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A child can 'see' maths though, they can see that if you have one apple over here and one orange over there, then you have two pieces of fruit all together.

If you only ever allowed a child to read about adding, without ever being able to physically experiment with putting pieces together and counting them, likely children would not be able to add either.

In fact, many teachers and schools teach children to add using blocks and physical manipulation of objects, not by giving countless examples and documents discussing addition and procedures of addition.

You may feel it's conclusive, and it's your right to think that. I am not sure.



Yet ChatGPT totally - apparently - gets 1 + 1. In fact it aces the addition table way beyond what a child or even your average adult can handle. It's only when you get to numbers in the billions that it's weaknesses become apparent. One thing it starts messing up is carry-over operations, from what I can see. Btw. the treshold used to be significanly lower, yet that doesn't convince me in the least that it's made progress in its understanding of addition. It's still just as much in the fog. And it cannot introspect and tell me what it's doing so I can point out where it's going wrong.

But I think you are right in what you are saying. Basically it not 'seeing' math as a child does, is just another way to say that it doesn't undestand math. It doesn't have a intuitive understanding of numbers. It also can't really experiment. What would experimenting mean in this context? Just more training cycles. This being math, one could have it run random sums and give it the correct answer each time. That's one way to experiment, but that wouldn't solve the issue. At some point it would reach its capacity of absorbing statistical corelations to deal with numbers large enough. It would need more neurons to progress beyond that stage.

Btw. I found this relevant article: https://bdtechtalks.com/2022/06/27/large-language-models-log...


That’s an interesting read, thank you. But my question is a bit more fundamental than that.

Ultimately, my point is that although the argument is that an LLM doesn’t “know” anything, I am not sure that there is something categorically different in terms of what we “know” vs what an LLM “knows”, we have just had more training on more different types of data (and the ability to experiment for ourselves).




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