Data Tech

Many diverse fields, such as applied mathematics, statistics, machine learning, data mining, econometrics, bioinformatics etc. are concerned with estimation of data-analytic models. More recently, due to the abundance of data and cheap computing power, machine learning (ML) algorithms have become very popular in various applications, even though many such algorithms are heuristics vaguely motivated by biological (as opposed to mathematical) arguments. This disconnect (between mathematics and practical applications) may seem strange, given the deep intrinsic connection between mathematics, science and engineering. The purpose of my talk is to explain various reasons for the current disconnect, including (a) conceptual (philosophical) aspects; (b) technical (mathematical) aspects and (c) non-technical (social) aspects. In particular, my talk will elaborate on different interpretation of philosophical concepts (of deductive and inductive reasoning), in classical science, statistics and ML.