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fossilesque@mander.xyzM to Science Memes@mander.xyzEnglish · 7 个月前

Is it worth it??

mander.xyz

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Is it worth it??

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

    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 个月前

      Run, the butlerian jihad is already going your way.

  • Clent@lemmy.world
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    7 个月前

    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@lemy.lol
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      7 个月前

      “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
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    7 个月前

    Source?

    • fossilesque@mander.xyzOPM
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      7 个月前

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

      • Dirac
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        7 个月前

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

  • fckreddit@lemmy.ml
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    7 个月前

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

    • SturgiesYrFase@lemmy.ml
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      7 个月前

      That’s just a saying.

  • propter_hog [any, any]@hexbear.net
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    7 个月前

    GET YOUR SHIT TOGETHER, CORAL

  • azi@mander.xyz
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    7 个月前

    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 个月前

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

  • Nasan@sopuli.xyz
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    7 个月前

    Coral*

  • Collatz_problem [comrade/them]@hexbear.net
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    7 个月前

    It is not even faster usually.

    • propter_hog [any, any]@hexbear.net
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      7 个月前

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

  • belated_frog_pants@beehaw.org
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    7 个月前

    Ai sucks ass, stop using it

    • ForeverComical@lemmy.ca
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      7 个月前

      It doesn’t. It’s just overhyped.

  • Buglefingers@lemmy.world
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    7 个月前

    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 个月前

      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.

  • bigbrowncommie69 [any]@hexbear.net
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    7 个月前

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

  • Reddfugee42@lemmy.world
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    7 个月前

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

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