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.

Our most powerful artificial agents cannot be told exactly what to do, especially in complex planning environments. They almost exclusively rely on neural networks to perform their tasks, but neural networks cannot easily be told to obey certain rules or adhere to existing background knowledge. While such uncontrolled behaviour might be nothing more than a simple annoyance next time you ask an LLM to generate a schedule for reaching a deadline in two days and it starts to hallucinate that days have 48 hours instead of 24, it can be much more impactful when that same LLM is controlling an agent responsible for navigating a warehouse filled with TNT and it decides to go just a little too close to the storage compartments. Luckily, controlling neural networks has gained a lot of attention over the last years through the development of . Neurosymbolic AI, or NeSy for short, aims to combine the learning abilities of neural networks with the guarantees that symbolic methods based on automated mathematical reasoning offer.

A 17-year-old British sailor saved Simon from the Hong Kong docks when he was likely a year old. Breakthroughs, discoveries, and DIY tips sent six days a week. One day in March of 1948, George Hickinbottom, a British sailor, was walking around the docks of Stonecutters Island in Hong Kong. When the 17-year-old spotted a small black-and-white tuxedo cat, barely out of kittenhood, he decided to smuggle the hungry, scrawny animal aboard his ship, the HMS . Hickinbottom didn't get in trouble.

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.

The other is GRL-SVO(NUP), a fast heuristic providing a total order directly.

With more than 4.5 million patent documents, HUPD is two to three times larger than comparable corpora.

In the United States, health insurance is a critical component of individuals' ability to access health care.