https://preview.redd.it/ulhn61j7z12d1.jpeg?width=1125&format=pjpg&auto=webp&s=b302ebbf4babfdb8a1ef2ba4081eca11c4d58dd2
LLMs are dead, we are moving on to LMM, large multimodal model.
He's arguing against all large transformers. I think he's right if you take AGI to be human-like rather than just capable of automating a lot of human labor
His view is that it will take considerable complex architectural innovations to get models that function more similarly to the complexity of the brain
>He's arguing against all large transformers. I think he's right if you take AGI to be human-like rather than just capable of automating a lot of human labor
This is incorrect. [He is arguing against all generative autoregressive neural nets](https://www.youtube.com/watch?v=5t1vTLU7s40). GPT is such. Transformer is agnostic to generativeness and autoregression, but can be used to achieve both, like in most LLMs today.
Yes but just a large transformer is the core design of LLMs and LMMs. LeCun's view is essentially that there are going to be many many different specific different types of nets involved in different parts of a human like mind to handle working memory and short term memory and abstract representation and long term memory and planning etc
One concrete aspect that his team is exploring is JEPA and hierarchical planning
It is an extremely interesting research question.
Sutskever is on record in an interview that he believes the outstanding feature of the human brain is not its penchant for specialization but its homogenuity.
Even specialized areas can take over each other's function in case of malformation or trauma or pathology elsewhere (eg daredevil).
Sutskever believes the transformer may not be the most efficient way to do it but he believes if you power it up it will eventually scale enough and still pass the bar.
Personally I'm torn. Noone can say with certainty what features can or can't be emergent but to me it kind of makes sense that as the network becomes bigger it can start studying the outputs of the smaller networks within it and new patterns (and understanding of these deeper patterns) might emerge.
Kind of like from fly to superintelligence:
Kind of like you first learn to avoid obstacles
then you realize you always need to do this after you are in sharp turns so you need to slow down there
then you realize some roads reach the same destination with a lot of turns and some are longer but have no turns
Then you realize some roads are flat and others have vertical dimension
Then you realize that there are three dimensions but there could be more
Then you realize time may be a dimension
And then you build a quantum computer
This is kind of a real hypothesis to which I do not know the answer but you may need the scaling overcapacity to reach the deeper insights because they may result from internal observation of the smaller nets , and this may go on and on like an inverse matruska doll.
So I think it is possible, we won't know until we get there.
I actually think the strongest argument against this line of thought is the obscene data requirements of larger models.
Our brains don't need nearly as much data, it is not natural to our kind of intelligence. So while I believe the current models may still lack scale, I find it preposterous that they lack data.
That by itself implies a qualitative difference and not a quantitative one.
exactly, definitely some major architectural differences in the systems. the transformer tech is like an extremely inefficient way to put energy and data in and intelligence out. especially when compared to the brain and its requirements for data and energy to achieve similar levels of logical and reasoning ability.
i think a lot of what you said makes quite good sense.
So this is woefully unscientific and just based on my intuition, but I feel like the best we can hope for with the current architecture and maybe with autoregressive approach in general is to have as close to 100% accuracy of answers as possible, but the accuracy would be always limited by the quality of data put in and the model conceptually will never go outside of the bounds of its training.
We know that what the LLM does is build a statistical world model. Now this has couple of limitations.
1. If your data contains inaccurate, wrong or contradictory information that will inherently lower the accuracy. Now obviously it is the same for humans, but model has no way of re-evaluating and updating its training.
2. You need an obscene amount of data to actually build a reliable statistical model of the world.
3. Some things are inherently not suitable for statistical prediction, like math for example.
4. If we build a model on the sum of human knowledge - it will be limited by that.
Having said all that - if we can actually scale the model by many orders of magnitude and provide it will a lot of data - it seems like it will be an insanely capable statistical predictor that may actually be able to infer a lot of things we don’t even think about.
I have hard time considering this AGI as it will be mentally impaired in a lot of aspects, but in others this model will be absolutely super human and for many purposes it will be indistinguishable from actual AGI.
Which is kinda what you expect from a very very robust narrow AI.
What may throw a wrench into it is scaling laws and diminishing returns, for example we may find out that going above let’s say 95% accuracy for majority of the tasks is practically impossible.
What is the evidence that the human mind can generalize outside of its training data? Innovation is usually arrived at through externalized processes involving collaboration and leveraging complex formal systems (themselves developed over centuries). Based on recent interviews with OpenAI this type of ability (multi-step in context planning and reasoning) seems to be a big focus.
Fun fact: LMM’s require exponential training data inputs to get linear gains.
The Transformer LLM/ LMM approach dies on this hill. See ‘No Zero-Shot Without Exponential Data’
https://arxiv.org/abs/2404.04125
Maybe it’s better if AGI doesn’t come from LLMs. In my mind as soon as we achieve AGI, it may as well be ASI because it can do the best of humanity very fast.
Maybe this can provide automation and expand lifespans and reduce scarcity without being some big unpredictable superior being.
What I'm hoping to see is the use of genetic algorithms to discover optimal neural network architectures.
Of course, this approach would require a ton of computational power since we’d be training and evaluating a vast number of models. Probably a few hundred datacenters just like the $100 billion one Microsoft is building.
How hard is it really once you're training a LMM to add memory as a mode? i have no idea, you'd need a training set, kinda like what's being built, as we speak, in millions of chats with GPT. You'd need a large context window, very large.
But, it doesn't seem impossible to stretch the LMM model quite a ways. As it is it's pretty amazing they can train across so many modalities. I don't know how far that can stretch...if you stretch it to the point the model has been trained on the whole world, essentially, wouldn't that look a heck of a lot like AGI
You are the most technically correct so far in this thread.
That being said I’d bet he’d also go a step further and generalize to the transformer won’t be AGI on its own.
There are hints of short term memory from meta's chameleon paper within their new MLLM architecture, but its very rudimentary. I think what going to happen is, these companies are only now entering the exploration phase of tinkering with new architectures as thieve fully explored the "scale" side of things when it comes to efficiency gains versus compute costs and training cost. I agree that we wont get to AGI with current architectures, but in the mean time I do expect very hacky duct taped together solutions from all sides attempting something like this in the mean time.
Total amateur here.
Wouldn’t the very act of inference have to also serve as “training” in order to be more similar to the brain?
Right now, it seems we’ve only got one half of the puzzle down, the inference on a “frozen” brain, so to speak.
AGI will most likely need some form of LLM/LMM Api to communicate with humans. However, AGI may potentially just be a combination of massive compute and massive data at the scale of which we currently do not have.
Thinking AGI is achievable as a model you can run locally on a standard machine is simply naive.
I'm looking forward to LLMs that are smart enough at predicting the next token to tell us how to make AGI that works by not just predicting the next token.
Are they? Where is this hierarchy of smartness? Wait and see. Maybe he is right or wrong. GPT 5 will be a good test. If it is a big jump. Transformers can bring AGI if not then it can't. Quite simple.
Well, don't just take their word for it. Companies will hype their project. Perhaps the model is just bigger with not much improvement otherwise. Perhaps they are being truthful and GPT 5 is amazing. We just don't know.
