Abstract

Machine Learning (ML) and its applications have over the last half decade become intricately intertwined with our lives and times. In diverse fields, ML and its many applications are creations of the specific needs within the fields, and the current crop of ML functionalities in any given sector is simply an adaptive evolutionary response to the epistemological pressures and necessities of the sector.   Across these fields ML itself is being changed, and expanded and diversified into various new roles and modalities customised to the questions in each field. Consequently, the time is now ripe to bring together thinkers and practitioners in mathematical physics as well as industry to exchange ideas, opinions and philosophies relating to the possibilities and burgeoning challenges of having ML reshape their respective landscapes.