A lot of your ideas involve machine learning. What's the hardest problem you've actually run to ground with machine learning? The point being: most practical m

A lot of your ideas involve machine learning. What's the hardest problem you've actually run to ground with machine learning? The point being: most practical machine learning I've seen has involved a lot of effort chasing 75-90% precision, which means that a lot of commonly considered problems can only be aided by machine learning for human-assisted or human-curated tasks, or cases where precision can be sacrificed and benefit still derived. If you haven't picked one idea and run it all the way through to a working solution, you have the choice of actually picking one and doing it, or not attributing more value to machine learning than it actually deserves. It seems to me that innovative uses of machine learning are more around finding a user interface or user experience that compensates for inaccuracy than it is the sophistication of the algorithm itself.