Analysis & Opinions

After viewing the movie Oppenheimer, Matthew Bunn, John P. Holdren, Mariana Budjeryn, Matthew J. Parent, and Calder Walton, and Sylvia Mishra offer their thoughts on its relevance to the world today. 

AI is shaping up to be part of surveillance capitalism, warn Bruce Schneier and Nathan Sanders.

Gabrielle Armstrong-Scott and Jim Waldo argue that machine learning tools and a better definition of "terrorist and extremist content" could help make the Web a safer place.

A future where A.I.s interpret, apply, and enforce most laws at societal scale like this will exponentially magnify problems around fairness, transparency, and freedom, warn Jonathon W. Penney and Bruce Schneier.

In May 2023, the Arctic Initiative held a closed-door workshop for climate scientists, regional experts, Indigenous and youth leaders, and national security officials from six Arctic states, with the goal of identifying the most plausible scenarios and pathways for how geopolitics linked to climate change in the Arctic might evolve and actionable steps that the U.S. government might consider taking today to manage emerging risks. Eyck Fremann summarizes his key takeaways from the event.

Big tech companies use citizens' photos, writing, and other data to train their AI models and turn a profit. Barath Raghavan and Bruce Schneier argue that you should be paid for your data.

Unlike industrial and other forms of pollution, the long-standing, pervasive problem of municipal solid waste in Arctic cities receives comparatively little attention. As rapid warming in the region compromises existing waste disposal methods such as landfills, Arctic cities will need to develop comprehensive solid waste management strategies for the health of residents and the environment.

A key constructive-use case for artificial intelligence in democratic processes is to serve as discussion moderator and consensus builder.

 The United States needs a government-funded and -directed AI program to develop widely reusable models in the public interest, guided by technical expertise housed in federal agencies, argue Bruce Schneier and Nathan E. Sanders.

Open-source large language models are dramatically altering AI research and innovation, wresting power from large tech companies and limiting the ability of governments to regulate this new technology.