All of the “AI” garbage that is getting jammed into everything is merely scaled up from what has been before. Scaling up is not advancement.
I disagree. Scaling might seem trivial now, but the state-of-the-art architectures for NLP a decade ago (LSTMs) would not be able to scale to the degree that our current methods can. Designing new architectures to better perform on GPUs (such as Attention and Mamba) is a legitimate advancement.
Furthermore, lots of advancements were necessary to train deep networks at all. Better optimizers like Adam instead of pure SGD, tricks like residual layers, batch normalization etc. were all necessary to allow scaling even small ConvNets up to work around issues such as vanishing gradients, covariate shift, etc. that tend to appear when naively training deep networks.
JayleneSlide@lemmy.world 3 weeks ago
You raise good points. Thank you for your replies. All of this still requires planet-cooking levels of power for garbage and to hurt workers.
KingRandomGuy@lemmy.world 3 weeks ago
Thanks for the respectful discussion! I work in ML (not LLMs, but computer vision), so of course I’m biased. But I think it’s understandable to dislike ML/AI stuff considering that there are unfortunately many unsavory practices taking place (potential copyright infringement, very high power consumption, etc.).