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.
I really like this approach. Showing that we must be doing it wrong because our brains are more efficient and we aren't doing it like our brains.
Is this a common thing in ML papers or something you came up with?
I believe human and machine learning unify into a pretty straightforward model and this shows that what we're doing that ML doesn't can be copied across, and I don't think the substrate is that significant.