Although artificial intelligence (AI; inclusive of machine learning) is gaining traction
supporting climate change projections and impacts, limited work has used AI to address …

Machine learning (ML) workloads have rapidly grown, raising concerns about their carbon
footprint. We show four best practices to reduce ML training energy and carbon dioxide …

Much of current machine learning (ML) research has lost its connection to problems of
import to the larger world of science and society. From this perspective, there exist glaring …

From an environmental standpoint, there are a few crucial aspects of training a neural
network that have a major impact on the quantity of carbon that it emits. These factors …

Summary Real-World Machine Learning is a practical guide designed to teach working
developers the art of ML project execution. Without overdosing you on academic theory and …

Modeling weather and climate is an essential endeavor to understand the near-and long-
term impacts of climate change, as well as to inform technology and policymaking for …

The emphasis of the book is on the question of Why–only if why an algorithm is successful is
understood, can it be properly applied, and the results trusted. Algorithms are often taught …

Recent advances in machine learning methods, along with successful applications across a
wide variety of fields such as planetary science and bioinformatics, promise powerful new …

Machine learning addresses the question of how to build computers that improve
automatically through experience. It is one of today's most rapidly growing technical fields …

Global climate change and its impact on human life has become one of our era's greatest
challenges. Despite the urgency, data science has had little impact on furthering our …