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Applying Artificial Intelligence to Built Environments through Machine Learning

Published by Johnson Controls

The document discusses how machine learning (ML) is transforming built environments by enabling systems to learn and improve from data without explicit programming. It highlights the application of ML in energy management, predictive maintenance, and biometric access control, showcasing its ability to optimize building operations, reduce energy consumption, and enhance security. The paper also explores different types of machine learning, including supervised, unsupervised, and reinforcement learning, and addresses challenges such as data quality, bias, and economic disruption as AI and ML technologies continue to evolve and reshape industries.

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Related Categories Artificial Intelligence, Deep Learning, AI in Healthcare, AI Applications, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Data Preprocessing, Model Evaluation, Inventory Management, Logistics Optimization, Supply Chain Visibility

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