Readers should make sure to contextualize this. We're talking about people researching AGI. Current LLM models are amazing, and will have business and societal impact. Previous ML models also had business and societal impact. None of that is contested here. The question is, what path leads to AGI, do LLM scale to AGI? That is the question being asked here, and some researchers think that it won't, it will scale superbly to many things, but something else might be needed for full AGI.
The relevant question is whether Humans + LLMs are much more likely to get to AGI than humans without LLMS. And the answer is pretty obviously yes. I don't think anyone was arguing that we would get to AGI by just training on more data with exactly the same models. Practically every advance in the last few years has been building additional functionality on top of LLMs, not just scaling up the same architecture to more data.
But zooming out, LLMs are universal approximators, so it's trivially true that they can approximate any function that describes AGI. It's also true that logic (from logos or "word") is about reasoning constrained by language and conversations. So an LLM is the right sort of device you'd expect to achieve general intelligence.
There are arguably non-linguistic forms of intelligence, such as visual intelligence. But those also can operate on written symbols (e.g. the stream of bits from an image file).
The other relevant question is why does Gary Macus always seem so angry? It's draining reading one of his posts.
The concept of mostly static weights holding the bulk of base intuition/knowledge (foundation if you will ;)) seems like a good bet, since it's how the mammalian brain works (with updates of those long term weights mostly happening while you sleep [1]).
I very naively assume the "easy" path will be similar: a very different system that's bolted on/references the foundation models, to enable the realtime/novel reasoning (outside the fixed latent space) bit that isn't possible now.
I think it's pretty rare for someone to use a pure LLM, today, or even a year ago. Function calls, MCP, tricks with thinking models, etc, all make these system "impure", and also much more capable.
Although it may be true that LLMs will not achieve AGI in the purest sense, they have at least forced us to move a lot of goalposts. I don't know what Gary Marcus was saying a few years ago, but I think many people would have said that e.g
achieving a gold medal at the Mathematics Olympiads would require AGI, not just LLMs.
Looking at the quoted tweet it is immediately obvious that these people have no clue about the current state of research. Yes they might have had some more or less relevant contributions to classical ML, but AI has taken off without (or rather despite) them and if history of AI has shown anything, it's that people like those are not the ones who will pave the way forward. In a field like this, there's no use to listen to people who still cling to their old ideas just because the current ideas don't seem "elegant" or "right" in their mind. The only thing you can trust is data and it proves we haven't peaked yet when it comes to LLMs.
more seriously though, as best as i can understand, what he is trying to say is that there must be a *LOGICAL* framework independent of compute or what you get is just a parrot (stochastic one at best) that operates within the smoothed edges of a distributed statistical field.
Gary Marcus has been taking victory laps on this since mid-2023, nothing to see here. Patently obvious to all that there will be additional innovations on top of LLMs such as test-time compute, which nonetheless are structured around LLMs and complementary
I just checked - he's right. Anthropic won't write code anymore. ChatGPT is just jumbled, dyslexic letters and nonsense. I generated a Midjourney image 10 times, each one was just TV static.
His work isn't all that different from what many other people in the space are doing. He just prefaces himself to be far more iconoclastic and "out there" than he actually is.
Someone who seems "addicted to feeling smug" is likely seeking constant validation for a grandiose sense of self importance. The smugness is the emotional payoff. The fix. That temporarily works up their fragile self-esteem.
This pattern of behavior is most closely associated with Narcissistic Personality Disorder in the DSM-5.
"We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done." - I am curious if people would read this as an advocacy or criticism of LLMs?
Discovery comes from search for both humans and AI agents. There is no magic in the brain or LLM except learning along the way and persistence. The search space itself is externalized.
So the AI agents are "good enough" but environment access is insufficient for collecting the required experience, this is the current bottleneck.
For example even a simple model like AlphaZero (just a CNN) was good enough to beat the best humans and rediscover game play from scratch, but it had the extensive access to the environment.
Readit News
But zooming out, LLMs are universal approximators, so it's trivially true that they can approximate any function that describes AGI. It's also true that logic (from logos or "word") is about reasoning constrained by language and conversations. So an LLM is the right sort of device you'd expect to achieve general intelligence.
There are arguably non-linguistic forms of intelligence, such as visual intelligence. But those also can operate on written symbols (e.g. the stream of bits from an image file).
The other relevant question is why does Gary Macus always seem so angry? It's draining reading one of his posts.
I very naively assume the "easy" path will be similar: a very different system that's bolted on/references the foundation models, to enable the realtime/novel reasoning (outside the fixed latent space) bit that isn't possible now.
[1] https://animalcare.umich.edu/our-impact/our-impact-monitorin...
more seriously though, as best as i can understand, what he is trying to say is that there must be a *LOGICAL* framework independent of compute or what you get is just a parrot (stochastic one at best) that operates within the smoothed edges of a distributed statistical field.
It's... it's over. The west has fallen.
> Yann LeCun was first, fully coming around to his own, very similar critique of LLMs by end of 2022.
> The Nobel Laureate and Google DeepMind CEO Sir Demis Hssabis sees it now, too.
He's personally moved on from LLM and exploring new architecture more built around world models.
Which he describes here: https://x.com/ylecun/status/1759933365241921817
Also I think the 2022 quoted refers to this Paper by Yann: https://openreview.net/pdf?id=BZ5a1r-kVsf
His work isn't all that different from what many other people in the space are doing. He just prefaces himself to be far more iconoclastic and "out there" than he actually is.
This pattern of behavior is most closely associated with Narcissistic Personality Disorder in the DSM-5.
Sutton... the patron saint of scaling...
Listen to people for the their ideas, not their label.
Regardless, Marcus is a bit late to comment on the bitter lesson. That is so 6 months ago lol
"We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done." - I am curious if people would read this as an advocacy or criticism of LLMs?
So the AI agents are "good enough" but environment access is insufficient for collecting the required experience, this is the current bottleneck.
For example even a simple model like AlphaZero (just a CNN) was good enough to beat the best humans and rediscover game play from scratch, but it had the extensive access to the environment.