Wladimir Silva and colleagues at North Carolina State University have developed an adaptive quantum algorithm for matrix multiplication that reduces the complexity of inner product calculations to O (log N) by using Quantum Random Access Memory. Their new “Adaptive Stacking” framework dynamically adjusts the algorithm’s execution, enabling compatibility with both near-term and fault-tolerant quantum systems and offering a flexible range of time complexities. Validation through a Quantum Machine Learning simulation on the MNIST dataset shows 96% accuracy and suggests a pathway towards sharply more efficient high-dimensional linear algebra operations. Demonstrated numerical stability and MNIST classification accuracy with a dynamically adjusted AQ-Stacker, a new hybrid quantum-classical algorithm, achieves 96 per cent accuracy on the MNIST dataset, exceeding the performance of previous methods with improved numerical stability in a practical quantum machine learning simulation. Realising super-classical efficiency in high-dimensional linear algebra has long been hindered by the difficulty of reconciling quantum speedups with the limitations of current quantum hardware. The MNIST dataset, comprising 70,000 labelled images of handwritten digits, serves as a standard benchmark for evaluating machine learning algorithms, particularly those dealing with image recognition and classification. Achieving high accuracy on this dataset demonstrates the algorithm’s ability to handle complex data representations and perform meaningful computations. AQ-Stacker overcomes this challenge by dynamically adjusting its execution pattern to optimise performance. This dynamic adjustment is crucial because the performance of quantum algorithms is heavily influenced by the number and quality of available qubits, as well as the coherence time, the duration for which qubits maintain their quantum state. Quantum Random Access Memory reduces the complexity of computing vector inner products to O(log N), enabling a tunable time-complexity range potentially reaching O(N2) on fault-tolerant systems. The use of QRAM allows for the preparation of quantum states in O(log N) time, significantly reducing data loading bottlenecks
Apr 9, 2026 · via quantumzeitgeist.com
This $99.99 AI tool combines writing, images, and more in one app TL;DR: 1min.AI is on sale for $99.99 (reg. $540), offering a lifetime subscription that combines multiple AI models and tools for writing, image generation, document processing, and more in one platform. Using AI efficiently often means using more than one tool, which is where things can start to feel a bit counterproductive. 1min.AI takes a more streamlined approach by combining those functions into a single platform. For a limited time, its lifetime plan is on sale for $99.99 (reg. $540). 1min.AI brings together multiple AI models like GPT, Claude, Gemini, MistralAI, and more into one workspace, so you can handle different tasks without switching between apps. Whether you’re writing, brainstorming, generating visuals, or analyzing documents, it’s designed to keep everything in one place. Instead of focusing on just one function, the platform covers a wide range of everyday use cases. For writing and content tasks, it can generate blog posts, rewrite drafts, expand or shorten content, and help with keyword research and SEO. It also includes tools for paraphrasing, summarizing, and even adapting content for platforms like LinkedIn or X, which can be useful if you’re managing multiple channels. On the visual side, it offers image generation and editing tools, including background removal, object editing, and upscaling. You can also generate images from prompts or sketches, adding a layer of flexibility to creative projects. For document-heavy work, it supports summarizing, translating, and interacting with files like PDFs and presentations. You can ask questions about documents or extract key information without manually digging through them. It also extends into audio and video, with features like text-to-speech, speech-to-text, voice tools, and basic video generation or editing, making it useful if your workflow includes multimedia content. Of course, like most AI
Apr 9, 2026 · via mashable.com

Constant Dullaart - The European Classes - Goethe-Institut United Kingdom
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Constant Dullaart The European Classes
Leighton, Room 107
As artificial intelligence progresses, so-called âconvolutional networksâ (ConvNets) now exist that can recognise objects within photographic images. Constant Dullaart has retrained these image recognition networks to focus purely on European images, creating an image dataset formed in collaboration with universities across Europe. He asks, âHow can Europeâs diverse cultural entities be represented within these networksâ capacity to recognise, create and interact?â The outcome will consist of terabytes of images that Dullaart hopes will represent the European collective identity in 2017.
