Ah, I see the disconnect and I think it's my own fault. Everything you are saying is totally true, but I think we are talking about two different parts of the same overall thing. You said "the model". I know exactly what you mean with your weather example and it's kind of a big deal when you think about it. I always wondered how scientists did "studies" and "research" with AI with all it's current limitations but at the time, I was still stuck on thinking AI = Chat Bot or Image Generator or what-have-you. The input vectors can represent anything and whether or not the system can produce anything useful with the data--that's all down to the training. But this isn't what I'm trying to point at.
Not the model. The model is a combination of multiple processes. There is a surrounding framework involved.
I'm talking about the system at the core of it all. Not just ChatGPT or whatever, but any feed-forward neural network at all.
The very concept of neural network technology appears to be an invariable expression of the idea of "navigation". As a matter of fact, there's nothing stopping anyone from building a 4x4 input neural network specifically for navigating the probabilities of a 4D environment. Once you cross that 4th dimension, you are plotting points in higher-dimensional space. That, as far as I can tell, is the function of a feed-forward of a neural network. I hate to reduce such a neat thing to such a description but... It's like a fancy graphing calculator. You could do the math by hand yourself, but you wouldn't want to lol.
I hope that makes sense.
"And it does have a memory for your session, but actually it does not have a memory. Just as you said, the previous text is just fed in again."
This part, I gotta push back on--respectfully--but hear me out: That's not memory. That's repetition. Which why it only works in the same session. Like I said, it isn't a conversation it's co-creating a composition but you go once and ChatGPT goes ~34,000 times per word of it's "replies". It isn't talking to you or me, it's talking to the conversation as a whole. New session, different co-composed conversation.
I noticed this before I studied neural networks, when I got real into setting up my own models on my PC.
The more "memory" my models "remembered", the worse they performed. Tracking the source, sure enough, there was no real "memory" but instead just brute-force reapplication of data over and over and over, chewing up token limits and taking up context space.
In my opinion, "memory" is one of the worst things you could possibly give a modern AI in any business environment. Any access to irrelevant context, in any way, opens the door to compounding instability. The current way of faking "memory" is probably what leads to a lot of cybersecurity woes.