Victor Schmidt

TL;DR: This work presents their Machine Learning Emissions Calculator, a tool for the community to better understand the environmental impact of training ML models and concrete actions that individual practitioners and organizations can take to mitigate their carbon emissions.

TL;DR: A project to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs), to enable individuals to make more informed choices about their climate future by creating a more visceral understanding of the effects ofClimate change, while maintaining scientific credibility by drawing on climate model projections.

TL;DR: The AI climate impact visualizer as mentioned in this paper uses cutting-edge artificial intelligence (AI) approaches to develop an interactive personalized visualization tool, which allows a user to enter an address and provide them with an AI-imagined possible visualization of the future of this location in 2050 following the detrimental effects of climate change.

TL;DR: The Machine Learning Emissions Calculator (MLEMC) as discussed by the authors is a tool for estimating the carbon impact of ML applications, which can be used to track and communicate the environmental impact of machine learning applications.

TL;DR: Methods that can be deployed to a smartphone to locally and proactively predict an individual's infectiousness based on their contact history and other information are developed, suggesting PCT could help in safe re-opening and second-wave prevention.