Comment by HarHarVeryFunny
10 hours ago
I'm not sure there is anything to disagree on here - just different definitions of what we personally associate the term "AGI" with. There is no magic to the name, and however you choose to define it there will be less and more capable levels of AI that precede and follow it.
Separate from what you choose to call any given set of AI capabilities, learning is generally more than retaining change over time. Learning isn't the same as memorization - it's the difference between memorizing a stack of how-to books and going out and practicing the skill for yourself, and all that entails.
A learnt skill needs to be internalized such that in the future it's what is driving behavior, which means it needs to be in the weights. Imagine a robot trying to learn to play tennis where rather than updating it's connections between perception and action it's trying to store everything as a bunch of notes!
It's not just physical tasks where this applies - the same would be true to trying to teach a multimodal model to recognize different types of mushrooms from photos, or even learning a new language. If a system can truly learn for itself then it shouldn't need to be pre-trained with language, but rather should be able to learn by immersion and practice the same way we do, and the same way a dog learns to follow commands. You can't do this by leaving notes to yourself - the learning mechanism needs to be animal-like and update the systems weights in response to prediction failure - a runtime version of the way SGD-based pre-training updates weights in response to prediction errors.
> If a system can truly learn for itself then it shouldn't need to be pre-trained with language, but rather should be able to learn by immersion and practice the same way we do, and the same way a dog learns to follow commands. You can't do this by leaving notes to yourself - the learning mechanism needs to be animal-like and update the systems weights in response to prediction failure - a runtime version of the way SGD-based pre-training updates weights in response to prediction errors
I am not sure why any of that is necessarily true. I think you are crediting the human brain with a lot more than it actually does - memory is something in the direction of a fine tune on existing neural hardware, it's really not anything special, and I absolutely think that "just taking notes", when taken to a sufficient fidelity, is enough to pass that bar.
You can't take notes before you've learned language, so if that's how you propose to learn then you are doomed to build something with less learning capability than an human brain.
Maybe you don't care - if you just want to push to see how far an LLM can go that's fine. but it's not going to be superhuman-level, or even human-level, if it doesn't have the learning ability of a human. If it relies on language and notes to itself then it is just an LLM.
Okay, nowhere did I say it would be before they learned language. I said we could use models that can trivially build notetaking systems to auto-improve themselves by taking, refining, and updating notes as a memory system to exceed the x token context threshold.
> but it's not going to be superhuman-level, or even human-level, if it doesn't have the learning ability of a human. If it relies on language and notes to itself then it is just an LLM.
Why do you think that? You do a lot of assuming a thing to be true without any clear reasoning or evidence. I'm talking about a system, including a LLM, that acts in a way that is equivalent to or better than an average human at a large range of tasks, such that is generally capable of learning and problem solving similar to the degree of success humans have. All these specific criteria about what is "actually human" is not the point. We know how to make more humans, I'm talking about a form of intelligence that we are creating that is genuinely alien, so holding to "well it doesn't work the same as a human" is an impossible bar. If you reverse the situation, humans are by no means LLM-equivalent intelligences - perfect memory of long form content, rapid calculation, instantaneous tool calling - a hypothetical silicon based lifeform would dismiss us as "really not intelligent at all, they can't even do multiple floating point calculations per second in their head", and I think that would be similarly silly.