There is no single all-purpose statistical algorithm that Explains All Of Life
Right, which is why I think the dimensionality reduction stuff, and in some sense all of machine learning, is kind of dishonestly packaged. These guys claim their algorithms are in some sense general, but that can’t really be true. There can never be a proof that a learning algorithm works for all situations. You can only prove statements of the form: “If the data exhibits property X, then algorithm Y will work”.
To live up to their promises, machine learning, computer vision, SNLP, and related fields need to become empirical sciences. They are currently strange hybrids of math and engineering (if you think they are empirical sciences, then what falsifiable predictions do they make? And if you don’t buy the falsifiability principle, then state an alternative answer to the Demarkationsproblem).
Right, which is why I think the dimensionality reduction stuff, and in some sense all of machine learning, is kind of dishonestly packaged. These guys claim their algorithms are in some sense general, but that can’t really be true. There can never be a proof that a learning algorithm works for all situations. You can only prove statements of the form: “If the data exhibits property X, then algorithm Y will work”.
To live up to their promises, machine learning, computer vision, SNLP, and related fields need to become empirical sciences. They are currently strange hybrids of math and engineering (if you think they are empirical sciences, then what falsifiable predictions do they make? And if you don’t buy the falsifiability principle, then state an alternative answer to the Demarkationsproblem).