That's a good read. Some of those problems are known from quantitative finance, where trying to extract statistical signals from data has been going on for decades. After much effort, all the easy wins (easy to find correlations) have been found and are no longer easy wins, because too many players have found them.

Some of the other problems listed are new, coming from taking what used to be research techniques and putting them into production programs. Those are more like ordinary big system problems, such as configuration management. The article points out, though, that your huge training set is now part of your configuration.

Then there's the problem of systems assigning excessive significance to features which happen to work but are really irrelevant. Those image recognition demos on HN last week illustrated this problem. At least there, though, there's a way to visualize what's happening. For many ML systems, humans have no clue what the system is actually recognizing. If your ML algorithm has locked onto the wrong features, it can become drastically wrong due to a minor change in the data. I saw an example of this in a web spam filter that was doing fairly well, working on 4-character sequences taken from web pages. It was actually recognizing specific code text used by one ad service. The page content seen by humans was totally irrelevant.