In some ways, you are correct. It is coming though. The psychological/neurological word you are searching for is “conceptualization”. The AI models lack the ability to abstract the text they know into the abstract ideas of the objects, at least in the same way humans do. Technically the ability to say “show me a chair” and it returns images of a chair, then following up with “show me things related to the last thing you showed me” and it shows couches, butts, tables, etc. is a conceptual abstraction of a sort. The issue comes when you ask “why are those things related to the first thing?” It is coming, but it will be a little while before it is able to describe the abstraction it just did, but it is capable of the first stage at least.
It doesn’t need to understand the words to perform logic because the logic was already performed by humans who encoded their knowledge into words. It’s not reasoning, but the reasoning was already done by humans. It’s not perfect of course since it’s still based on probability, but the fact that it can pull the correct sequence of words to exhibit logic is incredibly powerful. The main hard part of working with LLMs is that they break randomly, so harnessing their power will be a matter of programming in multiple levels of safe guards.
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https://en.m.wikipedia.org/wiki/Chinese_room
I think they’re wrong, as it happens, but that’s the argument.
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In some ways, you are correct. It is coming though. The psychological/neurological word you are searching for is “conceptualization”. The AI models lack the ability to abstract the text they know into the abstract ideas of the objects, at least in the same way humans do. Technically the ability to say “show me a chair” and it returns images of a chair, then following up with “show me things related to the last thing you showed me” and it shows couches, butts, tables, etc. is a conceptual abstraction of a sort. The issue comes when you ask “why are those things related to the first thing?” It is coming, but it will be a little while before it is able to describe the abstraction it just did, but it is capable of the first stage at least.
Some systems clearly do that though or are you just talking about llms?
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It’s like saying bro, this mouse can’t even type text if I don’t use an on screen keyboard
It doesn’t need to understand the words to perform logic because the logic was already performed by humans who encoded their knowledge into words. It’s not reasoning, but the reasoning was already done by humans. It’s not perfect of course since it’s still based on probability, but the fact that it can pull the correct sequence of words to exhibit logic is incredibly powerful. The main hard part of working with LLMs is that they break randomly, so harnessing their power will be a matter of programming in multiple levels of safe guards.