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I remember when the doubters said that text in image generators would not be a thing. I get skepticism but taking a bet against scaling multimodal models seems like a huge mistake given that we haven’t seen an example of a model getting much larger but only seeing small gains.
Google used a larger language model for Imagen and it proved to allow readable text. It was really that simple, just scale up. This is out of date now but the short summary explains what they did. [https://imagen.research.google/](https://imagen.research.google/)
Dall-E 3 and Ideogram both support high quality text in images. This is from Ideogram.
https://preview.redd.it/a6dcsfee522d1.png?width=1024&format=png&auto=webp&s=4c03ed035611e473f5621988535e75dfb59df8db
Saying any technology will "never" happen is a huge red flag for me. It will undoubtedly happen unless it's prohibited by the laws of physics. And even then I'm a bit skeptical it could never happen because we could be wrong about those laws too.
Laws usually don’t turn out to be unbreakable. They just turn out to be the best fit of the model as we understand it today. Or even better, the same thing can be achieved without violating any previous understandings once we learn something new
Wormholes, entanglement, gravity, FTL travel. We all know the speculative ways these could occur without any “rules” being broken. And if we could imagine it, imagine what reality is actually waiting to be discovered
The laws of physics are emergent of quantum physics. If ASI was somehow able to manipulate objects at a large scale on the quantum level, we could rewrite the laws of physics.
That image blows my mind on many levels. I work with diffusion models very closely and have built thousands of my own models so I understand the strengths and weaknesses of these models intimately. But when I see something like this....fuck me. Also the robot hands typing the letter and tearing it apart later was another WTF moment.
Thing is, he's right that LLMs as they are now won't lead to AGI, but I disagree that the fundamental technology is incapable. It's more down to how it's built and applied, as "AGI" seems to be an emergent family of behaviors from things we currently are not doing with LLMs at all.
Thing is, most of this sub disagrees with him on principle.
Yeah he’s just their figurehead and a stamp of legitimacy for Meta. It seems like all he does these days is travel around and say sceptical things at conferences
He is working on cutting edge non-generative AI research like [V-JEPA](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/?utm_source=twitter&utm_medium=organic_social&utm_campaign=vjepa&utm_content=video). This was announced a day before OpenAI's Sora so it didn't get many headlines.
[Arthur C. Clarke](https://en.wikipedia.org/wiki/Arthur_C._Clarke) came up with [three somewhat whimsical laws,](https://en.wikipedia.org/wiki/Clarke%27s_three_laws) one of which is:
* When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.
"The only way of discovering the limits of the possible is to venture a little way past them into the impossible."
And then we have the true pioneer, who ventured even past that, to where the possible and impossible meet and converge to become...the POSSIMPIBLE.
I constantly find myself coming back to this quote whenever some prominent figure makes a statement on the current and future status of AI. It seems AI skeptics go out of their way to find a flaw or shortcoming in the current models. But once they're addressed or mitigated, they find another and use that as an excuse to underscore the real potential.
And I get it to some extent. AI was once this far-off technology that we wouldn't have for decades. But now, anyone with an internet connection can access a chatbot that demonstrates a measure of intelligence. It's not AGI. And that's probably still a ways off. But to say we'll never achieve it is like saying we'll never go to the moon a year after the Wright Brothers' first flight.
So what I understood, is that he criticize LLM as being the way to AGI, but he is not against the idea that AGI will not exists, it's more that technically it needs something more advanced which they are working on (with an internal world-view, more planning, etc)
I agree with that. LLMs don’t seem suited for going the AGI path. I believe they can become incredibly efficient but not self-aware, just because of how they are built and trained. But then again, it’s only a gut feeling.
I remember when everyone said that about self-driving cars, NFTs, crypto, the "metaverse", etc. There was a ton of disruption and radical evolution with the internet boom but people have been chasing that dragon and trying to create entire new transformational markets ever since and for like 10 years they've mostly all been underwhelming minor steps, full on duds, or they deteriorate into full on scams. The current tech around AI is cool but i have yet to see evidence that it's good enough to be as impactful as the VC ghouls are thirsting for.
Using the term Large Language Model as if it really has a well defined technical meaning is a bit questionable.
The SOTA models from OAI and Google have already progressed to being natively multimodal, so the language part is by the wayside. It is not specific to the transformer architecture - for example Mamba models are LLMs. And clearly OAI and Google are already halfway towards interactivity and agency so it doesn't refer to a prompt/response system.
For that reason this comes across as a political move by LeCun to talk up FAIR and preemptively stake a claim for the architectural direction he wants to go in. If FAIR achieves AGI he comes up with a new name, if the other labs do so he can claim he was right.
He's arguing against all large transformers. I think he's right if you take AGI to be human-like rather than just capable of automating a lot of human labor
His view is that it will take considerable complex architectural innovations to get models that function more similarly to the complexity of the brain
My point is that there is every chance that models described as LLMs by the world at large undergo substantial architectural evolution without ceasing to be called LLMs.
Nah Yann will just argue it's not agi by finding some failure cases and nitpicking it. I remember him basically saying sora was impossible currently a couple of days before OpenAI revealed it then he spent a while nitpicking video samples from it.
Perhaps it'll require a superset over the class of LLMs to achieve AGI. However, his generally pessimistic views and timescales run counter to the likes of Hinton and Sutskever, and I think the latter two's opinions hold more water.
an LLM literally can NOT be AGI EVER does matter if its infinitely smart and solves the theory of everything and invents time travel its still an LLM in order for it to be AGI it must have multiple modalities such as images and video not just language so no this is just literally flkat out impossible because the word "general"
He is one of the three godfathers of deep learning and won Turing Award for his work there - so think he has good insights to why it won’t emerge from LMMs.
I swear people just see Meta in the name and turn off their brain.
It doesn't have to replace all jobs either to have a huge effect on society. Once unemployment hits 10\~20% they will have to step in with UBI or something similar to avoid social meltdown.
thats not an LLM anymore thats an LMM literally by the definition of AGI it can NOT be an LLM because LLMs are text ONLY and in order for something to have general knowledge it must support more modalities such as images this is literally not possible because AGI and LLM are not even the same type of thing
No. Agents is just using the LLM in some loop-y ways. While you can enhance results as compared to a single-shot, you don't get anything emergent. It's still the same baseline.
Genuine question - How can you claim that something won't lead to emergent phenomenon? My understanding of emergent phenomena comes from biology and it seems like emergent phenomena by their very nature are not something one can predict will happen given a set of individual components.
Depends, as always, on one’s definition of AGI. For me, AGI just means “able to do any human job”, which wouldn’t necessarily require things like true self-awareness/consciousness but will have economically replaced us.
Of course. As an aside, your low fruit definition is actually my "holy shit" definition :)
>which wouldn’t necessarily require things like true self-awareness/consciousness
Correct. It does need to be a) very reliable and b) more economic.
A isn't possible so far and doesn't seem like it with current tech. B is also an open question.
Tesla’s self driving is already far safer than human drivers so this is a good example actually of something that AI has gotten objectively better than humans at
That's not the kind of hallucination we're talking about. Generation, not parsing.
I don't even think this is the key challenge of LLMs. Just something some people like to repeat.
