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sapinker's profile
Steven Pinker
Steven Pinker
Steven Pinker
Verified account
@sapinker

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Steven PinkerVerified account

@sapinker

Cognitive scientist at Harvard.

Boston, MA
pinker.wjh.harvard.edu
Joined January 2010

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    Steven Pinker‏Verified account @sapinker Jan 2

    A critical appraisal of "Deep Learning" (which powers the best known current AI successes) by @garymarcus. Read it here first (ArXiv preprint server). https://arxiv.org/abs/1801.00631 

    5:59 PM - 2 Jan 2018
    • 104 Retweets
    • 235 Likes
    • AI PLOW 𝙹𝚘𝚗𝚊𝚝𝚑𝚊𝚗 𝚁𝚎𝚊𝚛𝚍𝚘𝚗 ifni888 ALFA Social Media Sanjay Kanvinde Dhurandhar B Hanne Rathcke FlySec Ash Jogalekar
    8 replies 104 retweets 235 likes
      1. New conversation
      2. Dan Kaminsky‏Verified account @dakami Jan 2
        Replying to @sapinker @GaryMarcus

        I wish this had been more informed by a practitioner. Even my *mild* explorations of the state of the art beg questions like -- if this is so incomplete, how can Neural Machine Translation be so superior to models crafted by humans over years?

        2 replies 0 retweets 4 likes
      3. Dan Kaminsky‏Verified account @dakami Jan 2
        Replying to @dakami @sapinker @GaryMarcus

        Causality, hierarchy, opening the black box...these are all things with *extensive* progress, even over the last year. The whole "Once something works, we no longer call it AI" is a real issue here.

        1 reply 0 retweets 1 like
      4. Dan Kaminsky‏Verified account @dakami Jan 2
        Replying to @dakami @sapinker @GaryMarcus

        And of course this is by no means the only segment of science that really wants to declare humans special in some way.

        1 reply 0 retweets 0 likes
      5. Dan Kaminsky‏Verified account @dakami Jan 2
        Replying to @dakami @sapinker @GaryMarcus

        The fundamental reason there's huge technical debt here is you _will_ not want the same architecture a year from now. It's like if they were figuring out how to make planes require 25% less fuel a year. That's not debt, that's progress.

        1 reply 0 retweets 2 likes
      6. Dan Kaminsky‏Verified account @dakami Jan 2
        Replying to @dakami @sapinker @GaryMarcus

        Both algorithmic efficiency *and* brute force capabilities are on unimaginable curves. It's like we finally figured out what to do with all this parallel computing capacity, after serial increases (and maybe developer cleverness) hit walls.

        1 reply 0 retweets 4 likes
      7. Dan Kaminsky‏Verified account @dakami Jan 2
        Replying to @dakami @sapinker @GaryMarcus

        What's wild is how *practical* all this stuff suddenly is, and that's the big difference that's hard to see from the outside. You actually have to be doing this stuff, in the field, to see how much it's changing the basic questions you can expect to get answers to.

        1 reply 0 retweets 5 likes
      8. Dan Kaminsky‏Verified account @dakami Jan 2
        Replying to @dakami @sapinker @GaryMarcus

        I think it's important to care about the ethics of Machine Learning, for the same reason I'm concerned about financial system ethics and (yes) bioethics: It's very easy to just "trust the system", turn your brain off, and do what the computer says.

        1 reply 1 retweet 0 likes
      9. Dan Kaminsky‏Verified account @dakami Jan 2
        Replying to @dakami @sapinker @GaryMarcus

        But, here more than in most places, it's important to get the tech right.

        0 replies 0 retweets 1 like
      10. End of conversation
      1. Louis Savain‏ @RebelScience Jan 2
        Replying to @sapinker @GaryMarcus

        "... I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence." You're kidding me? Deep learning must be abandoned completely. It has absolutely nothing to do with AGI.

        0 replies 0 retweets 2 likes
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      1. Timothy Duignan‏ @TimothyDuignan Jan 15
        Replying to @sapinker @GaryMarcus

        Really interesting. Seems like the summary is DL is great for interpolation but bad for extrapolation, which is the more important problem.

        0 replies 0 retweets 1 like
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      1. Leonardo Restivo‏ @ScipleNeuro Jan 4
        Replying to @sapinker @GaryMarcus

        I am sold on 5.2 and 5.3

        0 replies 0 retweets 1 like
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      1. d.robinson‏ @deanacusmaximus Jan 3
        Replying to @sapinker @GaryMarcus

        We design current neural networks, yet they remain/emerge as black boxes. Perhaps the behaiorists had it right.

        0 replies 0 retweets 1 like
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      1. Graham Chivers  ✏️‏ @deepgreendesign Jan 6
        Replying to @sapinker @GaryMarcus

        Alt-Ctrl-Del 🤓

        0 replies 0 retweets 0 likes
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      1. Patrick Vermeren‏ @pat_leader Jan 3
        Replying to @sapinker @GaryMarcus

        And in Europe it is going to be an even bigger problem as black box algorythms will be forbidden with regard to personal data. You will need to be able to explain them. Automated decisions are forbidden too. May 28 is the date #GDPR

        0 replies 0 retweets 0 likes
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      1. Postmodern Funhouse‏ @pomofunhouse Jan 3
        Replying to @sapinker @GaryMarcus

        Either mathematics is too big for the human mind or the human mind is more than a machine . . .pic.twitter.com/d5pWlxSRHf

        0 replies 0 retweets 0 likes
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