04-18, 14:55–15:30 (US/Eastern), Auditorium 5
This talk will survey how AI is currently used to address climate change, and describe possible future use cases. This high-level overview will touch on various aspects of climate change (e.g. energy, transportation, land use), of AI (e.g. image processing, reinforcement learning, LLMs), and of their intersection. The talk will conclude with resources for learning more about this area, and suggestions for contributing to current and future efforts.
Overview:
AI is profoundly shaping society. An equally forceful phenomenon is climate change; humanity is already feeling the impacts, and temperatures and greenhouse gas emissions keep rising. The goal of this talk is to briefly survey the many ways AI is and can be used to address climate change, and to provide pointers to anyone interested in contributing to the effort. The intended audience is anyone with an interest in this intersection of AI and climate change.
Climate Change:
We’ll briefly discuss aspects of climate change which AI is tackling, such as mitigating emissions from the five most carbon-intensive sectors: energy, manufacturing, land use, transportation, and buildings / infrastructure. We’ll also look at AI’s application to other areas such as climate modeling, carbon capture, climate finance, and reducing the carbon footprint of AI itself.
AI:
We’ll see how a number of AI methods can be used to address climate change, including: various neural net architectures (e.g. convolutional, recurrent, graph), LLMs, reinforcement learning, generative AI, neural operators, causality, and natural language processing.
Their intersection:
We’ll display a matrix of climate change domains and selected AI methods that can address them, as a guide to tractable areas to tackle. We’ll look at unsolved climate-related areas where AI could potentially help. We’ll conclude by providing resources for anyone wishing to learn more about this intersection, and for technologists wanting to plug into an existing community to contribute to this effort.
No previous knowledge expected
Dan Loehr earned his bachelor's in Computer Science from Cornell and a master's and PhD from Georgetown in Computational Linguistics. He 30 years experience leading large organizations in R&D and application of Machine Learning, AI, Natural Language & Speech Processing, and related fields. He has numerous publications and extensive experience teaching these topics at the graduate level. He's currently teaching a course on AI & Climate Change in Georgetown's Master of Science in Data Science and Analytics program