Real estate agent Kirsten Jordan joins'Fox News Live' to analyze the nation's housing market. Have you ever thought your dream house could offer skyline views without sacrificing style or space? Do you prefer the verticality of city apartments but wish you could also own a standalone home? These innovative prefab towers from the German company Moduleform make that possible. Named the DQ Tower, this micro-living residence is designed for backyards and small urban lots.

We present a challenging benchmark for the Open WorLd VISual question answering (OWLViz) task. OWLViz presents concise, unambiguous queries that require integrating multiple capabilities, including visual understanding, web exploration, and specialized tool usage. While humans achieve 69.2% accuracy on these intuitive tasks, even state-of-the-art VLMs struggle, with the best model, Gemini 2.0, achieving only 26.6% accuracy. Current agentic VLMs, which rely on limited vision and vision-language models as tools, perform even worse. This performance gap reveals significant limitations in multimodal systems' ability to select appropriate tools and execute complex reasoning sequences, establishing new directions for advancing practical AI research.

Recent open-vocabulary robot mapping methods enrich dense geometric maps with pre-trained visual-language features. While these maps allow for the prediction of point-wise saliency maps when queried for a certain language concept, large-scale environments and abstract queries beyond the object level still pose a considerable hurdle, ultimately limiting language-grounded robotic navigation. In this work, we present HOV-SG, a hierarchical open-vocabulary 3D scene graph mapping approach for language-grounded robot navigation. Leveraging open-vocabulary vision foundation models, we first obtain state-of-the-art open-vocabulary segment-level maps in 3D and subsequently construct a 3D scene graph hierarchy consisting of floor, room, and object concepts, each enriched with open-vocabulary features. Our approach is able to represent multi-story buildings and allows robotic traversal of those using a cross-floor Voronoi graph. HOV-SG is evaluated on three distinct datasets and surpasses previous baselines in open-vocabulary semantic accuracy on the object, room, and floor level while producing a 75% reduction in representation size compared to dense open-vocabulary maps. In order to prove the efficacy and generalization capabilities of HOV-SG, we showcase successful long-horizon language-conditioned robot navigation within real-world multi-storage environments. We provide code and trial video data at http://hovsg.github.io/.

Lifelong localization in a given map is an essential capability for autonomous service robots. In this paper, we consider the task of long-term localization in a changing indoor environment given sparse CAD floor plans. The commonly used pre-built maps from the robot sensors may increase the cost and time of deployment. Furthermore, their detailed nature requires that they are updated when significant changes occur. We address the difficulty of localization when the correspondence between the map and the observations is low due to the sparsity of the CAD map and the changing environment. To overcome both challenges, we propose to exploit semantic cues that are commonly present in human-oriented spaces. These semantic cues can be detected using RGB cameras by utilizing object detection, and are matched against an easy-to-update, abstract semantic map. The semantic information is integrated into a Monte Carlo localization framework using a particle filter that operates on 2D LiDAR scans and camera data. We provide a long-term localization solution and a semantic map format, for environments that undergo changes to their interior structure and detailed geometric maps are not available. We evaluate our localization framework on multiple challenging indoor scenarios in an office environment, taken weeks apart. The experiments suggest that our approach is robust to structural changes and can run on an onboard computer. We released the open source implementation of our approach written in C++ together with a ROS wrapper.

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Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardizing the quality of the production in industrial production lines. One of the more subtle kind of anomalies are uncalibrations. An uncalibration is said to take place when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. This solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. Then, after trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively. This solution can be adapted to different contexts by means of transfer learning, whose application allows for the addition of new sensors, the deployment into new environments and the retraining of the model with minimum amounts of data.

From Nintendo finally getting around to a new system with the Switch, to being able to play Snake on your phone again, to hacking your way to a better life -- there is a lot to be excited about. So break out the lotion, peach preserves and adapter cables and away we go. It's time to talk tech and make toast. The Nintendo Switch, which is a "technological work of art" according to some, should be the video game system of the year. It should propel Nintendo into the annuals of video game lore.

You're no match for Libratus, the new and powerful king of the felt. The artificial intelligence developed at Carnegie Mellon University is blowing away some of mankind's best in Heads-Up No Limit Texas Hold'Em, considered the final frontier of computer vs. human gamesmanship. Libratus and the pros – Dong Kim, Jimmy Chou, Jason Les and Daniel Mcauley – are playing a total of 120,000 hands over 20 days. With 101,908 hands in the bank, Libratus was ahead of all four by almost $1.4 million in virtual chips. Kim was down by $22,309; Mcauley, $271,233; Chou, $365,559; and Les, $718,341.

At the far end of the passage (#1): Historical Storage disks On the right side above table, left (#2): George Forsythe and students. In the table left (#6) Calculators used by Forsythe and Floyd. On the left side, left (#5): Stanford AI Lab (SAIL) history. More pictures of SAIL participants have been collected; many came from Bruce Baumgart who maintains a large archive. This poster was removed in 2015 in response to protest that considered that the poster perpetuated a gender biased view of Computer Science and Stanford CSD.

Larry Page, the billionaire co-founder of Google, is secretly backing a pair of startups that are working on flying cars, according to a report. Since 2010, Page has poured more than 100 million into Zee.Aero, a company that lately has been testing two flying-car prototypes at an airport hangar in Hollister, Calif., Bloomberg said Thursday, citing sources. Since last year, Page also has been funding another flying-car startup called Kitty Hawk -- and has cast it as a rival to Zee.Aero as he stages a top-secret race to develop a new class of vehicles that can soar above traffic jams and sidestep the hassles of the airport, Bloomberg said. "Page has drawn a line separating his two flying-car teams," the report said. "It's common for the Zee.Aero engineers to speculate over lunch about what their Kitty Hawk counterparts are up to."