She's right! So-called "subtraction" is really just addicating a negative number.
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Deep learning feels as memorizing how to add all possible numbers, hoping that if the model is large enough it will appear as being able to ‘add’. And for narrow (increasingly wider) use cases it works. But we know there will always be a pair of numbers that will ‘break’ it.
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Then the next is that real numbers are not real.
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