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.
I feel there are potential parallels between RAG and how human memory works. When we humans are prompted, I suspect we engage in some sort of relevant memory retrieval process and the retrieved memories are packaged up and factored in to our mental processing triggered by the prompt. This seems similar to RAG, where my understanding is that some sort of semantic search is conducted over a database of embeddings (essentially, "relevant memories") and then shoved into the prompt as additional context. Bigger context window allows for more "memories" to contextualise/inform the model's answer.
I've been wondering three things: (1) are previous user prompts and model answers also converted to embeddings and stored in the embedding database, as new "memories", essentially making the model "smarter" as it accumulates more "experiences" (2) could these "memories" be stored alongside a salience score of some kind that increases the chance of retrieval (with the salience score probably some composite of recency and perhaps degree of positive feedback from the original user?) (3) could you take these new "memories" and use them to incrementally retrain the model for, say, 8 hours every night? :)
Edit: And if you did (3), would that mean even with a temperature set at 0 the model might output one response to a prompt today, and a different response to an identical prompt tomorrow, due to the additional "experience" it has accumulated?
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.
Most of that is encoded into weights during training, though external function call interfaces and RAG are broadening this.
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.
I most probably am anthropomorphizing completely wrong. But point is humans may not be any more creative than an LLM, just that we have better computation and inputs. Maybe creativity is akin to LLMs hallucinations.
