Secure, Real-Time Decisions on Live Data

一个新的时代正在兴起，人工智能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识别有风险的患者，使用纳米探针进行细胞水平的诊断和治疗以及机器人手术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提供动力来帮助消除死亡事故。此外，它们还将通过实时检测和防御金融欺诈和互联网攻击，使企业更加安全。 更广泛地说，这些系统将改变人们感知和与周围世界互动的方式，使其更能适应和响应我们的需求。 为了实现这一愿景，需要新一代的人工智能系统来为人类安全和福祉受到威胁的关键任务应用提供动力，并且可以在不断变化和意外的对抗环境中工作。 除了智能之外，这些决策系统还需要解决四个挑战。首先，它们必须实时反应（即，在几秒钟甚至几毫秒内做出决定）来支持诸如机器人手术和自动驾驶汽车之类的应用。其次，人工智能系统需要在实时数据流上不断学习，因为它们的环境会发生混乱。 第三，这些系统需要是安全的，即，确保隐私、数据机密性和决策完整性。最后，当这些系统代表人类做出决策时，他们的决策需要向对人工智能理解有限的人解释。例如，如果人工智能系统诊断出患有罕见疾病的患者，或者认为某种测试是不必要的，系统应该根据患者的病史和更大人群的病史提供解释，而不是指向人工智能算法的内部计算。这个Expedition项目的目标是构建人工智能决策系统，通过开发开源平台、工具和算法来应对这些挑战，以实现实时、智能、安全和可解释（RISE）决策。实现这一目标需要一种结合人工智能、安全、系统和硬件研究的整体方法。 例如，要在拥挤的城市中成功部署一队送货机器人，不仅需要人工智能的进步（例如，感知和安全导航复杂城市环境的能力），而且系统的进步（例如，能够实时协调车辆的新混合边缘云系统），安全性（例如，确保机器人传感器收集的信息不会损害客户的隐私），以及计算机体系结构（例如，硬件和软件协同设计以降低功耗并提高安全性）。 RISE项目旨在授权一个大型的先驱社区，根据它将创建的工具和想法构建创新的应用程序和解决方案，并扩大研究参与，允许许多学科的学生和研究人员贡献和构建其工件。 围绕人工智能系统的通用开放平台构建和培育社区，将使未来十年的创新以广泛、智能和可信的计算为中心。关键的技术贡献是在系统，硬件和安全的接口领域，这将使实时人工智能成为可能。在系统领域，有两个关键思想：1）微内核的设计从根本上改变了使用深度模型做出决策的时间尺度; 2）整合了重播系统状态和决策历史的能力。在安全和硬件领域，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，并增加了可调安全性的关键功能，从而在安全性和性能之间进行权衡。在人工智能领域，该项目的主要贡献是开发实时系统和硬件支持，将任务适当地分配给后端和边缘，以快速准确地做出决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.