How much can fair budget-division rules resist manipulation? In our paper, we ask what the best achievable compromise is. We prove that the Nash product rule reaches the optimal frontier. We study settings in which a fixed, divisible resource must be distributed among candidates or projects. The resource might be public money, research funding, charitable donations or even screen time.

When asked why Jean-Olivier Irisson, a scientist at Sorbonne Université in Paris, decided to dedicate his life to studying microscopic creatures in the sea, his answer was simple: "They are beautiful." Beauty may not be the first thing that comes to mind when we think of plankton - organisms that drift in water and come in an extraordinary variety of shapes and sizes. But images by Irisson's team tell a different story. Shown in striking blues and oranges, as well as black and white, they reveal an unfamiliar and strangely beautiful world. "This one served as the model for the head of the creature in the Alien movie franchise," Irisson said, pointing to one particularly unusual specimen.

Interview with Yash Saxena: how is external knowledge used in AI systems? In a new series of interviews, we're meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) . In this first article of the series, we caught up with Yash Saxena to find out how he is trying to make it easier to understand how external knowledge is used in AI systems. Tell us a bit about your PhD - where are you studying, and what is the topic of your research? I am a PhD student in Computer Science at the University of Maryland, Baltimore County (UMBC), advised by Dr. Manas Gaur in the Knowledge Infused AI and Inference (KAI2) Lab.

When AI art has no author: Study finds generated images often can't be traced to training data When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Policymakers want a way to assign responsibility. New work from a team of researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) suggests that for models trained on large datasets, the question may often have no answer. It's not that the tools for finding it are inadequate.

For example, they might need to process decades of weather data or the movements of millions of insects. Up until now, these scientists and decision makers have had to manually find and sort information, then use statistical tools which often oversimplify the source information. Artificial intelligence (AI) tools now promise to help with all that. But can they deliver on the promise? They are far from perfect.

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.

Dr van Kolfschooten, the EU presented its at the end of last year. It aims to help member states develop new strategies in the fight against cardiovascular disease. What role does artificial intelligence (AI) play here? AI plays a key role in this plan. It is to be used extensively in all three areas on which the fight against cardiovascular disease is based: prevention, early detection and screening, as well as treatment and care.

Researchers have developed a low-cost, semi-automated, AI-driven method that uses remote cameras to survey bumblebees and potentially other insects. The new tool could have important implications for efforts to conserve declining insect populations . This includes bumblebee species, several of which have been petitioned to be listed under the Endangered Species Act. Researchers also say the technology could benefit agriculture, given that many crops depend on insects as pollinators. "Insects are vitally important, and we need methods to better understand their populations," said Michael Getz, a data scientist at Biodiversity Research Institute in Maine, who led this research as a master's student at Oregon State University.

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. Jie Chao (Concord Consortium) Raspberry PI Sign up here to join. Pierre Marion (INRIA) EPFL The Zoom link is here . Stefan Klein and Anna Bon The Digital Humanism (DIGHUM) Initiative The talk will be livestreamed on YouTube here .