331–340 of 430 posts
AI can be an amazing productivity multiplier for people who know what they're doing. This result reminded me of the C compiler case that Anthropic posted recently. Sure, agents wrote the code for hours but there was a human there giving them directions, scoping the problem, finding the test suites needed for the agentic loops to actually work etc etc. In general making sure the output actually works and that it's a s…
>AI can be an amazing productivity multiplier for people who know what they're doing. >[...] >The "AI replaces humans in X" narrative is primarily a tool for driving attention and funding. You're sort of acting like it's all or nothing. What about the the humans that used to be that "force multiplier" on a team with the person guiding the research? If a piece of software required a team of ten to people, and instead…
Maybe it requires fundamentally changing or economic systems? Who knows what the solution is, but the problem is most definitely rooted in lack of initiative by our representatives and an economic system that doesn't accommodate us for when shit inevitably hits the fan with labor markets.
The headline may make it seem like AI just discovered some new result in physics all on its own, but reading the post, humans started off trying to solve some problem, it got complex, GPT simplified it and found a solution with the simpler representation. It took 12 hours for GPT pro to do this. In my experience LLM’s can make new things when they are some linear combination of existing things but I haven’t been to g…
> but I haven’t been to get them to do something totally out of distribution yet from first principles Can humans actually do that? Sometimes it appears as if we have made a completely new discovery. However, if you look more closely, you will find that many events and developments led up to this breakthrough, and that it is actually an improvement on something that already existed. We are always building on the shou…
There are genuine creative insights that come from connecting two known semantic spaces in a way that wasn't obvious before (e.g, novel isomorphism). It is very conceivable that LLMs could make this kind of connection, but we haven't really seen a dramatic form of this yet. This kind of connection can lead to deep, non-trivial insights, but whether or not it is "out-of-distribution" is harder to answer in this case.
Earlier quoted context omitted.
> 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…
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.
How many people have tried to figure out a new maths, a GUT in physics, a more perfect human language (Esperanto for ex.) or programming language, only to fail in the vast majority of their attempts?
Do we think that anything but the majority of the attempts at a paradigm shift will end in failure?
If the majority end in failure, how is that not the same brute force methodology (brute force doesn’t mean you can’t respond to feedback from your failed experiments or from failures in the prevailing paradigms, I take it to just fundamentally mean trying “new” things with tools and information available to you, with the majority of attempts ending in failure, until something clicks, or doesn’t and you give up).
It's interesting to me that whenever a new breakthrough in AI use comes up, there's always a flood of people who come in to handwave away why this isn't actually a win for LLMs. Like with the novel solutions GPT 5.2 has been able to find for erdos problems - many users here (even in this very thread!) think they know more about this than Fields medalist Terence Tao, who maintains this list showing that, yes, LLMs hav…
I'm not blaming the model here, but Python is much easier to read and more universal than math notation in most cases (especially for whatever's going on at the bottom of page four). I guess I'll have one translate the PDF.
Man, I'd be more worried about the impact of this on Mathematica than actual humans.
AI can be an amazing productivity multiplier for people who know what they're doing. This result reminded me of the C compiler case that Anthropic posted recently. Sure, agents wrote the code for hours but there was a human there giving them directions, scoping the problem, finding the test suites needed for the agentic loops to actually work etc etc. In general making sure the output actually works and that it's a s…
> The "AI replaces humans in X" narrative is primarily a tool for driving attention and funding. It's also a legitimate concern. We happen to be in a place where humans are needed for that "last critical 10%," or the first critical 10% of problem formulation, and so humans are still crucial to the overall system, at least for most complex tasks. But there's no logical reason that needs to be the case. Once it's not,…
To think that whatever the AI is capable of solving is (and forever will be) the frontier of all problems is deeply delusional. AI got good at generating code, but it still can't even do a fraction of what the human brain can do.
Earlier quoted context omitted.
Go enough shoulders down, and someone had to have been the first giant.
A discovery by a giant is in some sense a new base vector in the space of discoveries. The interesting question is if a statistical machine can only perform a linear combination in the space of discoveries, or if a statistical machine can discover a new base vector in the space of discoveries.. whatever that is.
But yes, it is not yet clear to what degree there can be (non-linear) extrapolation in the learned semantic spaces here.
Earlier quoted context omitted.
When chess engines were first developed, they were strictly worse than the best humans. After many years of development, they became helpful to even the best humans even though they were still beatable (1985–1997). Eventually they caught up and surpassed humans but the combination of human and computer was better than either alone (~1997–2007). Since then, humans have been more or less obsoleted in the game of chess.…
We are already at stage 3 for software development and arguably step 4
The LLMs are very fast but the code they generate is low quality. Their comprehension of the code is usually good but sometimes they have a weightfart and miss some obvious detail and need to be put on the right path again. This makes them good for non-experienced humans who want to write code and for experienced humans who want to save time on easy tasks.