The Good Robot podcast: what makes a drone "good"? Hosted by Eleanor Drage and Kerry McInerney, The Good Robot is a podcast which explores the many complex intersections between gender, feminism and technology. What makes a drone "good"? In this episode, we talk to Beryl Pong, UKRI Future Leaders Fellow at the University of Cambridge, where she leads the Centre for Drones and Culture. Beryl reflects on what it means to think about drones as "good" or "ethical" technologies and how it can be assessed through its socio-political context.

AI enables a Who's Who of brown bears in Alaska Being able to distinguish individual animals - including their unique history, movement patterns and habits - can help scientists better understand how their species function, and therefore better manage habitats and study population dynamics. Today, most computer vision systems for tracking animals are effective on species with patterns and markings, such as zebras, leopards and giraffes. The task is much more complicated for unmarked species where individual differences are harder to spot. Distinguishing a particular brown bear from its peers in a non-invasive way requires an incredible eye for detail and years of viewing the same bears over time. What's more, these bears emerge from hibernation in the spring with shaggy fur and having lost quite a bit of weight and then substantially increase their body weight feasting on salmon, as well as fully shedding their winter coat - that's enough to throw off experts as well as AI algorithms.

What is your research area? My research topic, at a high level, hasn't changed much since my dissertation. It has always been the problem of physical scene understanding - building machines that see, reason about, and interact with the physical world. Besides learning algorithms, what are the levels of abstraction needed by Al systems in their representations, and where do they come from? I aim to answer these fundamental questions, drawing inspiration from nature, i.e., the physical world itself, and from human cognition.

'The search is soul-destroying': Young jobseekers on the struggle to find work Young people are bearing the brunt of the UK's weak labour market, according to new figures from the Office for National Statistics (ONS). Some 16.1% of people aged 16 to 24 are not able to find work, compared to a national unemployment figure of 5.1%. That does not include young people who are out of work but not looking for a job, due to ill health or who are still studying. Businesses, particularly in sectors that traditionally gave young people their first jobs, like retail and hospitality, say higher costs are leading them to cut staff or not take on new hires, which often hits young workers the hardest. But graduate-level roles are also proving harder to land.

Why apply AI to figure skating? Skaters can always keep pushing, higher, faster, stronger. OOFSkate is all about helping skaters figure out a way to rotate a little bit faster in their jumps or jump a little bit higher. The system helps skaters catch things that perhaps could pass an eye test, but that might allow them to target some high-value areas of opportunity. The artistic side of skating is much harder to evaluate than the technical elements because it's subjective.

'What's something you're passionate about that not many people know?' 'What's something you're passionate about that not many people know?' Agenic AI apps first interview you and then give you limited matches selected for'similarity and reciprocity of personality' Dating apps exploit you, dating profiles lie to you, and sex is basically something old people used to do. You might as well consider it: can AI help you find love? For a handful of tech entrepreneurs and a few brave Londoners, the answer is "maybe". No, this is not a story about humans falling in love with sexy computer voices - and strictly speaking, AI dating of some variety has been around for a while. Most big platforms have integrated machine learning and some AI features into their offerings over the past few years.

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 fourth panel, the AI experts tackle the topic of AI and sustainability, exploring the critical balance between harnessing AI's potential and managing its environmental impact. They talk about: the growth of AI and its impact on infrastructure, looking beyond energy use, AI for accelerating breakthroughs, and strategies for investing in grid capacity and innovations.

OpenAI's President Gave Millions to Trump. He Says It's for Humanity In an interview with WIRED, Greg Brockman says his political donations support OpenAI's mission--even if some employees at the company disagree. OpenAI's president and cofounder Greg Brockman doesn't consider himself political, which is surprising, because he was one of President Trump's biggest individual donors of 2025. Greg and his wife, Anna Brockman, gave $25 million to MAGA Inc--a super PAC that supports President Trump--in September of last year. The pair also gave $25 million to a bipartisan AI super PAC, Leading the Future, which says it plans to oppose politicians that jeopardize Americans' "ability to benefit from AI."

How can robots acquire skills through interactions with the physical world? One of the key challenges in building robots for household or industrial settings is the need to master the control of high-degree-of-freedom systems such as mobile manipulators. Reinforcement learning has been a promising avenue for acquiring robot control policies, however, scaling to complex systems has proved tricky. In their work SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL, and introduce a method that renders real-world reinforcement learning feasible for complex embodiments. We caught up with Jiaheng to find out more.

AI is no longer just a translator or image recognizer. Today, we engage with systems that remember our preferences, proactively manage our calendars, and even provide emotional support. They build ongoing bonds with users. They change their behavior based on our habits. They don't just wait for commands; they suggest next steps.