Gemini is available to consumers in Bard or Pixel 8 Pro now, with an enterprise model coming Dec. 13.

    • Orvanis@lemm.ee
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      11 months ago

      To be fair AI models are being replaced multiple times a year these days… So yeah, in 2 years it should be killed, because improved models should exist.

      • auf@lemmy.ml
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        11 months ago

        I think they can update it under the same name though. That’ll not be counted as killed

  • thejml@lemm.ee
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    11 months ago

    I like how literally 2 days ago the news was all “Google postpones Gemini until next year” and here we are.

      • GravityAce@lemmy.ca
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        11 months ago

        Some executive wanted the dots on the schedule to be green for his presentation to the board instead of red, orange, or yellow

      • Anony Moose@lemmy.ca
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        11 months ago

        They’ve only released the weaker version, Gemini Pro, which is integrated into Bard. It’s performance is comparable to GPT 3.5. the stronger version, which will go toe-to-toe with GPT4 will be Gemini Ultra, and will be released sometime in 2024.

    • zik@lemmy.world
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      11 months ago

      The name was in common usage a long time before that protocol. I don’t think they can claim any kind of rights over it.

  • Steve@lemmy.today
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    11 months ago

    I don’t know whether to cautiously applaud or be even more concerned about another “AI” being released way too early than it should.

    • AWittyUsername@lemmy.world
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      11 months ago

      Yeah this will be the theme of the apocalypse. First lower regulation, then companies rushing to compete to get market, a lack of testing and then boom.

  • catastrophicblues@lemmy.ca
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    11 months ago

    I’m pretty excited, honestly. From my limited testing, its answers are typically more elaborate when I ask it to explain a concept to me, and it also has some level of fact checking via Google Search.

      • UFO@programming.dev
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        11 months ago

        Thanks! Here’s the high level description from there:

        “Gemini models build on top of Transformer decoders (Vaswani et al., 2017) that are enhanced with improvements in architecture and model optimization to enable stable training at scale and optimized inference on Google’s Tensor Processing Units. They are trained to support 32k context length”