I often think the problem with LLMs is just with training. I think there exists a set of weights such that it produces an LLM that is functionally an agi.

Maybe self evolution will solve the training problem? Who knows.

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cjonas 4 days ago | parent | next [-]

The problem with LLMs reaching true AGI is it's basically "static" intelligence. Changing code, context, prompts and even fine tuning can improve output, but is still far from realtime learning.

The "weights" in our brains are constantly evolving.

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uripont 4 days ago | parent | next [-]

Interesting. The reason why companies aren't trying their best yet into non-static weights/online learning is probably (cloud) logistics. It seems simpler, easier and cheaper to serve a static, well-evaluated, and tuned model, rather than trying to let it learn alongside a specific user or all users.

	▲	cjonas 4 days ago \| parent [-]
		Oh absolutely. To be clear... I think this is probably a bad idea. It probably wouldn't be successful and if it was you'd have very little control of how it evolves.

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ninetyninenine 4 days ago | parent | prev [-]

Have you seen memento? Humans can be intelligent while losing the ability to learn and form new memories. See here: https://my.clevelandclinic.org/health/diseases/23221-anterog...

It is categorically wrong that non static learning is a requirement of agi. The biggest problem we face is hallucinations and this isn’t caused by the fact that agi can’t learn on the fly.

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wahern 3 days ago | parent | next [-]

Doesn't Memento prove the opposite? The character was basically stuck in a loop, and was taken advantage of by someone who held the real agency. (Notwithstanding the "happy ending" conceit at the end that resolves the audience's uncomfortableness with this lack of agency by intimating he may still possess some minimal agency despite the big reveal.)

But in any event, drawing conclusions about the real world from a fictional story seems fraught.

	▲	ninetyninenine 3 days ago \| parent [-]
		Leonard was still an intelligent entity despite lacking the ability to learn. In fact the amount of limited agency Leonard had is similar to the limits in agency we want for an agi that serves humanity. Anyway my main point is Leonard has intelligence well past what we expect from an agi. So because people like Leonard are documented to actually exist one can say live learning is not really needed to have Leonard levels ( aka agi levels) of intelligence

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mannykannot 4 days ago | parent | prev [-]

I take it that you are referring to the movie Memento? I had not heard of it, but I'll put it on my watch list.

I take your point about the non-necessity of dynamic learning for AGI.

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ivape 4 days ago | parent | prev | next [-]

Even the greatest LLM will only just give you a snapshot of a perceived world state. You’ll only ever get one state, input, to output. Each snapshot in sequence is what will perceptively appear to us as AGI initially.

If we stick with the frames analogy, we know the frames of a movie will never give us a true living and moving person (it will never be real). When we watch a movie, we believe we are seeing a living breathing thing that is deliberate in its existence, but we know that is not true.

So what the hell would real AGI be? Given that you provide the input, it can only ever be a super human augmentation. That along with your own biological world state forming, you have an additional computed world state that you can merge with your biological world state.

We will be AGI, is the implication. Perfect weights will never be perfect because they are historical. We have to embrace being part of the AI to maximize its potential to be AGI.

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voodooEntity 4 days ago | parent | prev [-]

I think, while i agree to "problem with LLMs is just with training" i also think to a certain degree we need to step back from LLM's as in text processors and to achieve "AI" as in something really intelligent we need to go more abstract back to NN and build a self learning "entity". While LLM's accomplish fascinating results, we are trying to force speech as the primary way of learning, tho this is a really limiting factor. If we would accomplish to create an NN driven AI in a virtual space which would have an simulated environment and learn from a base state like a "newborn" it still could accomplish the skills to understand language as we humans prefer to use it, tho it wouldn't be limited in "thinking" in and only based on this.

I know this is a very simple and abstract way to explain it but i think you get my point.

Towards the simulated AI learning environment, theres this interview with Jensen Huang that i can recommend in which he touches on the topic and how nvidia is working on such https://www.youtube.com/watch?v=7ARBJQn6QkM

While im not a "expert" in this topic, i might have spend quite a portion of the past 10 years in my freetime to think about it and tinker, and ill stick with the point - we need a free self-trained system to actually call it AI, and while LLM's as GPT's nowadays are powerfull tools, for me those are not "Artificial Intelligence" (intelligence from my pov must include reasoning, understanding of its own action, pro-active acting, self-awareness). And even tho the LLM's we use can "answer" to certain questions as if they would have any of those, its just pre-trained answers and they dont bring any of those (we work on reasoning but lets be fair its not that great yet).

Just my two cents.