What’s the difference between machine learning, deep learning, big data, statistics, decision & risk analysis, probability, fuzzy logic, and all the rest?
Not NONE. machine learning is the super discipline, an ensemble of techniques. Deep learning is a specific type of machine learning, focusing on higher order statistics. Big data is a term from business not academia, but deals with the unique challenges of machine learning with very big data sets. Fuzzy logic was the fashionable way of dealing with uncertainty before Bayesian logic came to the front (particular flavour of probability). Probability and fuzzy logic should be considered notation.
So what’s the difference between probability and logic?
Not Not much. There are lots of types of logic, Bayesian probability can be considered a type of logic. Most people when they think of logic they think of propositional logic, boolean algebra, predicate calculus, formal methods etc. That kind of logic is a billion miles away from probability in culture and the kind of things you can conclude from its application. Logic is rigid truths, undecidability. Probability is a representation of uncertainty and half truths, from which you hope to extract the most likely explanation from partial or noisy data. Formal logic is next to useless on noisy data. Probability is a good fit for noisy problems (like perception).
Logic is classic AI. Probability is Modern AI. There are attempts at unification (e.g. Bayesian Logic networks, but that's cutting edge stuff). Saying they are basically the same is lunacy.