[utopiah]

> modern LLMs are incredibly capable, and relentless, at solving problems that have a verification test suite.

Feel like it's a bit what I tried to expressed few weeks ago https://news.ycombinator.com/item?id=46791642 namely that we are just pouring computational resources at verifiable problems then claim that astonishingly sometimes it works. Sure LLMs even have a slight bias, namely they do rely on statistics so it's not purely brute force but still the approach is pretty much the same : throw stuff at the wall, see what sticks, once something finally does report it as grandiose and claim to be "intelligent".

[virgildotcodes]

> throw stuff at the wall, see what sticks, once something finally does report it as grandiose and claim to be "intelligent".

What do we think humans are doing? I think it’s not unfair to say our minds are constantly trying to assemble the pieces available to them in various ways. Whether we’re actively thinking about a problem or in the background as we go about our day.

Every once in a while the pieces fit together in an interesting way and it feels like inspiration.

The techniques we’ve learned likely influence the strategies we attempt, but beyond all this what else could there be but brute force when it comes to “novel” insights?

If it’s just a matter of following a predefined formula, it’s not intelligence.

If it’s a matter of assembling these formulas and strategies in an interesting way, again what else do we have but brute force?

[utopiah]

See what I replied just earlier https://news.ycombinator.com/item?id=47011884 namely the different regimes, within paradigm versus challenging it by going back to first principles. The ability to notice something is off beyond "just" assembling existing pieces, to backtrack within the process when failures get too many and actually understand the relationship is precisely different.

[tsimionescu]

While I don't think anyone has a plausible theory that goes to this level of detail on how humans actually think, there's still a major difference. I think it's fair to say that if we are doing a brute force search, we are still astonishingly more energy efficient at it than these LLMs. The amount of energy that goes into running an LLM for 12h straight is vastly higher than what it takes for humans to think about similar problems.

[sheepscreek]

The field of medicine - pharmacology and drug discovery, is an optimized version of that. It works a bit like this:

Instead of brute-forcing with infinite options, reduce the problem space by starting with some hunch about the mechanism. Then the hard part that can take decades: synthesize compounds with the necessary traits to alter the mechanism in a favourable way, while minimizing unintended side-effects.

Then try on a live or lab grown specimen and note effectiveness. Repeat the cycle, and with every success, push to more realistic forms of testing until it reaches human trials.

Many drugs that reach the last stage - human trials - often end up being used for something completely other than what they were designed for! One example of that is minoxidil - designed to regular blood pressure, used for regrowing hair!

[piombisallow]

That's also what most grad students are doing. Even in the unlikely case they completely stop improving, it's still a massive deal.

[btown]

Once heard someone call it "graduate student descent" and I've never heard a more apt term!