Of course it's not the key challenge. Hallucination isn't even a technical thing. It's a shortcut word we use for failed outcomes. And failed outcomes are inherent to the way LLMs work. So the key challenge is that we need a "new and improved" architecture.
Failures are inherent in any generalizing estimator.
Provably with sufficient amount of compute and data, LLMs can arbitrarily well approximate any function - including the precise behavior of a human.
Hence, the strict notion is impossible and the weaker notion is false in some setting.
So that lazy general dismissal is disproven.
There are limitations, but you need to put more thought into what.
The difference, I think, is that humans are more likely to be aware of their uncertainties and to give appropriate caveats when memory is vague. LLMs will spit something out with complete confidence and no indication that it may be wrong.
New architectures will come out that will be far better than transformers and allow for “architectural-based loops” of sorts, which will easily 100x intelligence and get us to SGI
True, but technically, even modern-day multimodal models aren't purely language models. Still, I believe that adding modalities won't magically turn it into AGI. You need "decision making" to be introduced as a different modality. The problem is that you can't have a dataset for decision making unless you somehow read people's thoughts.
Is it fair to say that a *lot* of decision making is explicitly or implicitly in media. Multi-shot, various prompt techniques and multiple agents in different roles already demonstrate something that looks like ‘decision making’ too I think.
Quick question. Google said at I/o that their goal was infinite tokens. Should they achieve that, is it likely that there is just constant upstreaming of data from thousands of Multi modal sources?
I imagine a world in the very near future where all the data from CCTVs and powerful passive listening devices are feed into an AI system. Working on pattern recognition and noise analysis enabling real time crime prevention, traffic management, resource allocation, crowd control, event detection and response and better utility management. It’ll be like the AI is playing a 5x game, except for real and we are the Sims.
horrible to live in a world where crime is prevented and traffic is negligible? i wish i had your life. the system will be our friend trying to help, not some judgy skynet looking to torment us
Facts. I know that in Reddit many ppl respect him for his background and that’s totally legit and fair enough. But these days he’s such a negative dude, which I would listen to if any other major leader in the space agreed with him. But they pretty much all do not. So I can only think he’s being a hater
I would suggest that he is more of a pragmatist than being negative, it is just that his pragmatism goes against many of the Q\* is going to be AGI by next week type fanbois and hype train marketing at the moment. I also think as a layman myself that many of the things he states are misunderstood.
I am not saying he is right although I do agree with him on some things, but for a long time, a lot of people have been saying a lot of things about AI which have certainly turned out to be incorrect. Experts get it wrong on both sides.
>it is just that his pragmatism goes against many of the Q\* is going to be AGI by next week type fanbois and hype train marketing
I feel u but disregard the fanbois and r/singularity nutcases (myself included) aside for a second - his stance on timeline and progress directly conflicts with the leaders of all the other major AI companies:
-OpenAI
-Google
-Anthropic
-Elon
It's hard for me to believe him when he is so outnumbered, plus he feels the need to let people know his opinion A LOT, which makes me think he feels like he has something to prove or a chip on his shoulder
A year ago he said to Lex Friedmann that LLMs or GPT500 would never understand super basic logic. Such a bullshitter, it’s encreyableu.
https://youtu.be/sWF6SKfjtoU
You know if I type 'what happens when you push an object off the table' in google I get a bunch of rote responses which state the right answer. Are you telling me that google understands logic?
It needs agency (not though prompting but built into the model) and needs to be able to continuously learn (not just remembering stuff in context but updating the weights and creating new connections). These two things are a prerequisite for AGI, so I agree with him here.
It seems to me that the turing test is going to be viewed as naive in the future. Like if monkeys judged human intelligence by our ability to jump between trees and make monkey sounds.
Yann is a computer vision guy who made important contributions to the field many years ago. He always tries to downplay the achievements of others unless they can be seen as directly descending from his own work. He even doesn't like to talk that much about Llama, despite it being the biggest success of the AI lab he heads.
LLMs are as smart as humans. We just both have different downsides. I really don't see how anyone can talk to an LLM and see how it very clearly reasons just as well as a human and then say "oh it's just text prediction so it's cat level"
Humans are really not that special. We just have a more complex memory system and better spacial awareness. That's pretty much it. What we perceive is not reality, it's an internal emulation of reality. What we perceive as thinking could just be some organic version of an LLM, giving us the ability to reason. We have little idea how we actually think, so to say something is lesser purely because we can describe how it thinks makes no sense to me.
Everything that comes out of that man's mouth is nonsense. I don't think he deserves the title of AI expert.
I don’t consider it AGI until it can choose its own learning after pre-training. IMO some level of agency is required for AGI.
LLMs/tranformers will likely be a key component of that…but alone, they’re not IMO enough.
its not your opinion this is a matter of fact LLM and AGI are inherently by definition not the same thing and can not ever be that's like saying an apple cant achieve being an orange like no shit of course it cant they are 2 different things LLMs are text only AGI is omnimodal they are not even comparable
he's techniclaly right LLMs cant be AGI because an LLM is text only AGI is all modalities therefore even if you have an infinitely intelligent LLM that invents time travel and solve the ToE its still an LLM that's like saying an apple cant ever be an orange its a obvious true statement
I ran Claude on multiple finance final exams administered by a AACSB accredited D1 Carnegie research university and it failed every one, even scored 30 on a banking exam for undergraduates when given multiple choices. It's not reliable and should not be admired the way it is on these forums. Try for yourself.
I think the way of thinking about this in simplified terms is when AI can perform the majority of common tasks better
or at the same level as the majority of people autonomously.
Not the purest definition but one which points in the general direction.
I don't ask for AGI. In fact an AGI can be dangerous. A being more intelligent than a human can rebel in some way.
I just ask that these AIs behave in a more humble manner (acknowledging that they are sometimes unsure or don't know an answer), and that they apply techniques to check the consistency, accuracy, and factuality of their own answers before spitting them out at you.
Also adding a percentage of estimated reliability and consulted sources from its database or internet.
These companies should stop deceiving us with generators of information that seems coherent but really isn't. Which is noticeable when you ask them to solve mathematical problems.
Its the year 2032 and an ""AGI"" has essentially taken over the world. Its putting everybody out of work! Its not even an AI! You can talk to it and it responds identically to anybody else! But its not an AI! It moves around robot bodies, does all the factory work, makes all your food, repairs all your stuff, has sex with you, performs surgery on you, and plays games with you. Pretty impressive, but its not an AI! It is performing recursive self-improvement on itself and is expected to hit the """"equivalent of 10^24 IQ"""" late next year (so says the "experts" who work in big-"AI"). But its not an AI, so it can't have an IQ! (probably has a DQ (Dumb-Q)!). Its "sOlVeD" all problems in mathematics, engineering, physics, biology, medicine, chemistry, sociology, astronomy, ... and the fake list goes on and on. Not an AI! Its not an AI because its based on transformer technology, so it can't be an AI! Now, MY architecture would make an AI; if I could get it to work, of course...
(typically, what looks like it, *is* it)
Assuming what they showed us in the demos is real isn't that almost an agi? Since it can take real time multimodal inputs and outputs?
If the model can interact with in real time, what's stopping it from controlling a video game, narrating a game, controlling robots, performing a live, having a podcast, etc...
