I'm sympathetic to your argument, but isn't this at least partially because the cost of training LLMs and other very large/deep networks is so high (in the tens of millions of $$). The models themselves could be (and sometimes are) open source, but without the trained parameters they're not very useful.

The broader question here is how can open source play in this space? Given the impact these technologies are going to be having should we have some kind of public funding? Otherwise only very large tech companies are going to be controlling this space.

Inference is much more common than training just as using a word processor is much more common than writing one. Even from a training standpoint, there are a lot of interesting things you can do with DNN models that aren't at the large end (GPT-3/ChatGPT/DALL-E/Stable Diffusion/etc) of the spectrum.

Right now there's not really a coherent open source stack to even run pre-trained models on a GPU even though most of the pieces are open source... so I find it a bit baffling that no one is even talking about trying to put the pieces together. The whole GPU compute world still basically needs to be side-loaded on many distros today with the core of it (the GPU compute drivers) still proprietary. There are things like RustICL coming along that give me some hope but I'm more lamenting what appears to be a lack of interest in the larger open source world in changing things even when all the pieces are there. If open source seems 'a bit' (lol) behind on mobile, that's nothing compared to how it's going to feel on the desktop in a few years.

I think this will come when the technology is more mature.

Looking forward to my local only, super efficient GNU Brain in 2030 :)

This stuff is all community run, there isn't some overarching theme or control over the content.

If you think there should be more ML content in FOSS, get out there and do it or organize others to do it.

That's fair push back. I absolutely get that re: no centralized control/decision making. However, places like FOSDEM are where I've seen people raising community awareness on larger issues like this in the past. So I just figured I'd throw my $.02 out there in case it gets anyone thinking more about it or starting to work more in that direction as that's what I've got the time to do right now... yeah, I know: lame.

ML and AI have existed for decades, there's little new to say.

Yet there were many sessions touching on ML and AI already in the abstract:

https://fosdem.org/2023/schedule/event/fast_data_realtime_st...

https://fosdem.org/2023/schedule/event/overengineering_ml_pe...

https://fosdem.org/2023/schedule/event/cicd_formachine_learn...

https://fosdem.org/2023/schedule/event/ai_discussion/

https://fosdem.org/2023/schedule/event/appinventor/

https://fosdem.org/2023/schedule/event/graph_ml_visualizatio...

https://fosdem.org/2023/schedule/event/20_minutes_from_zero_...

https://fosdem.org/2023/schedule/event/saass/

https://fosdem.org/2023/schedule/event/graph_ldbc/

https://fosdem.org/2023/schedule/event/publiccode_dpg_public...

https://fosdem.org/2023/schedule/event/ldcb_benchmark_suite/

https://fosdem.org/2023/schedule/event/network_cni_automagic...

Seems plenty to me.

FOSS is being sent to the same place as evil soviet communism went to...

all that "FUD" about free and open source being communist were onto something.

Isn't basically all machine learning infra open source? Pytorch, tensorflow, mlir, llvm.

With the exception of drivers (currently), yes. But almost everything that exists is targeting developers and server infrastructure. If open source graphics infrastructure were in such a state, no one would even be running a GUI desktop on Linux, for example. (and things like Steam on Linux wouldn't be possible)

In fairness, I'm coming at this from the standpoint of someone who uses Linux as a daily driver on the desktop so I know I'm an extreme minority. It's also not lost on me that I'm probably also an extreme minority within that extreme minority...

FOSS and open source ML/AI are a perfect combination that can change the world of computing forever. Crowdsource the training of the models, embed them transparently in the apps and tools and never look back.