@ylecun
Founder/Chair, AMI Labs; Professor, NYU; Partner, 224 Ventures; Ex-Chief AI Scientist, Meta. Researcher in AI, ML, Robotics, etc. ACM Turing Award Laureate.
@bedilbek @knarfroeder @rsalakhu s/self-service/self-serving/ I hate sneaky spelling correctors
I absolutely never said that LLMs were not useful. They obviously are. This was already obvious in 2022. I have said that LLMs are not a path towards human-level intelligence. Therefore, *IF* you are interested in pushing AI towards human-like intelligence, do not work on LLMs. This is not "bashing LLMs"
@Noahpinion Also:
Yann LeCun@ylecun·I said "auto-regressive LLMs, in and of themselves, will not lead human-level AI" That statement is still totally true. First, the reasoning abilities of current AI systems are based non-auto-regressive search (which is what I have always advocated for). But AFAICT, they do it in token space, which is limited and inefficient. I have claimed that human-like reasoning must be a search in continuous representation space. It looks like the industry is moving towards that. Second, the self-improvement methods, as currently practiced, only work for domains where the quality of outputs can be scored without human intervention, such as mathematics, code, and scenarios that can be simulated accurately. Not anything else. Humans and animals learn new skills way more efficiently than current RL methods. Third, the multimodal capabilities of current AI assistants generally use separately-trained encoders (that are not LLMs). This is also what I've been advocating. Except that I think the best way to do this is with JEPA trained with self-supervised learning. The research community is clearly moving towards that (3000 papers on JEPA in just 4 years). Fourth, if LLMs were a path to human-level AI, we would have domestic robots and Level-4 or Level-5 self-driving cars for consumers by now. And we don't. We certainly don't have cars that can learn to drive in 20 hours or practice like any teenager. We're still missing something pretty huge to claim human-level intelligence (let alone superhuman). Sure, we now have computer systems that are impressive, very useful, and whose performance is superhuman in an increasing number of domains (coding being one of them). But that's true of the entire history of progress in computer technology. Lastly, there is a basic confusion about what intelligence actually is. It is not the mere accumulation and regurgitation of existing declarative knowledge (which is essentially what LLMs do). As Jean Piaget famously said, "intelligence is not what you know, it is what you do when you don't know." It is your ability to solve new problem without any prior training, to act in previously-unknown scenarios, and to adapt very quickly to new situations with minimal training. We're still far from that.
@geoffreyhinton 3 years ago ⬆️
But there is a 1 hour talk, with results and evidence, that leads to these conclusions. Also, I am not saying that LLM-based AI tools are useless. They are very useful. We all use them. But, by themselves, they are not a path to human-level AI. Architectures that understand the real world are not LLMs. The architectural components that enable current LLM-based AI systems to interpret images, videos, and other real-world signals are not LLMs.
@dylanbowmanSF Slide deck here: drive.google.com/file/d/1sD5Vov…
@andrewgwils @perplexity_ai Congrats Andrew !
@JitendraMalikCV Exactly. We also should not confuse world models, as per your definition, with video prediction or video generation models. Understanding the dynamics of a system in order to control it is not the same as producing cute videos.
@giffmana The curse of dimensionality hits histogram-based methods particularly harshly. But it also hits every method, either by increasing the memory footprint, the computational requirements, or the training set size required for a given level of performance.
@ziv_ravid No
@elonmusk @EricTopol And your shameful campaign against Anthony Fauci makes you even more guilty. Fauci helped save millions of lives.
Only in America. Tired of winning?
Reginald@ginald87·Giving birth in Europe vs in America 😂
When you work on research (particularly on long-term research), you have to accept the fact that corporate management, who is often interested in short-term impact, may think you are wasting your time and company resources. But it very much depends what you objectives and your horizon is. - If your goal is keeping up with the current AI product race, then you might think that long-term bets are a waste. - If your goal is "AGI" (whatever you mean by that) then your horizon is likely longer and your modus operandi is very different. - However, If you think "AGI is just around the corner through LLM scaling", then you might think working on world models and other new AI concepts is a waste. - But if you know that AGI requires a few more conceptual advances (as Demis and I do), you pick a role that maximizes progress towards that goal. That role is necessarily less connected with products, less connected with management, and more connected with research. - I can't speak for Demis, but in my case, I've always been supportive of high-quality work on LLMs and impressed by the results. My personal interest was to advance towards human-level AI, and I never thought LLMs were the ticket. But I always thought LLMs were incredibly useful as a technology, and eventually as the centerpieces of a family of products. As a high-level IC (not a manager), I would not have had the influence or power to stifle work on LLM even if I had wanted to (which I had not).
@willdepue Relevant talk: youtube.com/watch?v=72Xj8k…
Tired of not winning x.com/RpsAgainstTrum…
@Viktoria5z @HaiyuWu1 This has more to do with the amount of data curation, engineering/tweaking, and compute that went into it than with the intrinsic quality of the methods.
@nicochristie Also, to first approximation, every computer in the world that runs an operating system runs Linux, precisely because it's open source. The entire software infrastructure of the mobile communication network is open source.
Whatever your definition of AGI: 1. It's emergence will not be a singular event, 2. Assuming folks agree on some sort threshold (which is unlikely), Anthropic is unlikely to be the one crossing it first. 3. If some company crosses that threshold first, 5 others will be crossing it within 6 months. 4. Whoever crosses it will rely on open research / open source that does not yet exist.
Tired of winning x.com/SteveRattner/s…
@Holding_Frame @Kathleen_Tyson_ @KenRoth Humanities hardly get any federal funding. The budgets being cut are destroying research in STEM.
Tired of winning x.com/maddenifico/st…
@MaulikAtanu @ZhugeEX s/warms/earns/
Tired of winning x.com/SteveRattner/s…
@caleb_shepherd @andrewgwils Also, I don't complain. I try to rally people to working on the Right Things.