Constant Dullaart was born in the Netherlands. He reflects on the cultural and social effects of communication and image processing technologies. His work includes websites, performances, fake armies and manipulated found images, presented both offline and in the public space of the Internet.
Apr 9, 2026 · via goethe.de
Notes from the Asia-Pacific region: Robust conversations at richly rewarding Summit The IAPP Global Summit 2026 brought together diverse voices — from scientists and legal scholars to authors and global figures — to discuss privacy as a deeply human, culturally significant and increasingly urgent challenge. Contributors: Charmian Aw AIGP, CIPP/A, CIPP/E, CIPP/US, CIPM, FIP Partner Hogan Lovells Editor's note The IAPP is policy neutral. We publish contributed opinion pieces to enable our members to hear a broad spectrum of views in our domains. This year's IAPP Global Summit 2026, held just last week, featured a series of robust, potent conversations, which lingered long after attendees departed. Highlights continuing to brew in my mind are distilled below. The conference opened with cognitive scientist Maya Shankar, whose work and book on decision-making invite us to reconsider the invisible forces shaping human behavior. Shankar's keynote was nothing short of a hand-crafted, house-blend reflection on how small interventions can stir profound change. In a conversation with IAPP Vice President and Chief Knowledge Officer Caitlin Fennessy, CIPP/US, world-renowned author Salman Rushdie added a deeply human and cultural dimension. Reflecting on his experiences and observations, including life in India, Rushdie highlighted how privacy is neither universal nor evenly distributed. In the slums, privacy is a luxury. Cultural norms, social structures and economic realities shape what privacy means — and whether it is even attainable. Boston University School of Law Professor Woodrow Hartzog's keynote was a calibrated dark roast — deep, complex and intentionally uncomfortable. He spoke candidly about privacy's ongoing identity crisis. Against the backdrop of shrinking budgets and expanding technological capability, Hartzog warned of the risks posed by surveillance systems, including facial recognition, and the creeping normalization of intrusive technologies. Unfettered artificial intelligence, he argued, threatens not just data protection, but the very fabric of
Apr 9, 2026 · via iapp.org
Abstract Automatic and high-precision video-based animal re-identification (Re-ID) has become an important tool for wildlife conservation and behavioral research. However, state-of-the-art image-based methods, while effective on static images, fail to make full use of the rich dynamic information in videos, such as gait and motion patterns. To bridge this gap, we propose HST-Former, a novel framework that extends data efficiency and species-agnostic principles into the temporal domain. The core innovation of this method is the Hierarchical Spatio-Temporal Transformer Aggregator (HSTTA)—a customized transformer architecture designed to process and integrate all local features from an entire animal trajectory. By modeling both spatial and temporal feature dependencies, HSTTA learns complex long-range relationships and produces a single, highly discriminative video-level descriptor. The hierarchical design first summarizes intra-frame features and then aggregates them across frames, effectively addressing the computational challenges of standard transformers on long sequences. In addition, we introduce a spatio-temporal consistency constraint to enhance the geometric verification step, improving re-ranking accuracy. Our model significantly outperforms current state-of-the-art baselines. To validate its effectiveness, we conduct comprehensive evaluations on three public datasets, where HST-Former achieves the best performance across all key metrics, including Top-1, Top-3, and Top-5. Similar content being viewed by others Data availability The datasets used during the current study are available from the corresponding author on reasonable request. References Araujo, A. et al. Recurrent neural networks for person re-identification revisited. IEEE Trans. Multimed. 21, 2813–2824 (2019). Whytock, R. C. et al. Optimizing the automated recognition of individual animals to support population monitoring. Ecol. Evol. 13, e10210 (2023). Crouse, D. et al. Identification of animal individuals using deep learning: A case study of the giant panda using infrared camera images. Biol. Conserv. 242, 108405 (2020). Francis, D. Review of the current research in animal individual recognition. Modern Sci. 1, 80–85 (2023). Clapham, M.,
Apr 9, 2026 · via nature.com
