I *think* @halhod was doing some A+ nudge-wink trolling of @GaryMarcus with the CNN comment.
But yes, NNs are not at all suited to many-step-until-reward puzzles like this without very strong hints (like carefully engineered reward functions or watching lots of successful runs).
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Replying to @_brohrer_ @deliprao and
And I absolutely agree with the rest of Hal’s assessment. Handling things is hard, and sim-to-real progress is the most important contribution of this paper.
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um, wasn’t that close to my assessment, too? i wrote “the system is (very impressively) trained to do the perception and manual dexterity parts, but cube solving algorithm itself is innate, and symbolic, not acquired via training.”
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ask 500 people on the street what they think “solving” a rubik cubes mean.
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importantly, the “easy” thing was not done in the way that they imply, via DRL, but by hybrid model. not clear from your tweets that you have gotten that point. it still seems like you think a CNN or DRL has solved Rubik’s cube; that is NOT what the paper from last year showed.
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did you read the last paragraph? “In summary, combining MCTS with neural networks is a powerful technique that helps to bridge the gap between symbolic AI and connectionism.“ it’s (also) a hybrid model.
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you are getting warmer, yes. it was certainly a seriously misleading piece for DRL, which is what they mostly work on. since they have become a private company, they stand to make money from that misrepresentation. isn’t that what hype is about?
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Replying to @halhod @GaryMarcus and
I mean, hal, robots can totally solve rubiks cubes. This is not a big open problem Here's a robot built with legos! Legos, hal. And a cell phone! And it's infinitely more capable than the openai "demo."https://www.youtube.com/watch?v=X0pFZG7j5cE …
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