h-jones

I’ve had to debug DSV4 issues with both SGLang and vLLM in the last month. FWIW current SGLang nightly seems fine.

My understanding is that this is in a different league (Smale problem) than a lot of the other results that have been coming out (Erdos), though I could be wrong.

Anyone know how this compares to GROBID [1]? I'm looking at alternatives to GROBID as I'm not super pleased with its outputs. GROBID has a lot of great features for journal papers (reference extraction / parsing), but I'm only interested in cleanly extracting the body. Also considering nougat [2] but I haven't tried it yet.

[1] https://github.com/kermitt2/grobid

[2] https://github.com/facebookresearch/nougat

If you’re looking for research along these directions, Melanie Mitchell at the Santa Fe institute explores these areas. There are better references from her, but this is what came to mind https://medium.com/p/can-a-computer-ever-learn-to-talk-cf47d....

The PyTorch docs give a pretty good overview of AMP here https://pytorch.org/tutorials/recipes/recipes/amp_recipe.htm... and an overview of which operations cast to which dtype can be found here https://pytorch.org/docs/stable/amp.html#autocast-op-referen....

Edit: Fixed second link.

This helps lessen issues like inference speed / requirements but it doesn't address the environmental impact of training.

How does this play in with the fact that the DDR5 is clocked at 4800mHz and the DDR4 is at 3200? Would we not expect a 50% improvement with respect to transfer rates with a 50% increase in clock? I really don't know.

There are even 4800mHz DDR4 DIMMs available now, even if they are niche.

EDIT: DDR4 is 3200 not 3800.

I hope I didn't make it seem like I was pushing the solution onto others when I said that it will move outside the domain of computer science. My research is in the area of detecting deep fakes.

You're definitely right about how little we attend to the consequences of our advancements, especially in the field of AI/ML. This area is fraught with ethical and moral issues that have taken a backseat to the ideal of progress and I think we are making a mistake there.

There are plenty of publicly published papers detailing detection of deep fakes.

We do have methods to detect deepfakes and it often works very well (at least for any given generator, perhaps not multiple generators), but you're right, at some point deepfakes will be indistinguishable from real media. At that point I think the problem is largely outside the domain of computer science and we will need to start redefining "trust" looks like.

I think differential geometry may present the closest exception to this, not only is the notation often incredibly dense and subtle (i.e. spacing between indices when raising and lowering) but often everyone seems to have their personal favorite take on any given notation.

I actually really enjoy using it, why do you not like it? It has its quirks but for me they almost make me enjoy the creative process more.