Deep Learning and Neural Nets for Ecological Restoration, hosted by the Earthshot Institute

​Neural Nets are eating software!

​Come learn with world-class ecological deep learning experts Brookie Williams (Chief Data Scientist @ WRI), Trevor Keenan (UC Berkeley Climate and Ecology Lab), Patrick Leung (CTO @ Earthshot Labs) and more! Learn about the current uses for machine learning in ecological restoration and where the frontier of neural architecture is headed.

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​Remember maps? Not the app, the actual paper sheets that you would hold while driving and yell at your partner when they missed a turn. Well, that’s the equivalent to the state of the art for carbon and ecosystem measurement: pay $150,000 for a consultant from Norway to walk around your land with a tape measure for a few days measuring trees, then plugging the diameters into an excel sheet to approximate biomass.

​The academic frontier is a bit better: think MapQuest. A series of numerical heuristics (i.e. turn right, left, stop) based on the past and other regions help predict ecological state in any given area.

​The holy grail is a comprehensive ecological simulator that understands ecological state everywhere in real time, and can make recommendations for interventions anywhere on earth to optimize for a theorized ideal ecological balance. Building this simulator is a nuanced and complex task that will have planetary consequences for policy, investment, and individual land steward decision making. How we get there is the theme of this event. Neural architecture and novel machine learning techniques, satellite + drone + smartphone + alternative data sources, data sharing and best practices.

​The Earthshot Labs team is leading a coalition to create a comprehensive ecological simulator. This will not be our exclusive “Intellectual property”, but an open-source public good so we can reach scientific consensus on how to incentivize restoration of nature. If you feel called to contribute, we’re hiring, and you can join the Earthshot Institute as a mentor or individual contributor.