  Despite the widespread usage of machine learning throughout organizations,
there are some key principles that are commonly missed. In particular: 1) There
are at least four main families for supervised learning: logical modeling
methods, linear combination methods, case-based reasoning methods, and
iterative summarization methods. 2) For many application domains, almost all
machine learning methods perform similarly (with some caveats). Deep learning
methods, which are the leading technique for computer vision problems, do not
maintain an edge over other methods for most problems (and there are reasons
why). 3) Neural networks are hard to train and weird stuff often happens when
you try to train them. 4) If you don't use an interpretable model, you can make
bad mistakes. 5) Explanations can be misleading and you can't trust them. 6)
You can pretty much always find an accurate-yet-interpretable model, even for
deep neural networks. 7) Special properties such as decision making or
robustness must be built in, they don't happen on their own. 8) Causal
inference is different than prediction (correlation is not causation). 9) There
is a method to the madness of deep neural architectures, but not always. 10) It
is a myth that artificial intelligence can do anything.
