Is it worth it??

Comments

• BugleFingers@lemmy.world ⁨1⁩ ⁨year⁩ 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?

  source

  • rando895@lemmygrad.ml ⁨1⁩ ⁨year⁩ 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.

    source
• azi@mander.xyz ⁨1⁩ ⁨year⁩ 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.

  source
• Nasan@sopuli.xyz ⁨1⁩ ⁨year⁩ ago

  Coral*

  source
• belated_frog_pants@beehaw.org ⁨1⁩ ⁨year⁩ ago

  Ai sucks ass, stop using it

  source

  • UnrepententProcrastinator@lemmy.ca ⁨1⁩ ⁨year⁩ ago

    It doesn’t. It’s just overhyped.

    source
• slackassassin@sh.itjust.works ⁨1⁩ ⁨year⁩ ago

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

  source

  • daniskarma@lemmy.dbzer0.com ⁨1⁩ ⁨year⁩ ago

    Run, the butlerian jihad it’s already going your way.

    source
• fckreddit@lemmy.ml ⁨1⁩ ⁨year⁩ ago

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

  source

  • SturgiesYrFase@lemmy.ml ⁨1⁩ ⁨year⁩ ago

    That’s just a saying.

    source
• bigbrowncommie69@hexbear.net ⁨1⁩ ⁨year⁩ ago

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

  source
• Clent@lemmy.world ⁨1⁩ ⁨year⁩ 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.

  source

  • Krauerking@lemy.lol ⁨1⁩ ⁨year⁩ 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?

    source
• propter_hog@hexbear.net ⁨1⁩ ⁨year⁩ ago

  GET YOUR SHIT TOGETHER, CORAL

  source
• Reddfugee42@lemmy.world ⁨1⁩ ⁨year⁩ ago

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

  source
• Collatz_problem@hexbear.net ⁨1⁩ ⁨year⁩ ago

  It is not even faster usually.

  source

  • propter_hog@hexbear.net ⁨1⁩ ⁨year⁩ ago

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

    source
• Alexstarfire@lemmy.world ⁨1⁩ ⁨year⁩ ago

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

  source
• Dirac@lemmy.today ⁨1⁩ ⁨year⁩ ago

  Source?

  source

  • fossilesque@mander.xyz ⁨1⁩ ⁨year⁩ ago

    reddit.com/…/machine_learning_in_physics_research…

    source

    • Dirac@lemmy.today ⁨1⁩ ⁨year⁩ ago

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

      source