"We could just try to figure out how humans work because humans work pretty well in a variety of domains. And so one program would be to say, "Well, we forget how humans work. And then that's what we do. We make robots that work like that." So first of all, that's a hard biology problem. I think it's very important that people work on it. But it's also not a general engineering methodology because for instance, I might want robots that work in certain kinds of circumstances or problem domains that are really different from the niche that humans are well tuned for. And so I might want to make a robot that isn't really human-like in its intelligence. And then it seems like what we're left with that maybe we could just say, well, we'll somehow recapitulate evolution. Like we just search around in the space of programs and try to find ones that work well and then eventually get ones that are great for our environment. But that seems slow and complicated."
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Computer scientists from the United StatesWomen academics from the United StatesStanford University alumniWomen scientists from the United StatesMassachusetts Institute of Technology faculty
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Leslie P. Kaelbling
Leslie Pack Kaelbling is an American roboticist and the Panasonic Professor of Computer Science and Engineering at the Massachusetts Institute of Technology. She is widely recognized for adapting partially observable Markov decision processes from operations research for application in artificial intelligence and robotics. Kaelbling received the IJCAI Computers and Thought Award in 1997 for applying reinforcement learning to embedded control systems and developing programming tools for robot nav
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