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I'm not sure humans meet the definition here. If you took the average human from birth and gave them only 'the most primitive first principles', the chance tha

by Closi 1y ago

I'm not sure humans meet the definition here. If you took the average human from birth and gave them only 'the most primitive first principles', the chance that they would have novel insights into medicine is doubtful. I also disagree with your following statement: > Right now we're trying to essentially train on the entire corpus of human writing. That is a defacto acknowledgement that the absolute endgame for current tech is simple mimicry At worst it's complex mimicry! But I would also say that mimicry is part of intelligence in general and part of how humans discover. It's also easy to see that AI can learn things - you can teach an AI a novel language by feeding in a fairly small amount of words and grammar of example text into context. I also disagree with this statement: > One fundamental difference is that AGI would not need some absurdly massive data dump to become intelligent I don't think how something became intelligent should affect whether it is intelligent or not. These are two different questions.

Jensson 1y ago
> you can teach an AI a novel language by feeding in a fairly small amount of words and grammar of example text into context. You didn't teach it, the model is still the same after you ran that. That is the same as a human following instructions without internalizing the knowledge, he forgets it afterward and didn't learn what he performed. If that was all humans did then there would be no point in school etc, but humans do so much more than that. As long as LLM are like an Alzheimer's human they will never become a general intelligence. And following instructions is not learning at all, learning is building an internal model for those instructions that is more efficient and general than the instructions themselves, humans do that and that is how we manage to advance science and knowledge.
- Closi 1y ago
  It depends what you count as learning - you told it something, and it then applied that new knowledge, and if you come back to that conversation in 10 years, it will still have that new knowledge and be able to use it. Then when OpenAI does another training run it can also internalise that knowledge into the weights. This is much like humans - we have short term memory (where it doesn't get into the internal model) and then things get baked into long term memory during sleep. AI's have context-level memory, and then that learning gets baked into the model during additional training. Although whether or not it changed the weights IMO is not a prerequisite for whether something can learn something or not. I think we should be able to evaluate if something can learn by looking at it as a black-box, and we could make a black-box which would meet this definition if you spoke to a LLM and limited it to it's max context length each day, and then ran an overnight training run to incorporate learned knowledge into weights.

by Closi 1y ago

Jensson 1y ago

> you can teach an AI a novel language by feeding in a fairly small amount of words and grammar of example text into context. You didn't teach it, the model is still the same after you ran that. That is the same as a human following instructions without internalizing the knowledge, he forgets it afterward and didn't learn what he performed. If that was all humans did then there would be no point in school etc, but humans do so much more than that. As long as LLM are like an Alzheimer's human they will never become a general intelligence. And following instructions is not learning at all, learning is building an internal model for those instructions that is more efficient and general than the instructions themselves, humans do that and that is how we manage to advance science and knowledge.

Closi 1y ago
It depends what you count as learning - you told it something, and it then applied that new knowledge, and if you come back to that conversation in 10 years, it will still have that new knowledge and be able to use it. Then when OpenAI does another training run it can also internalise that knowledge into the weights. This is much like humans - we have short term memory (where it doesn't get into the internal model) and then things get baked into long term memory during sleep. AI's have context-level memory, and then that learning gets baked into the model during additional training. Although whether or not it changed the weights IMO is not a prerequisite for whether something can learn something or not. I think we should be able to evaluate if something can learn by looking at it as a black-box, and we could make a black-box which would meet this definition if you spoke to a LLM and limited it to it's max context length each day, and then ran an overnight training run to incorporate learned knowledge into weights.