A Berkeley View of Systems Challenges for AI
  With the increasing commoditization of computer vision, speech recognition
and machine translation systems and the widespread deployment of learning-based
back-end technologies such as digital advertising and intelligent
infrastructures, AI (Artificial Intelligence) has moved from research labs to
production. These changes have been made possible by unprecedented levels of
data and computation, by methodological advances in machine learning, by
innovations in systems software and architectures, and by the broad
accessibility of these technologies.
  The next generation of AI systems promises to accelerate these developments
and increasingly impact our lives via frequent interactions and making (often
mission-critical) decisions on our behalf, often in highly personalized
contexts. Realizing this promise, however, raises daunting challenges. In
particular, we need AI systems that make timely and safe decisions in
unpredictable environments, that are robust against sophisticated adversaries,
and that can process ever increasing amounts of data across organizations and
individuals without compromising confidentiality. These challenges will be
exacerbated by the end of the Moore's Law, which will constrain the amount of
data these technologies can store and process. In this paper, we propose
several open research directions in systems, architectures, and security that
can address these challenges and help unlock AI's potential to improve lives
and society.
