Secure, Real-Time Decisions on Live Data

根据实时数据做出安全、实时的决策

• 批准号: 1730628
• 负责人: Ion Stoica
• 金额: $ 1000万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-03-01 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1730628&HistoricalAwards=false
• 关键词: Secure; Real; Time; Decisions; Live

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.

一个新时代正在上升，其中AI系统将在人们的生活中发挥越来越重要的作用。 这些系统将通过早期鉴定使用纳米探针和机器人手术的风险，细胞水平诊断和治疗的患者来彻底改变医疗保健。它们将减少交通拥堵，并通过为自动驾驶汽车和无人驾驶飞机供电，帮助消除死亡。而且，他们将通过实时检测和防御财务欺诈和互联网攻击来使企业更安全。 更普遍地，这些系统将改变人们如何感知和与周围世界互动，从而使其对我们的需求更加适应性和响应。 为了实现这一愿景，需要新一代的AI系统来为关键任务和福祉受到威胁，并可以在不断和出乎意料地改变的对抗环境中工作。 除了聪明之外，这些决策系统还需要解决四个挑战。首先，他们必须实时反应（即，以几秒钟甚至毫秒做出决定），以支持机器人手术和自动驾驶汽车等应用。其次，随着环境的发展，AI系统需要在实时数据流上不断学习。 第三，这些系统需要安全，即确保隐私，数据机密性和决策完整性。最后，由于这些系统代表人类做出决定，因此对AI了解有限的人需要解释他们的决定。例如，如果AI系统诊断患有罕见疾病的患者或认为某些未经根据的测试，则该系统应根据患者的病史和较大的人群提供解释，而不是指向AI算法的内部计算。该探险项目的目标是建立AI决策系统，通过为实时，智能，安全和可解释的（上升）决策开发开源平台，工具和算法来应对这些挑战。实现这一目标需要一种结合AI，安全性，系统和硬件研究的整体方法。 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 （例如，硬件和软件共同设计以减少功耗并提高安全性）。 RISE项目旨在授权大批开拓者社区，以基于其将创造的工具和想法的工具和思想来构建创新的应用程序和解决方案，并扩大研究参与，使许多学科的学生和研究人员能够在其文物上做出贡献和建立。 建立和培育社区围绕一个通用的AI系统开放平台建立和培养社区将使接下来的十年创新以广泛，智能和值得信赖的计算为中心。关键的技术贡献是在系统，硬件和安全性接口的领域，这将实时AI。在系统域中，有两个关键的想法：1）微核的设计从根本上改变了使用深层模型的决策的时间尺度； 2）结合了重播状态和系统决策历史的能力。在安全性和硬件域中，研究人员正在设计能够在各种硬件和云平台上运行的通用系统，并具有可调安全性的附加关键功能，该功能可在安全性和性能之间进行权衡。在AI域中，该项目的主要贡献在于开发实时系统和硬件支持方面，可以将任务适当地分配给后端和优势以进行快速准确的决策。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。

项目成果

Gauss: program synthesis by reasoning over graphs

高斯：通过图推理进行程序综合

• DOI: 10.1145/3485511
• 发表时间: 2021
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Bavishi, Rohan;Lemieux, Caroline;Sen, Koushik;Stoica, Ion
• 通讯作者: Stoica, Ion

Jiffy: elastic far-memory for stateful serverless analytics

• DOI: 10.1145/3492321.3527539
• 发表时间: 2022-03
• 期刊: Proceedings of the Seventeenth European Conference on Computer Systems
• 作者: Anurag Khandelwal;Yupeng Tang;R. Agarwal;Aditya Akella;I. Stoica

How Computer Science and Statistics Instructors Approach Data Science Pedagogy Differently: Three Case Studies

计算机科学和统计学教师如何以不同的方式处理数据科学教学法：三个案例研究

• DOI: 10.1145/3478431.3499384
• 发表时间: 2022
• 期刊: SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education
• 作者: Lau, Sam;Nolan, Deborah;Gonzalez, Joseph;Guo, Philip J.
• 通讯作者: Guo, Philip J.

Hoplite: efficient and fault-tolerant collective communication for task-based distributed systems

• DOI: 10.1145/3452296.3472897
• 发表时间: 2020-02
• 期刊: Proceedings of the 2021 ACM SIGCOMM 2021 Conference
• 作者: Siyuan Zhuang;Zhuohan Li;Danyang Zhuo;Stephanie Wang;Eric Liang;Robert Nishihara;Philipp Moritz

e-mission: An Open-Source, Smartphone Platform for Collecting Human Travel Data

e-mission：用于收集人类旅行数据的开源智能手机平台

• DOI: 10.1177/0361198118770167
• 发表时间: 2018
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Shankari, K.;Bouzaghrane, Mohamed Amine;Maurer, Samuel M.;Waddell, Paul;Culler, David E.;Katz, Randy H.
• 通讯作者: Katz, Randy H.