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Joined 10 days ago
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Cake day: May 20th, 2026

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  • Doing with it, sure, but the creation of LLMs, and the algorithms behind them, especially the training, are what I’m talking about. It’s a lot of very impressive, complicated math

    I think it’s pretty pathetic that “fuck AI” has become the trendy, cool thing. It really misses the mark. It should be fuck capitalism and the sociopathic CEOs abusing AI and shoving it down our throats. AI is not the problem.



  • Such a fallacy. Anything that falls under the umbrella of machine learning will contribute to future AI. We certainly won’t improve LLMs such that they become AGI, but all of it contributes.

    And, whether or not future AI even uses traditional silicon computing is also irrelevant.

    What matters is improved understanding of mathematics, neurons, chemistry, electronics, etc. That all happens each step of the way, even if the next technology is completely different.









  • That’s some pretty absurd logic. It is clear there is no point in continuing this discussion, as you are too easily trapped by logical fallacies and are too quick to jump to insults. Hopefully you can take some time to try to grow as a person.

    The field of Machine Learning and Artificial Intelligence does not depend on the success of Large Language Models, and LLMs quite possibly have reached their limits. I do not see that as relevant.


  • I am far from dumb. I got 60/60 problems correct on a proctored Raven’s Progressive Matrices as a teenager. That places my pattern recognition much higher than most of the world’s population. I taught myself C++ when I was six years old in 1997.

    You’ve created a strawman by recasting my argument to a point that I am not making. I did not say “trust vendor promises” or “ignore current failures.” I said present-state-only evaluation is a bad way to judge emerging technology.

    Engineering forecasting is not limited to closed-form physics. It also uses empirical progress, deployment data, cost curves, failure modes, and observed capability gains. Your pig analogy only works if AI had no demonstrated utility and no measurable improvement. That is plainly false.

    Criticize cost, energy use, incentives, etc. Those are real arguments. But “some promises are hype, therefore the whole field is slop and cannot improve” is not analysis. It is just cynicism with insults attached.