Technology AI Robots can't replace guide dogs Man's best friend shares an'invisible care world' with humans that AI can't beat--yet. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Guide dogs are highly trained and can help people with vision loss navigate the world, open doors, and more. Breakthroughs, discoveries, and DIY tips sent six days a week. On paper, few physical jobs seem as ripe for AI takeover as that of the loyal service dog .

Decision tree ensembles are widely used in critical domains, making robustness and sensitivity analysis essential to their trustworthiness. We study the feature sensitivity problem, which asks whether an ensemble is sensitive to a specified subset of features -- such as protected attributes -- whose manipulation can alter model predictions. Existing approaches often yield examples of sensitivity that lie far from the training distribution, limiting their interpretability and practical value. We propose a data-aware sensitivity framework that constrains the sensitive examples to remain close to the dataset, thereby producing realistic and interpretable evidence of model weaknesses. To this end, we develop novel techniques for data-aware search using a combination of mixed-integer linear programming (MILP) and satisfiability modulo theories (SMT) encodings. Our contributions are fourfold. First, we strengthen the NP-hardness result for sensitivity verification, showing it holds even for trees of depth 1. Second, we develop MILP-optimizations that significantly speed up sensitivity verification for single ensembles and for the first time can also handle multiclass tree ensembles. Third, we introduce a data-aware framework generating realistic examples close to the training distribution. Finally, we conduct an extensive experimental evaluation on large tree ensembles, demonstrating scalability to ensembles with up to 800 trees of depth 8, achieving substantial improvements over the state of the art. This framework provides a practical foundation for analyzing the reliability and fairness of tree-based models in high-stakes applications.

Who cares about kids when adult collectors are willing to pay top dollar? From the moment a pixelated Gengar and Nidorino faced off in the opening animation of the first games on the original Game Boy back in 1996, the franchise has been a perennial favorite of kids and adults alike. With 2026 marking 30th anniversary, Lego's first-ever collaboration with the enduringly popular monster-catching megahit is perfectly timed--a crossover of pop culture titans with just one problem: Anyone who isn't an ultra-fan with cavernously deep pockets isn't invited. The recent announcement of a line of Lego Pokémon wasn't a surprise--the Danish brick brand first revealed it had entered into a "multi-year partnership" with The Pokémon Company back in March 2025 --but the makeup of the range itself was. Despite the mass appeal, Lego is launching with just three sets, and every single one is age-rated 18+.

Lego's Smart Brick Gives the Iconic Analog Toy a New Digital Brain The new sensor-packed Smart Play Brick will land this spring as part of a special Star Wars collection. The update adds interactive lights and sound to the Lego experience--including the minifigs. At CES in Las Vegas today, Lego has unveiled its new Smart Play platform, aimed at taking its distinctly analog plastic blocks and figures into a new world of tech-powered interactive play--but crucially one without any reliance on screens. Smart Play revolves around Lego's patented sensor-and tech-packed brick. It's the same size as a standard 2 x 4 Lego brick, but it is capable of connecting to compatible Smart Minifigures and Smart Tags and interacting with them in real time.

This paper reports on pretraining ModernBERT encoder models in six different sizes, ranging from 51M to 475M parameters, with a focus on limited multilingualism, emphasizing languages relevant to Finland. Our models are competitive with, or superior to, existing multilingual models. They outperform monolingual models on tasks that require a context longer than 512 tokens. We present empirical results on using different data in the final stage of training. The code and models are publicly released.

Data preparation (DP) transforms raw data into a form suitable for downstream applications, typically by composing operations into executable pipelines. Building such pipelines is time-consuming and requires sophisticated programming skills, posing a significant barrier for non-experts. To lower this barrier, we introduce Text-to-Pipeline, a new task that translates NL data preparation instructions into DP pipelines, and PARROT, a large-scale benchmark to support systematic evaluation. To ensure realistic DP scenarios, PARROT is built by mining transformation patterns from production pipelines and instantiating them on 23,009 real-world tables, resulting in ~18,000 tasks spanning 16 core operators. Our empirical evaluation on PARROT reveals a critical failure mode in cutting-edge LLMs: they struggle not only with multi-step compositional logic but also with semantic parameter grounding. We thus establish a strong baseline with Pipeline-Agent, an execution-aware agent that iteratively reflects on intermediate states. While it achieves state-of-the-art performance, a significant gap remains, underscoring the deep, unsolved challenges for PARROT. It provides the essential, large-scale testbed for developing and evaluating the next generation of autonomous data preparation agentic systems.