligence.
“My biggest fear is not Skynet,” Marcus said, referring to the killer artificial intelligence in the Terminator films. “It’s getting stuck.”
Marcus argued that despite recent technical advances, there are many simple things that computers cannot do, and that these limitations are holding back efforts to make progress towards real general intelligence.
Marcus is a well-known critic of AI hype, but his comments contrasted starkly with those of Ilya Sutskever, the research director of OpenAI, a non-project created by Elon Musk and others to do fundamental and open research on AI.
In an earlier talk, in which Sutskever described a new approach to machine learning, he suggested that general, or human-level, AI might not be so far away.
“[It] seems far off right now but [was] way more far off five years ago,” he said. “The number of people and the amount of effort going into developing these algorithms is extremely high—things are moving forward at a very healthy pace.”
Marcus, who left Uber earlier this month to spend more time with his family, and who is also a professor at NYU, said during his talk that corporate investment in AI might not be such a good thing for the field’s long-term goals. In times past, a lack of progress caused investment in AI to dry up. This time around, Marcus suggested, too much investment might cause researchers to lose sight of the long-term goals.
Asked if the commercial interest in AI, which is pulling many academics into industry, was a good thing, Marcus, said it was a double-edged sword. “Certain things can be done at a company like Uber, they have enormous resources,” Marcus said. “The problem with the corporate market is there’s a short-term focus. How do you make money out of deep learning today?”
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