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Ask HN: Any insider takes on Yann LeCun's push against current architectures?

385 points vessenes | 1 comments | 10 Mar 25 19:41 UTC | HN request time: 0.311s | source

So, Lecun has been quite public saying that he believes LLMs will never fix hallucinations because, essentially, the token choice method at each step leads to runaway errors -- these can't be damped mathematically.

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.

1. nonagono ◴[15 Mar 25 01:36 UTC] No.43369139[source]▶
Many of his arguments make “logical” sense, but one way to evaluate them is: would they have applied equally well 5 years ago? and would that have predicted LLMs will never write (average) poetry, or solve math, or answer common-sense questions about the physical world reasonably well? Probably. But turns out scale is all we needed. So yeah, maybe this is the exact point where scale stops working and we need to drastically change architectures. But maybe we just need to keep scaling.