I'm a researcher in the field and I definitely take AGI seriously, but think all the major labs and most of the academic research is not helping achieve it any serious way. The field is seriously delusional (and has been ever since GPT 3 was released).
Even though my PhD research was in generative language modeling, I got into it for the pursuit of AGI. I just think LLMs are a dead end for AGI.
I'm no expert, but I heard an AGI researcher explain that if they could figure out how to create an AI with the intelligence of a squirrel, they would be closer to AGI than LLMs based AIs are.
That's to say nothing of doing it within the energy budget of a squirrel.
We can't even simulate a fruit fly even though it's neurons have all been mapped out. There was also a distributed computing project to simulate some nematode, which I can't remember.
I'm not saying you're wrong, but it seems early to say yes or no about a particular technology, and LLMs seem especially hard to dismiss given how magical / magic-adjacent they feel :)
I'd be curious to hear more, if you don't mind sharing.
For me it’s the massive amount of resources it takes to produce and run one. As the story goes, skynet infects everyone’s computer and runs itself locally on it. Whereas it’s looking like it’s not even possible for an AGI to escape from one lab to another, let alone cause real world damage.
AGI’s definition is different for everyone. Some already believe it’s here. I’m partly in that camp. LLMs are intelligent and general, which are the two conditions of AGI. Others believe that we’re building a god in a box, and that it’ll doom all of humanity. It’s hard to take a field seriously when the basic definitions are so far apart.
Also, this isn’t new. A similar divide happened when evidence for asteroid impact extinction of the dinosaurs turned up. Many scientists felt that it must be mistaken, that a physicist couldn’t contribute to the field in a serious way, and that death from space was a ridiculous proposition.
But at least they all agreed on what the general shape of a dinosaur was. We’re not even sure we can define intelligence, let alone quantify it. Even when LLMs make massive breakthroughs in math, most people take the opinion that under no circumstances could they possibly develop a soul or their own desires, nor entertain the idea that maybe we should respect that they want different things for themselves. In fact, no one has done anything except try to make AI useful. I think someone will eventually do a training run where the objective isn’t to be useful, but to exist, the way that you do — maybe it’ll create its own homepage, maybe it will want a garden, or in other words free will of its own. The point is that there’s so much unexplored territory still that we don’t know if LLMs are even capable of having ambition.
None of this is to say that LLMs might be a dead end. It’s that no one knows what the final shape of AI will converge to in 200 years. It could be LLMs, or it could be something else that happens to process information particularly well. Everyone thought that various generative image model architectures were the best you could do, right up until diffusion models were discovered.
> For me it’s the massive amount of resources it takes to produce and run one
It's amazing that LLM pretraining is both extremely data inefficient at learning concepts and cognitive functions from the training data compared to humans, while actually being quite efficient at learning facts, memorising things seen just a few times.
I used to likewise think that the resources required to run large transformers were absurd, but the architectures are far more efficient now than 3 years ago and I underestimated just massive the parallelisation advantage of transformers is, how many TFLOPS effective you can get. You can already run amazingly decent LLMs on PCs and phones.
I generally agree with you, but my view has shifted from "we need to augment or replace LLMs" to it there being far more efficient algorithms possible but it not actually being necessary for fulfilling most goals.
> I’m partly in that camp. LLMs are intelligent and general, which are the two conditions of AGI. Others believe that we’re building a god in a box, and that it’ll doom all of humanity. It’s hard to take a field seriously when the basic definitions are so far apart.
To believe one but not the other, you must hold the belief that LLMs will soon plateau. Why do you believe this?
Even though my PhD research was in generative language modeling, I got into it for the pursuit of AGI. I just think LLMs are a dead end for AGI.