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John Platt (Google): AI for Climate and Science

Machine Learning, Applied Mathematics and Artificial Intelligence podcast with John Platt

The Information Bottleneck, Hosted by Ravid Shwartz Ziv and Allen Roush

Friday 1 h 13 min

About

John Platt discusses how Google’s applied-science team chooses problems where AI can make a useful contribution. Topics include satellite-based detection and prediction of aircraft contrails, evaluation of rerouting trials, limitations of physical simulation, and the role of AI agents in scientific modelling and data work. The conversation also examines overfitting, action incentives, gradient-free optimisation, SMO and support-vector machines, the physical constraints on scaling, and opportunities for robotics and manufacturing to support climate technology. It concludes with FireSat’s approach to detecting small wildfires from orbit and the practical issues of coverage, cost and false positives.

Topics

physical simulationAI agentsscientific modellingoverfittinggradient-free optimisationsupport-vector machinesFireSat

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