Abstract This work introduces SqueezeViT (Squeeze Vision Transformers), a compact yet effective architecture based on Vision Transformers (ViT) designed for chest X-ray (CXR) image classification. In contrast to traditional ViT architectures, which are computationally demanding, SqueezeViT employs a novel squeezing procedure that effectively lowers token dimensions without compromising important visual components, leading to expedited inference and decreased memory consumption. The designed model is tested for two commonly used public datasets, NIH Chest X-ray and CheXpert, providing a diverse range of thoracic pathologies. SqueezeViT reduces the number of parameters by 43.2% compared to the baseline MobileViT1, and up to 95.4% compared to other state-of-the-art (SOTA) models. The suggested model offers up to 16.5% improvement in the area under the receiver operating characteristic curve (AUROC) compared to SOTA models, and it is, in general, superior to the baseline and effective convolutional neural networks CNNs2 in numerous tasks. Such developments make the proposed SqueezeViT approach an attractive option for a wide variety of applications. The findings indicate that SqueezeViT outperforms the current SOTA classifiers while maintaining a lightweight model architecture. In turn, such results emphasize the possibilities of using SqueezeViT in real clinical environment, where the amount of computational resources can be constrained. Similar content being viewed by others Data availability The datasets used in this work are available at: [https://www.kaggle.com/datasets/nih-chest-xrays/data](https:/www.kaggle.com/datasets/nih-chest-xrays/data)[https://aimi.stanford.edu/datasets/chexpert-chest-x-rays](https:/aimi.stanford.edu/datasets/chexpert-chest-x-rays)The model of the proposed SqueezeViT is available on the link: [https://github.com/vijay13787/SqueezeVIT.git](https:/github.com/vijay13787/SqueezeVIT.git). References Wang, X. et al. ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. in. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017. (2017). Haritha, D., Pranathi, M. K. & Reethika, M. COVID detection from chest X-rays with DeepLearning: CheXNet. in 5th international conference on computing, communication and security (ICCCS). 2020. IEEE. 2020. IEEE. (2020). Hasanah, U. et al. CheXNet and feature
Apr 9, 2026 · via nature.com
European Union’s Digital Omnibus Package Heats Up the Debate on Human Rights Protection - Human Rights Research Center - 8 hours ago - 6 min read Author: Ana Budeanu April 9, 2026 HRRC acknowledges the efforts of the European Union to establish a more cohesive set of digital rules across its territory, but emphasises that any policy must adhere to the principles found in the international human rights body of laws. The European Union (EU) Digital Omnibus Package has member states and human rights organizations in the EU debating whether this set of legislative acts is a good choice for EU citizens, especially those in vulnerable groups such as people with disabilities or members of LGBTQIA+, as they seem more likely to diminish already established rules that protect privacy, prevent discrimination, and ensure accountability in digital systems. To date, the European Commission has introduced the “Digital Package,” a comprehensive regulatory framework encompassing both the Digital Omnibus Regulation and the parallel Digital Omnibus on Artificial Intelligence Regulation. The EU’s digital regulatory construction is currently undergoing a significant change through the Digital Omnibus Package, which has two main acts: the Digital Omnibus on AI and the Digital Omnibus Regulation. The package is intended to help the EU’s digital economy and lessen administrative pressures on businesses by creating a more cohesive set of rules for everything which takes place online, from purely business transactions to communications, aiming to resolve legal uncertainties within the complex web of the General Data Protection Regulation (GDPR), the Data Act, and the ePrivacy Directive. The more important of the two acts is the EU Digital Omnibus on AI proposal, a legislative initiative seeking to simplify and modernize the existing regulations for data protection, artificial intelligence, and cybersecurity within broader online activities, including business, education or recruitment. The starting
Apr 9, 2026 · via humanrightsresearch.org
Abstract Image defect detection of power transmission and transformation equipment is a key technology in the operation and maintenance of power systems, which is of vital importance for ensuring the safe and stable operation of the power grid and enhancing its self-healing capacity. However, this task has long been confronted with the core challenge of a limited number of defect samples for specific categories. Traditional deep learning models trained on large-scale data find it difficult to achieve accurate detection under small-sample conditions. To address this issue, this study proposes a lightweight segmentation network based on the Mamba architecture, Residual Mamba (ResMamba). This network adopts a six-level U-shaped codec structure and innovatively integrates the Mamba module into the