How the hell can people be so confident about this? You describe two smart people reasonably disagreeing about a complicated topic
Given that AGI means reaching "any intellectual task that human beings can perform", we need a system that can go beyond lexical reasoning and actually contribute (on it's own) to advance our total knowledge. Anything less isn't AGI.
Ilya may be right that a super-scaled transformer model (with additional mechanics beyond today's LLMs) will achieve AGI, or he may be wrong.
Therefore something more than an LLM is needed to reach AGI, what that is, we don't yet know!
Without persistence outside of the context window, they can't even maintain a dynamic, stable higher level goal.
Whether you can bolt something small to these architectures for persistence and do some small things and get AGI is an open question, but what we have is clearly insufficient by design.
I expect it's something in-between: our current approaches are a fertile ground for improving towards AGI, but it's also not a trivial further step to get there.
My beef with RAG is that it doesn't match on information that is not explicit in the text, so "the fourth word of this phrase" won't embed like the word "of", or "Bruce Willis' mother's first name" won't match with "Marlene". To fix this issue we need to draw chain-of-thought inferences from the chunks we index in the RAG system.
So my conclusion is that maybe we got the model all right but the data is too messy, we need to improve the data by studying it with the model prior to indexing. That would also fix the memory issues.
Everyone is over focusing on models to the detriment of thinking about the data. But models are just data gradients stacked up, we forget that. All the smarts the model has come from the data. We need data improvement more than model improvement.
Just consider the "Textbook quality data" paper Phi-1.5 and Orca datasets, they show that diverse chain of thought synthetic data is 5x better than organic text.
Nope, and not all people can achieve this as well. Would you call them less than humans than? I assume you wouldn't, as it is not only sentience of current events that maketh man. If you disagree, then we simply have fundamental disagreements on what maketh man, thus there is no way we'd have agreed in the first place.
I don't claim that RAG + LLM = AGI, but I do think it takes you a long way toward goal-oriented, autonomous agents with at least a degree of intelligence.
I mean, can't you say the same for people? We are easily confused and manipulated, for the most part.
You're right: I haven't seen evidence of LLM novel pattern output that is basically creative.
It can find and remix patterns where there are pre-existing rules and maps that detail where they are and how to use them (ie: grammar, phonics, or an index). But it can't, whatsoever, expose new patterns. At least public facing LLM's can't. They can't abstract.
I think that this is an important distinction when speaking of AI pattern finding, as the language tends to imply AGI behavior.
But abstraction (as perhaps the actual marker of AGI) is so different from what they can do now that it essentially seems to be futurism whose footpath hasn't yet been found let alone traversed.
When they can find novel patterns across prior seemingly unconnected concepts, then they will be onto something. When "AI" begins to see the hidden mirrors so to speak.
Who cares? Sometimes the remixation of such patterns is what leads to new insights in us humans. It is dumb to think that remixing has no material benefit, especially when it clearly does.
The only think flawed here is this statement. Are you even familiar with the premise of Turing test?
