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.

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

The Corporation of London is calling on employers to re-skill female workers not currently in technical roles. The Corporation of London is calling on employers to re-skill female workers not currently in technical roles. 'Mid-career' females also being sidelined by rigid hiring processes, says City of London Corporation Women working in tech and financial services are at greater risk of losing their jobs to increased use of AI and automation than their male peers, according to a report that found experienced females were also being sidelined as a result of "rigid hiring processes". "Mid-career" women - with at least five years' experience - are being overlooked for digital roles in the tech and financial and professional services sectors, where they are traditionally underrepresented, according to the report by the City of London Corporation. The governing body that runs the capital's Square Mile found female applicants were discriminated against by rigid, and sometimes automated, screening of their CVs, which did not take into account career gaps related to caring for children or relatives, or only narrowly considered their professional experience.

The Meross smart plug mini boasts excellent compatibility and slim construction. On the hunt for new smart plugs to upgrade your home automation? One of our favorite picks, the Meross MSS110 Smart Plug Mini, is currently marked down on Amazon. The two pack is discounted from $34 to $27, and the four pack is down to $34 from its usual price of $52 . These plugs help add smart functionality to otherwise dumb devices around your home, like lamps or fans, so they can be included in your routines.

By focusing on the economic opportunities and economic data, fears about AI investment can turn into smart business decisions. There are many paths AI evolution could take. On one end of the spectrum, AI is dismissed as a marginal fad, another bubble fueled by notoriety and misallocated capital. On the other end, it's cast as a dystopian force, destined to eliminate jobs on a large scale and destabilize economies. Markets oscillate between skepticism and the fear of missing out, while the technology itself evolves quickly and investment dollars flow at a rate not seen in decades. All the while, many of today's financial and economic thought leaders hold to the consensus that the financial landscape will stay the same as it has been for the last several years.

The company also introduced Apple Home support and natural language automations at CES. Philips Hue has introduced a new software feature called SpatialAware at CES 2026 designed to ensure that all the lights in a space are in harmony with each other. Available exclusively for the Hue Bridge Pro, it takes into account each light point in a room and tailors the colors to ensure a natural representation. In a sunset scene, for example, the lights on one side of the room emit warm yellow tones to mimic the setting sun, while the ceiling lights on the opposite side reflect darker shades, the company wrote on its blog. The new feature is set to launch in the spring of 2026. To use the feature, you scan a room with your smartphone camera and use augmented reality to determine the positions of individual lights.

Existing ML benchmarks lack the depth and diversity of annotations needed for evaluating models on business process management (BPM) tasks. BPM is the practice of documenting, measuring, improving, and automating enterprise workflows. However, research has focused almost exclusively on one task -- full end-to-end automation using agents based on multimodal foundation models (FMs) like GPT-4. This focus on automation ignores the reality of how most BPM tools are applied today -- simply documenting the relevant workflow takes 60% of the time of the typical process optimization project. To address this gap we present WONDERBREAD, the first benchmark for evaluating multimodal FMs on BPM tasks beyond automation. Our contributions are: (1) a dataset containing 2928 documented workflow demonstrations; (2) 6 novel BPM tasks sourced from real-world applications ranging from workflow documentation to knowledge transfer to process improvement; and (3) an automated evaluation harness. Our benchmark shows that while state-of-the-art FMs can automatically generate documentation (e.g.

High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained human experts. A common source of concern about automation in these settings is that experts may exercise intuition that is difficult to model and/or have access to information (e.g., conversations with a patient) that is simply unavailable to a would-be algorithm. This raises a natural question whether human experts add value which could not be captured by an algorithmic predictor.We develop a statistical framework under which we can pose this question as a natural hypothesis test. Indeed, as our framework highlights, detecting human expertise is more subtle than simply comparing the accuracy of expert predictions to those made by a particular learning algorithm. Instead, we propose a simple procedure which tests whether expert predictions are statistically independent from the outcomes of interest after conditioning on the available inputs ('features'). A rejection of our test thus suggests that human experts may add value to any algorithm trained on the available data, and has direct implications for whether human-AI'complementarity' is achievable in a given prediction task.We highlight the utility of our procedure using admissions data collected from the emergency department of a large academic hospital system, where we show that physicians' admit/discharge decisions for patients with acute gastrointestinal bleeding (AGIB) appear to be incorporating information that is not available to a standard algorithmic screening tool. This is despite the fact that the screening tool is arguably more accurate than physicians' discretionary decisions, highlighting that - even absent normative concerns about accountability or interpretability - accuracy is insufficient to justify algorithmic automation.