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    Deep learning and computational science’s theoretical efficiency limits

    A user argues that today’s best human solutions fall far short of theoretical efficiency limits and believes deep learning can efficiently simulate the universe.

    BA
    1 Source, 15d ago, first seen 15d ago

    TLDR

    A user argues that today’s best human solutions in computational science are much less efficient than theoretical limits allow. They believe the universe can be simulated efficiently and that deep learning can do it all. They also expect few “verifier bits” to be needed, though more may make things faster.

    Combined views

    1.7K

    1 Source, first seen 15d ago

    Combined views

    1.7K

    1 Source, first seen 15d ago

    43 likes
    43 likes
    5 comments
    4 saves
    1 reposts

    Sentiment

    Positive——Negative

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    5 comments
    4 saves
    1 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    1 Source

    @bayeslordI believe today’s best human solutions are way worse than the theoretical efficiency limits for computational sciences The universe is efficiently simulable and deep learning can do it all. I don’t think we’ll even need that many verifier bits, though more may make things faster

    1 Source

    @bayeslordI believe today’s best human solutions are way worse than the theoretical efficiency limits for computational sciences The universe is efficiently simulable and deep learning can do it all. I don’t think we’ll even need that many verifier bits, though more may make things faster