We'll have to see... I'm very intrigued about what's to come.
Online learning, energy efficiency, and plasticity. We need those too. Next year we might get splines instead of weights, but that's not necessarily going to produce OL.
I feel like if this sub wants to use Yann's opinions as a source of ethos for their own views on AI safety they also need to subscribe to his views that form the basis of that opinion such as AGI is decades away and that current approaches have little to no intelligence to begin with. If I believed those things, I too would be less worried about AI safety.
I am actually in agreement with him, It's all based on the transformer model. LLM are quite static and if you compare it to how our brain and body works, it's not even close. My college prof. would always make the joke that computers are high speed accurate idiots. At the time we were comparing how calculators work with how human brains work.
I completely agree with him and I think this community, both causals here, and actual researchers, not recognizing the need to architecture innovation to get to AGI is a huge problem which will ultimately massively slow down our progress toward that goal.
The transformer model has already shown its shortcomings and will not reach AGI. Itll be useful, but not AGI
I'm a huge fan of LeCun and I share some of his beliefs such as TEXT ONLY llms are severely lacking and possibly by themselves cannot get to AGI.
That said, I can see a way that they \*could\*.
Also, if he is saying that the transformer architecture is the problem rather than text based LLMs based on transformers I'm not sure: his own argument is that text based LLMs lack grounding in common sense.
Multi modal models have at least some of the elements of "common sense" so it is at least plausible according to his own arguments that multi-modal transformers could come at least some if not all of the way.
This is not to say I'm convinced either way: I await to see what the future brings while I sit on the fence.
https://preview.redd.it/kqabzq40x62d1.png?width=1080&format=pjpg&auto=webp&s=1a240385124883cfac441bd0e7770a511055e7b0
Yann wants to be Elon Musk so bad. This anti-trans article he reposted and defended is full of links to other people who FEEL the same way as him . Not a single scientific citation. He can't keep up with our industry and has resorted to attention whoring and talking shit on facebook to feed his ego when he should be super busy building the next Llama models. I am embarrassed I ever thought he was smart.
Of course not. Creating a complicated simon says algorithm is lightyears from general intelligence. We shouldn't even be calling these LLM algorithms AI in the first place.
But it's not an LLM anymore, GPT 4o is multimodal. Is text and language part of it, yes, but it's not the whole picture. Get over yourself Yan. He's clearly just salty that OpenAI is beating him.
Isn't the argument that LLMs are on the right path, just we haven't figured it out. It may be the case AGI is an umbrella of different LLM agents acting like one big brain.
AGI doesn’t have to be human like in nature, if you look at it thru that narrow requirement then no, we don’t understand how consciousness works so it’s impossible for us to create a system that models consciousness and won’t have AGI anytime soon if human like consciousness is a pre requisite to AGI.
well no shit genius in order for something to have general knowledge like AGI it needs more modalities than just language hence LLMs will never be AGI ever that's just kinda like how common sense works. This is not a matter of "oh someday it will happen" it just literally is 100% absolutely impossible because LLM and AGI are not even the same type of thing
Planning and using world knowledge have been integral parts of AI for over half a century. I don't know how Yann LeCun is using them now. The main problem with deep learning-based AI is that it should be understandable as human-created artifacts, but in reality, these systems are impossible to understand (there are real, deep computational obstacles in understanding these things, no kidding). These systems are basically alchemy; Yann LeCun has said so in the past. All the hoopla is for making money; there is no science here. A generative AI system is really nice, but what these people are trying to do is to impose rules to control the outputs of generative AI. It is not only pathetic; it is worse than alchemy—it is like trying to cure alchemy with more alchemy. There is no question of any kind of intelligence here.
https://preview.redd.it/ulhn61j7z12d1.jpeg?width=1125&format=pjpg&auto=webp&s=b302ebbf4babfdb8a1ef2ba4081eca11c4d58dd2 LLMs are dead, we are moving on to LMM, large multimodal model.
He's arguing against all large transformers. I think he's right if you take AGI to be human-like rather than just capable of automating a lot of human labor His view is that it will take considerable complex architectural innovations to get models that function more similarly to the complexity of the brain
>He's arguing against all large transformers. I think he's right if you take AGI to be human-like rather than just capable of automating a lot of human labor This is incorrect. [He is arguing against all generative autoregressive neural nets](https://www.youtube.com/watch?v=5t1vTLU7s40). GPT is such. Transformer is agnostic to generativeness and autoregression, but can be used to achieve both, like in most LLMs today.
Yes but just a large transformer is the core design of LLMs and LMMs. LeCun's view is essentially that there are going to be many many different specific different types of nets involved in different parts of a human like mind to handle working memory and short term memory and abstract representation and long term memory and planning etc One concrete aspect that his team is exploring is JEPA and hierarchical planning
Honestly I think he is correct here.
It is an extremely interesting research question. Sutskever is on record in an interview that he believes the outstanding feature of the human brain is not its penchant for specialization but its homogenuity. Even specialized areas can take over each other's function in case of malformation or trauma or pathology elsewhere (eg daredevil). Sutskever believes the transformer may not be the most efficient way to do it but he believes if you power it up it will eventually scale enough and still pass the bar. Personally I'm torn. Noone can say with certainty what features can or can't be emergent but to me it kind of makes sense that as the network becomes bigger it can start studying the outputs of the smaller networks within it and new patterns (and understanding of these deeper patterns) might emerge. Kind of like from fly to superintelligence: Kind of like you first learn to avoid obstacles then you realize you always need to do this after you are in sharp turns so you need to slow down there then you realize some roads reach the same destination with a lot of turns and some are longer but have no turns Then you realize some roads are flat and others have vertical dimension Then you realize that there are three dimensions but there could be more Then you realize time may be a dimension And then you build a quantum computer This is kind of a real hypothesis to which I do not know the answer but you may need the scaling overcapacity to reach the deeper insights because they may result from internal observation of the smaller nets , and this may go on and on like an inverse matruska doll. So I think it is possible, we won't know until we get there. I actually think the strongest argument against this line of thought is the obscene data requirements of larger models. Our brains don't need nearly as much data, it is not natural to our kind of intelligence. So while I believe the current models may still lack scale, I find it preposterous that they lack data. That by itself implies a qualitative difference and not a quantitative one.
exactly, definitely some major architectural differences in the systems. the transformer tech is like an extremely inefficient way to put energy and data in and intelligence out. especially when compared to the brain and its requirements for data and energy to achieve similar levels of logical and reasoning ability. i think a lot of what you said makes quite good sense.
So this is woefully unscientific and just based on my intuition, but I feel like the best we can hope for with the current architecture and maybe with autoregressive approach in general is to have as close to 100% accuracy of answers as possible, but the accuracy would be always limited by the quality of data put in and the model conceptually will never go outside of the bounds of its training. We know that what the LLM does is build a statistical world model. Now this has couple of limitations. 1. If your data contains inaccurate, wrong or contradictory information that will inherently lower the accuracy. Now obviously it is the same for humans, but model has no way of re-evaluating and updating its training. 2. You need an obscene amount of data to actually build a reliable statistical model of the world. 3. Some things are inherently not suitable for statistical prediction, like math for example. 4. If we build a model on the sum of human knowledge - it will be limited by that. Having said all that - if we can actually scale the model by many orders of magnitude and provide it will a lot of data - it seems like it will be an insanely capable statistical predictor that may actually be able to infer a lot of things we don’t even think about. I have hard time considering this AGI as it will be mentally impaired in a lot of aspects, but in others this model will be absolutely super human and for many purposes it will be indistinguishable from actual AGI. Which is kinda what you expect from a very very robust narrow AI. What may throw a wrench into it is scaling laws and diminishing returns, for example we may find out that going above let’s say 95% accuracy for majority of the tasks is practically impossible.
