MIT Robotics Pioneer Rodney Brooks On Generative AI

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MIT Robotics Pioneer Rodney Brooks On Generative AI






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from the take-note dept.

An anonymous reader quotes a report from TechCrunch: When Rodney Brooks talks about robotics and artificial intelligence, you should listen. Currently the Panasonic Professor of Robotics Emeritus at MIT, he also co-founded three key companies, including Rethink Robotics, iRobot and his current endeavor, Robust.ai. Brooks also ran the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) for a decade starting in 1997. In fact, he likes to make predictions about the future of AI and keeps a scorecard on his blog of how well he’s doing. He knows what he’s talking about, and he thinks maybe it’s time to put the brakes on the screaming hype that is generative AI. Brooks thinks it’s impressive technology, but maybe not quite as capable as many are suggesting. “I’m not saying LLMs are not important, but we have to be careful [with] how we evaluate them,” he told TechCrunch.

He says the trouble with generative AI is that, while it’s perfectly capable of performing a certain set of tasks, it can’t do everything a human can, and humans tend to overestimate its capabilities. “When a human sees an AI system perform a task, they immediately generalize it to things that are similar and make an estimate of the competence of the AI system; not just the performance on that, but the competence around that,” Brooks said. “And they’re usually very over-optimistic, and that’s because they use a model of a person’s performance on a task.” He added that the problem is that generative AI is not human or even human-like, and it’s flawed to try and assign human capabilities to it. He says people see it as so capable they even want to use it for applications that don’t make sense.

Brooks offers his latest company, Robust.ai, a warehouse robotics system, as an example of this. Someone suggested to him recently that it would be cool and efficient to tell his warehouse robots where to go by building an LLM for his system. In his estimation, however, this is not a reasonable use case for generative AI and would actually slow things down. It’s instead much simpler to connect the robots to a stream of data coming from the warehouse management software. “When you have 10,000 orders that just came in that you have to ship in two hours, you have to optimize for that. Language is not gonna help; it’s just going to slow things down,” he said. “We have massive data processing and massive AI optimization techniques and planning. And that’s how we get the orders completed fast.”

“People say, ‘Oh, the large language models are gonna make robots be able to do things they couldn’t do.’ That’s not where the problem is. The problem with being able to do stuff is about control theory and all sorts of other hardcore math optimization,” he said.

“It’s not useful in the warehouse to tell an individual robot to go out and get one thing for one order, but it may be useful for eldercare in homes for people to be able to say things to the robots,” he said.

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