Interesting paper attempting to prove that hallucinations are unavoidable in those models. It is well balanced though, and explains why it's not necessarily a bad thing in theory. In my opinion, the problem is the marketing talk around those models making grand claims or denying the phenomenon.

Interesting tool for file type detection. Seems very accurate too.

Interesting paper evaluating a Life Cycle Assessment (LCA) method to estimate the power consumption and environmental impact of generative AI services. This is illustrated on a single service, hopefully we'll see more such assessments.

Very nice progress on this type of architecture. It's definitely needed in part because it lowers the inference cost quite a lot. It's also nice to see it released with under the Apache 2 license and the training set be documented.

This is an interesting move, we'll see if this certification gets any traction.

The tooling to protect against the copyright theft of image generator models training is making progress. This will clearly turn into an arm race.

Interesting vulnerability, not all vendors are impacted though. GPU memory leaks can have unforeseen impacts.

The tone pointing at "open models" is wrong but the research is interesting. It still proves models can be poisoned (open or not) so traceability and secured supply-chains will become very important when using large language models.

When bug bounty programs meet LLM hallucinations... developer time is wasted.

It was only a question of time until we'd see such lawsuits appear. We'll see where this one goes.

When underfunded schools systems preaching obedience and conformity meet something like large language models, this tips over the balance enough that no proper learning can really happen anymore. Time to reform our school systems?

Very interesting paper about the energy footprint of the latest trend in generator models. The conclusion is fairly clear: we should think twice before using them.

Interesting inference engine. The design is clever with an hybrid CPU-GPU approach to limit the memory demand on the GPU and the amount of data transfers. The results are very interesting, especially surprising if the apparently very limited impact on the accuracy.

Here we are... We're really close to crossing into this territory where any fiction can disguise itself for reality. The problem is that we'll literally be drowning in such content. The social impacts can't be underestimated.

Interesting technique to speed up the generation of large language models.

There's definitely a problem here. The lack of transparency from the involved companies doesn't help. It's also a chance for local and self-hostable models, let's hope their use increases.

Important and interesting study showing how the new generation of models are driving energy consumption way up. As a developer, do the responsible thing and use smaller, more specific models.

The Large Language Model arm race is still going strong. Models are still mostly hidden behind APIs of course, and this is likely consuming lots of energy to run. Results seem interesting though, even though I suspect they're over inflating the "safety" built in all this. Also be careful of the demo videos, they've been reported as heavily edited and misleading...

A glimpse into how those generator models can present a real copyright problem... there should be more transparency on the training data sets.

This is clearly an uphill battle. And yes, this is because it's broken by design, it should be opt-in and not opt-out.