Collaborative Intelligence

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Artificial Intelligence (AI) is moving from research labs to the real world. One of the most promising avenues for bringing AI "to the edge" is Collaborative Intelligence (CI), a framework in which AI inference is shared between the edge devices and the cloud. In CI, typically, the front-end of an AI model is deployed on an edge device, where it performs initial processing and feature computation. These intermediate features are then sent to the cloud, where the back-end of the AI model completes the inference. CI has been shown to have the potential for energy and latency savings compared to the more typical cloud-based or fully edge-based AI model deployment, but it also introduces new challenges, which require new science and engineering principles to be developed in order to achieve optimal designs. In CI, a capacity-limited channel is inserted in the information pathway of an AI model. This necessitates compression of features computed at the edge sub-model. Moreover, errors introduced into features due to channel imperfections would need to be handled at the cloud side in order to perform successful inference. Finally, issues related to the privacy of transmitted data need to be addressed. The proposed research program aims to discover fundamental insights and develop practical designs for CI systems. On the theory side, we plan to use information-theoretic tools to study information flow in CI systems. Specifically, we plan to extend the Information Bottleneck principle to capacity-limited learning models in order to gain insight into performance limits of CI systems. On the practical side, we will focus on the following short-term objectives: develop novel feature compression techniques for CI systems; study error resilience of CI systems and develop appropriate error control methods; study privacy issues and propose solutions for privacy-friendly inference in CI systems; and extend the above solutions to multi-input, multi-task CI systems. The proposed research program stands at the intersection of two technological areas of great strategic importance to Canada: AI and Information and Communication Technologies (ICT). The outcomes of the proposed research will have significant impact on both areas, and will spur further innovation at their intersection. The result will be the improvement of energy efficiency, responsiveness, privacy and security of emerging applications in remote monitoring, transportation, automation and manufacturing, smart homes and cities, agriculture and other industries, while setting the stage for new, yet-to-be-imagined applications that will shape industries of tomorrow. The HQP trained in the program will gain skills and knowledge that will position them to become scientific and technological leaders in these important areas, in Canada and beyond.