Nice. I have daydreamed about something like this for years, and this is not it.
I'd imagine a series of left/right comparisons, like a visit to an eye doctor, where the machine learning is rewarded for its ability to predict my preferences. Eventually (a time commitment for me) it will be able to build from scratch color designs that I love.
This is like an early application of machine learning: What are the odds of victory for this backgammon position? Here, instead, we've estimating a preference function on color triples. Is RGB even the right domain, or do we want to work in some frequency transform, to capture the equivalent to musical chords. This is an empirical question, that can only be answered by trying to estimate this preference function, and noticing ripples better resolved by a different parametrization.
This would be easy, compared to the Riemannian geometry used in medical imaging. There's more money there.
For commercial use one cares what others think. There's the speciation question: You won't synthesize deep jazz tracks and deep blues tracks without separating the advice into species. Identifying clusters in data is something statisticians have worried about since the dawn of statistics.
I'd imagine a series of left/right comparisons, like a visit to an eye doctor, where the machine learning is rewarded for its ability to predict my preferences. Eventually (a time commitment for me) it will be able to build from scratch color designs that I love.
This is like an early application of machine learning: What are the odds of victory for this backgammon position? Here, instead, we've estimating a preference function on color triples. Is RGB even the right domain, or do we want to work in some frequency transform, to capture the equivalent to musical chords. This is an empirical question, that can only be answered by trying to estimate this preference function, and noticing ripples better resolved by a different parametrization.
This would be easy, compared to the Riemannian geometry used in medical imaging. There's more money there.
For commercial use one cares what others think. There's the speciation question: You won't synthesize deep jazz tracks and deep blues tracks without separating the advice into species. Identifying clusters in data is something statisticians have worried about since the dawn of statistics.