I think I just figured out why @ylecun is so defensive: Samsung’s Starcraft classical-AI heavy bot is (so far) still better than Facebook’s ML system. Results, discussions, links here, h/t @jackclarkSF http://www.cs.mun.ca/~dchurchill/starcraftaicomp/2018/aiide/ …
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I'm defensive? I don't need to be. But why are you so offensive? CherryPi (the FAIR StarCraft bot) is largely rule based too, with a bit of ML/RL. It's also open source. The Samsung bot is not "still better" than CherryPi since it didn't even exist until now.
ha, i was kidding, but that’s a funny contrast to you yesterday: “If we are talking about Facebook and Google, it's all deep learning ... hard pressed to find any trace of symbolic AI in any operational code.”
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You do know that with your "just kidding" you're insulting the team who open sourced FB's StarCraft work, in front of all of your audience, forcing
@ylecun to step in to defend their work as you didn't present it fairly? Stop making research a zero sum fight for your own benefit. -
i am not following; please clarify in DM
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I don’t think Starcraft is considered operational code for Facebook.
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no i realize that but (a) it is still a striking contrast and (b) someone immediately posted a counterexample in production (ontologies) at facebook and (c) i posted one earlier today (text to speech for numbers) from Google.
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The machine learning research enterprise seeks to replace hand-written code incrementally with learned components. The fact that these systems are mostly hand-written today doesn’t tell us much. It will be interesting to see which parts are the hardest to replace with ML.
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you presume the answer to a question that is actual empirical and not yet answered
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CherryPi is definitely not operational code.
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Do you guys think there is potential in trying to formalize new computational paradigms and topologies in neuroscience and apply them in ML? I'm risking my MS thesis on it
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