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  3. ChatGPT 'got absolutely wrecked' by Atari 2600 in beginner's chess match — OpenAI's newest model bamboozled by 1970s logic

ChatGPT 'got absolutely wrecked' by Atari 2600 in beginner's chess match — OpenAI's newest model bamboozled by 1970s logic

Technology
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  • J jsomae@lemmy.ml

    If you believe LLMs are not good at anything then there should be relatively little to worry about in the long-term, but I am more concerned.

    It's not obvious to me that it will backfire for them, because I believe LLMs are good at some things (that is, when they are used correctly, for the correct tasks). Currently they're being applied to far more use cases than they are likely to be good at -- either because they're overhyped or our corporate lords and masters are just experimenting to find out what they're good at and what not. Some of these cases will be like chess, but others will be like code*.

    (* not saying LLMs are good at code in general, but for some coding applications I believe they are vastly more efficient than humans, even if a human expert can currently write higher-quality less-buggy code.)

    K

    I believe LLMs are good at some things

    The problem is that they're being used for all the things, including a large number of tasks that thwy are not well suited to.

    0
  • K kent_eh@lemmy.ca

    I believe LLMs are good at some things

    The problem is that they're being used for all the things, including a large number of tasks that thwy are not well suited to.

    J

    yeah, we agree on this point. In the short term it's a disaster. In the long-term, assuming AI's capabilities don't continue to improve at the rate they have been, our corporate overlords will only replace people for whom it's actually worth it to them to replace with AI.

    1
  • S stevedice@sh.itjust.works

    That's exactly what thinking is, though.

    A

    An LLM is an ordered series of parameterized / weighted nodes which are fed a bunch of tokens, and millions of calculations later result generates the next token to append and repeat the process. It's like turning a handle on some complex Babbage-esque machine. LLMs use a tiny bit of randomness ("temperature") when choosing the next token so the responses are not identical each time.

    But it is not thinking. Not even remotely so. It's a simulacrum. If you want to see this, run ollama with the temperature set to 0 e.g.

    ollama run gemma3:4b
>>> /set parameter temperature 0
>>> what is a leaf

    You will get the same answer every single time.

    0
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