Meta absolutely has (or at least had) a word class industry AI lab and has published a ton of great work and open source models (granted their LLM open source stuff failed to keep up with chinese models in 2024/2025 ; their other open source stuff for thins like segmentation don't get enough credit though). Yann's main role was Chief AI Scientist, not any sort of product role, and as far as I can tell he did a great job building up and leading a research group within Meta.
He deserved a lot of credit for pushing Meta to very open to publishing research and open sourcing models trained on large scale data.
Just as one example, Meta (together with NYU) just published "Beyond Language Modeling: An Exploration of Multimodal Pretraining" (https://arxiv.org/pdf/2603.03276) which has a ton of large-experiment backed insights.
Yann did seem to end up with a bit of an inflated ego, but I still consider him a great research lead. Context: I did a PhD focused on AI, and Meta's group had a similar pedigree as Google AI/Deepmind as far as places to go do an internship or go to after graduation.
Creating a startup has to be about a product. When you raise 1B, investors are expecting returns, not papers.
Speaking of returns - Apple absolutely fucked Meta ads with the privacy controls, which trashed ad performance, revenue and share price. Meta turned things around using AI, with Yann as the lead researcher. Are you willing to give him credit for that? Revenue is now greater than pre-Apple-data-lockdown
> I wasn't criticising his scientific contribution at all, that's why I started my comment by appraising what he did.
You were criticising his output at Facebook, though, but he was in the research group at facebook, not a product group, so it seems like we did actually see lots of things?
That's true for 99% of the scientists, but dismissing their opinion based on them not having done world shattering / ground breaking research is probably not the way to go.
> I sincerely wish we will see more competition
I really wish we don't, science isn't markets.
> Understanding world through videos
The word "understanding" is doing a lot of heavy lifting here. I find myself prompting again and again for corrections on an image or a summary and "it" still does not "understand" and keeps doing the same thing over and over again.
But often passion and freedom to explore are often more important than resources
Or, maybe it's just hard?
Source: himself https://x.com/ylecun/status/1993840625142436160 (“I never worked on any Llama.”) and a million previous reports and tweets from him.
Quite a big contribution in practice.
Is it a troll? Even if we just ignore Llama, Meta invented and released so many foundational research and open source code. I would say that the computer vision field would be years behind if Meta didn't publish some core research like DETR or MAE.
>My only contribution was to push for Llama 2 to be open sourced.
For a hot minute Meta had a top 3 LLM and open sourced the whole thing, even with LeCunn's reservations around the technology.
At the same time Meta spat out huge breakthroughs in:
- 3d model generation
- Self-supervised label-free training (DINO). Remember Alexandr Wang built a multibillion dollar company just around having people in third world countries label data, so this is a huge breakthrough.
- A whole new class of world modeling techniques (JEPAs)
- SAM (Segment anything)
If it was a breakthrough, why did Meta acquire Wang and his company? I'm genuinely curious.
Unfotunately the dude knows very little about ai or ml research. He's just another wealthy grifter.
At this point decision making at Meta is based on Zuckerberg's vibes, and i suspect the emperor has no clothes.
So I keep wondering: if his idea is really that good — and I genuinely hope it is — why hasn’t it led to anything truly groundbreaking yet? It can’t just be a matter of needing more data or more researchers. You tell me :-D
Lecun introduced backprop for deep learning back in 1989 Hinton published about contrastive divergance in next token prediction in 2002 Alexnet was 2012 Word2vec was 2013 Seq2seq was 2014 AiAYN was 2017 UnicornAI was 2019 Instructgpt was 2022
This makes alot of people think that things are just accelerating and they can be along for the ride. But its the years and years of foundational research that allows this to be done. That toll has to be paid for the successsors of LLMs to be able to reason properly and operate in the world the way humans do. That sowing wont happen as fast as the reaping did. Lecun was to plant those seeds, the others who onky was to eat the fruit dont get that they have to wait
If he still hasn’t produced anything truly meaningful after all these years at Meta, when is that supposed to happen? Yann LeCun has been at Facebook/Meta since December 2013.
Your chronological sequence is interesting, but it refers to a time when the number of researchers and the amount of compute available were a tiny fraction of what they are today.