Sam claims LLMs aren't sufficient for AGI (rightfully so).
Ilya claims the transformer architecture, with some modification for efficiency, is actually sufficient for AGI.
Obviously transformers are the core component of LLMs today, and the devil is in the details (a future model may resemble the transformers of today, while also being dynamic in terms of training data/experience), but the jury is still out.
In either case, publicly disagreeing on the future direction of OpenAI may be indicative of deeper problems internally.
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.
I mean, can't you say the same for people? We are easily confused and manipulated, for the most part.
I can reason about something and then combine it with something I reasoned about at a different time.
I can learn new tasks.
I can pick a goal of my own choosing and then still be working towards it intermittently weeks later.
The examples we have now of GPT LLM cannot do these things. Doing those things may be a small change, or may not be tractable for these architectures to do at all... but it's probably in-between: hard but can be "tacked on."
Our brain actually uses many different functions for all of these things. Intelligence is incredibly complex.
But also, you don't need all of these to have real intelligence. People can problem solve without memory, since those are different things. People can intelligently problem-solve without a task.
And working towards long-term goals is something we actually take decades to learn. And many fail there as well.
I wouldn't be surprised if, just like in our brain, we'll start adding other modalities that improve memory, planning, etc etc. Seems that they started doing this with the vision update in GPT-4.
I wouldn't be surprised if these LLMs really become the backbone of the AGI. But this is science– You don't really know what'll work until you do it.