Good perspective on how the generative AI space evolved in 2024. There are good news and more concerning ones in there. We'll see what 2025 brings.
The results are unsurprising. They definitely confirm what we expected. The models are good at predicting the past, they're not so great at solving problems.
This highlights quite well the limits of the models used in LLMs.
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.
It looks like analog chips for neural network workloads are on the verge of finally becoming reality. This would reduce consumption by an order of magnitude and hopefully more later on. Very early days for this new attempt, let's see if it holds its promises.
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.
Looks like a nice model to produce 3D assets. Should speed up a bit the work of artists for producing background elements, I guess there will be manual adjustments needed in the end still.
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.
Nice vision model. Looks like it strikes and interesting balance between performance and memory consumption. Looks doable to run cheaply and on premise.
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.