@lmorchard this is an interesting perspective, thanks for sharing it.
I would imagine the attribution would be a mess. I'm not sure it's possible to draw a line from a particular input to a particular output when using machine learning. Even if you could it might be something like 1/10,000 from source A, 7/100,000 from source B, on and on for thousands of inputs.
@bcgoss I'm less versed in the ML algorithms than I'd like, but I'd like to imagine legible attribution might get easier the more specific the code suggestion gets.
Which could also be a warning sign that it's "filing the serial numbers off" something specific and to decide not to accept the suggestion.
That said, a legible process seems like a non-trivial change from the ground up
@lmorchard I've made neural nets as a hobbyist, and one attempt at a k-nearest neighbor classifier for work. In both, the algo has a complex set of weighted equations. The weights are tuned to the "right" value using "reinforcement". Given an input and an expected output, weights are changed one direction if the actual output matches the expected output, another direction if it's different.
In the end all you have are the weights and the algorithm, no real connection to the inputs.
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