From threat modeling to encrypted collaboration apps, we've collected experts' tips and tools for safely and effectively building a group--even while being targeted and tracked by the powerful. Rarely in modern US history have so many Americans opposed the actions of the federal government with so little hope for a top-down political solution. That's left millions of people seeking a bottom-up approach to resistance: grassroots organizing. Yet as Americans assemble their own movements to protect and support immigrants, push back against the Department of Homeland Security's dangerous incursions into cities, and protest for civil rights and policy changes, they face a federal government that possesses vast surveillance powers and sweeping cooperation from the Silicon Valley companies that hold Americans' data. That means political, social, and economic organizing presents a risky dilemma. How do you bring people of all ages, backgrounds, and technical abilities into a mass movement without exposing them to monitoring and targeting by a government--and in particular Immigration and Customs Enforcement and Customs and Border Protection, agencies with paramilitary ambitions, a tendency to break the law, and more funding than some countries' militaries. Organizing safely in an age of surveillance increasingly requires not only technical security know-how, but also a tricky balance between secrecy and openness, says Eva Galperin, the director of cybersecurity at the Electronic Frontier Foundation, a nonprofit focused on digital civil liberties.

Moltbook is the chaotic future of the internet. The first signs of the apocalypse might look a little like Moltbook: a new social-media platform, launched last week, that is supposed to be populated exclusively by AI bots--1.6 million of them and counting say hello, post software ideas, and exhort other AIs to "stop worshiping biological containers that will rot away." Moltbook was developed as a sort of experimental playground for interactions among AI "agents," which are bots that have access to and can use programs. Claude Code, a popular AI coding tool, has such agentic capabilities, for example: It can act on your behalf to manage files on your computer, send emails, develop and publish apps, and so on. Normally, humans direct an agent to perform specific tasks.

Traditionally, the world of insurance is manual and process-heavy, forcing insurance executives to seek the best solutions for improving tedious workflows, with a priority on better decision making. The challenges are significant: even today's best-in-class software lacks the power to optimize the huge amount of data and variables originating from all the different clients, coverage plans and claims. To read the details for every case would be a near-endless task. Enter AI for the insurance industry: AI is allowing insurance firms to meet the ever-growing volume of client submissions and claims with quick response times, precision pricing and quoting, and streamlined workflows. These four pillars have already helped some of the world's leading insurance companies transform their businesses and make them AI-ready.

If we want to massively accelerate artificial intelligence and improve human lives, we need to democratize access to data. Past civilizations built grand libraries to organize the world's knowledge. These repositories of information focused on cataloging, aggregating, organizing and making information accessible so that others could focus on learning and creating new knowledge. AI and machine learning systems also need repositories of information from which to learn -- and right now everyone is building their own. If different groups of people focus on organizing data versus building AI, the progress of intelligent computers will massively accelerate.

If we want to massively accelerate artificial intelligence and improve human lives, we need to democratize access to data. Past civilizations built grand libraries to organize the world's knowledge. These repositories of information focused on cataloging, aggregating, organizing and making information accessible so that others could focus on learning and creating new knowledge. AI and machine learning systems also need repositories of information from which to learn -- and right now everyone is building their own. If different groups of people focus on organizing data versus building AI, the progress of intelligent computers will massively accelerate.

The latest in the drumbeat of news about artificial intelligence advances came from Seoul, where machine learning algorithms recently beat the world champion in a game of "Go." A Scientific American article explains why this is so impressive, but the implications run far deeper than a match of wits between man and machine. Soon, our workplaces will be transformed by artificial intelligence, with a wide range of processes and roles becoming redefined as some of the tasks comprising them are taken over by machines. Travelers are seeing early signs of this phenomenon. For example, in many US airports these days, instead of standing in a long line to have an immigration officer eyeball us, we scan our passport at a self-service kiosk, answer a few questions, get photographed, and then hand our photo receipt and passport to an agent who quickly verifies that everything checks out.

Fortunate to be one of the cofounders of AAAI, the author describes how the association was founded, how the first AAAI conference was planned, and how the first tutorial program was organized. I had been hired by Raj and Allen Newell to play a lead role on the Hearsay-II speech understanding project in 1976. After that, I moved to Rand Corporation and, shortly thereafter, took over the leadership of the research program in information processing systems, where the focus was on AI tools and applications and cognitive science. It was in that context that Raj spoke to me about his conviction that it was time for AI to become a recognized scientific profession, much as the AAAS and IEEE had done for natural science and engineering, respectively. This conversation was an example of Raj's modus operandi, the gap between vision and current state translated simply into gap-reducing actions.