AGI is here? Yann Le Cun has a few weeks ago once more presented his PoV about how current LLMs fail: https://youtu.be/nqDHPpKha_A?is=sQsO57UWwR8LGZkW

in french ...so in my own words:

1) Still unreliable at logic and general inference: try and try again seems to be SoTA...

2) Comically bad at pro-activity and taking the right initiative: eg. "You're right to be upset."

3) Most likely already reaching the end of the line in terms of available good training data: looking at the posted article here, I would tend to agree...

The problem is that LeCun was obviously wrong on LLMs before. You have to take what he says with the caveat that he probably talks about these in a purist (academic) way. Most of the "downsides" and "failures" are not really happening in the real world, or if they happen, they're eventually fixed / improved.

~2 years ago he made 3 statements that he considered failures at the time, and he was quite adamant that they were real problems:

1. LLMs can't do math

2. LLMs can't plan

3. (autoregressive) LLMs can't maintain a long session because errors compound as you generate more tokens.

ALL of these were obviously overcome by the industry, and today we have experts in their field using them for heavy, hard math (Tao, Knuth, etc), anyone who's used a coding agent can tell you that they can indeed plan and follow that plan, edit the plan and generally complete the plan, and the long session stuff is again obvious (agentic systems often remain useful at >100k ctx length).

So yeah, I really hope one of Yann, Ilya or Fei-Fei can come up with something better than transformers, but take anything they say with a grain of salt until they do. They often speak on more abstract, academic downsides, not necessarily what we see in practice. And don't dismiss the amout of money and brainpower going into making LLMs useful, even if from an academic pov it seems like we're bashing a square peg into a round hole. If it fits, it fits...