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Today's Large Language Models are Essentially BS Machines
(quandyfactory.com)
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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.