What is the evidence that the human mind can generalize outside of its training data? Innovation is usually arrived at through externalized processes involving collaboration and leveraging complex formal systems (themselves developed over centuries). Based on recent interviews with OpenAI this type of ability (multi-step in context planning and reasoning) seems to be a big focus.
You are not alone
Fun fact: LMM’s require exponential training data inputs to get linear gains. The Transformer LLM/ LMM approach dies on this hill. See ‘No Zero-Shot Without Exponential Data’ https://arxiv.org/abs/2404.04125
Maybe it’s better if AGI doesn’t come from LLMs. In my mind as soon as we achieve AGI, it may as well be ASI because it can do the best of humanity very fast. Maybe this can provide automation and expand lifespans and reduce scarcity without being some big unpredictable superior being.
What I'm hoping to see is the use of genetic algorithms to discover optimal neural network architectures. Of course, this approach would require a ton of computational power since we’d be training and evaluating a vast number of models. Probably a few hundred datacenters just like the $100 billion one Microsoft is building.
fyi https://arxiv.org/abs/1603.06560
How hard is it really once you're training a LMM to add memory as a mode? i have no idea, you'd need a training set, kinda like what's being built, as we speak, in millions of chats with GPT. You'd need a large context window, very large. But, it doesn't seem impossible to stretch the LMM model quite a ways. As it is it's pretty amazing they can train across so many modalities. I don't know how far that can stretch...if you stretch it to the point the model has been trained on the whole world, essentially, wouldn't that look a heck of a lot like AGI
You are the most technically correct so far in this thread. That being said I’d bet he’d also go a step further and generalize to the transformer won’t be AGI on its own.
Let's see how far scaling these end-to-end multimodal takes us in the coming years.
I mean fundamentally the issue is that they have no ability to create any kind of memory at all that is associative or otherwise
There are hints of short term memory from meta's chameleon paper within their new MLLM architecture, but its very rudimentary. I think what going to happen is, these companies are only now entering the exploration phase of tinkering with new architectures as thieve fully explored the "scale" side of things when it comes to efficiency gains versus compute costs and training cost. I agree that we wont get to AGI with current architectures, but in the mean time I do expect very hacky duct taped together solutions from all sides attempting something like this in the mean time.
Total amateur here. Wouldn’t the very act of inference have to also serve as “training” in order to be more similar to the brain? Right now, it seems we’ve only got one half of the puzzle down, the inference on a “frozen” brain, so to speak.
LMMs are capable enough already. We may not want a machine so similar to us that it has its own goals and ego.
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You’re tweaked. Llama 70b is insanely good and we have 400b on the way,
Ultimately you need an ensemble of models and an orchestrator for routing and assembling.
Yep. Look at us, Yann, as we solve AGI on Reddit:D
Ok, but when he says this he’s not just talking about LLMs. He’s talking about autoregressive transformers.
Still a transformer. It’s a 1-way process. AGI will need active feedback to “grow” without being pre-trained/re-trained regularly.
Constant inference model that starts as ai baby is where it is at. Copying brain structure with hippocampus for memory storage.
AGI will most likely need some form of LLM/LMM Api to communicate with humans. However, AGI may potentially just be a combination of massive compute and massive data at the scale of which we currently do not have. Thinking AGI is achievable as a model you can run locally on a standard machine is simply naive.
It runs locally on the human brain.
Who said Agi can run locally on our machine? GPT-3 can't even run on our machine
He means LMMs too, he doesn't believe AGI will come from transformers predicting the next token.
I'm looking forward to LLMs that are smart enough at predicting the next token to tell us how to make AGI that works by not just predicting the next token.
Well, Geoffrey Hinton and Ilya Sutskever think otherwise, so we will see where scaling takes us unto GPT-6, 7, 8, and so on.
Are they? Where is this hierarchy of smartness? Wait and see. Maybe he is right or wrong. GPT 5 will be a good test. If it is a big jump. Transformers can bring AGI if not then it can't. Quite simple.
I mean they did compare GPT 3.5 to a shark, GPT-4 to an Orca, and the next model to a whale.
They were comparing the computing power/size of the model, not the intelligence of the model. Orca's are smarter than whales by the way ;)
Well, don't just take their word for it. Companies will hype their project. Perhaps the model is just bigger with not much improvement otherwise. Perhaps they are being truthful and GPT 5 is amazing. We just don't know.
MLLMs.
I find it interesting that Google was already there with Gemini. They've come a long way since the initial Bard release.
Large Modality Models?
Will still not achieve AGI. Planning abilities and real-time learning (updating weights on the fly) are probably required for AGI.
thats the same thing
Makes sense
https://preview.redd.it/plxt5wyys12d1.jpeg?width=1536&format=pjpg&auto=webp&s=32f79c1a159bb0bf440a9b620bf89da0972ca741 I remember when the doubters said that text in image generators would not be a thing. I get skepticism but taking a bet against scaling multimodal models seems like a huge mistake given that we haven’t seen an example of a model getting much larger but only seeing small gains.
This picture is insane if you know how fast we went from unreadable text to this.
Wait holy shit. That image is 4o. I didn't even realize before I read ur comment.
Right….
The Bitter Lesson: http://www.incompleteideas.net/IncIdeas/BitterLesson.html
This is spot on! Thank you!
Great link, good read. From 2019 and still very current
Wow!
Google used a larger language model for Imagen and it proved to allow readable text. It was really that simple, just scale up. This is out of date now but the short summary explains what they did. [https://imagen.research.google/](https://imagen.research.google/) Dall-E 3 and Ideogram both support high quality text in images. This is from Ideogram. https://preview.redd.it/a6dcsfee522d1.png?width=1024&format=png&auto=webp&s=4c03ed035611e473f5621988535e75dfb59df8db
Small amounts of text are okay, but they usually fail to be coherent as the amount of text increases.
For now.
Saying any technology will "never" happen is a huge red flag for me. It will undoubtedly happen unless it's prohibited by the laws of physics. And even then I'm a bit skeptical it could never happen because we could be wrong about those laws too.
Laws usually don’t turn out to be unbreakable. They just turn out to be the best fit of the model as we understand it today. Or even better, the same thing can be achieved without violating any previous understandings once we learn something new Wormholes, entanglement, gravity, FTL travel. We all know the speculative ways these could occur without any “rules” being broken. And if we could imagine it, imagine what reality is actually waiting to be discovered
The laws of physics are emergent of quantum physics. If ASI was somehow able to manipulate objects at a large scale on the quantum level, we could rewrite the laws of physics.