visual state space (VSS) as the codec link. Its core ResVSS module significantly reduces the number of parameters by removing a redundant linear layer (accounting for 38% of the original VSS module parameters) in the internal shortcut connection of the original VSS module, and introduces deep convolutional blocks and learnable scale parameters to dynamically scale residual connections. It enhances feature representation ability while reducing model complexity. In addition, the skip connection part introduces a multi-level and multi-scale information fusion mechanism, effectively integrating cross-scale features by generating spatial and channel attention maps, thereby enhancing the model’s performance in multi-specification defect detection. Experimental results on public datasets (including specialized small-sample settings) show that ResMamba achieves superior segmentation accuracy while maintaining a low parameter count, effectively balancing computational efficiency and detection performance. It outperforms both general segmentation models and domain-specific models for power equipment defect detection, providing a reliable new solution for small-sample defect detection of power equipment and enhancing the self-healing ability of power grids. Similar content being viewed by others Data availability The data cannot be shared publicly because it contains sensitive information/is subject
Apr 9, 2026 · via nature.com
Woodchuck Named “Waste Diversion Solution of the Year” In 2026 CleanTech Breakthrough Awards Program Published Thursday, April 9, 2026 | 5:01 a.m. Updated Thursday, April 9, 2026 | 5:03 a.m. GRAND RAPIDS, Mich.--(BUSINESS WIRE)--Apr 9, 2026-- Woodchuck , the AI-powered climate-tech startup redefining how construction and manufacturing industries handle wood waste, today announced it has been awarded “Waste Diversion Solution of the Year” in the 3 rd annual CleanTech Breakthrough Awards program conducted by CleanTech Breakthrough , a leading independent market intelligence organization that evaluates and recognizes standout climate and clean technology companies, products and services around the globe. This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20260409517100/en/ Woodchuck was recognized for its AI-enabled waste diversion platform that transforms wood waste into renewable energy by combining AI-driven technology, on-site logistics, and bioenergy partnerships to deliver a scalable, measurable solution that turns waste into clean baseload power. Woodchuck deploys AI-enabled image recognition directly on job sites. Instead of relying on manual sorting, Woodchuck integrates smart containers, sensor technology, and computer vision systems that identify, classify, and track wood waste in real time. This increases diversion rates, reduces contamination, reduces landfill dependency while supplying regional bioenergy facilities with renewable feedstock and generates transparent, auditable data for contractors and project owners. Woodchuck’s model helps contractors routinely achieve 30–40% cost savings compared to traditional waste hauling while meeting aggressive ESG and diversion targets. Waste becomes a measurable sustainability advantage to help companies win bids on high-profile projects, including EV battery plants and hyperscale AI data centers. “We’re not just simply diverting waste, but building a circular system where technology, economics, and climate impact reinforce one another. Our solution turns sustainability into cost savings and demonstrates that climate leadership can be both profitable and scalable. Woodchuck’s impact also extends beyond carbon accounting into
Apr 9, 2026 · via lasvegassun.com
For those of you just tuning in, I’ll repeat that a little over a year ago I made a visit to the archives belonging to the Missouri History Museum in Forest Park. There I met the archivist Lauren Sallwasser who it turned out is a fellow Maplewoodian. She showed me their collection of documents and photographs that had been donated by Margaret Sappington Townsend. She was kind enough to provide me with digital copies of two of the photo albums from that collection. It was a mind-blowing experience. I’ve been trying to get back ever since. Although, I have posted many of the images from those two albums that I consider solid gold nuggets from the rich vein of Maplewood history. There are many more that are worth a look. Last post we took a look at the first ten pages from Album One Click on this if you would like to see the First Ten Pages from Album One again. Still from Album One, here are ten more pages beginning with page 11. Page eleven. The address on this house is very clearly 7177. From other images in this collection, I have learned that it was on Manchester, west of Sutton even though the address would have you think otherwise. Images added to the album later show that the address had been changed to 7477. This is a fairly common problem for historians. As the population increased, it was sometimes necessary for the post office to change the addresses. Page twelve. Philip’s page. Since I have had much enjoyment from having the whole of the two albums to examine, I thought that the only way for the followers of this space to experience that was to post all of the images contained within the albums. That is a tall
