The Conference for Machine Learning Innovation

Scaling up AI for Confronting Climate Change

Keynote
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Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos

Big data and artificial intelligence have enabled numerous applications for humanitarian and social good. In terms of climate change, machine learning, deep learning, and computer vision approaches have proven to be useful for adaptation and mitigation. We’ll highlight nine major areas in which artificial intelligence is key in the fight against this crisis: electricity systems, transportation, buildings and cities, industry, farms and forests, carbon dioxide removal, climate prediction, societal impacts, solar geoengineering. From harnessing deep learning-based computer vision techniques for infrastructure damage assessment after natural disasters using satellite imagery, to utilizing natural language processing technologies to analyze climate-related legislation, we contend that AI is a necessary tool in years ahead. At the same time, sustainable and responsible use of deep learning models is key. Particularly, the notably large energy consumption of AI systems themselves have come under scrutiny; especially with the recent popularity of deep learning (DL) since approximately 2012, high-level computations have raised the overall energy consumption by 300,000 times or more. Balancing this concern, in addition to other considerations like model interpretability, accessibility, and fairness, are crucial challenges to tackle ahead.

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