rho50

Power management is going to be a huge emerging issue with the deployment of transformer model inference to the edge.

I foresee some backpedaling from this idea that "one model can do everything". LLMs have their place, but sometimes a good old LSTM or CNN is a better choice.

Yeah, this is actually a pretty great application for AI. It's local, privacy-preserving and genuinely useful for an underserved demographic.

One of the most wholesome and actually useful applications for LLMs/CLIP that I've seen.

(6.9-4.2)/(2024-2018) = 0.45 "version increments" per year.

4.2/(2018-1991) = 0.15 "version increments" per year.

So, the pace of version increases in the past 6 years has been around triple the average from the previous 27 years, since Linux' first release.

I guess I can see why 6.9 would seem pretty dramatic for long-time Linux users.

I wonder whether development has actually accelerated, or if this is just a change in the approach to the release/versioning process.

I don't think it's necessarily a bad thing that an AI got it wrong.

I think the bigger issue is why the AI model got it wrong. It got the diagnosis wrong because it is a language model and is fundamentally not fit for use as a diagnostic tool. Not even a screening/aid tool for physicians.

There are AI tools designed for medical diagnoses, and those are indeed a major value-add for patients and physicians.

Precisely. Many of the narrowly scoped solutions work really well, too (for what they're advertised for).

As of today though, they're nowhere near reliable enough to replace doctors, and any breakthrough on that front is very unlikely to be a language model IMO.

Exactly. So the organisations creating and serving these models need to be clearer about the fact that they're not general purpose intelligence, and are in fact contextual language generators.

I've seen demos of the models used as actual diagnostic aids, and they're not LLMs (plus require a doctor to verify the result).

There are some very impressive AI/ML technologies that are already in use as part of existing medical software systems (think: a model that highlights suspicious areas on an MRI, or even suggests differential diagnoses). Further, other models have been built and demonstrated to perform extremely well on sample datasets.

Funnily enough, those systems aren't using language models 🙄

(There is Google's Med-PaLM, but I suspect it wasn't very useful in practice, which is why we haven't heard anything since the original announcement.)

It is quite terrifying that people think these unoriginal and inaccurate regurgitators of internet knowledge, with no concept of or heuristic for correctness... are somehow an authority on anything.

I know of at least one other case in my social network where GPT-4 identified a gas bubble in someone's large bowel as "likely to be an aggressive malignancy." Leading to said person fully expecting they'd be dead by July, when in fact they were perfectly healthy.

These things are not ready for primetime, and certainly not capable of doing the stuff that most people think they are.

The misinformation is causing real harm.

Ohh, my bad! I thought the person you were replying to was asking about Gitea. Yeah, Forgejo seems truly free and also looks like it has a strong governance structure that is likely to keep things that way.

This sadly isn't true anymore - they now have Gitea Enterprise, which contains additional features not available in the open source version.

From here:

• SAML
• Branch protection for organizations
• Dependency scanning (yes, there are other tools for this, but it's still a feature the open source version doesn't get).
• Additional security controls for users (IP allowlisting, mandatory MFA)
• Audit logging