Yann LeCun to depart Meta and launch AI startup focused on 'world models'

Making LeCun report to Wang was the most boneheaded move imaginable. But… I suppose Zuckerberg knows what he wants, which is AI slopware and not truly groundbreaking foundation models.

He is also not very interested in LLMs, and that seems to be Zuck's top priority.

Yeah I think LeCun is underestimating the impact that LLM's and Diffusion models are going to have, even considering the huge impact they're already having. That's no problem as I'm sure whatever LeCun is working on is going to be amazing as well, but an enterprise like Facebook can't have their top researcher work on risky things when there's surefire paths to success still available.

I politely disagree - it is exactly an industry researcher's purpose to do the risky things that may not work, simply because the rest of the corporation cannot take such risks but must walk on more well-trodden paths.

Corporate R&D teams are there to absorb risk, innovate, disrupt, create new fields, not for doing small incremental improvements. "If we know it works, it's not research." (Albert Einstein)

I also agree with LeCun that LLMs in their current form - are a dead end. Note that this does not mean that I think we have already exploited LLMs to the limit, we are still at the beginning. We also need to create an ecosystem in which they can operate well: for instance, to combine LLMs with Web agents better we need a scalable "C2B2C" (customer delegated to business to business) micropayment infrastructure, because as these systems have already begun talking to each other, in the longer run nobody would offer their APIs for free.

I work on spatial/geographic models, inter alia, which by coincident is one of the direction mentioned in the LeCun article. I do not know what his reasoning is, but mine was/is: LMs are language models, and should (only) be used as such. We need other models - in particular a knowledge model (KM/KB) to cleanly separate knowledge from text generation - it looks to me right now that only that will solve hallucination.

Knowledge models, like ontologies, always seem suspect to me; like they promise a schema for crisp binary facts, when the world is full of probabilistic and fuzzy information loosely categorized by fallible humans based on an ever slowly shifting social consensus.

Everything from the sorites paradox to leaky abstractions; everything real defies precise definition when you look closely at it, and when you try to abstract over it, to chunk up, the details have an annoying way of making themselves visible again.

You can get purity in mathematical models, and in information systems, but those imperfectly model the world and continually need to be updated, refactored, and rewritten as they decay and diverge from reality.

These things are best used as tools by something similar to LLMs, models to be used, built and discarded as needed, but never a ground source of truth.

I have vague notions of there being an entire hidden philosophical/political battlefield (massacre?) behind the whole "are knowledge models/ontologies a realistic goal" debate.

Starting with the sophomoric questions of the optimist who mistakes the possible for the viable: how definite of a thing is "the world", how knowable is it, what is even knowledge... and then back through the more pragmatic: by whom is it knowable, to what degree, and by what means. The mystics: is "the world" the same thing as "the sum of information about the world"? The spooks: how does one study those fields of information which are already agentic and actively resist being studied by changing themselves, such as easily emerge anywhere more than n(D) people gather?

Plenty of food for thought from why ontologies are/aren't a thing. The classical example of how this plays out in the market being search engines winning over internet directories. But that's one turn of the wheel. Look at what search engines grew into quarter century later. What their outgrowths are doing to people's attitude towards knowledge. Different timescale, different picture.

Fundamentally, I don't think human language has sufficient resolution to model large spans of reality within the limited human attention span. The physical limits of human language as information processing device have been hit at some point in the XX century. Probably that 1970s divergence between productivity and wages.

So while LLMs are "computers speak language now" and it's amazing if sad that they cracked it by more data and not by more model, what's more amazing is how many people are continually ready to mistake language for thought. Are they all P-zombies or just obedience-conditioned into emulating ones?!?!?

Practically, what we lack is not the right architecture for "big knowing machine", but better tools for ad-hoc conceptual modeling of local situations. And, just like poetry that rhymes, this is exactly what nobody has a smidgen of interest to serve to consumers, thus someone will just build it in their basement in the hope of turning the tables on everyone. Probably with the help of LLMs as search engines and code generators. Yall better hurry. They're almost done.