The current paradox is: Why can you evolve differential computing but not be able to do as well with discrete computing? Why does evolutionary methods not work as well as gradient descent in learning?
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Why is that a paradox? Directed search is going to be more efficient than undirected search. Large search spaces are often only tractable when you discover and exploit gradients.
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this argument doesn't work because hypercomputers aren't Turing machines – so the standard halting contradiction proof fails since you can't simulate a hypercomputer on a TM
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you might be interested in reading about what happens when you augment a TM with an oracle that can magically decide the halting problem though – the new machine will have its own halting problem! and you get the arithmetical hierarchy https://en.wikipedia.org/wiki/Arithmetical_hierarchy#Relation_to_Turing_machines …
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Also, so far, Universe seems to be discreet anyway? (as in quantum physics)
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Yes, but most of current physics is built on continuous foundations and assumes that the observed discreteness is an emergent result of wave harmonics.
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Wait you're saying we're not yet sure the universe is discrete? I thought that was pretty much established since the 90s?

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No, digital physics is still fringe. Even QM is not time discrete.
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this plus https://twitter.com/browserdotsys/status/1009272621500960768 … have soothed me
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