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Nameless1995 t1_j0a97yt wrote

Reply to comment by Purplekeyboard in [R] Talking About Large Language Models - Murray Shanahan 2022 by Singularian2501

> But it would all be nonsense.

Modeling the data generating rules (even if arbitrarily created rules) and relations from data, seems to be close to "understanding". I don't know what would even count as a positive conception of understanding. In our case, the data that we recieve is not just generated by an arbitrarily created algorithm, but by the world - and so the models we create helps us orient better to the world and in that sense "more senseful", but at a functional level not necessarily fundamentally different.

More this applies to any "intelligent agent". If you feed it arbitrary procedurally generated data what it can "understand" will be restricted to that specific domain (and not reach the larger world).

> GPT-3 only knows the text world, it only knows what words tend to follow what other words.

One thing to note that text world is not just something that exists in the air, it is a part of the larget world and created by social interactions. In essence they are "offline" expert demonstrations in virtual worlds (forums, QA, reviews, critics etc.).

However, obviously, GPT3 cannot go beyond that, and cannot comprehend the multimodal associations (images, proprioception, bodily signals etc.) beyond text (it can still associate different sub-modalities within text like programs vs natural texts and so on), and whatever it "understands" would be far alien from what a human understands (having much limited text data, but much richer multimodally embodied data). But that doesn't mean it doesn't have any form of understanding (understood in a functionalist (multiply realizable) sense -- ignoring any matter about "phenomenal consciousness") at all; and moreover, none of these mean somehow "making likely prediction from statistics" is dichotomous with understanding.

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Purplekeyboard t1_j0aayw0 wrote

One thing that impresses me about GPT-3 (the best of the language models I've been able to use) is that it is functionally able to synthesize information it has about the world to produce conclusions that aren't in its training material.

I've used a chat bot prompt (and now ChatGPT) to have a conversation with GPT-3 regarding whether it is dangerous for a person to be upstairs in a house if there is a great white shark in the basement. GPT-3, speaking as a chat partner, told me that it is not dangerous because sharks can't climb stairs.

ChatGPT insisted that it was highly unlikely that a great white shark would be in a basement, and after I asked it what would happen if someone filled the basement with water and put the shark there, once again said that sharks lack the ability to move from the basement of a house to the upstairs.

This is not information that is in its training material, there are no conversations on the internet or anywhere about sharks being in basements or unable to climb stairs. This is a novel situation, one that has not been discussed anywhere likely before, and GPT-3 can take what it does know about sharks and use it to conclude that I am safe in the upstairs of my house from the shark in the basement.

So we've managed to create intelligence (text world intelligence) without awareness.

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