somethingsome

Thank-you, it's a longtime idea, but I was finally able to implement it, but now I'm trying to learn the procedure

I like Julia approach, compile everything on first launch, then reuse without speed penality, if you change a function, recompile that.

On another note, I'm working on a (big) PEP for python, and looking for maintainers and sponsors (as per python's rules). Do someone know where I could find such amazing persons? (I already have the code, works in cython,graalVM and pypy, and it's kind of a big change but I believe it will help a lot the language)

"leading them to suggest that LLMs will hit a wall in mathematical capability just beyond the convex hull of human mathematics."

This was to be expected, llms are trained on a very large corpus of data, they 'look' like creative from our point of view, as humans don't have that much knowledge, so, if the LLM proposes something that a human does not know, it looks like creative. But they stay neural networks after all, so very good in interpolation, very bad at extrapolation. Building new knowledge is extrapolation if it's truly outside the convex hull how the dataset.

I usually write code and ideas that are at the border of the convex hull, I haven't seen yet an llm being able to propose a really innovative solution. All the time they reuse building blocks that are already there. And I really tried pushing by making them creating proofs.

If I have an insight on some problem and I ask for the proof for it, it will usually be able to generate the proof, but that proof is never creative, and often it will use novel ideas only if I explain the novel idea. It will not come up with it alone. (And sometimes it's very hard to even explain the ideas, as it tends to return to knew waters)

Its only a data point, but I haven't seen a paper that handle this issue yet, and I don't expect them to magically become good at extrapolation due to the theory behind neural networks.

Sure, and I hope LLMs will at some point be able to do it. It would simplify greatly my work.

However, at the moment I consider that they stay in the 'convex hull' of their training set + a provided context, and I don't see that much research that made real improvements to the situation.

In my experience, LLMs are becoming very good at executing, but not a creating novel ideas or being creative.

Most of programming is reusing existing ideas in new shapes to solve new problems, but all the building blocks are there in the training set. Or new blocks can (easily) be derived from existing ones.

Math is different, it requires quite a bit of creativity, it's not just 'reuse all existing blocks'.

For the moment LLMs are good at discovering things that we overlooked in maths, or apply cleverly existing math blocks to make new results, but making a new theory that is really useful is out of reach for the moment in my opinion.

Very interesting!

I was looking at your website and wondering if there is a way to have access to the course material/videos?

In particular: Spring 2025 @ UR : CSC 209/409 Seminar on Artificial General Intelligence

Fall 2024 @ UR : CSC 277/477 End-to-End Deep Learning

Spring 2023 @ UR : CSC 266/466 Frontiers in Deep Learning

Spring 2022 @ Cornell Tech : CS 5787 – Deep Learning

Fall 2021 @ RIT : IMGS 684 – Deep Learning for Vision

Maybe what I'm saying is stupid or malinformed, but why not having the doc as a separate repo, and set it as a submodule to the repo where the code lives? Agent should be able to push code to the submodule independently from the code, and it should not be problematic for the main repository

I'm in the weights quite correctly, nice, but it also seem to hallucinate football players

Hi! Thanks for the effort!

It would be lovely to parse which datasets/benchmarks were used in the comparisons and select papers by dataset!

In many fields the datasets vary greatly depending on the subfield and its very difficult to find what other benchmarks could be used.

At least onedrive do not corrupt my powerpoints, while pcloud systematically break them, police change, elements resized randomely. It happens systematically with powerpoints weighting more than 100Mb.

My workplace has named (forcefully) the onedrive folder with around 35 characters. You add to that the path to that folder on the computer that is (forcefully) not on the root of a disk. I now mostly need a flat structure for my files. 4-5 subfolders and a file and onedrive dies.

While I agree with the message, I don't agree on the tools. It's very difficult to define a specification that works as intended, even with tools. Most waterfall software methodologies failed for a reason. And tools of the past are really not usable with AI. We need tools where it is way easier to adapt the specification iteratively, and even better, to have a bidirectional conversion. You define the spec, the LLM generate code, from the code you extract the spec, now you can compare and iterate. Then the model can focus only on the differences.

The other main issue that I see, is that even if there is a formally verified specification, at the moment, LLMs will not respect it perfectly. As long as LLMs are not able to non-deterministically follow a spec, the technology is not good enough.

A part from that, imo, in this age we should focus more on the mathematical aspect of computations, and I think we need to develop novel theories that take into account the non deterministic nature of LLMs in the process. I'm not sure this will ever work by merely extending current practices, as software design practices are extremely poorly defined from an engineer point of view. Just extending them by including randomeness does not seem a good idea.

Thanks, I just tried, for me it worked on 2x L40S with vLLM. I had some issues due to the model name, forge was forwarding 'default' instead of the real model name 'Qwen2.5-Coder-14B-Instruct'.

If someone else struggle on this step, I added in vLLM args: --served-model-name "Qwen2.5-Coder-14B-Instruct" --served-model-name "default"

So default becomes an alias.

I didn't yet test Forge, I was just happy that it worked at the moment ;)

I was hoping it would work with vLLM (openai compatible) to test it, does anyone know a similar proxy for local coding models?

Not really, Hardware didn't really change that much, of course you'll not find Tensor or raytracing cores, but you will have a very solid grasp of gpu programming and the cuda language (that didn't change that much either), and then you can easily learn those more modern things with blog posts or even, at worst, chatgpt.