really? so what is the bright red line? amount of compute used? use of linear algebra? Is https://github.com/google-deepmind/c3_neural_compression AI enough? </snrk>

Because I really want to see difference between approximating input signal using smooth orthogonal basis functions, and approximating those using quantization with rate/distortion feedback as "violates copyright, compression algorithm", and then approximating input signal using perceptron, with backprop loss based off original media as "not compression algorithm, skip jail"