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Thinking

Let’s imagine a future where our cities, transportation systems, utilities, and production chains become self-motivated environmental learners. How can we embed environmental sensing and learning into complex social/technological/economic systems?

  • applying artificial intelligence (AI) and machine learning to environmental problem solving and environmental protection
  • autonomous vehicles (cars, trucks, boats, planes)cybernetics
  • distributed smart science (labs in the cloud, cognitive computing, massively distributed sensing)
  • improving human thinking and decisionmaking (overcoming cognitive biases and shortfalls in thinking)

Environmentalism in the Next Machine Age. Let’s imagine a future where our cities, transportation systems, utilities, and production chains become self-motivated learners. Think “iRobot meets EPA.” Read the blog for more.

When Cars Lie.  "Software rules"  But what if software is programed to deceive regulators?  The recent diesel emissions scandal involved machines talking to machines (M2M) and kept the regulators out of the loop for years. This should be a warning about the need for software oversight.  Read more.

Blindsided by Change: Slow Threats and Environmental Policy.  Why is it so difficult to galvanize attention to slow environmental threats and sustain efforts to deal with them?  This paper examines insights from evolutionary psychology, neuroscience, behavioral economics, decision science, social psychology and journalism. Check out this report for more.

Also see this earlier report, Missing the Slow Train: How Gradual Change Undermines Public Policy and Collective Action (Wilson Center 2016).