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.”
Another lesson Brooks has learned when it comes to robots and AI is that you can’t try to do too much. You should solve a solvable problem where robots can be integrated easily.
“We need to automate in places where things have already been cleaned up. So the example of my company is we’re doing pretty well in warehouses, and warehouses are actually pretty constrained. The lighting doesn’t change with those big buildings. There’s not stuff lying around on the floor because the people pushing carts would run into that. There’s no floating plastic bags going around. And largely it’s not in the interest of the people who work there to be malicious to the robot,” he said.
Brooks explains that it’s also about robots and humans working together, so his company designed these robots for practical purposes related to warehouse operations, as opposed to building a human-looking robot. In this case, it looks like a shopping cart with a handle.
“So the form factor we use is not humanoids walking around — even though I have built and delivered more humanoids than anyone else. These look like shopping carts,” he said. “It’s got a handlebar, so if there’s a problem with the robot, a person can grab the handlebar and do what they wish with it,” he said.
After all these years, Brooks has learned that it’s about making the technology accessible and purpose-built. “I always try to make technology easy for people to understand, and therefore we can deploy it at scale, and always look at the business case; the return on investment is also very important.”
Even with that, Brooks says we have to accept that there are always going to be hard-to-solve outlier cases when it comes to AI, that could take decades to solve. “Without carefully boxing in how an AI system is deployed, there is always a long tail of special cases that take decades to discover and fix. Paradoxically all those fixes are AI complete themselves.”
Brooks adds that there’s this mistaken belief, mostly thanks to Moore’s law, that there will always be exponential growth when it comes to technology — the idea that if ChatGPT 4 is this good, imagine what ChatGPT 5, 6 and 7 will be like. He sees this flaw in that logic, that tech doesn’t always grow exponentially, in spite of Moore’s law.
He uses the iPod as an example. For a few iterations, it did in fact double in storage size from 10 all the way to 160GB. If it had continued on that trajectory, he figured out we would have an iPod with 160TB of storage by 2017, but of course we didn’t. The models being sold in 2017 actually came with 256GB or 160GB because, as he pointed out, nobody actually needed more than that.
Brooks acknowledges that LLMs could help at some point with domestic robots, where they could perform specific tasks, especially with an aging population and not enough people to take care of them. But even that, he says, could come with its own set of unique challenges.
“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.
Brooks explains that this could eventually lead to robots with useful language interfaces for people in care situations. “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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