That was the first significant new result in AI since 1987, IMO! I’m waiting to be impressed by any other deep learning results… not yet.
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Replying to @Meaningness @Intrinsic29
I'd frame this as "DL seems generally capable at doing visual cortex-like stuff that traditional AI couldn't, AG being a special case of it"
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with AG, relevant similarity is that it uses intuitive pattern-matching + traditional tree calculation, same as human players
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Replying to @xuenay @Intrinsic29
I don’t know of a reason to believe that its pattern matching is “intuitive” or “similar to human”?
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Replying to @Meaningness @Intrinsic29
"Intuitive" in that the trained value network can't be described with any explicit or crisp rules, anymore than human could give a fully
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satisfactory explanation to why some moves feel good - they just do, based on pattern recognition built up from countless previous games.
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Replying to @xuenay @Intrinsic29
I don’t think “we don’t know in detail how it works” is either interesting or meaningfully similar to humans (though also true of us). YMMV!
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Replying to @Meaningness @Intrinsic29
Most relevantly it was a capability AI didn't really have before; we can train humans by throwing them lots of training examples and without
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fully understanding *how* they learn; now we can do same with AI. That opens up huge practical applications, we need to understand less.
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Replying to @xuenay @Intrinsic29
This isn’t new at all. Machine learning has been doing this since the 1950s.
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Relevant, from http://catb.org/jargon/html/koans.html … . Story may not be exactly accurate, but broadly reflective. MIT-AI PDP-6 decommissioned ~1976.pic.twitter.com/11nbKg9EV8
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