>Machine learning is powerful in many domains, but it's giving engineers license to be lazy. The problem is that if you do machine learning, you just throw dat

>Machine learning is powerful in many domains, but it's giving engineers license to be lazy. The problem is that if you do machine learning, you just throw data at it to correct wrong behaviors. It's difficult to marry this with a logical approach, like dictionary frequency. Which one do you weight how much? 50:50? What if the user dislikes one of the two models more often, do you add another layer of machine learning here?