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文件名称:A Berkeley View of Systems Challenges for AI
文件大小:1.12MB
文件格式:PDF
更新时间:2021-04-08 06:56:49
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 (Articial 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 condentiality. 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.