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By AnonymPedro Domingos
Despite all its successes, machine learning is still in the alchemy stage of science.
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By AnonymPedro Domingos
One of the most important problems in machine learning—and life—is the exploration-exploitation dilemma. If you’ve found something that works, should you just keep doing it? Or is it better to try new things, knowing it could be a waste of time but also might lead to a better solution? Would you rather be a cowboy or a farmer? Start a company or run an existing one? Go steady or play the field? A midlife crisis is the yearning to explore after many years spent exploiting.
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By AnonymPedro Domingos
People worry that computers will get too smart and take over the world, but the real problem is that they're too stupid and they've already taken over the world.
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By AnonymPedro Domingos
We should therefore welcome with open arms computers that are vastly more powerful than our brains, safe in the knowledge that our job is exponentially easier than theirs. They have to solve the problems; we just have to check that they did so to our satisfaction. AIs will think fast what we think slow, and the world will be the better for it. I, for one, welcome our new robot underlings.
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