@themattsimpson > you’d expect if you did some digging, and when I’ve done the digging, they turned out not to work better than expected.
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Replying to @Meaningness
@themattsimpson The dramatic seeming-successes, in each case, turn out to be because the problem is much easier than it looked at first, and
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Replying to @Meaningness
@themattsimpson once you understand the problem itself, you can see that solving it was not a big deal. The ImageNet results *might* be >
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Replying to @Meaningness
@themattsimpson > an exciting partial exception, but having done some digging on that, I’m pretty sure the problem is easier than it looks,
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Replying to @Meaningness
@Meaningness @themattsimpson What about other deep learning applications? Also, would we need "dramatic successes" to get human level AI?1 reply 0 retweets 0 likes -
Replying to @davidmanheim
@davidmanheim @themattsimpson I haven’t seen any others that looked exciting, but maybe I missed something!2 replies 0 retweets 0 likes -
Replying to @Meaningness
@Meaningness @themattsimpson I'd think the other obviously exciting application of deep learning and NNs is AlphaGo, right?1 reply 0 retweets 0 likes -
Replying to @davidmanheim
@davidmanheim @themattsimpson Haven’t yet had time to read the AlphaGo paper, but my subjective probability that it’s meaningless is 0.94.1 reply 0 retweets 1 like -
Replying to @Meaningness
@Meaningness @themattsimpson "Meaningless" implying that it's just applying now-well understood techniques to a new area?1 reply 0 retweets 0 likes -
Replying to @davidmanheim
@davidmanheim @themattsimpson Meaningless in that it shows that Go is trivial if you throw enough computrons at it.2 replies 0 retweets 0 likes
@davidmanheim @themattsimpson Solution is probably mostly table lookup, or something equally uninteresting.
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