The argument for current LLM AIs leading to AGI has always been that they would spontaneously develop independent reasoning, through an unknown emergent property that would appear as they scale. It hasn't happened, and there's no sign that it will.
That's a dilemma for the big AI companies. They are burning through billions of dollars every month, and will need further hundreds of billions to scale further - but for what in return?
Current LLMs can still do a lot. They've provided Level 4 self-driving, and seem to be leading to general-purpose robots capable of much useful work. But the headwinds look ominous for the global economy, - tit-for-tat protectionist trade wars, inflation, and a global oil shock due to war with Iran all loom on the horizon for 2025.
If current AI players are about to get wrecked, I doubt it's the end for AI development. Perhaps it will switch to the areas that can actually make money - like Level 4 vehicles and robotics.
Not entirely true, the big change was multi-headed attention and the transformer model.
It's not just being used for language but anything where sequence and context patterns are really important. Some stuff is still using convolutional networks and RNNs etc. but transformers aren't just for LLMs. There's definitely a lot of algorithmic advances driving the wave of new ai implementations, not just hardware improvements.
Thanks for the clarification. The point remains that it's not true to say that LLMs have "provided Level 4 self-driving and ... general-purpose robots."
Agreed. It's a lot of the same tech that powers both, but it's not like a self driving car contains a language model that's fine tuned on the adventures of Steve MacQueen or something.