AI and ML are making a significant contribution to climate change. Developers can help reverse the trend with best practices and tools to measure carbon efficiency.
The growth of computationally intensive technologies such as machine learning incurs a high carbon footprint and is contributing to climate change. Alongside that rapid growth is an expanding portfolio of green AI tools and techniques to help offset carbon usage and provide a more sustainable path forward.
The cost to the environment is high, according to research published last month by Microsoft and the Allen Institute for AI, with co-authors from Hebrew University, Carnegie Mellon University and Hugging Face, an AI community. The study extrapolated data to show that one training instance for a single 6 billion parameter transformer ML model — a large language model — is the CO2 equivalent to burning all the coal in a large railroad car, according to Will Buchanan, product manager for Azure machine learning at Microsoft, Green Software Foundation member and co-author of the study.