exactly, Judea Pearl's The Book of Why opened my eyes to the fact that most of what happens in machine learning is really just curve fitting It connected with

exactly, Judea Pearl's The Book of Why opened my eyes to the fact that most of what happens in machine learning is really just curve fitting It connected with what i've heard Chomsky say about trying to develop laws of physics by filming what's happening outside the window. We need to do experiments and interventions to learn the dynamics of a system "What do you think the role is, if any, of other uses of so-called big data? [...] NOAM CHOMSKY: It’s more complicated than that. Let’s go back to the early days of modern physics: Galileo, Newton, and so on. They did not organize data. If they had, they could never have reached the laws of nature. You couldn’t establish the law of falling bodies, what we all learn in high school, by simply accumulating data from videotapes of what’s happening outside the window. What they did was study highly idealized situations, such as balls rolling down frictionless planes. Much of what they did were actually thought experiments. Now let’s go to linguistics. Among the interesting questions that we ask are, for example, what’s the nature of ECP violations? You can look at 10 billion articles from the Wall Street Journal, and you won’t find any examples of ECP violations. It’s an interesting theory-determined question that tells you something about the nature of language, just as rolling a ball down an inclined plane is something that tells you about the laws of nature. Scientists use data, of course. But theory-driven experimental investigation has been the nature of the sciences for the last 500 years. In linguistics we all know that the kind of phenomena that we inquire about are often exotic. They are phenomena that almost never occur. In fact, those are the most interesting phenomena, because they lead you directly to fundamental principles. You could look at data forever, and you’d never figure out the laws, the rules, that are structure dependent. Let alone figure out why. And somehow that’s missed by the Silicon Valley approach of just studying masses of data and hoping something will come out. It doesn’t work in the sciences, and it doesn’t work here." - https://www.rochester.edu/newscenter/conversations-on-linguistics-and-politics-with-noam-chomsky-152592/ https://www.rochester.edu/newscenter/conversations-on-lingui... It is actually a really interesting subject, marketing people doing a/b tests for ads/features seem at least a little closer to the experimental ideal, not just fitting curves to data For further reading, I'd recommend the epilogue of Casuality (Pearl 2000), it's from a 1996 lecture at UCLA: - http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf http://bayes.cs.ucla.edu/BOOK-2K/causality2-epilogue.pdf