Not sure what other people were claiming, but normally the point being made is that it's not possible for a network to memorize a significant portion of its training data. It can definitely memorize significant portions of individual copyrighted works (like shown here), but the whole dataset is far too large compared to the model's weights to be memorized.
I'm a researcher in ML and that's not the definition that I've heard. Normally the way I've seen AI defined is any computational method with the ability to complete tasks that are thought to require intelligence.
This definition admittedly sucks. It's very vague, and it comes with the problem that the bar for requiring intelligence shifts every time the field solves something new. We sort of go "well, given these relatively simple methods could solve it, I guess it couldn't have really required intelligence."
The definition you listed is generally more in line with AGI, which is what people likely think of when they hear the term AI.
I think what they mean is that ML models generally don't directly store their training data, but that they instead use it to form a compressed latent space. Some elements of the training data may be perfectly recoverable from the latent space, but most won't be. It's not very surprising as a result that you can get it to reproduce copyrighted material word for word.
The big thing you get with frameworks is super simple repairability. This means service manuals, parts availability, easy access to components like the battery, RAM, ssd, etc. Customizable ports are also a nice feature. You can even upgrade the motherboard later down the line instead of buying a whole new laptop.
It's unfortunately super clear from their Steam charts. When they had creator events and whatnot, the player count spiked, but other than that they only have about 1000 players active and I seriously doubt many people spend money on the game since it's already rather F2P friendly.
It's a shame, the game was a lot of fun and I still play with friends.
Right, as someone in the field I do try to remind people of this. AI isn't defined as this sentient general intelligence (frankly its definition is super vague), even if that's what people colloquially think of when they hear the term. The popular definition of AI is much closer to AGI, as you mentioned.
Afaik the StarFive SOCs used in SBCs are a lot slower than current ARM offerings. Part of that might be because software support is worse, so maybe compilers and related tooling aren't yet optimized for them?
Hopefully development on these continues to improve though. The biggest nail in the coffin for Pi alternatives has been software support.
Yep, and for good reason honestly. I work in CV and while I don't work on autonomous vehicles, many of the folks I know have previously worked at companies or research institutes on these kinds of problems and all of them agree that in a scenario like this, you should treat the state of the vehicle as compromised and go into an error/shutdown mode.
Nobody wants to give their vehicle an override that can potentially harm the safety of those inside it or around it, and practically speaking there aren't many options that guarantee safety other than this.
Also one very important aspect of this is that it must be possible to backpropagate the discriminator. If you just have access to inference on a detector of some kind but not the model weights and architecture itself, you won't be able to perform backpropagation and therefore can't generate gradients to update your generator's weights.
That said, yes, GANs have somewhat fallen out of favor due to their relatively poor sample diversity compared to diffusion models.
I haven't read the article myself, but it's worth noting that in CS as a whole and especially ML/CV/NLP, selective conferences are generally seen as the gold standard for publications compared to journals. The top conferences include NeurIPS, ICLR, ICML, CVPR for CV and EMNLP for NLP.
It looks like the journal in question is a physical sciences journal as well, though I haven't looked much into it.
For reference, ICML is one of the most prestigious machine learning conferences alongside ICLR and NeurIPS.
I'm curious what field you're in. I'm in computer vision and ML and most conferences have clauses saying not to use ChatGPT or other LLM tools. However, most of the folks I work with see no issue with using LLMs to assist in sentence structure, wording, etc, but they generally don't approve of using LLMs to write accuracy critical sections (such as background, or results) outside of things like rewording.
I suspect part of the reason conferences are hesitant to allow LLM usage has to do with copyright, since that's still somewhat of a gray area in the US AFAIK.
I believe this is the referenced article:
https://arxiv.org/abs/2311.03348