Same probably goes for slot machines if you strip them of all the bright, flashy colours and comforting sounds and make it a black metal box, with only a number display.
Its almost like... it was inherently designed to be as addictive as possible?
I hope the point is bigger than a nit to pick, but a plain slot machine is still bad. Social media used to be good or at least interesting in a good way. Maybe it can be again.
Genuinely curious, what is the benefit of doing this over just programming it yourself? It sounds to me like this is just an extra step that will grant you less control?
When programming a feature, you are simultaneously doing at least four things:
1. Implementing.
2. Building highly detailed mental models.
3. Learning and expanding your skillset.
4. Quality control and scope limiting.
And this process can be iterative and dynamic. Writing massive, super detailed specs that you then hand off to a undeterministic model feels like doing step one and three, while skipping two and four, which you then have to do after. What is the benefit? The speed up, in my opinion, comes if you skip step two and four, but then your product WILL be worse. Feels like I am going crazy?
Programming was never the bottleneck for software dev?
Except it is very easy to verifiy whether or not someone has used a nuclear bomb, where as LLM's can be used in complete secrecy. MAD only works if you can verify that the other part is not using it.
LLM’s seem very good at solving mathematical problems of which there is an enormous amount of exisiting work/attempts in their training data. This is an amazing capability, but does not convince me that these models are «thinking» or «reasoning» in the way a human does. A human mathematician could in theory categorize/discover an entirely new field of mathematics tomorrow, based purely on their «human intelligence», I wonder if we will see similar examples by LLM’s soon. It seems to me currently impossible that LLM’s can replace human mathematicians, because of their (assumption) likely dependence on human input in the sense of enormous amounts of pre-existing attempts/data.
If an entirely new problem, within a new field of mathematics were to appear tomorrow, I highly doubt an LLM would be useful at all on their own. Is this the «ultimate ASI test»?
This is also what I've been thinking. The result itself is amazing but it's not like this was completely unexpected. There has been a huge amount of progress on the problem in the last 10 years without which it seems unlikely today's full resolution would have been possible. It is not clear what strategy was taken but it sounds like it borrowed heavily from the two spanish mathematicians. Experts will scrutinize the proof and it will be interesting to see if anything truly original or unexpected was done, outside of known techniques, a move 37.
That doesn't really matter. Currently we're in the "this is a dog" (marks a muffin) stage of "AI as a scientist". It's totally reasonable to expect that considering how rapidly AI is advancing, the frontier of knowledge and research won't be universities anymore, but rather AI companies running their models in a loop.
Imagine that 20 years from now nobody really does science by hand because AI is just better at it, everyone just runs models, but these models require so much memory that only datacenters can realistically handle them, and it just so happens that the public gets access to nerfed models, while privately, companies actually break all asymmetrical encryption ciphers.
If an entirely new problem, within a new field of mathematics were to appear tomorrow, I highly doubt a human mathematician would be useful at all on their own.
How have we got to the place we are today then? Someone must have made the first steps onto uncharted territory, otherwise we would be in a homogeneous state frozen in time.
I am not saying that LLM intelligence can not be the same, that they are uncapable of dicovering new fields/problems that they have no training on. I am just pointing out that historically it kind of "must" be true that humans are capalbe of this, but we have yet to see an LLM do something like this, something radically "new" in a sense. All of these breakthroughs appear to me (not a mathematician) to be more a case of "digging" through millions of existing attempts/work, patching it together into a result.
This would already make LLM's one of the greatest tool mankind has ever made, but it has yet to display what I would consider a necessity for human level intelligence, which is this ability to discover entirely "new" things.
Would an LLM, given enough time and only the currently available trainingdata with no further input from humans, be able to solve something that was discovered tomorrow?
For humans my answer would be: maybe, probably, because this has been done historically.
For LLM's I would not be comfortable in claiming that they could. I think they would not be any better at this than traditional computational bruteforce.
