←back to thread

765 points MindBreaker2605 | 6 comments | | HN request time: 0.867s | source | bottom
Show context
lm28469 ◴[] No.45897524[source]
But wait they're just about to get AGI why would he leave???
replies(1): >>45897571 #
killerstorm ◴[] No.45897571[source]
LeCun always said that LLMs do not lead to AGI.
replies(2): >>45897613 #>>45897683 #
NitpickLawyer ◴[] No.45897683[source]
He also said other things about LLMs that turned out to be either wrong or easily bypassed with some glue. While I understand where he comes from, and that his stance is pure research-y theory driven, at the end of the day his positions were wrong.

Previously, he very publicly and strongly said:

a) LLMs can't do math. They trick us in poetry but that's subjective. They can't do objective math.

b) they can't plan

c) by the very nature of autoregressive arch, errors compound. So the longer you go in your generation, the higher the error rate. so at long contexts the answers become utter garbage.

All of these were proven wrong, 1-2 years later. "a" at the core (gold at IMO), "b" w/ software glue and "c" with better training regimes.

I'm not interested in the will it won't it debates about AGI, I'm happy with what we have now, and I think these things are good enough now, for several usecases. But it's important to note when people making strong claims get them wrong. Again, I think I get where he's coming from, but the public stances aren't the place to get into the deep research minutia.

That being said, I hope he gets to find whatever it is that he's looking for, and wish him success in his endeavours. Between him, Fei Fei Li and Ilya, something cool has to come out of the small shops. Heck, I'm even rooting for the "let's commoditise lora training" that Mira's startup seems to go for.

replies(3): >>45897933 #>>45898169 #>>45905642 #
tonii141 ◴[] No.45898169[source]
a) Still true: vanilla LLMs can’t do math, they pattern-match unless you bolt on tools.

b) Still true: next-token prediction isn’t planning.

c) Still true: error accumulation is mitigated, not eliminated. Long-context quality still relies on retrieval, checks, and verifiers.

Yann’s claims were about LLMs as LLMs. With tooling, you can work around limits, but the core point stands.

replies(2): >>45898248 #>>45898683 #
1. killerstorm ◴[] No.45898683[source]
My man, math is pattern matching, not magic. So is logic. And computation.

Please learn the basics before you discuss what LLMs can and can't do.

replies(1): >>45899359 #
2. ozgrakkurt ◴[] No.45899359[source]
I'm no expert on math but "math is pattern matching" really sounds wrong.

Maybe programming is mostly pattern matching but modern math is built on theory and proofs right?

replies(2): >>45900035 #>>45905753 #
3. noddybear ◴[] No.45900035[source]
Nah, its all pattern matching. This is how automated theorem provers like Isabelle are built, applying operations to lemmas/expressions to reach proofs.
replies(2): >>45900776 #>>45901563 #
4. staticman2 ◴[] No.45900776{3}[source]
I'm sure if you pick a sufficiently broad definition of pattern matching your argument is true by definition!

Unfortunately that has nothing to do with the topic of discussions, which is the capabilities of LLMs, which may require a more narrow definition of pattern matching.

5. vbarrielle ◴[] No.45901563{3}[source]
Automated theorem provers are also built around backtracking, which is absent in LLMs.
6. HarHarVeryFunny ◴[] No.45905753[source]
When an LLM does it, it's pattern matching.

RL training amounts to pattern matching.

How does an LLM decode Base64? Decode algorithm? No - predictive pattern matching.

An LLM isn't predicting what a person thinks - it's predicting what a person does.