In exchange, he offers the idea that we should have something that is an 'energy minimization' architecture; as I understand it, this would have a concept of the 'energy' of an entire response, and training would try and minimize that.
Which is to say, I don't fully understand this. That said, I'm curious to hear what ML researchers think about Lecun's take, and if there's any engineering done around it. I can't find much after the release of ijepa from his group.
The short answer should be that it's obvious LLM training and inference are both ridiculously inefficient and biologically implausible, and therefore there has to be some big optimization wins still on the table.
What I mean is this: A brain today is obviously far more efficient at intelligence than our current approaches to AI. But a brain is a highly specialized chemical computer that evolved over hundreds of millions of years. That leaves a lot of room for inefficient and implausible strategies to play out! As long as wins are preserved, efficiency can improve this way anyway.
So the question is really, can we short cut that somehow?
It does seem like doing so would require a different approach. But so far all our other approaches to creating intelligence have been beaten by the big simple inefficient one. So it’s hard to see a path from here that doesn’t go that route.