Machine Learning for Tomorrow: Efficient, Flexible, Robust and Automated

Artificial intelligence systems have recently led to significant advances in the state-of-the-art in downstream fields including computer vision, speech and natural language processing, and game playing. Although impressive, these advances mask a set of fundamental limitations of the underlying machine learning technology that need to be addressed to unlock gains in a wide variety of applications relevant to industry and society. These limitations come in four main forms. First current approaches are data-inefficient requiring extremely large and painstakingly curated datasets. Second, they are inflexible solving single tasks that are fixed through time. Third, the current approaches are brittle as performance can degrade catastrophically in the face of noise, missing data or adversarially selected data points. Fourth, the approaches are only semi-automated requiring an expert to design and tune them. These limitations mean that many important application domains are currently out of reach. For example, in medicine we typically have only small and noisy datasets which requires data-efficient and robust machine learning. Providing machine learning as a service requires fully-automated machine learning. This Prosperity Partnership will develop machine learning that is data-efficient, robust, flexible and automated by leveraging recently developed technology from the University of Cambridge's Machine Learning Group and deep expertise from Microsoft Research Cambridge. This partnership has identified a unique testbed of impactful application domains: health, enterprise tools and games development. This research programme is central to realising Microsoft's vision to empower every developer, organization and individual to innovate and transform the world with AI. Moreover, this area of immediate and wide-ranging national importance, and provides pathways to impact by partnering with one of the world's largest technology companies.