Explore the future of energy-efficient artificial intelligence in this thought-provoking lecture by Professor Max Welling from the University of Amsterdam. Delve into the challenges of modern deep learning architectures, which require extensive computational resources and data for training and execution. Learn about innovative approaches to maximize AI capabilities per kilowatt-hour, including neural network compression techniques, low-precision computing, and spiking neural networks. Gain insights into the potential shift of AI computation to edge devices and understand the importance of energy efficiency in the next phase of AI development. Discover how these advancements could shape the future of machine learning across various applications, from medical imaging to reinforcement learning.