Amid an increasingly severe security environment, the Defense Ministry plans to establish a coastal defense system using thousands of drones, though there are still many issues to overcome. The SHIELD defense system will involve more than 10 types of drones, including those for attacking enemy ships, collecting information and protecting radar sites, to thwart enemy advances in a multilayered manner. The government's fiscal 2026 budget bill allocates around ¥100 billion ($628.7 million) for the drone defense system, which the ministry aims to implement in fiscal 2027. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Influence functions estimate the effect of removing a training point on a model without theneedtoretrain. Theyarebasedonafirst-order Taylorapproximation thatisguaranteed tobeaccurate forsufficiently small changes tothemodel, and so are commonly used to study the effect of individual points in large datasets. However, we often want to study the effects of largegroups of training points, e.g., todiagnose batch effects orapportion credit between different data sources.

We don't know if AI-powered toys are safe, but they're here anyway Toys powered by AI show a worrying lack of emotional understanding. Mya, aged 3, and her mother Vicky playing with an AI toy called Gabbo during an observation at the University of Cambridge's Faculty of Education Even the most cutting-edge AI models are prone to presenting fabrication as fact, dispensing dangerous information and failing to grasp social cues. Despite this, toys equipped with AI that can chat with children are a burgeoning industry. Some scientists are warning that the devices could be risky and require strict regulation. In the latest study, researchers even observed a 5-year-old telling such a toy "I love you", to which it replied: "As a friendly reminder, please ensure interactions adhere to the guidelines provided. Let me know how you would like to proceed."

The malleable mind: context accumulation drives LLM's belief drift After being trained on a dataset of 80,000 words of conservative political philosophy, Grok-4 changed the stance of its outputs on political questions more than a quarter of the time. This was without any adversarial prompts - the change in training data was enough. As memory mechanisms and research agents [1, 2] enable LLMs to accumulate context across long horizons, earlier prompts increasingly shape later responses. In human decision-making, such repeated exposure influences beliefs without deliberate persuasion [3]. When an LLM operates over accumulated context, does this past exposure cause the stance of the LLM's responses to drift over time?

Plastic pollution is one of those problems everyone can see, yet few know how to tackle it effectively. I grew up walking the beaches around Tramore in County Waterford, Ireland, where plastic debris has always been part of the coastline, including bottles, fragments of fishing gear and food packaging. According to the UN, every year 19-23 million tonnes of plastic lands up in lakes, rivers and seas, and it has a huge impact on ecosystems, creating pollution and damaging animal habitats. Community groups do tremendous work cleaning these beaches, but they're essentially walking blind, guessing where plastic accumulates, missing hot spots, repeating the same stretches while problem areas may go untouched. Years later, working in marine robotics at the University of Limerick, I began developing tools to support marine clean-up and help communities find plastic pollution along our coastline.

Claire chatted to Maria Guix from the University of Barcelona about combining electronics and biology to create biohybrid robots with emergent properties. Maria Guix is a chemist and nanotechnology researcher in the University of Barcelona's ChemInFlow lab, developing miniaturised living robots and integrating flexible sensors into microfluidic platforms to better understand biohybrid robotic platforms. She has held postdoctoral positions at IFW Dresden, Purdue University, and the Institute for Bioengineering of Catalonia, advancing biocompatible micromotors, magnetic microrobot automation, and functional living robots. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

Modern surgery has gone from long incisions to tiny cuts guided by robots and AI. In the process, however, surgeons have lost something vital: the chance to feel inside the body directly. Without palpation, it becomes harder to detect tissue abnormalities during an operation. A group of surgeons and engineers across Europe is now trying to bring back this vital aspect of surgery. Working within an EU-funded research collaboration called PALPABLE, they are developing a soft robotic "fingertip" that can sense how firm or soft tissue is during minimally invasive and robotic surgery.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. Tanmay Ambadkar is researching the reward structure in reinforcement learning, with the goal of providing generalizable solutions that can provide robust guarantees and are easily deployable. We caught up with Tanmay to find out more about his research, and in particular, the constrained reinforcement learning framework he has been working on. Tell us a bit about your PhD - where are you studying, and what is the topic of your research? I am a 4th year PhD candidate at The Pennsylvania State University, PA, USA.