Apr 9, 2026 · via 40southnews.com
Abstract Ultrasound imaging is a crucial modality for breast cancer diagnosis, with lesion segmentation being an essential component of computer-aided diagnosis systems. However, the high cost and effort associated with pixel-level annotation significantly hinder the advancement of breast ultrasound (BUS) image segmentation algorithms. In this paper, we propose a multi-level semi-supervised generative adversarial network (MLSS-GAN) that incorporates a generator and a novel multi-level classifier acting as the discriminator. While some previous approaches have incorporated multi-level adversarial learning, our discriminator employs an encoder for feature extraction and two decoders designed for distinct image-level and pixel-level adversarial tasks, enabling more comprehensive segmentation supervision. Specifically, one decoder performs image-level classification, while the other conducts pixel-level classification. This multi-level adversarial strategy compels the generator to produce images that closely resemble real BUS images at different feature representations, thereby enriching the classification feature space. To assess the effectiveness of the proposed method, comprehensive comparisons with both fully supervised and semi-supervised approaches are conducted on the BUSI dataset. Experimental results demonstrated the superior performance of our method, especially under conditions with only a limited number of annotated BUS images. Similar content being viewed by others Data availability The BUSI dataset analyzed during the current study is available in the BUSI repository, https://doi.org/10.1016/j.dib.2019.104863. References Bray, F. et al. Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. (2021). Masood, S., Sharif, M., Masood, A., Yasmin, M. & Raza, M. A survey on medical image segmentation. Curr. Med. Imaging 11, 3–14 (2015). Gu, Y. et al. Deep learning based on ultrasound images assists breast lesion diagnosis in china: a multicenter diagnostic study. Insights Imaging 13, 124 (2022). Ranjbarzadeh, R. et al. Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods.
Apr 9, 2026 · via nature.com
U.S. Air Force Senior Master Sgt. Matthew Broussard, front right, Air Force Global Strike Command fire and emergency services program manager, receives a medallion from his wife as part of the Chief Recognition Ceremony at Barksdale Air Force Base, Louisiana, April 3, 2026. The ceremony is held to recognize Airmen who have achieved the rank of chief master sergeant. (U.S. Air Force photo by Senior Airman Rhea Beil)
| Date Taken: | 04.03.2026 |
| Date Posted: | 04.08.2026 17:28 |
| Photo ID: | 9603888 |
| VIRIN: | 260403-F-DY500-1009 |
| Resolution: | 8202x5468 |
| Size: | 4.61 MB |
| Location: | BARKSDALE AIR FORCE BASE, LOUISIANA, US |
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This work, Chief Recognition Ceremony 2026 [Image 8 of 8], by SrA Rhea Beil, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Apr 9, 2026 · via dvidshub.net
Scientists develop three-in-one diode PN junction diodes are the underpinning of modern optoelectronic systems. But, typically, they perform only a single task. To process complex visual signals, optical imaging systems often rely on the introduction of a third electrode or integrating diodes with external logic circuits. But these architectures increase hardware complexity and bring von Neumann issues — the physical separation of sensing, memory, and computing—leading to larger areas and increased power consumption. There is a lack of a compact, single-device solution. Now, in a study titled 'A single diode with integrated photosensing, memory and processing for neuromorphic image sensors' published in Nature Electronics, a research team led by SUN Haiding from the University of Science and Technology of China of the Chinese Academy of Sciences, along with the collaborators from domestic and foreign universities, have developed a multifunctional photodiode architecture. They appraoch, they say, provides a hardware solution for low-power neuromorphic visual cameras with integrated sensing, memory, and computing, breaking through the traditional semiconductor physical limit of unidirectional conductivity. The new PN junction design is based on energy band engineering. The team constructed high-crystal-quality vertical GaN-based PN junction diode arrays on conductive silicon substrates. By inserting a wide-bandgap n-AlGaN layer into the GaN-based PN junction, they formed a localised 'charge storage layer' internally via energy band bending. This embedded structure endowed the device with unique charge trapping and releasing capabilities, enabling arbitrarily mode switch through simple external bias voltages without altering the device structure. The team demonstrated that a single device could exhibit three independent and freely switchable working modes: photosensing, photosynapse, and photomemory. Under zero bias, the device exhibited stable self-driven photosensing characteristics for real-time image sensing. Under a constant bias, it demonstrated photosynaptic behaviour modulated by pulse frequencies. Under pulsed bias operation, it achieved multi-state optical memory
