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)
- distributed smart science (labs in the cloud, cognitive computing, massively distributed sensing)
- improving human thinking and decisionmaking (overcoming cognitive biases and shortfalls in thinking)
Blockchain Salvation: The hype around blockchains—the programming protocol originally created for the Bitcoin—is bidirectional, ranging from apocalyptic predictions of bitcoin energy use that will “destroy our clean energy future” to rosy scenarios that “blockchain technology can usher in a halcyon age of prosperity for all.” The question for policymakers, therefore, is how to ensure that the environment profits in the end. This ELI Research Policy Brief looks at the challenges and opportunities presented by blockchain technology, and it urges environmental professionals to take part in an ongoing conversation with software developers and other stakeholders that will shape the social contract affecting the blockchain’s environmental costs and benefits—plus shape emerging policy and governance responses.
When Software Rules: Rule of Law in the Age of Artificial Intelligence. Artificial intelligence (AI) has the potential to improve human interaction with the environment, but it can also exacerbate existing environmental issues. Some form of governance is needed to ensure that AI is deployed in a manner that is beneficial for our environment. This report offers a set of recommendations on how AI governance can include consideration of environmental impacts. Listen to Dave Rejeski discuss this report on WCAI radio!
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).