Saving Israeli and Palestinian Lives: Technology For Life: Disaster relief and life-saving tech *From the Archives*

My guest today is Nabiha Syed, Executive Director of the Mozilla Foundation, and one of the leading voices working at the intersection of technology, journalism, civil liberties, and the public interest.

Last week, the International Rescue Committee published an article titled “I’m a Humanitarian Leader Who’s All In on AI. Here’s Why,” written by its Senior Vice President and Chief Research and Innovation Officer, Jeannie Annan. Annan argues that humanitarian organizations have a moral obligation to become some of its most ambitious users, reasoning that the decision not to explore technologies that could help organizations reach more people, more effectively, is itself an ethical choice—with potentially devastating consequences. So what does responsible innovation look like when the stakes are literally life and death? How do we balance the risks of deploying imperfect technologies against the risks of failing to deploy them? And what might the technology industry learn from humanitarian organizations that have spent decades making consequential decisions under conditions of uncertainty, scarcity, and profound human vulnerability? Jeannie Annan is Senior Vice President for Research and Innovation, AI, and People at the International Rescue Committee, one of the world's largest humanitarian organizations, working in around 40 countries. She co-founded and leads the Airbel Impact Lab, IRC's research and innovation engine, where researchers, designers and technologists build, adapt and test solutions for people caught in conflict and disaster, from getting vaccines to children to predicting climate disasters and responding before they strike. She also leads IRC's AI strategy, and recently launched Airbel Ventures, a first-of-its-kind humanitarian impact investing fund. Jeannie began her career running programs in Kosovo, northern Uganda and South Sudan. She holds a PhD in psychology. Her research on preventing violence against women and children has been published in leading academic journals and her work on AI in humanitarian response has been featured in Fast Company and Fortune.This episode was produced by Jack Lucas Chang.

What happens to mathematics when doing mathematics is no longer an exclusively human activity? For years, we've watched artificial intelligence get progressively better at math: solving equations, passing exams, succeeding at mathematical competitions, and generating proofs. But recent developments seem to cross a different kind of threshold. This month, OpenAI announced that an internal AI system had produced a solution to the Navier–Stokes existence and smoothness problem—one of the seven Millennium Prize Problems, designed to represent some of the most difficult unsolved questions in mathematics. The announcement has generated excitement, skepticism, and controversy. But it also raises a much larger set of questions. What does it actually mean to do mathematics? Is mathematics about arriving at a correct answer, or understanding why that answer is true? What happens to ideas like discovery, creativity, and authorship when machines can participate in producing new mathematical knowledge? And what does that tell us about artificial intelligence—and about ourselves? My guest today is Dr. Noah Giansiracusa, a math professor at Bentley University and a faculty associate at Harvard, with a PhD in algebraic geometry from Brown University. Noah is cohost of the podcasts Breaking Math and AI in Academia, and his writing has appeared in The Washington Post, Scientific American, TIME, Wired, Slate, and The Boston Globe, among others. His first book, How Algorithms Create and Prevent Fake News, examined the mathematics and technology underlying our information ecosystem. His latest, Robin Hood Math, asks how people from all backgrounds can use mathematics to navigate—and reclaim some control over—a world increasingly governed by algorithms. Today, we're talking about what happens when those algorithms begin doing mathematics themselves.

For most of the web’s history, we’ve operated on a fairly simple assumption: if you put something on a public website, people could find it, search engines could index it, and information could move relatively freely across the internet. But as AI systems increasingly crawl, extract, learn from, and act on information from the web, that bargain is beginning to change.

My guest today is Dave Merritt, cofounder and Chief Technology Officer of Watch Duty.