Apr 9, 2026 · via compoundsemiconductor.net
Wrongfully Arrested Peppermill Truck Driver Slams Facial Recognition Tech Posted on: April 8, 2026, 03:49h. Last updated on: April 9, 2026, 06:14h. Lawyers acting for a man wrongly arrested at Reno’s Peppermill Casino following misuse of AI-based facial recognition say poor training has led police to unlawfully detain “thousands” in similar cases. In September 2023, truck driver Jason Killinger was flagged as a “100% match” to an individual named Michael Ellis who had been banned from the venue months earlier for sleeping on the premises. Held for 11 Hours Killinger was detained by casino security and then arrested by Richard Jager, a rookie officer from the Reno Police Department. Jager refused to believe Killinger was not who he said he was and accused him of obtaining a fraudulent ID when he provided evidence of his identity. He was detained for 11 hours, and handcuffed for four, which resulted in bruises and shoulder pain, according to a lawsuit he later filed against Jager. The suit claims that even after his real identity was confirmed by a fingerprint check at Washoe County jail, Jager filed a police report saying that Killinger had given conflicting IDs to Peppermill security. City Added to Lawsuit Last week, federal Judge Miranda Du granted a motion to add the City of Reno to the lawsuit as a defendant. The primary reason was the city’s continued pursuit of legal action against Killinger after he had been exonerated, and because it allegedly failed to properly train police officers on the limits of facial recognition technology at the time of Killinger’s arrest. However, a new filing from Killinger’s lawyers alleges that Reno police have long been aware that facial recognition results aren’t reliable enough to justify an arrest, but the department continued to follow that approach under Chief Kathryn Nance.
Apr 9, 2026 · via casino.org
Adversarial Examples in Computer Vision: How Attacks Work and How to Build Robust Models Adversarial examples in computer vision are inputs that appear normal to humans but cause neural networks to make confident, incorrect predictions. What began as small, gradient-based pixel tweaks has expanded into physically realizable attacks (patches, textures, camouflages) and latent-space manipulations that target internal representations. By early 2026, research has increasingly focused on vision foundation models and vision-language systems, where multimodal attack surfaces such as prompt injection and jailbreaking have become practical concerns. This article explains how adversarial attacks work, why they transfer across models, how physical attacks succeed outside the lab, and which defenses actually improve robustness in real deployments. Adversarial attacks exploit model sensitivity to input perturbations, impacting vision systems and inference reliability-build defensive expertise with an AI Security Certification, implement robust CV pipelines using a Python Course, and align model outputs with real-world applications through an AI powered marketing course. What Are Adversarial Examples in Computer Vision? Adversarial examples are intentionally crafted perturbations applied to an image that cause a machine learning model to misclassify it. The defining property is that the perturbation is often imperceptible or resembles benign noise, yet reliably changes the model output. In computer vision, this can affect: Image classification (mislabeling an object) Object detection (missing an object or hallucinating one) Segmentation (corrupting pixel-level masks) Biometrics (face recognition evasion or impersonation) Autonomous driving perception (sign detection, vehicle detection, sensor fusion) Recent surveys underscore a dual reality: adversarial examples represent a security threat and also serve as a testing tool for building more resilient models. Many modern defense strategies explicitly reuse attack methods to harden systems. How Adversarial Attacks Work Most adversarial attacks exploit how deep neural networks respond to small input changes in high-dimensional spaces. Even a tiny change in
Apr 9, 2026 · via blockchain-council.org
