[Terretta]

> thinking how every neural network is equivalent to a lookup table where the input is all numbers up to what can be expressed within the context window and the output is the result of the arithmetic operations applied to that number... no neural network is actually doing any thinking other than uncompressing the table and looking up the value corresponding to the input number

You're proposing the lookup table as one possible mechanism in Searle's chinese room, then proposing Searle's conclusion?

“Searle argues that, without ‘understanding’ (or ‘intentionality’), we cannot describe what the machine is doing as ‘thinking’ and, since it does not think, it does not have a ‘mind’ in anything like the normal sense of the word. Therefore, he concludes that the ‘strong AI’ hypothesis is false.‘

https://en.wikipedia.org/wiki/Chinese_room

I think you've said Chinese room, run as many times as it takes to get all possible sequences of Chinese characters to cache the results, then using those run it and ask if it's still or yet ‘thinking’.

PS. Where did the arithmetic operations come from? How did they come to be as they are? Is iterating to an algo that does that, ‘learning’? What's the difference between this and lossy or non-lossy compression of information? Could it be said the arithmetic operations are a compression of the lookup table into that which has the ‘right’ response given the inputs? If two different sets of arithmetic operations give by and large the same outputs from inputs, is one of them more ‘reasoning’ than the other depending how it's derived? What do we mean by ‘learning’ and ‘reasoning’ when applying those words to humans? Are teachers telling students to ‘show your work’ searching for explainable intelligence? :-)