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

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.

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 don't disagree that the world is full of fuzziness. But the problem I have with this portrayal is that formal models are often normative rather than analytical. They create reality rather than being an interpretation or abstraction of reality.

People may well have a fuzzy idea of how their credit card works, but how it really works is formally defined by financial institutions. And this is not just true for software products. It's also largely true for manufactured products. Our world is very much shaped by artifacts and man-made rules.

Our probabilistic, fuzzy concepts are often simply a misconception. That doesn't mean it's not important of course. It is important for an AI to understand how people talk about things even if their idea of how these things work is flawed.

And then there is the sort of semi-formal language used in legal or scientific contexts that often has to be translated into formal models before it can become effective. Law makers almost never write algorithms (when they do, they are often buggy). But tax authorities and accounting software vendors do have to formally model the language in the law and then potentially change those formal definitions after court decisions.

My point is that the way in which the modeled, formal world interacts with probabilistic, fuzzy language and human actions is complex. In my opinion we will always need both. AIs ultimately need to understand both and be able to combine them just like (competent) humans do. AI "tool use" is a stop-gap. It's not a sufficient level of understanding.