Lip reading, emotion sensing, face biometrics vie for place in smart auto stack Your car may soon know when you’re tipsy, stressed, or just mouthing words into the wind. From Ford’s lip-reading patent to radar systems that sense your mood and (of course) biometric driver identification, the latest wave of automotive AI is turning vehicles into surprisingly perceptive co-pilots. Ford publishes patent for in-car lip reading Vehicle voice control systems have been around for a while – but soon, they may be able to listen in on our most secretive conversations. Ford has filed a patent application for software that reads the occupant’s lips using cameras and sensors inside the vehicle. The feature would not be used for lip-based biometric authentication. Instead, the automaker says the vehicle would turn on lip reading in situations where normal voice controls do not work due to wind or other noise, for example, while driving in a convertible with the top down. Aside from lip movement captured by cameras, the system could also rely on acoustic signals: the vehicle could emit inaudible sound waves and analyze the echoes bouncing back from the user’s lips and mouth with the help of machine learning. “The captured video and sensor data are processed using machine learning algorithms that are trained on large datasets of lip movements and corresponding speech to learn the patterns and nuances of lip reading,” says patent 20260095520, recently published by the USPTO. The patent also predicts the use of gesture- and facial-expression-detection software to determine whether the user is having difficulty interacting with the system. Nodding the head could be interpreted as acknowledging a verbal interaction with the vehicle, while shaking the head or appearing confused would lead the system to believe that the user is having difficulties. The vehicle could then choose
Apr 9, 2026 · via biometricupdate.com
PRAGUE, CZECH REPUBLIC - Apr 8, 2026 - Future of Gaming, the strategic intelligence platform tracking gaming patents from the USPTO, has published its Q1 2026 Quarterly Granted Report analyzing 124 patents awarded to 21 companies. The report reveals a consistent industry priority: eliminating player friction through automation, from controller drift calibration to backend service integration to crafting systems that don't pull players out of the game world. "The granted patents tell you what's actually shipping," said Alex Kirillov, founder of Future of Gaming. "Filed patents show intent. Granted patents show commitment. When Sony gets 27 grants in a single quarter spanning everything from gesture recognition to spatial anchoring to procedural animation, that's not speculative R&D. That's infrastructure for products in development right now." Key Findings Sony received more than triple the next-largest company. Sony's 27 granted patents span six categories, with 16 in AI/ML alone. Their grants cover gesture recognition that prevents false positives during gameplay, spectator-driven AI adjustment, session duration prediction, customizable flat controllers for accessibility, and spatial anchoring for mixed-reality experiences. Cross-platform led granted patents with 52. Unified progression tracking, backend service integration, and multi-game loyalty systems dominated. Betty Gaming received a patent for universal progress bars converting game-specific metrics into platform-wide rewards. Amazon patented pre-configured workflows connecting authentication, matchmaking, leaderboards, and voice chat without custom integration code. AI/ML accounted for 31 grants. EA's Motion Variational Autoencoders generate character poses in real-time without storing animation data. Cygames patented automated bug detection for collectible card games by comparing irregular and regular gameplay session logs. Colopl received a patent for AI-generated NPC dialogue replacing scripted responses. Hardware grants focused on accessibility and durability. Sony received patents for adaptive dead-zone calibration, touch-sensitive control surfaces, and customizable flat controllers. Wi-Charge patented laser-based wireless charging for controllers during active gameplay. Backbone Labs
Apr 8, 2026 · via markets.financialcontent.com
ICE Already Knew Her Name The Tech: Mobile Fortify ICE agents are using a phone app called Mobile Fortify. Mobile Fortify allows agents to: - Take your photo; - Scan your face and fingerprints; - Instantly search state and federal government databases. With Mobile Fortify, agents simply need to point a phone at anyone in public in order to compare their face against databases containing 200 million images and instantly access their name, date of birth, and other data. You May Already Be In the Network Hundreds of millions of people are in these systems, including U.S. citizens here in Wisconsin. You don’t need to have previously interacted with ICE to already be in the database. Mobile Fortify connects to databases including: - Federal biometric systems; - Passport and visa records; - State driver’s license photos. If you do happen to enter the database