So it's no wonder artists would denounce generative AI as mass-plagiarism when it showed up. It's also no wonder that a bunch of tech entrepreneurs and data janitors wouldn't understand this at all, and would in fact embrace the plagiarism wholesale, training their models on every pirated shadow library they can get. Or indeed, every code repository out there.
NFAs are cheaper to construct, but have a O(n*m) matching time, where n is the size of the input and m is the size of the state graph. NFAs are often seen as the reasonable middle ground, but i disagree and will argue that NFAs are worse than the other two. they are theoretically “linear”, but in practice they do not perform as well as DFAs (in the average case they are also much slower than backtracking). they spend the complexity in the wrong place - why would i want matching to be slow?! that’s where most of the time is spent. the problem is that m can be arbitrarily large, and putting a large constant of let’s say 1000 on top of n will make matching 1000x slower. just not acceptable for real workloads, the benchmarks speak for themselves here.
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