About this episode
Host Chris Adams is joined by Charles Tripp and Dawn Nafus to explore the complexities of measuring AI's environmental impact from a novice’s starting point. They discuss their research paper, A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning, breaking down key insights on how energy efficiency in AI systems is often misunderstood. They discuss practical strategies for optimizing energy use, the challenges of accurate measurement, and the broader implications of AI’s energy demands. They also highlight initiatives like Hugging Face’s Energy Score Alliance, discuss how transparency and better metrics can drive more sustainable AI development and how they both have a commonality with eagle(s)! Learn more about our people:Chris Adams: LinkedIn | GitHub | WebsiteDawn Nafus: LinkedInCharles Tripp: LinkedInFind out more about the GSF:The Green Software Foundation Website Sign up to the Green Software Foundation NewsletterNews:The paper discussed: A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learning [01:21] Measuring the Energy Consumption and Efficiency of Deep Neural Networks: An Empirical Analysis and Design Recommendations [13:26]From Efficiency Gains to Rebound Effects: The Problem of Jevons' Paradox in AI's Polarized Environmental Debate | Luccioni et al [45:46]Will new models like DeepSeek reduce the direct environmental footprint of AI? | Chris Adams [46:06]Frugal AI Challenge [49:02] Within Bounds: Limiting AI's environmental impact [50:26]Events:NREL Partner Forum Agenda | 12-13 May 2025Resources:Report: Thinking about using AI? - Green Web Foun
