The One Hundred Year Study on Artificial Intelligence (AI100) is a longitudinal study that assesses AI's development and societal impact. Reports are released once every five years, with the 2026 edition being the third in the series. The title of this year's report is . The panel behind the project is an international committee of experts tasked with examining AI's evolution across technical, ethical, economic, and societal domains. They respond to 12 standing questions about AI's advances, opportunities, dangers, and societal effects, and compare developments since 2021 with the previous panel's assessments.

Today, most of us interact with AI assistants - reactive bots that wait for human instructions. Yet, AI assistants are rapidly being replaced by agentic AI agents, that can interact with external tools, browse the web, generate images, send emails, and perform increasingly complex workflows on behalf of users. As these agentic agents become more capable, they may be exploited by people with bad intentions. Safety tests have generally only checked whether an AI agent refuses a single harmful request - they don't measure what happens when an attacker gradually persuades the AI through a series of seemingly harmless conversations. Now, researchers from EPFL's Natural Language Processing Laboratory have developed STING (Sequential Testing of Illicit N-step Goal execution), an automated testing framework that simulates how a real attacker might manipulate large language model (LLM) agents into carrying out harmful tasks over multiple interactions.

Minji Park is a PhD candidate at the Korea Institute of Energy Technology (KENTECH). In her paper, with co-authors Seunghyun Yoon and Hyuk Lim, she introduces a framework which aims to help make large language models stay more up-to-date when pieces of information change. We hear from Minji about inspired her work, how the framework operates, and her plans to further develop this research. What is the topic of the research in your paper and why is it an interesting area for study? The paper is fun, because we are challenging the norm.

Cornell researchers are using artificial intelligence to speed the search for better battery materials while using a creative approach to chemistry to expand the design space for electrolytes, unlocking new possibilities for safer, higher-performing energy storage. Electrolytes shuttle ions between battery electrodes and are key to a battery's energy density, lifetime and charging speed, yet they are difficult to design because small changes in composition can dramatically change how they work. "Electrolytes are not just supporting materials in batteries; they are a design space," said Fengqi You . You's research group integrates theoretical frameworks, artificial intelligence and other computational methods to help discover next-generation battery materials, among other research themes. His group has introduced IonNet, an AI framework that predicts how well lithium ions move through solid materials using only their chemical composition.

"With AI, we need to reconsider what it means to learn" Generative AI has worked its way into every field over the past four years and is prompting us to reconsider our habits. At universities, students are using it to write, find information and solve problems, while scientists have already incorporated it largely into their research. Teachers are being called on to rethink how they do their job now that AI programs can complete - in just seconds - a number of tasks traditionally assigned to students. These are just some of the questions we explore in our special online report on AI at universities, published at the start of this new school year . These are also questions that Schwartz - a professor of educational technology at the Stanford Graduate School of Education and a faculty director at the Stanford Accelerator for Learning - has been studying for the past few years.

This post contains a list of the AI-related seminars that are scheduled to take place in the next couple of months. All events detailed here are free and open for anyone to attend virtually. Stephanie Gil (Harvard University) EPFL The Zoom link is here . Timo Bertholdt and Imre Polik (FICO) Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list . Jonah Botvinick-Greenhouse (Yale University) University of Minnesota Zoom registration is here .

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we chat to AI pioneer, Michael Littman, learn about time-series forecasting for clinical data, investigate evolutionary game theory for cybersecurity, and get the scoop on neuromorphic computing. In the latest interview in our series meeting AI pioneers, Michael Littman reflects on the evolution of reinforcement learning, the power of science communication, and how generative AI is changing the way we teach and learn. Mayra Elwes is a PhD student developing machine learning methods for clinical time-series data. She is working on practical strategies to evaluate methods in a more contextualised way and on improving existing domain adaptation methods specific to time-series forecasting.

Can you teach yourself to detect AI writing? AI is quietly reshaping writing. It's not just that prose is changing -- the very structure of written language is genuinely changing too. Did this article's introduction seem AI generated? What made you feel that way?

Hosted by Ben Byford, The Machine Ethics Podcast brings together interviews with academics, authors, business leaders, designers and engineers on the subject of autonomous algorithms, artificial intelligence, machine learning, and technology's impact on society. This time we're chatting with E.M. about the promise of AI and making human connection easier, speech recognition and supporting linguistic diversity, making useful technologies that have a purpose, Mozilla Data Collective, under-represented cultures in datasets, accidental monocultures with technology, negative uses of datasets, AI literacy, the instability of LLMs and more E.M. Lewis-Jong is a Founder, Impact Entrepreneur and HCI researcher working at the intersection of community technology, open data, and inclusive AI. They are the Founder and CEO of the Mozilla Data Collective, a community-led platform for ethical creation, curation, and control of AI training datasets; built on the principle that people should be able to share their data on their own terms. They previously served as a VP at Mozilla Foundation, and the Director for Mozilla's Common Voice, an open-source platform enabling communities worldwide to preserve, revitalise, and contribute their languages to the future of speech tech. E.M. holds an MA in Modern History from the University of Oxford and is expecting a PhD in Informatics and Engineering at the University of Sussex, with research focused on controllability in conversational and voice AI for adolescents.

Taken from high resolution satellite imagery. Can you tell us a bit about your background and your current area of research? I use computational tools to study animal behaviour. After my PhD, I joined the U.S. Army Research Office, where I used satellite imagery to follow animals across whole landscapes, which is a powerful technique for seeing broad patterns, but far too coarse to capture what individuals are actually doing. That gap is what drives my current work at Princeton: I combine drone video with AI methods to resolve movement at much finer scales, as I have done in studies of Olive Baboons and lions.