Sustainability in AI: Challenges and Efficient Methods

This lecture explores sustainability in AI, focusing on revealing biases in datasets, making AI more sustainable, and the environmental impact of large models. It discusses challenges with scaling AI, the staggering cost of training state-of-the-art models, and the importance of inclusiveness. The lecture delves into the energy and policy considerations for deep learning in NLP, the impact of language models on the environment, and the need for efficient methods in natural language processing. It also covers the efficiency in NLP, including data-efficient training, model distillation, and structured pruning. The lecture concludes with discussions on tackling climate change with machine learning, the role of AI in informing policy makers, and the opportunities and challenges in the field.