through a face scan in the app during an interaction with federal agents, the government keeps the scan up to 15 years regardless of immigration status. In comparison, TSA claims to delete your facial recognition data at airports after 24 hours if you are a U.S. citizen. Error-Prone Privacy Nightmare Facial recognition doesn’t confirm identity — it guesses. False matches happen. One person can be matched to multiple different identities (including wrongly flagging U.S. citizens as undocumented immigrants). Furthermore, there are higher error rates for Black and Brown people. Congressman Bennie Thompson, ranking member of the House Homeland Security Committee, stated that ICE indicated that it treats Mobile Fortify as a “definitive” determination of a person’s status and that an ICE officer may ignore evidence of U.S. citizenship, including a birth certificate, when the app says a person is undocumented. Technology that is unreliable even in controlled settings should not be used to doll out consequences
Apr 8, 2026 · via aclu-wi.org
Shoplifting and thefts down 10%, says police force Shoplifting and theft offences in Cheshire have fallen by 10% during the last 12 months, police have said. According to Cheshire Constabulary, this means the county has been bucking the national trend. Assistant Chief Constable Alison Ross said her force was working more closely with retailers and local authorities, as well as making good use of CCTV footage and facial recognition technology. "All the different technologies we now have to support businesses, not only to gather the evidence, but also identify the suspect, is incredibly important," she said. Ross urged shop owners to continue to work with Cheshire Police to ensure their CCTV cameras are working properly, and to effectively share footage of any suspicious activity. "Wherever you are in the community - whether that's a large business or a small business - we can give you advice relevant to your particular business," said Ross. Carl Critchlow, chief executive of the Chester Business Improvement District, said while businesses had reported a reduction in shoplifting over the last six months, it remained a significant issue. He said he wondered whether "we're seeing less shoplifting or whether we're just getting better detecting it". Critchlow added: "I think that's the challenge and I think that's what we're working with - our businesses and the police - to try and understand. "We are seeing improvements, but there is still probably a lot of work that we need to do in terms of reporting to make sure that all incidents do get reported and the police have a true understanding and a real picture of what the issue is." Sophie Bryant, who runs The Gift Box in the Hoole area of Chester, said she continued to have problems with shoplifters. She said police were inconsistent in the
Apr 8, 2026 · via bbc.com
What began as low-budget, authentic programming evolved into a cable TV industry capable of producing prestige content that redefined media and entertainment. Are today’s creators traversing the same trajectory? The parallels are striking, but so are the differences, and those differences present both potential limitations and inherent advantages as the creator economy continues to evolve and grow. Cable TV’s Evolution: From Raw Authenticity to Prestige Storytelling in Three Phases From the 1990s into the 2020s, the cable TV industry* delivered ever-increasing levels of fidelity in content and achieved greater and greater heights, cementing itself as a backbone of modern media. This evolution unfolded across three distinct phases, each representing a leap in storytelling ambition and production value. With each phase, cable’s audience followed and the industry thrived. Phase 1 – Unscripted Reality: First-Person Storytelling + Low-to-Mid Fidelity – Cable first captured audiences through reality content built on first-person, unscripted authenticity. In first-person storytelling, stories unfold through an individual’s direct perspective and lived experience. The audience participates from within rather than observing from the outside. This immediacy is the core of the parasocial bond that makes reality content so compelling. In the 1990s, cable pioneered this format with shows such as MTV’s The Real World (1992 – 2017) and Road Rules (1995 – 2007), which later extended into Laguna Beach (2004 – 2006), Deadliest Catch (2005 – present), The Real Housewives (2006 – present), Keeping Up With The Kardashians (2007 – 2021) and many others, giving viewers unfiltered access to “real people” in niche communities. This Phase 1 content is cost-effective to produce and sustains engagement across long seasons and/or reruns, building deeply engaged fan bases. Phase 2 – Scripted Animation: Third-Person Storytelling + Low-to-Mid Fidelity/Budget – Soon after reality content, low-to-mid-budget animation found a home on cable. Cable’s Phase 2
Apr 8, 2026 · via jdsupra.com