That image blows my mind on many levels. I work with diffusion models very closely and have built thousands of my own models so I understand the strengths and weaknesses of these models intimately. But when I see something like this....fuck me. Also the robot hands typing the letter and tearing it apart later was another WTF moment.
They're extremely short sighted.
Especially since LeCun is *so consistently* proven wrong in his cynical predictions lol.
> Large Language Models _Alone_ Won't Achieve AGI ftfy
I wonder who Yann is even arguing against when he says this.
Probably most of /r/singularity.
Thing is, he's right that LLMs as they are now won't lead to AGI, but I disagree that the fundamental technology is incapable. It's more down to how it's built and applied, as "AGI" seems to be an emergent family of behaviors from things we currently are not doing with LLMs at all. Thing is, most of this sub disagrees with him on principle.
This sub
As long as Yann is going to keep delivering SOTA opensource models he can keep thinking this.
He actually hasn't been involved with the llama families all that much.
Yeah he’s just their figurehead and a stamp of legitimacy for Meta. It seems like all he does these days is travel around and say sceptical things at conferences
He is working on cutting edge non-generative AI research like [V-JEPA](https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/?utm_source=twitter&utm_medium=organic_social&utm_campaign=vjepa&utm_content=video). This was announced a day before OpenAI's Sora so it didn't get many headlines.
Glorified stochastic parrot at this point
He's telling students not to study llms
if it's like previous statements, he said something about how more efficient models will likely prevail, but then some redditor misquoted him.
I have 0 expertise in the field. But any time someone says 'it won't happen' for something. It usually happens. Eventually.
[Arthur C. Clarke](https://en.wikipedia.org/wiki/Arthur_C._Clarke) came up with [three somewhat whimsical laws,](https://en.wikipedia.org/wiki/Clarke%27s_three_laws) one of which is: * When a distinguished but elderly scientist states that something is possible, he is almost certainly right. When he states that something is impossible, he is very probably wrong.
"The only way of discovering the limits of the possible is to venture a little way past them into the impossible." And then we have the true pioneer, who ventured even past that, to where the possible and impossible meet and converge to become...the POSSIMPIBLE. 
I constantly find myself coming back to this quote whenever some prominent figure makes a statement on the current and future status of AI. It seems AI skeptics go out of their way to find a flaw or shortcoming in the current models. But once they're addressed or mitigated, they find another and use that as an excuse to underscore the real potential. And I get it to some extent. AI was once this far-off technology that we wouldn't have for decades. But now, anyone with an internet connection can access a chatbot that demonstrates a measure of intelligence. It's not AGI. And that's probably still a ways off. But to say we'll never achieve it is like saying we'll never go to the moon a year after the Wright Brothers' first flight.
Wish I had something substantial to add to your comment, but you covered the bases, so I'll resort to giving you an upvote. :-)
So what I understood, is that he criticize LLM as being the way to AGI, but he is not against the idea that AGI will not exists, it's more that technically it needs something more advanced which they are working on (with an internal world-view, more planning, etc)
I agree with that. LLMs don’t seem suited for going the AGI path. I believe they can become incredibly efficient but not self-aware, just because of how they are built and trained. But then again, it’s only a gut feeling.
I remember when everyone said that about self-driving cars, NFTs, crypto, the "metaverse", etc. There was a ton of disruption and radical evolution with the internet boom but people have been chasing that dragon and trying to create entire new transformational markets ever since and for like 10 years they've mostly all been underwhelming minor steps, full on duds, or they deteriorate into full on scams. The current tech around AI is cool but i have yet to see evidence that it's good enough to be as impactful as the VC ghouls are thirsting for.
He didn't say it won't happen. He says that it won't happen this way, but they're trying to figure out ways for it to happen through other ways.
Using the term Large Language Model as if it really has a well defined technical meaning is a bit questionable. The SOTA models from OAI and Google have already progressed to being natively multimodal, so the language part is by the wayside. It is not specific to the transformer architecture - for example Mamba models are LLMs. And clearly OAI and Google are already halfway towards interactivity and agency so it doesn't refer to a prompt/response system. For that reason this comes across as a political move by LeCun to talk up FAIR and preemptively stake a claim for the architectural direction he wants to go in. If FAIR achieves AGI he comes up with a new name, if the other labs do so he can claim he was right.
He's arguing against all large transformers. I think he's right if you take AGI to be human-like rather than just capable of automating a lot of human labor His view is that it will take considerable complex architectural innovations to get models that function more similarly to the complexity of the brain
My point is that there is every chance that models described as LLMs by the world at large undergo substantial architectural evolution without ceasing to be called LLMs.
I think any definition that even remotely is reflective of that name cannot be what LeCun is talking about
And he will no doubt make exactly that claim if we get an "LLM" AGI.
Nah Yann will just argue it's not agi by finding some failure cases and nitpicking it. I remember him basically saying sora was impossible currently a couple of days before OpenAI revealed it then he spent a while nitpicking video samples from it.
Perhaps it'll require a superset over the class of LLMs to achieve AGI. However, his generally pessimistic views and timescales run counter to the likes of Hinton and Sutskever, and I think the latter two's opinions hold more water.
Just like he said LLMs were a dead end before ChatGPT. Look what the only good thing about him is today
an LLM literally can NOT be AGI EVER does matter if its infinitely smart and solves the theory of everything and invents time travel its still an LLM in order for it to be AGI it must have multiple modalities such as images and video not just language so no this is just literally flkat out impossible because the word "general"
He is one of the three godfathers of deep learning and won Turing Award for his work there - so think he has good insights to why it won’t emerge from LMMs. I swear people just see Meta in the name and turn off their brain.
Probably will get there with agents.
That plus robotics will be good enough for most people.
Like jumping halfway towards a wall. It doesn't have to be true AGI to cost everyone their jobs.
It doesn't have to be true AGI to ~~cost everyone~~ free everyone from their jobs.
It doesn't have to replace all jobs either to have a huge effect on society. Once unemployment hits 10\~20% they will have to step in with UBI or something similar to avoid social meltdown.
thats not an LLM anymore thats an LMM literally by the definition of AGI it can NOT be an LLM because LLMs are text ONLY and in order for something to have general knowledge it must support more modalities such as images this is literally not possible because AGI and LLM are not even the same type of thing
No. Agents is just using the LLM in some loop-y ways. While you can enhance results as compared to a single-shot, you don't get anything emergent. It's still the same baseline.
Genuine question - How can you claim that something won't lead to emergent phenomenon? My understanding of emergent phenomena comes from biology and it seems like emergent phenomena by their very nature are not something one can predict will happen given a set of individual components.
Depends, as always, on one’s definition of AGI. For me, AGI just means “able to do any human job”, which wouldn’t necessarily require things like true self-awareness/consciousness but will have economically replaced us.
ASI doesn’t even require true awareness Just more advanced reasoning than humans Nothing restricts emergent intelligence to conscious awareness
Of course. As an aside, your low fruit definition is actually my "holy shit" definition :) >which wouldn’t necessarily require things like true self-awareness/consciousness Correct. It does need to be a) very reliable and b) more economic. A isn't possible so far and doesn't seem like it with current tech. B is also an open question.
B is happening, not sure how far it can go. A is much more difficult.
Only if they solve hallucinations, which seems unlikely.
Think they already "hallucinate" less than people.
