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So, I’ve been reading articles on this and I think I have a fuzzy idea of some of these solutions would entail. But what I’m hung up on is this: if Deep learnin

by gdubs 7y ago

So, I’ve been reading articles on this and I think I have a fuzzy idea of some of these solutions would entail. But what I’m hung up on is this: if Deep learning is about coming up with solutions to problems that are too hard for humans, how do we hope to understand the rationale behind whatever solutions the machine comes up with?

ben_w 7y ago
Sometimes it’s easier to understand a solution then it is to produce the solution in the first place. I don’t need to be Newton or Leibniz to understand calculus. That said, in general I don’t expect that we could understand any particular solution produced by an AI, be it deep or otherwise, but I do expect it to be possible quite often.
dreamcompiler 7y ago
A lot of DL is about teaching computers to solve problems that are easy for humans (like driving and recognizing your grandmother) but for which humans have a tough time explaining how they do it. The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work. We're not there yet but progress has been made. Eventually we'll understand NNs well enough to explain not only themselves but also human brains. In any case we have no choice because we cannot deploy NNs in life critical situations until we understand how they work, because that's the only way to understand how they fail.
- thelazydogsback 7y ago
  > The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work. I'd say that's a goal for some people -- for those whose goal is to figure out how the brain works, rather than constructing a more ideal and powerful GI. Remember the brain is great at some things, but laughable at others -- such as a "7 +/- 2" items in short term memory, inability to immediately retain rote knowledge after one instance and in great numbers, etc. It's the merging of the fuzzy, goal-directed behavior of the mind, in conjunction with its ability to effect the "real world", and the super-human memory and computational capabilities of computers that makes possible future GAIs that are so powerful and possibly scary.
charleskinbote 7y ago
I've asked myself the same. I think of it this way: we can imagine that, in the set of all possible solutions to a particular problem, a subset of those are too hard for humans because they are solved with systems too large for a human to have designed in a reasonable amount of time, or the systems themselves contain pieces that are composed in some previously unrealized way. Of that subset, there may be solutions whose pieces and compositions are well-understood. In these cases (perhaps not exclusively), it might be possible for us to begin understanding any rationale. edit: after noticing this other hacker news article (https://news.ycombinator.com/item?id=21107706 https://news.ycombinator.com/item?id=21107706), I wanted to add that this line of thinking is applicable to understanding programs and proofs written by humans as well. Programs and proofs can be well-understood when their pieces, and the way those pieces compose, are well-understood. When the pieces, e.g. lemmata in a proof, are large or hard to decompose, the proof (i.e. the solution to a problem) is harder to verify and understand.

by gdubs 7y ago

ben_w 7y ago

Sometimes it’s easier to understand a solution then it is to produce the solution in the first place. I don’t need to be Newton or Leibniz to understand calculus. That said, in general I don’t expect that we could understand any particular solution produced by an AI, be it deep or otherwise, but I do expect it to be possible quite often.

dreamcompiler 7y ago

A lot of DL is about teaching computers to solve problems that are easy for humans (like driving and recognizing your grandmother) but for which humans have a tough time explaining how they do it. The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work. We're not there yet but progress has been made. Eventually we'll understand NNs well enough to explain not only themselves but also human brains. In any case we have no choice because we cannot deploy NNs in life critical situations until we understand how they work, because that's the only way to understand how they fail.

thelazydogsback 7y ago
> The holy grail of neural nets has always been to build a simulation of the brain, figure out how it works, and apply that knowledge to how the human brain might work. I'd say that's a goal for some people -- for those whose goal is to figure out how the brain works, rather than constructing a more ideal and powerful GI. Remember the brain is great at some things, but laughable at others -- such as a "7 +/- 2" items in short term memory, inability to immediately retain rote knowledge after one instance and in great numbers, etc. It's the merging of the fuzzy, goal-directed behavior of the mind, in conjunction with its ability to effect the "real world", and the super-human memory and computational capabilities of computers that makes possible future GAIs that are so powerful and possibly scary.

charleskinbote 7y ago

I've asked myself the same. I think of it this way: we can imagine that, in the set of all possible solutions to a particular problem, a subset of those are too hard for humans because they are solved with systems too large for a human to have designed in a reasonable amount of time, or the systems themselves contain pieces that are composed in some previously unrealized way. Of that subset, there may be solutions whose pieces and compositions are well-understood. In these cases (perhaps not exclusively), it might be possible for us to begin understanding any rationale. edit: after noticing this other hacker news article (https://news.ycombinator.com/item?id=21107706 https://news.ycombinator.com/item?id=21107706), I wanted to add that this line of thinking is applicable to understanding programs and proofs written by humans as well. Programs and proofs can be well-understood when their pieces, and the way those pieces compose, are well-understood. When the pieces, e.g. lemmata in a proof, are large or hard to decompose, the proof (i.e. the solution to a problem) is harder to verify and understand.