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Sensor-powered devices offer safe global connections; cloud scalability and flexibility, and new business value driven by data. The constraints that have historically obstructed major innovations in technology can be addressed by advancements in Artificial Intelligence (AI) and Machine Learning (ML), cloud, quantum computing, and the ubiquitous availability of data. Edge AI (Edge Artificial Intelligence) refers to the deployment of AI applications on the edge device near the data source rather than in a cloud computing environment. Although edge data has been utilized to make inferences in real-time through predictive models, real-time machine learning has not yet been fully adopted. Real-time machine learning utilizes real-time data to learn on the go, which helps in faster and more accurate real-time predictions and eliminates the need to store data eradicating privacy issues. In this article, we present the practical prospect of developing a physical threat detection system using real-time edge data from security cameras/sensors to improve the accuracy, efficiency, reliability, security, and privacy of the real-time inference model.
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Secure artificial intelligence at the edge
Sensors for the perception of multimodal stimuli—ranging from the five senses humans possess and beyond—have reached an unprecedented level of sophistication and miniaturization, raising the prospect of making man-made large-scale complex systems that can rival nature a reality. Artificial intelligence (AI) at the edge aims to integrate such sensors with real-time cognitive abilities enabled by recent advances in AI. Such AI progress has only been achieved by using massive computing power which, however, would not be available in most distributed systems of interest. Nature has solved this problem by integrating computing, memory and sensing functionalities in the same hardware so that each part can learn its environment in real time and take local actions that lead to stable global functionalities. While this is a challenging task by itself, it would raise a new set of security challenges when implemented. As in nature, malicious agents can attack and commandeer the system to perform their own tasks. This article aims to define the types of systemic attacks that would emerge, and introduces a multiscale framework for combatting them. A primary thesis is that edge AI systems have to deal with unknown attack strategies that can only be countered in real time using low-touch adaptive learning systems. This article is part of the theme issue ‘Emerging technologies for future secure computing platforms’.
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- Award ID(s):
- 2303115
- PAR ID:
- 10611974
- Publisher / Repository:
- Royal Society
- Date Published:
- Journal Name:
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
- Volume:
- 383
- Issue:
- 2288
- ISSN:
- 1364-503X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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