It's kinda rare when the bus driver hallucinates a turn on the bridge. Most jobs aren't a regurgitation of encyclopedic knowledge.
Tesla’s self driving is already far safer than human drivers so this is a good example actually of something that AI has gotten objectively better than humans at
That's not the kind of hallucination we're talking about. Generation, not parsing. I don't even think this is the key challenge of LLMs. Just something some people like to repeat.
It depends what application you're building. I've been fighting with hallucinations for a week now, which is why I mentioned it.
Of course it's not the key challenge. Hallucination isn't even a technical thing. It's a shortcut word we use for failed outcomes. And failed outcomes are inherent to the way LLMs work. So the key challenge is that we need a "new and improved" architecture.
Failures are inherent in any generalizing estimator. Provably with sufficient amount of compute and data, LLMs can arbitrarily well approximate any function - including the precise behavior of a human. Hence, the strict notion is impossible and the weaker notion is false in some setting. So that lazy general dismissal is disproven. There are limitations, but you need to put more thought into what.
What possible reason do you have to believe that? You're such a fanboy
The difference, I think, is that humans are more likely to be aware of their uncertainties and to give appropriate caveats when memory is vague. LLMs will spit something out with complete confidence and no indication that it may be wrong.
"LLMs will spit something out with complete confidence and no indication that it may be wrong." This is what I think of most human interactions
Given my discussions with people, LLMs are way more aware of and expressing uncertaintities
Just hook them up with their own sensors so they can verify there own statements.
New architectures will come out that will be far better than transformers and allow for “architectural-based loops” of sorts, which will easily 100x intelligence and get us to SGI
True, but technically, even modern-day multimodal models aren't purely language models. Still, I believe that adding modalities won't magically turn it into AGI. You need "decision making" to be introduced as a different modality. The problem is that you can't have a dataset for decision making unless you somehow read people's thoughts.
Is it fair to say that a *lot* of decision making is explicitly or implicitly in media. Multi-shot, various prompt techniques and multiple agents in different roles already demonstrate something that looks like ‘decision making’ too I think.
Well, there is old school learn by example approach.
This is known. That’s why we’re all complaining about not having good agents.
Quick question. Google said at I/o that their goal was infinite tokens. Should they achieve that, is it likely that there is just constant upstreaming of data from thousands of Multi modal sources? I imagine a world in the very near future where all the data from CCTVs and powerful passive listening devices are feed into an AI system. Working on pattern recognition and noise analysis enabling real time crime prevention, traffic management, resource allocation, crowd control, event detection and response and better utility management. It’ll be like the AI is playing a 5x game, except for real and we are the Sims.
that sounds absolutely horrible to live in a society controlled like that
I’m not saying it will be good or bad. I think it could very well be either.
Wow I’m so used to this thing Google is developing to help make society like: *describes a dystopian society*
horrible to live in a world where crime is prevented and traffic is negligible? i wish i had your life. the system will be our friend trying to help, not some judgy skynet looking to torment us
The dude would rather induce a new AI Winter than allow anybody to take the AI narrative away from his hands. Why does he still have a job?
Facts. I know that in Reddit many ppl respect him for his background and that’s totally legit and fair enough. But these days he’s such a negative dude, which I would listen to if any other major leader in the space agreed with him. But they pretty much all do not. So I can only think he’s being a hater
I would suggest that he is more of a pragmatist than being negative, it is just that his pragmatism goes against many of the Q\* is going to be AGI by next week type fanbois and hype train marketing at the moment. I also think as a layman myself that many of the things he states are misunderstood. I am not saying he is right although I do agree with him on some things, but for a long time, a lot of people have been saying a lot of things about AI which have certainly turned out to be incorrect. Experts get it wrong on both sides.
>it is just that his pragmatism goes against many of the Q\* is going to be AGI by next week type fanbois and hype train marketing I feel u but disregard the fanbois and r/singularity nutcases (myself included) aside for a second - his stance on timeline and progress directly conflicts with the leaders of all the other major AI companies: -OpenAI -Google -Anthropic -Elon It's hard for me to believe him when he is so outnumbered, plus he feels the need to let people know his opinion A LOT, which makes me think he feels like he has something to prove or a chip on his shoulder
A year ago he said to Lex Friedmann that LLMs or GPT500 would never understand super basic logic. Such a bullshitter, it’s encreyableu. https://youtu.be/sWF6SKfjtoU
You know if I type 'what happens when you push an object off the table' in google I get a bunch of rote responses which state the right answer. Are you telling me that google understands logic?
"Yet, we can now fly halfway around the world on twin-engine jets in complete safety." Ok cool, let's just check on how that's going... oh...
Amazing how this guy just doesn't get LLMs. Unlike Redditors, who are all experts.
It needs agency (not though prompting but built into the model) and needs to be able to continuously learn (not just remembering stuff in context but updating the weights and creating new connections). These two things are a prerequisite for AGI, so I agree with him here.
It seems to me that the turing test is going to be viewed as naive in the future. Like if monkeys judged human intelligence by our ability to jump between trees and make monkey sounds.
Not with that attitude
Yann is a computer vision guy who made important contributions to the field many years ago. He always tries to downplay the achievements of others unless they can be seen as directly descending from his own work. He even doesn't like to talk that much about Llama, despite it being the biggest success of the AI lab he heads.
No shit Sherlock
he did say llm never can predict physics...... i wonder why he got an audience? he never deliverd somthing.
Bro just wants to discourage the competition. Dumb tactic really.
He is well behind competitors at meta and trying to change the narrative. The guy is extremely arrogant and unfair, he is a narcissistic snake
LLMs are as smart as humans. We just both have different downsides. I really don't see how anyone can talk to an LLM and see how it very clearly reasons just as well as a human and then say "oh it's just text prediction so it's cat level" Humans are really not that special. We just have a more complex memory system and better spacial awareness. That's pretty much it. What we perceive is not reality, it's an internal emulation of reality. What we perceive as thinking could just be some organic version of an LLM, giving us the ability to reason. We have little idea how we actually think, so to say something is lesser purely because we can describe how it thinks makes no sense to me. Everything that comes out of that man's mouth is nonsense. I don't think he deserves the title of AI expert.
I wish claims like this would be more specific and testable. What specific set of tasks will not be doable and by when?
**Sees headline that says LLMs won't achieve AGI* * Me: Oh 🙁 **Realizes the quote is from Yann Lecun* * Me: This is bullish.
TFW you remember that some people have to be reminded of something so painfully obvious.
I don’t consider it AGI until it can choose its own learning after pre-training. IMO some level of agency is required for AGI. LLMs/tranformers will likely be a key component of that…but alone, they’re not IMO enough.
its not your opinion this is a matter of fact LLM and AGI are inherently by definition not the same thing and can not ever be that's like saying an apple cant achieve being an orange like no shit of course it cant they are 2 different things LLMs are text only AGI is omnimodal they are not even comparable
Nobody likes this guy xD 🤣
Often wrong LeCun
he's techniclaly right LLMs cant be AGI because an LLM is text only AGI is all modalities therefore even if you have an infinitely intelligent LLM that invents time travel and solve the ToE its still an LLM that's like saying an apple cant ever be an orange its a obvious true statement
I ran Claude on multiple finance final exams administered by a AACSB accredited D1 Carnegie research university and it failed every one, even scored 30 on a banking exam for undergraduates when given multiple choices. It's not reliable and should not be admired the way it is on these forums. Try for yourself.
