The fields of machine learning and 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. Nowadays, with the advent of modern digital computers and the availablity of enormous amount of raw data, these questions have taken a computational flavor.
As it often happens with scientific research, in the early days of machine learning there used to be a genuine interest around philosophical and conceptual issues, but over time the interest shifted almost entirely to technical and algorithmic aspects and became driven mainly by practical applications. In recent years, however, there has been a renewed interest around the foundational and/or philosophical problems of machine learning and pattern recognition, from both the computer scientist's and the philosopher's camps. This suggests that the time is ripe to initiating a long-term dialogue between the philosophy and the machine learning communities with a view to foster cross-fertilization of ideas.
In particular, we do feel the present moment is appropriate for reflection, reassessment and eventually some synthesis, with the aim of providing the machine learning field a self-portrait of where it currently stands and where it is going as a whole, and hopefully suggesting new directions. The aim of this workshop is precisely to consolidate research efforts in this area, and to provide an informal discussion forum for researchers and practitioners interested in this important yet diverse subject.
The workshop is planned to be a one-day meeting. The program will feature invited as well as contributed presentations. We feel that the more informal the better and we would like to solicit open and lively discussions and exchange of ideas from researchers with different backgrounds and perspectives. Plenty of time will be allocated to questions, discussions, and breaks.