Environmental & Green AI

Everyone wants to use AI but is AI Environmental friendly? Let's find out in our panel discussion with Green AI experts!

🌱 Should efficiency be considered as one of the metrics along with performance in deep learning research? What can we do to offset the large carbon footprint of training neural networks? The research field of Green AI tackles the sustainability aspect of AI and the costs that come with digitalization. There is an increasing need to talk about the environmental, economic, and social costs of AI. 🌱

🟢 Our panelists, Wiebke Gergeleit, Xinchi Qiu, and Lucas Spreiter, who are making a mark in the field of Green AI!

🍀 Wiebke Gergeleit who is currently pursuing her Ph.D. at Hasso Plattner Institute conducts research on digital sustainability. Her work involves improving techniques and employing best practices for sustainable development of technology.

🍀 Xinchi Qiu who is pursuing her Ph.D. at the University of Cambridge conducts research on federated learning and published the very first measurements of carbon emission of AI systems resulting in the paper “A First Look into the Carbon Footprint of Federated Learning”.

🍀 Lucas Spreiter is passionate about mitigating the threats posed by climate change by using AI. With this purpose, he founded the company Unetiq which uses expertise in data science, software development, and design thinking to identify carbon emissions of companies and develops innovative solutions to offset it.

🟢 How can we develop models to quantify carbon footprint? What are the most energy-efficient ways of training models? What are the different components that contribute to carbon emissions? How can we implement these measures practically?