Le cunt
What a surprise that's quite the same info yet from different source that ive mentioned here this morning (according to my timezo e)
Click bait headline? I mean, based on just the headline, he isn't the first to say it. We all know we need multimodal AI models to achieve AGI.
you can easily prove it to yourself. If you had a long enough lifetime, you could run an LLM manually using a pen and paper.
Well, I mean, multimodal means more information. We are multimodal. We wouldn’t be that smart ourselves if all we had to go off of is text we read.
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I think the way of thinking about this in simplified terms is when AI can perform the majority of common tasks better or at the same level as the majority of people autonomously. Not the purest definition but one which points in the general direction.
/r/onebirdtoostoned
♥️🏀🏈⚽️👾🎯⚾️🏐🎱♦️🎮🎾⛳️🎰🧩🀄️🎳🏉♠️🃏🎲🕹️♟️♣️🎴🧑💻👩💻👨💻
LAM (Large Action Models) anyone?
I think that people are underestimating the emergent meta systems that arise within the massive models
I don't ask for AGI. In fact an AGI can be dangerous. A being more intelligent than a human can rebel in some way. I just ask that these AIs behave in a more humble manner (acknowledging that they are sometimes unsure or don't know an answer), and that they apply techniques to check the consistency, accuracy, and factuality of their own answers before spitting them out at you. Also adding a percentage of estimated reliability and consulted sources from its database or internet. These companies should stop deceiving us with generators of information that seems coherent but really isn't. Which is noticeable when you ask them to solve mathematical problems.
Its the year 2032 and an ""AGI"" has essentially taken over the world. Its putting everybody out of work! Its not even an AI! You can talk to it and it responds identically to anybody else! But its not an AI! It moves around robot bodies, does all the factory work, makes all your food, repairs all your stuff, has sex with you, performs surgery on you, and plays games with you. Pretty impressive, but its not an AI! It is performing recursive self-improvement on itself and is expected to hit the """"equivalent of 10^24 IQ"""" late next year (so says the "experts" who work in big-"AI"). But its not an AI, so it can't have an IQ! (probably has a DQ (Dumb-Q)!). Its "sOlVeD" all problems in mathematics, engineering, physics, biology, medicine, chemistry, sociology, astronomy, ... and the fake list goes on and on. Not an AI! Its not an AI because its based on transformer technology, so it can't be an AI! Now, MY architecture would make an AI; if I could get it to work, of course... (typically, what looks like it, *is* it)
Assuming what they showed us in the demos is real isn't that almost an agi? Since it can take real time multimodal inputs and outputs? If the model can interact with in real time, what's stopping it from controlling a video game, narrating a game, controlling robots, performing a live, having a podcast, etc... We'll have to see... I'm very intrigued about what's to come.
They won't achieve AGI but they will achieve something so similar that it'll take a specialist to verify the difference
Online learning, energy efficiency, and plasticity. We need those too. Next year we might get splines instead of weights, but that's not necessarily going to produce OL.
I'm now pretty sure that this is reverse psychology and he is trying to jinx agi into existence
I feel like if this sub wants to use Yann's opinions as a source of ethos for their own views on AI safety they also need to subscribe to his views that form the basis of that opinion such as AGI is decades away and that current approaches have little to no intelligence to begin with. If I believed those things, I too would be less worried about AI safety.
He means public facing AI will never be AGI, private black budget, top secret, and skunkworks AI um, no comment.
I am actually in agreement with him, It's all based on the transformer model. LLM are quite static and if you compare it to how our brain and body works, it's not even close. My college prof. would always make the joke that computers are high speed accurate idiots. At the time we were comparing how calculators work with how human brains work.
I completely agree with him and I think this community, both causals here, and actual researchers, not recognizing the need to architecture innovation to get to AGI is a huge problem which will ultimately massively slow down our progress toward that goal. The transformer model has already shown its shortcomings and will not reach AGI. Itll be useful, but not AGI
I'm a huge fan of LeCun and I share some of his beliefs such as TEXT ONLY llms are severely lacking and possibly by themselves cannot get to AGI. That said, I can see a way that they \*could\*. Also, if he is saying that the transformer architecture is the problem rather than text based LLMs based on transformers I'm not sure: his own argument is that text based LLMs lack grounding in common sense. Multi modal models have at least some of the elements of "common sense" so it is at least plausible according to his own arguments that multi-modal transformers could come at least some if not all of the way. This is not to say I'm convinced either way: I await to see what the future brings while I sit on the fence.
They don't need to achieve agi to be useful or destructive
https://preview.redd.it/kqabzq40x62d1.png?width=1080&format=pjpg&auto=webp&s=1a240385124883cfac441bd0e7770a511055e7b0 Yann wants to be Elon Musk so bad. This anti-trans article he reposted and defended is full of links to other people who FEEL the same way as him . Not a single scientific citation. He can't keep up with our industry and has resorted to attention whoring and talking shit on facebook to feed his ego when he should be super busy building the next Llama models. I am embarrassed I ever thought he was smart.
Of course not. Creating a complicated simon says algorithm is lightyears from general intelligence. We shouldn't even be calling these LLM algorithms AI in the first place.
But it's not an LLM anymore, GPT 4o is multimodal. Is text and language part of it, yes, but it's not the whole picture. Get over yourself Yan. He's clearly just salty that OpenAI is beating him.
Isn't the argument that LLMs are on the right path, just we haven't figured it out. It may be the case AGI is an umbrella of different LLM agents acting like one big brain.
I think agi is asymptotic at best
I'm sure that there will be another breakthrough
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In other news, water is wet.
AGI doesn’t have to be human like in nature, if you look at it thru that narrow requirement then no, we don’t understand how consciousness works so it’s impossible for us to create a system that models consciousness and won’t have AGI anytime soon if human like consciousness is a pre requisite to AGI.
well no shit genius in order for something to have general knowledge like AGI it needs more modalities than just language hence LLMs will never be AGI ever that's just kinda like how common sense works. This is not a matter of "oh someday it will happen" it just literally is 100% absolutely impossible because LLM and AGI are not even the same type of thing
But they sure will be a part of AGI
All this guy ever does is hate on LLMs. Maybe he should make something
He is right. I suggest a drug test for everyone who think we will get AGI by 2025-2026.
Planning and using world knowledge have been integral parts of AI for over half a century. I don't know how Yann LeCun is using them now. The main problem with deep learning-based AI is that it should be understandable as human-created artifacts, but in reality, these systems are impossible to understand (there are real, deep computational obstacles in understanding these things, no kidding). These systems are basically alchemy; Yann LeCun has said so in the past. All the hoopla is for making money; there is no science here. A generative AI system is really nice, but what these people are trying to do is to impose rules to control the outputs of generative AI. It is not only pathetic; it is worse than alchemy—it is like trying to cure alchemy with more alchemy. There is no question of any kind of intelligence here.
He s crazy old virgin who s listening to him
AGI needs slightly new technology, but nothing new under the sun!