Comment on Goldman Sachs: AI Is Overhyped, Wildly Expensive, and Unreliable

• EatATaco@lemm.ee ⁨2⁩ ⁨months⁩ ago

  You know it’s funny how many times I’ve heard that “it’s just predictive text algorithms!” As a dismissal that I’m beginning to think we’re just predictive text algorithms.

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  • sudoreboot@slrpnk.net ⁨2⁩ ⁨months⁩ ago

    We are prediction machines, but nothing like chatgpt. Current AI has no ability to learn, adapt, or even consider the future.

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    • CanadaPlus@lemmy.sdf.org ⁨2⁩ ⁨months⁩ ago

      Current AI has no ability to learn, adapt, or even consider the future.

      BS. The first two are all a neural net does.

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      • sudoreboot@slrpnk.net ⁨2⁩ ⁨months⁩ ago

        Once. They do not have the ability to learn or adapt on their own. They are created by humans through “deep learning”, but that is fundamentally different from continuously learning based on one’s own actions and experiences.

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  • CanadaPlus@lemmy.sdf.org ⁨2⁩ ⁨months⁩ ago

    Yep. All the reasons cited could pretty much apply to a person as well. GPT-4 is pretty damn smart by every reasonable measure.

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• tyler@programming.dev ⁨2⁩ ⁨months⁩ ago

  LLMs have been shown to have emergent math capabilities (that are the opposite of what is trained) so you’re simplifying way too much. Yes a lot is just “predictive text” but there’s a ton of “this was not the training and we don’t know how it knows this” as well.

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  • anachronist@midwest.social ⁨2⁩ ⁨months⁩ ago

    Game of Life has cool emergent properties that are a lot more interesting and fun to play with than LLMs. LLMs also have emergent properties like, for instance, failing classification due to the manipulation of individual image pixels.

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• jarfil@beehaw.org ⁨2⁩ ⁨months⁩ ago

  Not exactly.

  LLMs are predictive-associative token algorithms with a degree of randomness and some self-reflection. A key aspect is that anything can be a token, they can self-feed their own output, creating the basis for a thought cycle, as well as output control input for other algorithms. It remains to be seen whether the core of “(human) intelligence” is much more than that, and by how much.

  Stable Diffusion is a random image generator that refines its output based on perceptual traits associated with a prompt. It’s like a “lite” version of human dreaming, only with a super-human training set. Kind of an “uncanny valley” version of dreaming.

  It just so happens that both algorithms have been showcased at about the same time, and it’s the first time we can build a “set and forget” AI system that can both make decisions about its own next steps, and emulate human creativity… which has driven the hype into overdrive.

  I don’t think we’ll stop hearing about it, but I do think there is much more to be done, and it’s pretty much impossible to feed any of the algorithms with human experience data, without registering at least one human learning cycle, as in over many years from inside a humanoid robot.

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  • AVincentInSpace@pawb.social ⁨1⁩ ⁨month⁩ ago

    LLMs are predictive associative token algorithms

    Ah, so they produce parts of words instead of whole words at a time. Totally different.

    with a degree of randomness and self reflection.

    Much like predictive text.

    A key aspect is that anything can be a token

    …much like predictive text. Rarely will you find one that doesn’t suggest punctuation on occasion.

    they can self feed their own output

    …much like predictive text.

    as well as output control input for other algorithms.

    Oh, so you can tell it to suggest certain tokens more or less often. How fancy.

    It remains to be seen whether the core of human intelligence is much more than that.

    I mean, I’d say the ability to visualize things and reason about scenarios it hasn’t experienced before are a good start.

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    • jarfil@beehaw.org ⁨1⁩ ⁨month⁩ ago

      Not sure if you were unable or unwilling to understand anything of what I wrote, and I don’t like your tone. Feel free to come back with something more serious.

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