I’m an anarchocommunist, all states are evil.

Your local herpetology guy.

Feel free to AMA about picking a pet/reptiles in general, I have a lot of recommendations for that!

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Joined 1 year ago
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Cake day: June 8th, 2023

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  • It literally can’t worry about its own existence; it can’t worry about anything because it has no thoughts or feelings. Adding computational power will not miraculously change that.

    Who cares? This has no real world practical usecase. Its thoughts are what it says, it doesn’t have a hidden layer of thoughts, which is quite frankly a feature to me. Whether it’s conscious or not has nothing to do with its level of functionality.


  • And (from what I’ve seen) they get things wrong with extreme regularity, increasingly so as thing diverge from the training data. I wouldn’t say they’re a “stochastic parrot” but they don’t seem to be much better when things need to be correct… and again, based on my (admittedly limited) understanding of their design, I don’t anticipate this technology (at least without some kind of augmented approach that can reason about the substance) overcoming that.

    Keep in mind, you’re talking about a rudimentary, introductory version of this, my argument is that we don’t know what will happen when they’ve scaled up, we know for certain hallucinations become less frequent as the model size increases (see the statistics on gpt3 vs 4 on hallucinations), perhaps this only occurs because they haven’t met a critical size yet? We don’t know.

    There’s so much we don’t know.

    That’s missing the forest for the trees. Of course an AI isn’t going to go fishing. However, I should be able to assert some facts about fishing and it should be able to reason based on those assertions. e.g. a child can work off of facts presented about fishing, “fish are hard to catch in muddy water” -> “the water is muddy, does that impact my chances of a catching a bluegill?” -> “yes, it does, bluegill are fish, and fish don’t like muddy water”.

    https://blog.research.google/2022/05/language-models-perform-reasoning-via.html

    they do this already, albeit imperfectly, but again, this is like, a baby LLM.

    and just to prove it:

    https://chat.openai.com/share/54455afb-3eb8-4b7f-8fcc-e144a48b6798


  • You’re assuming i’m saying something that i’m not, and then arguing with that, instead of my actual claim.

    I’m saying we don’t know for sure what they will be able to do when they’re scaled up. That’s the end of my assertion. I don’t have to prove that they will suddenly come alive, i’m not claiming they will, i’m just claiming we don’t know what will happen when they’re scaled, and they seem to have emergent properties as they scale up. Nobody has devised a way of predicting what emergent properties happen when, nobody has made any progress whatsoever on knowing what scaling up accomplishes.

    Can they reason? Yes, but poorly right now, will that get better? Who knows.

    The end of my claim is that we don’t know what’ll happen when they scale up, and that you can’t just write it off like you are.

    If you want proof that they reason, see the research article I linked. If they can do that in their rudimentary form that we’ve created with very little time, we can’t write off the possibility that they will scale.

    Whether or not they reason LIKE HUMANS is irrelevant if they can do the job.

    And i’m not anthropomorphizing them without reason, there aren’t terms for this already, what would you call this behavior of answering questions significantly better when asked to fully explain reasoning? I would say it is taking the easiest option that still meets the qualifications of what it is requested to do, following the path of least resistance, I don’t have a better word for this than laziness.

    https://www.downtoearth.org.in/news/science-technology/artificial-intelligence-gpt-4-shows-sparks-of-common-sense-human-like-reasoning-finds-microsoft-89429

    Furthermore predictive power is just another way of achieving reasoning, better predictive power IS better reasoning, because you can’t predict well without reasoning.



  • If I teach a real AI about fishing, it should be able to reason about fishing and it shouldn’t need to have read a supplementary knowledge of mankind to do it.

    This is a faulty assumption.

    In order for you to learn about fishing, you had to learn a shitload about the world. Babies don’t come out of the womb able to do such tasks, there is a shitload of prerequisite knowledge in order to fish, it’s unfair to expect an ai to do this without prerequisite knowledge.

    Furthermore, LLM’s have been shown to do many things that aren’t in their training data, so the notion that it’s a stochastic parrot is also false.


  • You’re guessing, you don’t actually know that for sure, it seems intuitively correct, but we simply do not know enough about cognition to make that assumption.

    Perhaps our ability to reason exclusively comes from our ability to predict, and by scaling up the ability to predict, we become more and more able to reason.

    These are guesses, all we have now are guesses, you can say “it doesn’t reason” and “it’s just autocorrect” all you want, but if that were the case why did scaling it up eventually enable it to perform basic math? Why did scaling it up improve its ability to problemsolve significantly (gpt3 vs gpt4), there’s so many unknowns in this field, to just say “nah, can’t be, it works differently from us” doesn’t mean it can’t do the same things as us given enough scale.