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 .

What happens when AI runs out of pictures? A hospital may only ever collect a few dozen scans of a rare condition - for example, an unusual tumour. The radiology department wants software to flag this on a scan - not to replace the specialist, but so a hospital without one still gets their scan checked the same way. The clinicians know what they're looking for. Over a decade, the hospital might gather 40 confirmed cases.

The winners of three International Joint Conferences on Artificial Intelligence (IJCAI) awards have been announced . These three distinctions are: the, the and the . The Research Excellence award is given to a scientist who has carried out a program of research of consistently high quality throughout an entire career yielding several substantial results. The winner of the 2026 Award for Research Excellence is Nicholas R. Jennings, Vice-Chancellor and President of Loughborough University, UK. Professor Jennings is recognized for his seminal contributions to the field of multi-agent systems, including algorithms for multi-agent coordination and the principles of human-agent teamwork, and for his pioneering applications of autonomous agents and multi-agent systems.

The Future of AI Research report, published in March 2025, aims to clearly identify the trajectory of AI research in a structured way. The report was led by outgoing AAAI President Francesca Rossi and covers 17 different AI topics . Members of the report team, and other selected AI practitioners, are taking part in a series of video panel discussions covering selected chapters from the report. In the next discussion in the collection, the four panellists tackle AI and scientific integrity. Lucy Smith is Senior Managing Editor for AIhub.

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 find out about time-series anomaly detection, delve into music generation, honour award winners, and catch up on the action from the RoboCup humanoid soccer league. We caught up with Thi Kieu Khanh Ho to find out more about her work on time-series anomaly detection, what inspired her to study AI, and what she plans to work on next. This interview is part of our series featuring the AAAI Doctoral Consortium participants. In the latest in our series of IJCAI interviews, AIhub ambassador Liliane-Caroline Demers spoke to François Pachet to find out more about his work on music generation with AI.

Humans have been successfully trained to spot AI-generated faces in a study led by researchers at the Australian National University (ANU) Emotions and Faces Lab. AI-generated deepfake faces have become so realistic that it is difficult for people to tell them apart from photos of real humans, contributing to increases in AI-related fraud. "Training on visual artifacts, like looking for a sixth finger or odd earrings, has had limited success, partly because the AI is getting too good, and fraudsters may avoid using pictures with obvious flaws anyway," lead researcher Associate Professor Amy Dawel said. "Our training directs people's attention to global qualities that differ between AI and human faces. AI faces tend to be more symmetrical, proportional and attractive, but without training we often think these are markers of being human."