Comment by himata4113
6 days ago
It's less of solving a problem, but trying every single solution until one works. Exhaustive search pretty much.
It's pretty much how all the hard problems are solved by AI from my experience.
6 days ago
It's less of solving a problem, but trying every single solution until one works. Exhaustive search pretty much.
It's pretty much how all the hard problems are solved by AI from my experience.
If LLMs really solved hard problems by 'trying every single solution until one works', we'd be sitting here waiting until kingdom come for there to be any significant result at all. Instead this is just one of a few that has cropped up in recent months and likely the foretell of many to come.
In other words, it's solving a problem.
Yes, but is it "intelligence" is a valid question. We have known for a long time that computers are a lot faster than humans. Get a dumb person who works fast enough and eventually they'll spit out enough good work to surpass a smart person of average speed.
It remains to be seen whether this is genuinely intelligence or an infinite monkeys at infinite typewriters situation. And I'm not sure why this specific example is worthy enough to sway people in one direction or another.
Someone actually mathed out infinite monkeys at infinite typewriters, and it turns out, it is a great example of how misleading probabilities are when dealing with infinity:
"Even if every proton in the observable universe (which is estimated at roughly 1080) were a monkey with a typewriter, typing from the Big Bang until the end of the universe (when protons might no longer exist), they would still need a far greater amount of time – more than three hundred and sixty thousand orders of magnitude longer – to have even a 1 in 10500 chance of success. To put it another way, for a one in a trillion chance of success, there would need to be 10^360,641 observable universes made of protonic monkeys."
Often infinite things that are probability 1 in theory, are in practice, safe to assume to be 0.
So no. LLMs are not brute force dummies. We are seeing increasingly emergent behavior in frontier models.
4 replies →
Maybe infinite monkeys at infinite typewriters hitting the statistically most likely next key based on their training.
The real question is how to define intelligence in a way that isn't artificially constrained to eliminate all possibilities except our own.
Bet you didn't come up with that comment by first discarding a bunch of unsuitable comments.
I hired an artist for an oil painting.
The artist drew 10 pencil sketches and said "hmm I think this one works the best" and finished the painting based on it.
I said he didn't one shot it and therefore he has no ability to paint, and refused to pay him.
You learned what was unsuitable over your entire life until now by making countless mistakes in human interaction.
A basic AI chat response also doesn't first discard all other possible responses.
How often do you self edit before submitting?
because commenting is easy and solving hard problems is hard
A random sentence can also generate correct solution to a problem once in a long while...does not mean that it "solved" anything..
The link has an entire section on "The infeasibility of finding it by brute force."
No, that's precisely solving a problem.
Shotgunning it is an entirely valid approach to solving something. If AI proves to be particularly great at that approach, given the improvement runway that still remains, that's fantastic.
But this is exactly how we do math.
We start writing all those formulas etc and if at some point we realise we went th wrong way we start from the begignning (or some point we are sure about).
How do you think mathematicians solve problems?
That's also the only way how humans solve hard problems.
Not always, humans are a lot better at poofing a solution into existence without even trying or testing. It's why we have the scientific method: we come up with a process and verify it, but more often than not we already know that it will work.
Compared to AI, it thinks of every possible scientific method and tries them all. Not saying that humans never do this as well, but it's mostly reserved for when we just throw mud at a wall and see what sticks.
More often than not, far, far, far more often than not, we do not already know that it will work. For all human endeavors, from the beginning of time.
If we get to any sort of confidence it will work it is based on building a history of it, or things related to "it" working consistently over time, out of innumerable other efforts where other "it"s did not work.
That's just not true at all. There are entire fields that rest pretty heavily on brute force search. Entire theses in biomedical and materials science have been written to the effect of "I ran these tests on this compound, and these are the results", without necessarily any underlying theory more than a hope that it'll yield something useful.
As for advances where there is a hypothesis, it rests on the shoulders of those who've come before. You know from observations that putting carbon in iron makes it stronger, and then someone else comes along with a theory of atoms and molecules. You might apply that to figuring out why steel is stronger than iron, and your student takes that and invents a new superalloy with improvements to your model. Remixing is a fundamental part of innovation, because it often teaches you something new. We aren't just alchemying things out of nothing.
1 reply →
AI can one shot problems too, if they have the necessary tools in their training data, or have the right thing in context, or have access to tools to search relevant data. Not all AI solutions are iterative, trial and error.
Also
> humans are a lot better at (...)
That's maybe true in 2026, but it's hard to make statements about "AI" in a field that is advancing so quickly. For most of 2025 for example, AI doing math like this wouldn't even be possible
There have been both inductive and deductive solutions to open math problems by humans in the past decade, including to fairly high-profile problems.