> Also, probably something like the creation of the image of the black hole is closer to computational photography than “AI”, and it seems a bit like yours is a populist argument against it.
I do have reservations about that image, and don't consider it a photograph, because it took lots of crazy math to assemble it from a weak signal, and as anyone in software who ever wrote simulations should know, it's very hard to notice subtle mistakes when they give you results you expected.
However, this was a high-profile case with a lot of much smarter and more experienced people than me looking into it, so I expect they'd raise some flags if the math wasn't solid.
(What I consider precedent to highly opaque computational photography is MRI - the art and craft of producing highly-detailed brain images from magnetic field measurements and a fuck ton of obscure maths. This works, but no one calls MRI scans "photos".)
So where do you draw the line? Summation and multiplication of signals is fine, but conditionals are bad?
That’s just arbitrary bullshitting around a non-existing problem.
Hell, it is pretty easy to check iphone’s performance with measurement metrics — make the same photo in identical environments with the iphone and with a camera with much bigger sensors and compare the results. Hell, that’s literally how apple calibrated their algorithm.
> So where do you draw the line? Summation and multiplication of signals is fine, but conditionals are bad?
Effectively, yes. Hard conditionals create discontinuities in the mathematical sense. So while I'm not sure of the rest of line's path, conditionals are on it (and using generative AI to hide discontinuities doubly so!).
In general, I'm fine with information loss. I'm not fine with adding information that wasn't in the photo originally.
I do have reservations about that image, and don't consider it a photograph, because it took lots of crazy math to assemble it from a weak signal, and as anyone in software who ever wrote simulations should know, it's very hard to notice subtle mistakes when they give you results you expected.
However, this was a high-profile case with a lot of much smarter and more experienced people than me looking into it, so I expect they'd raise some flags if the math wasn't solid.
(What I consider precedent to highly opaque computational photography is MRI - the art and craft of producing highly-detailed brain images from magnetic field measurements and a fuck ton of obscure maths. This works, but no one calls MRI scans "photos".)