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A Berkeley View of Systems Challenges for AI 评分:

ABSTRACT 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 (Arti€cial Intelligence) has moved from research labs to production. Œese changes ha ve been made possible by unprecedented levels of data and computation, by methodological advances in machine learning, by innovations in systems so‰ware and architectures, and by the broad accessibility of these technologies. Œe next generation of AI systems promises to accelerate these developments and increasingly impact our lives via frequent interactions and making (o‰en mission-critical) decisions on our behalf, o‰en 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 con€dentiality. Œese 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.
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