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 .

Bringing back in the specialists and generalists idea - could you have several specialists on each node, and then a generalist agent overseeing all the nodes? Yes, you can think of it that way. We use the term "orchestrator" - an orchestrator is trying to manage everything, but the complexity gets balanced with a hierarchical structure. You tested this policy on some leading LLMs, and they performed differently to each other. Why do you think Gemini did better than, say, Claude or ChatGPT?

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.

AIhub monthly digest: August 2026 - IJCAI-ECAI in Bremen, the mathematics of simplicity, and does AI change the way we think? 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 report on events at IJCAI-ECAI 2026, learn about the mathematics of simplicity, investigate the accountability vacuum, and find out how AI changes the way we think. On Rashomon sets, the mathematics of simplicity, and why we don't need black boxes: an interview with Cynthia Rudin In the latest in our series of interviews with AI pioneers, we hear from Cynthia Rudin about interpretability, noise, and the case against complexity for complexity's sake. The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) was held from 15-21 August, in Bremen, Germany.

Robots walking down the street, surrounded by astounded onlookers, is an increasingly common sight. But these machines aren't yet the do-it-all assistants you'd want working in a kitchen or factory, and a major bottleneck is data. Much like humans, robots learn best by experience. The challenge is that it's labor-intensive and time-consuming to physically teach these machines so many actions across different settings. "One natural idea is to use simulation as a training ground. While there has been significant progress over the last few years in the physics engines that power robotics simulators, one of the remaining challenges has been creating sufficiently rich and diverse simulation content to capture the complexity of the real world," says Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science (EECS), Aeronautics and Astronautics, and Mechanical Engineering at MIT, and a principal investigator at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).