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.
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If you think LLMs are not the future then you need to come with something better
If you have a theoretical idea that's great, but take to at least GPT2 level first before writing off LLMs
Theoretical people love coming up with "better ideas" that fall flat or have hidden gotchas when they get to practical implementation
As Linus says, "talk is cheap, show me the code".
Are all critiques of the obvious decline in physical durability of American-made products invalid unless they figure out a solution to the problem? Or may critics of a subject exist without necessarily being accredited engineers themselves?
And while we've been able to approximate the world behind the words, it's just full of hallucinations because the AI's lack axiomatic systems beyond much manually constructed machinery.
You can probably expand the capabilties by attaching to the front-end but I suspect that Yann is seeing limits to this and wants to go back and build up from the back-end of world reasoning and then _among other things_ attach LLM's at the front-end (but maybe on equal terms with vision models that allows for seamless integration of LLM interfacing _combined_ with vision for proper autonomous systems).
Oh god, that is massively under-selling their learning ability. These models are able to extract and reply with why jokes are funny without even knowing basic vocab, yet there are pure-code models out there with lingual rules baked in from day one which still struggle with basic grammar.
The _point_ of LLMs arguably is there ability to learn any pattern thrown at it with enough compute. With an exception to learning how logical processes work, and pure LLMs only see "time" in the sense of a paragraph begins and ends.
At the least they have taught computers, "how to language", which in regards to how to interact with a machine is a _huge_ step forward.
Unfortunately the financial incentives are split between agentic model usage (taking the idea of a computerised butler further), maximizing model memory and raw learning capacity (answering all problems at any time), and long-range consistency (longer ranges give better stable results due to a few reasons, but we're some way from seeing an LLM with a 128k experts and 10e18 active tokens).
I think in terms of building the perfect monkey butler we already have most or all of the parts. With regard to a model which can dynamically learn on the fly... LLMs are not the end of the story and we need something to allow the models to more closely tie their LS with the context. Frankly the fact that DeepSeek gave us an LLM with LS was a huge leap since previous model attempts had been overly complex and had failed in training.
The problem isn't LLMS, the problem is that everyone is trying to build bigger/better llms or manually code agents around LLMs. Meanwhile, projects like Mu Zero are forgotten, despite being vastly more important for things like self driving.