A new era is rising in which AI systems will play an increasingly central role in people's lives. These systems will revolutionize healthcare through early identification of patients at risk, cell-level diagnosis and treatment using nanoprobes, and robotic surgery. They will reduce traffic congestion and help eliminate fatalities by powering autonomous vehicles and unmanned drones. And, they will make businesses safer by detecting and defending in real-time against financial fraud and internet attacks. More generally, these systems will transform how people sense and interact with the surrounding world making it more adaptive and responsive to our needs. In order to fulfill this vision, a new generation of AI systems is needed to power mission-critical applications where human safety and well-being are at stake, and can work in adversarial environments that change continually and unexpectedly. Besides being intelligent, these decision systems need to address four challenges. First, they must react in real-time (i.e., making decisions in seconds or even milliseconds) to support applications such as robotic surgery and self-driving cars. Second, AI systems need to learn continually on live data streams as their environments evolve chaotically. Third, these systems need to be secure, i.e., ensure privacy, data confidentiality, and decision integrity. Finally, as these systems make decisions on behalf of humans, their decisions need to be explainable to someone with limited understanding of AI. For example, if an AI system diagnoses a patient with a rare disease or deems a certain test unwarranted, the system should provide an explanation in terms of the patient's history and that of the larger population, and not point to the AI algorithm's internal computations. The goal of this Expedition project is to build AI decision systems to address these challenges by developing open source platforms, tools, and algorithms for Real-time, Intelligent, Secure, and Explainable (RISE) decisions. Achieving this goal requires a holistic approach that combines AI, security, systems, and hardware research. For example, to successfully deploy a fleet of delivery robots in a crowded city requires not only advances in AI (e.g., the ability to perceive and safely navigate complex urban environments), but also advances in systems (e.g., new hybrid edge-cloud systems able to coordinate vehicles in real-time), security (e.g., ensure the information collected by robots' sensors does not compromise customer's privacy), and computer architecture (e.g., hardware and software co-design to reduce power consumption and improve security).

The RISE project aims to empower a large community of pioneers to build innovative applications and solutions based on the tools and ideas it will create, and broaden research participation, allowing students and researchers across many disciplines to contribute and build on its artifacts. Building and fostering a community around a common open platform for AI systems will enable the next decade of innovation centered around widespread, intelligent, and trustworthy computing. The key technical contribution is in the areas at the interface of systems, hardware, and security, which would enable real-time AI. In the Systems domain, there are two key ideas: 1) the design of micro-kernel to fundamentally transform the time scale at which decisions using deep models are made; and, 2) incorporating the ability to replay the state and the decision history of the system. In the security and hardware domain, investigators are designing general purpose systems capable of running on a variety of hardware and cloud platform with an added key feature of tunable security that provides a trade-off between security and performance. In the AI domain, the major contributions of the project are in developing real-time systems and hardware supports that would assign tasks suitably to back-end and edge for fast accurate decision making.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Computer and Network Systems (CNS)
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Darleen Fisher
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University of California Berkeley
United States
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