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It's great to see Marcus's 3.8: "Deep learning presumes a largely stable world, in ways that may be problematic." Most interesting problems in the world don't
by GCA10 7y ago
It's great to see Marcus's 3.8: "Deep learning presumes a largely stable world, in ways that may be problematic."
Most interesting problems in the world don't mirror Go's orderly rules. Players don't always take turns; the size of the board changes, and the pieces' powers mutate.
Marcus cites politics and economics as fields that pose fierce challenges to deep learning. I'd add marriage counseling, stand-up comedy, full-length fiction, and most sustained interactions between multiple people.
- another-one-off 7y ago> the pieces' powers [don't] mutate [in a Go game] That comment in particular suggests to me that you havn't spent a lot of time playing Go. The pieces' powers mutate quite spectacularly as the situation around them changes. > Most interesting problems in the world don't mirror Go's orderly rules This is true, but it isn't obvious this is going to slow deep learning down. For example, you cite players taking turns which is a significant handicap for a computer. If it comes down to reflexes, robots can win even with worse decision making algorithms than a human. In the context of deep learning, if the situation mutates in ways that the training regime didn't then an AI will have trouble. However, people love to overestimate both how often exceptional circumstances come up (the correct answer is rarely) and how good humans are at responding to them (correct answer is badly). My favorite part of learning the training system for artificial neural networks was that it incidentally explains a lot of human failure modes really well. It isn't at all obvious the humans have a sustainable advantage here.
- SubiculumCode 7y agoIf I kick over the Go board, how is the AI going to respond.
- scottLobster 7y ago"Rarely" over large populations comes out to quite a few people. And humans may very well fail in more predictable ways (that can then be accounted for) than multiple AIs trained on widely varying data sets. In fact AI's advantage, speed of processing ("reflexes") means that it's capable of failing in all sorts of ways that humans physically couldn't. Deep Learning is great for pure-data problems. I doubt it will be sufficient when interacting with the real world's inherent real-time randomness on large scales.
- exelius 7y agoAbsolutely agree — a computer algorithm can never capture the complex motivations behind human decisions. Two humans can have exactly opposite reactions to the same situation. Any algorithm under which decisions are not random can be deceived — see also Google’s search results which are full of garbage for any topic outside popular keywords. The history of law is effectively the history of humans trying to game the rules of whatever systems are in place; and I fail to see how AI would function any differently.