Looks like we're still a long way from mathematical accuracy with the current generation of models. It made progress of course.
OK, this is a nice parabole. I admit I enjoyed it.
A good balanced post on the topic. Maybe we'll finally see a resurgence of real research innovation and not just stupid scaling at all costs. Reliability will stay the important factor of course and this one is still hard to crack.
Kind of unsurprising right? I mean LinkedIn is clearly a deformed version of reality where people write like corporate drones most of the time. It was only a matter of time until robot generated content would be prevalent there, it's just harder to spot since even humans aren't behaving genuinely there.
Indeed, we'll have to relearn "internet hygiene", it is changing quickly now that we prematurely unleashed LLM content on the open web.
Excellent post showing all the nuances of AI skepticism. Can you find in which category you are? I definitely match several of them.
Let's hope security teams don't get saturated with low quality security reports like this...
Another lawsuit making progress against OpenAI and their shady practice.
More shady practices to try to save themselves. Let's hope it won't work.
The water problem is obviously hard to ignore. This piece does a good job illustrating how large the impact is.
Good reminder that models shouldn't be used as a service except maybe for prototyping. This has felt obvious to me since the beginning of this hype cycle... but here we are people are falling in the trap today.
More signs of the generative AI companies hitting a plateau...
It shouldn't be, but it is a big deal. Having such training corpus openly available is one of the big missing pieces to build models.
This is an interesting and balanced view. Also nice to see that local inference is really getting closer. This is mostly a UI problem now.
This is what you get by making bots spewing text based on statistics without a proper knowledge base behind it.
More marketing announcement than real research paper. Still it's nice to see smaller models being optimized to run on mobile devices. This will get interesting when it's all local first and coupled to symbolic approaches.
This is still an important step with LLM. It's not because the models are huge that tokenizers disappeared or that you don't need to clean up your data.
Using the right metaphors will definitely help with the conversation in our industry around AI. This proposal is an interesting one.
More signs of the current bubble being about to burst?
Now this is an interesting paper. Neurosymbolic approaches are starting to go somewhere now. This is definitely helped by the NLP abilities of LLMs (which should be used only for that). The natural language to Prolog idea makes sense, now it needs to be more reliable. I'd be curious to know how many times the multiple-try path is exercised (the paper doesn't quite focus on that). More research is required obviously.