Understanding the Innovations Required for a Green & Secure Artificial Intelligence Paradigm

Artificial Intelligence (AI) is being widely used in diverse domains such as industrial automation, traffic control, precision agriculture, and smart cities for major heavy lifting in terms of data analysis and decision making. However, the AI life-cycle is a major source of greenhouse gas (GHG) emission leading to devastating environmental impact. This is due to expensive neural architecture searches, training of countless number of models per day across the world, in-field AI processing of data in billions of edge devices, and advanced security measures across the AI life-cycle. In this work, we explore the impact of reckless AI computation on the environment for every stage of the AI life-cycle with deeper dives into specific algorithms via case studies. We also propose a systematic knowledge-guided AI system design framework that leverages past design experiences towards limiting GHG emissions during future AI system design.