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fossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 7 months ago

Is it worth it??

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Is it worth it??

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fossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 7 months ago
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  • slackassassin@sh.itjust.works
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    7 months ago

    Working with pretrained models implemented in FPGAs for particle identification and tracking. It’s much faster and exactly as accurate. ¯\_(ツ)_/¯

    • daniskarma@lemmy.dbzer0.com
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      7 months ago

      Run, the butlerian jihad is already going your way.

  • Clent@lemmy.world
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    7 months ago

    The actual model required for general purpose likely lies beyond the range of petabytes of memory.

    These models are using gigabytes and the trend indicates its exponential. A couple more gigabytes isn’t going to cut it. Layers cannot expand the predictive capabilities without increasing the error. I’m sure a proof of that will be along within in the next few years.

    • Krauerking
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      7 months ago

      “Come on man, I just need a couple more pets of your data and I will totally be able to predict you something useful!”.
      It’s capacitors flip polarity in anticipation.

      “I swear man! It’s only a couple of orders of magnitude more, man! And all your dreams will come true. I’m sure I’ll service you right!”

      Well if it needs it, right?

  • Dirac@lemmy.today
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    7 months ago

    Source?

    • fossilesque@mander.xyzOPM
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      7 months ago

      https://www.reddit.com/r/okbuddyphd/comments/1gpphow/machine_learning_in_physics_research_meme/

      • Dirac@lemmy.today
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        7 months ago

        Hahahahaha I meant for the statistics, but I appreciate ya!

  • fckreddit@lemmy.ml
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    7 months ago

    “There is no free lunch.”, is a saying in ML research.

    • SturgiesYrFase@lemmy.ml
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      7 months ago

      That’s just a saying.

  • propter_hog [mirror/your pronouns]@hexbear.net
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    7 months ago

    GET YOUR SHIT TOGETHER, CORAL

  • azi@mander.xyz
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    7 months ago

    There’s plenty of stuff where ML algorithms the state of the art. For example the raw data from nanopore DNA sequencing machines is extremely noisy and ML algorithms clean it up with much less error than the Markov chains used in years previous.

  • Alexstarfire@lemmy.world
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    7 months ago

    For the meme? The Walking Dead. For the content? No idea.

  • Nasan@sopuli.xyz
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    7 months ago

    Coral*

  • Collatz_problem [comrade/them]@hexbear.net
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    7 months ago

    It is not even faster usually.

    • propter_hog [mirror/your pronouns]@hexbear.net
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      7 months ago

      And if it is faster, it just converges to the wrong answer faster

  • Buglefingers@lemmy.world
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    7 months ago

    A lot of new tech is not as efficient or equally so at the get go. Learning how to properly implement and utilize it is part of the process.

    Right now we are just throwing raw computing power in ML format at it. As soon as it catches and shows a little promise in an area we can focus and refine. Sometimes you need to use the shotgun to see the rabbits ya know?

    • rando895@lemmygrad.ml
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      7 months ago

      Physicists abhor a black box. So long as it is an option, most will choose not to use AI to any great extent, and will chastise those who do.

  • belated_frog_pants@beehaw.org
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    7 months ago

    Ai sucks ass, stop using it

    • UnrepententProcrastinator@lemmy.ca
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      7 months ago

      It doesn’t. It’s just overhyped.

  • bigbrowncommie69 [any]@hexbear.net
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    7 months ago

    Pretty much the only thing it’s even remotely good for is as a toy.

  • Reddfugee42@lemmy.world
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    7 months ago

    So what you’re saying, Dad, is it’s nascent and already faster? Gotcha.

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