Comment by ramraj07
4 hours ago
The fundamental idea that modern LLMs can only ever remix, even if its technically true (doubt), in my opinion only says to me that all knowledge is only ever a remix, perhaps even mathematically so. Anyone who still keeps implying these are statistical parrots or whatever is just going to regret these decisions in the future.
But all of my great ideas are purely from my own original inspiration, and not learning or pattern matching. Nothing derivative or remixed. /sarcasm
> Anyone who still keeps implying these are statistical parrots or whatever is just going to regret these decisions in the future.
You know this is a false dichotomy right? You can treat and consider LLMs statistical parrots and at the same time take advantage of them.
Yeah, Yann LeCun is just some luddite lol
I don't think he's a luddite at all. He's brilliant in what he does, but he can also be wrong in his predictions (as are all humans from time to time). He did have 3 main predictions in ~23-24 that turned out to be wrong in hindsight. Debatable why they were wrong, but yeah.
In a stage interview (a bit after the "sparks of agi in gpt4" paper came out) he made 3 statemets:
a) llms can't do math. They can trick us with poems and subjective prose, but at objective math they fail.
b) they can't plan
c) by the nature of their autoregressive architecture, errors compound. so a wrong token will make their output irreversibly wrong, and spiral out of control.
I think we can safely say that all of these turned out to be wrong. It's very possible that he meant something more abstract, and technical at its core, but in the real life all of these things were overcome. So, not a luddite, but also not a seer.
Have this shortcomings of llms been addressed by better models or by better integration with other tools? Like, are they better at coding because the models are truly better or because the agentic loops are better designed?
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