zlacker

>>amrrs+(OP)
I think the intuition the authors are trying to capture is that they believe the models are omniscient, but also dim-witted. And the question they are collectively trying to ask is whether this will continue forever.

I've never seen this question quantified in a really compelling way, and while interesting, I'm not sure this PDF succeeds, at least not well-enough to silence dissent. I think AI maximalists will continue to think that the models are in fact getting less dim-witted, while the AI skeptics will continue to think these apparent gains are in fact entirely a biproduct of "increasing" "omniscience." The razor will have to be a lot sharper before people start moving between these groups.

But, anyway, it's still an important question to ask, because omniscient-yet-dim-witted models terminate at "superhumanly assistive" rather than "Artificial Superintelligence", which in turn economically means "another bite at the SaaS apple" instead of "phase shift in the economy." So I hope the authors will eventually succeed.

>>antics+4Q
> I think the intuition the authors are trying to capture is that they believe the models are omniscient, but also dim-witted.

We keep assigning adjectives to this technology that anthropomorphize the neat tricks we've invented. There's nothing "omniscient" or "dim-witted" about these tools. They have no wit. They do not think or reason.

All Large "Reasoning" Models do is generate data that they use as context to generate the final answer. I.e. they do real-time tuning based on synthetic data.

This is a neat trick, but it doesn't solve the underlying problems that plague these models like hallucination. If the "reasoning" process contains garbage, gets stuck in loops, etc., the final answer will also be garbage. I've seen sessions where the model approximates the correct answer in the first "reasoning" step, but then sabotages it with senseless "But wait!" follow-up steps. The final answer ends up being a mangled mess of all the garbage it generated in the "reasoning" phase.

The only reason we keep anthropomorphizing these tools is because it makes us feel good. It's wishful thinking that markets well, gets investors buzzing, and grows the hype further. In reality, we're as close to artificial intelligence as we were a decade ago. What we do have are very good pattern matchers and probabilistic data generators that can leverage the enormous amount of compute we can throw at the problem. Which isn't to say that this can't be very useful, but ascribing human qualities to it only muddies the discussion.