An AI technique called reinforcement learning could help us solve some of the world’s most complex problems. It enables an algorithm to learn how to perform a task through trial and error in a simulator, or digital twin. Applying this technique to test climate-saving initiatives across a digital twin of Earth could help us tackle climate change. Last year we embarked on an ambitious project with the Emirates Team New Zealand sailing team to build an AI bot that could sail a digital version of any type of boat design they engineered in digitally simulated, real-world sailing conditions. This would allow engineers to test various boat designs much faster than having to secure time with the team’s human sailors, who could only step away from practice a few hours here and there. To sail as well as the world’s best sailors, the AI bot needed to learn to execute many different maneuvers in varying conditions, choosing the best course to set under a wide variety of winds and seas, adjusting 14 different boat controls accordingly, assessing the results of its decisions, and continually improving decisions over long time horizons. We trained the bot using an AI technique called reinforcement […]

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