Data-driven Pattern Recognition: Philosophical, Historical, and Technical issues
The field of pattern recognition can arguably be considered as a modern-day incarnation of an endeavor which has challenged mankind since antiquity. In fact, fundamental questions pertaining to categorization, abstraction, generalization, induction, etc., have been on the agenda of mainstream philosophy, under different names and guises, since its inception. With the advent of modern digital computers and the availability of enormous amount of raw data, these questions have now taken a computational flavor. As it often happens with scientific research, in the early days of pattern recognition there used to be a genuine interest around philosophical and conceptual issues (see, e.g., Minsky, 1961; Sutherland, 1968; Watanabe, 1969, 1985; Bongard, 1970; Nelson, 1976; Good, 1983), but over time the interest shifted almost entirely to technical and algorithmic aspects, and became driven mainly by practical applications. With this reality in mind, it is instructive to remark that although the dismissal of philosophical inquiry at times of intense incremental scientific progress is understandable to allow time for the immediate needs of problem-solving, it is also sometimes responsible for preventing or delaying the emergence of true scientific progress (Kuhn, 1962).
The goal of this tutorial is to provide researchers of the pattern recognition community, in particular the youngers who did not live the early days of research in pattern recognition, a picture of the philosophical and historical issues that are behind the current paradigm of Big Data + Deep Learning + GPUs, also discussing a few technical issues that are relevant for a comprehension that goes beyond the mainstream and trends. We do feel that this could be an opportunity for reflection, reassessment and eventually some synthesis, with the aim of providing the field a self-portrait of where it currently stands and where it is going as a whole, and hopefully suggesting new directions.