It's good to see major institutions like this get out of contracts with scientific publishing companies. Those unfortunately became mostly parasitic. Open access should be the norm for research.

More discussion about models collapse. The provenance of data will become a crucial factor to our ability to train further models.

Further clues that transformer models can't learn logic from data.

Interesting paper showing a promising path to reduce the memory and workload of transformer models. This is much more interesting than the race to the gigantic size.

Another cruel reminder that basic reasoning is not to be expected from LLMs. Here is a quote from the conclusion of the paper which makes it clear:
"We think that observations made in our study should serve as strong reminder that current SOTA
LLMs are not capable of sound, consistent reasoning, as shown here by their breakdown on even such a simple task as the presented AIW problem, and enabling such reasoning is still subject of basic research. This should be also a strong warning against overblown claims for such models beyond being basic research artifacts to serve as problem solvers in various real world settings, which are often made by different commercial entities in attempt to position their models as a strong mature product for end-users. [...] Observed breakdown of basic reasoning capabilities, coupled with such public claims (which are also based on standardized benchmarks), present an inherent safety problem. Models with insufficient basic reasoning are inherently unsafe, as they will produce wrong decisions in various important scenarios that do require intact reasoning."

Nice article. It's a good reminder that the benchmarks used to evaluate generative AI systems have many caveats.

This is how it should be done. This one comes with everything needed to reproduce the results. This is necessary to gain insights into how such models work internally.

More work about eco-design of software. This is definitely welcome. I found this work a bit weak on the state of the art and the interview parts (10 people in the same company). But the field is so nascent that it's to be expected I guess, PhD students have to do with what they have access to. Unsurprisingly this shows a great lack of proper tools to tackle the measurement problem. This thesis shows interesting prospects to reduce variations in measurements though, some of the proposed guidelines might help but cannot offset the hardware heterogeneity completely... The parts focusing on practical advices around Java use and deployment are interestingly easy to apply though. You need to take into account the context of your application to make the right choices of course.

This is great news, more scientific papers from the past decades will be accessible to everyone.

An important question for proper statistics about the content itself. Surprisingly harder to get an answer to it than one would think.

Interesting research, this shows opportunities to push CRDTs to the next level.

Very comprehensive (didn't read it all yet) guide about self-supervised learning. It'll likely become good reference material.

We got a problem with research around software estimates. This won't help us get better at it as an industry...

This is indeed very much true... there's a clear crisis in research. It turned into a hamster wheel of publishing articles at a constantly faster pace. The incentives are misguided which pushes that behavior to even have a career. Meanwhile, knowledge building suffers.

The rebellion against the academic publishers is still going on. Hopefully this will really change soon. That cartel of publishers needs to go back to its rightful place.