Extreme temperature levels (>2.0) can push a GPT of its manifold, essentially producing predictions barely distinguishable from random noise (it flattens the probability distribution of the next token). In theory this could predict anything including the next field of mathematics (infinite monkey theorem) but realistically that would never happen.
However, how to we know the next field of mathematics isn't a novel combinations of several other sub-fields? That level of mathematics would be indistinguishable from magic to most people and so in their eyes the GPT did something truly inventive.
The kind of humans who invent entire fields of science on their own come by a few times a generation. It’s fine to say AI isn’t anywhere as close to them in intelligence, but instead is comparable to the “average” mathematician who is building on the work done by others and taking it a bit further.
Yeah, thats likely true to some extent. But my point is perhaps a "counter-thought" to the idea that this is ASI level intelligence and that math is now a computer task and not a human one. If it is incapable of moving maths forwards into the "unknown" it simply can not replace humans, and this might be a hard structural block for LLM's.
And an LLM could in theory also stumble upon entirely new ideas: there's randomness in how they generate their reasoning and answers after all.
I suspect that we are seeing a lot of advances coming from the combination of existing but somewhat obscure knowledge coming from LLMs at the moment, because LLMs are really good at this. At least compared to humans.
Even before our AI friends became good, they were already known for having read approximately every paper and every textbook published in any language. You only need to increase intelligence a fairly small amount from there to get to something like the 'convex hull' of human knowledge.
The gist is that basically whenever anyone comes up with a new method you get a big burst of activity of picking up all the now lower hanging fruit, that was previously out of reach.
Running Q3 on my AMD RX 9070XT. 32k context and 32/TPS. Apart from the context window preventing it from doing any large tasks, this thing is seriously powerful. I could probably push it to 64k context. Local open models are the future, and I am definitely getting a more powerful card. Very fun!
9070XT operator here: I'm using llama.cpp with the same model and quant and I'm getting 87,000 for my context limit. I tried the Unsloth models but they lowered it to around 30-40K so I went back to upstream.
I'm on Linux and using some sort of unholy mess of ROCM libraries that I don't understand.
I’m running Qwen3.8-27B-Unleashed UD-Q3_K_XL, which is a ~12.3 GiB Q3 quant, fully offloaded to the 16 GB 9070 XT. I disabled the vision projector to save VRAM and use one inference slot, Flash Attention, Q4 KV cache, --fit off, and --ctx-checkpoints 0. I’m running it with a 64K context window. The AMD driver also needs to be recent enough for ROCm 7.14; I targeted Adrenalin 26.6.4 or newer.
Could you comment more on how you set this up? I have a mostly idle 9070XT I use for gaming, and I was considering using it with the newer local open models. Many thanks.
I got Qwen3.8-27B running on my RX 9070 XT by setting up AMD’s ROCm 7.14/TheRock runtime for gfx1201 and adding the required HIP/LLVM paths to Windows. The official llama.cpp ROCm build detected the card correctly, but kept crashing with 0xC0000005 while loading the model, so I switched to the Windows Vulkan build of llama.cpp instead, which worked reliably.
I’m running Qwen3.8-27B-Unleashed UD-Q3_K_XL, which is a ~12.3 GiB Q3 quant, fully offloaded to the 16 GB 9070 XT. I disabled the vision projector to save VRAM and use one inference slot, Flash Attention, Q4 KV cache, --fit off, and --ctx-checkpoints 0. I’m running it with a 64K context window. The AMD driver also needs to be recent enough for ROCm 7.14; I targeted Adrenalin 26.6.4 or newer.
Hardly an accurate statement. Harnessed LLM’s are clearly capable at finding an answer to a mathematical problem in a pool of existing attempts by mathematicians, but I highly doubt that it will be able to solve math that it does not have massive amounts of pre-existing training data for. Which means we are entirely dependent upon intelligent mathematicians to continue their work and implement LLM’s as what it is: a great tool.
If I was a supervillain working towards the extinction of humanity I probably wouldn’t boast too loudly about it. Might anger, lets see… at least a couple billion people?
reply