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> you don't need any percepts, or experience of reality or simulated unreality to know". That's a big leap, and I can't disagree more.

I still feel this the the point where you're making a difference based on you desired outcome vs the actual system. ChatGPT absolutely does have precepts / a sense. It has a sense of "textual language". It also has a level of sequencing or time w.r.t. word order of that text.

While you're saying experience, it seems like in your definition experience only counts if there is a spatial component to it. Any experience without a physical spatial component to you seems like it's not valid sense or perception.

Again taking this in the specific, imagine someone could only hear via one ear, and that is their only sense. So there is no multi-dimensional positioning of audio, just auditory input. It's clear to me that person can still know things. Now if you also made all audio the same loudness so there is no concept of distance with it, it still would know things. This is now the same a simple audio stream, just like ChatGPT's langauge stream. Spatial existence is not required for knowledge. And from what I'm understanding that is what underpins your definition of a reality/experience (whether physical or virtual).

Or as a final example lets say you are Magnus Carlson. You know a ton about chess, best in the world. You know so much about chess that you can play entire games via chess notation (1. e4, e6 2. d4 e5 ...). So now an alternate world where there is even a version of Magnus that has never sat in front of a chess board and only ever learned chess by people reciting move notation to him. Does the fact that no physical chess boards exist and there is no reality/environment where chess exists mean he doesn't know chess? Even if chess were nothing but streams of move notations it still would be the same game, and someone could still be an expert at it knowing more than anyone else.

I feel your intuition is leading your logic astray here. There is no need for a physical or virtual environment/reality for something to know.



You're still fighting a strawman. You're the only participant in this thread that's talking about space. I'm going to discontinue this conversation with this message since (aptly), you seem happy responding to views whether or not they come from an actual interlocutor.

- I disagree that inputs to an LLM as a sequence of encoded tokens constitute a "a sense" or "percepts". If inputs are not related to any external reality, I don't consider those to be perception, any more than any numpy array I feed to any function is a "percept".

- I think you're begging the question by trying to start with a person and strip down their perceptual universe. I think that comes with a bunch of unstated structural assumptions which just aren't true for LLMs. I think space/distance/directionality aren't necessary for knowing some things (but bags, chocolate and popcorn as lsy raised at the root of this tree probably require notions of space). I can imagine a knowing agent whose senses are temperature and chemosensors, and whose action space is related to manipulating chemical reactions, perhaps. But I think action, causality and time are important for knowing almost anything related to agenthood, and these are structurally absent in ChatGPT UUIC. The RLHF loop used for Instruct/ChatGPT is a bandit setup. The "episodes" it's playing over are just single prompt-response opportunities. It is _not_ considering "If I say X, the human is likely to respond Y, an I can then say Z for a high reward". Though we interact with ChatGPT through a sequence of messages, it doesn't even know what it just said; my understanding is the system has to re-feed the preceding conversation as part of the prompt. In part, this is architecturally handy, in that every request can be answered by whichever instance the load-balancer picks. You're likely not talking to the same instance, so it's good that it doesn't have to reason about or model state.

But I actually think both of these are avenues towards agents which might actually have a kind of ToM. If you bundled the transformer model inside a kind of RNN, where it could preserve hidden state across the sequence of a conversation, and if you trained the RLHF on long conversations of the right sort, it would be pushed to develop some model of the person it's talking to, and the causes between its responses and the human responses. It still wouldn't know what a bag is, but it could better know what conversation is.




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