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fchollet's profile
François Chollet
François Chollet
François Chollet
Verified account
@fchollet

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François CholletVerified account

@fchollet

Deep learning @google. Creator of Keras. Author of 'Deep Learning with Python'. Opinions are my own.

United States
fchollet.com
Joined August 2009

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    François Chollet‏Verified account @fchollet 1 Jul 2019

    Gradient descent will take any shortcut available to map inputs to targets. Human perception works differently: it starts from a different input (embodied stream vs static images), it doesn't have a target for each input, and it isn't trained with SGD.https://www.quantamagazine.org/where-we-see-shapes-ai-sees-textures-20190701/ …

    11:02 AM - 1 Jul 2019
    • 115 Retweets
    • 405 Likes
    • りゅうすけ⛅ Astro Tapan jain Gabriel M. Stoppini Neil Fernandes lguanq P.MOHANSRINIVAS pwangbot_tech Peter Wang
    8 replies 115 retweets 405 likes
      1. New conversation
      2. Duncan MacGregor‏ @blueapex 1 Jul 2019
        Replying to @fchollet

        The key thing that David Marr proposed was structured hierarchy in classification. Less easy to get working than the simplified form that became Deep Learning, but ultimately far more powerful and efficient.

        3 replies 1 retweet 5 likes
      3. Drago Indjic/Инђић‏ @dindjic 1 Jul 2019
        Replying to @blueapex @fchollet

        Exactly what I wanted to say - #AI miss him, sadly passed at 35 years of age

        0 replies 0 retweets 1 like
      4. End of conversation
      1. This Tweet is unavailable.
      2. joshcryer‏ @joshcryer 1 Jul 2019
        Replying to @jeshiphryn @tuespetre @fchollet

        I think our best success is going to be in making very good simulations of reality, then teaching our nets on that, since we'll be able to classify everything in the simulation. And thus the map becomes the territory and the AI is in the box.

        0 replies 0 retweets 0 likes
      3. End of conversation
      1. Rishabh Patil‏ @RishabhPatil 1 Jul 2019
        Replying to @fchollet

        Brilliant point. This paper shows how NNs perform way worse than humans at classification when the images are degraded. https://arxiv.org/pdf/1705.02498.pdf …

        0 replies 2 retweets 5 likes
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      1. New conversation
      2. Fadi Badine‏ @fadibadine 1 Jul 2019
        Replying to @fchollet

        This reminded me of a small experience I had when I was preparing a training on the MNIST dataset while having my 3-year-old son next to me: while the NN failed at guessing inverted images, 90 degrees rotated ones, my son guessed every single one of them.

        1 reply 0 retweets 6 likes
      3. François Chollet‏Verified account @fchollet 1 Jul 2019
        Replying to @fadibadine

        By default we store visual representations in a symmetry-invariant way; letter orientation is something we have to learn, typically from ages 5 to 7. It's common for 5 y.o. to confuse d/b, write inverted letters, or even write in full mirror writing.

        1 reply 0 retweets 9 likes
      4. Show replies
      1. Haavepaja‏ @Haavepaja 1 Jul 2019
        Replying to @fchollet

        This is fundamental. Style transfer is always the right answer.

        0 replies 0 retweets 0 likes
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      1. Bug Generator  💙‏ @BugGenerator 1 Jul 2019
        Replying to @fchollet

        Is this what Jeff Hawkins is espousing? (Hierarchical Temporal Memory)

        0 replies 0 retweets 0 likes
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      1. Nova Introvert 醉舞游魂‏ @NovaIntrovert 1 Jul 2019
        Replying to @fchollet @hardmaru

        (Words/opinions of elite engineers like you guys are, every now & then, inspiring.)

        0 replies 0 retweets 0 likes
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