A lawsuit against Amazon is seeking financial damages for millions of Americans whose faces may have been recorded by Ring cameras since the Familiar Faces feature was rolled out late last year. Plaintiff Charles Sigwalt yesterday filed a class action suit that aims to represent all people in the US “who had their facial recognition data collected, retained, and otherwise used by the Familiar Faces feature created and implemented by Defendant.” The lawsuit will seek “far” more than $5 million, but the $5 million figure was given in the complaint because US district courts have jurisdiction for civil actions seeking at least that amount. “Here, there are millions of Americans who have walked by Ring cameras which have activated the Familiar Faces feature… the damages in this action far exceed $5,000,000.00 when calculating the statutory damages that may be owed to each Class member in addition to the actual damages caused by the aggregate loss of value of biometric information,” the lawsuit said. Ring’s Familiar Faces feature is designed to identify people who appear at one’s door and provide alerts to the owner of the camera. Amazon says Familiar Faces is not enabled by default but that owners of Ring cameras can turn it on. Ring camera users can create a “personal directory of up to 50 familiar faces” so they can be alerted when one comes to the door. Sigwalt lives in Virginia and filed the suit in US District Court for the Western District of Washington, where Amazon is headquartered. He proposes a nationwide class of all people in the US whose faces were scanned and a subclass for Virginia residents. “Familiar Faces uses facial recognition technology to scan the face of all guests and passersby before categorizing who they are using artificial intelligence,” the lawsuit said. “AI
Jun 2, 2026 · via arstechnica.com
Partenope Ristorante in Dallas ranked No. 18 on the 50 Top Pizza USA 2026 list released this week, marking its sixth consecutive appearance among the nation’s best pizzerias. The Naples-style pizzeria, with locations in downtown Dallas and Richardson, is the only Texas restaurant on the annual ranking compiled by the Italy-based 50 Top Pizza organization. It also received the “Made in Italy 2026 – Salumi Coati Award” for its commitment to authentic Italian culinary traditions. Partenope Ristorante opened in downtown Dallas in 2019 in the historic Titche-Goettinger Building at 1903 Main St. Founded by Naples-born pizzaiolo Dino Santonicola and his wife Megan, the restaurant emphasizes traditional Neapolitan pizza made with a custom oven imported from Naples. A second location operates at 110 S. Greenville Ave. in Richardson. Reviewers with 50 Top Pizza praised its dough as “traditional Neapolitan, soft and melt-in-your-mouth, with precise cooking techniques,” noting it as the top reference for Neapolitan-style pizza in North Texas. Dino Santonicola said in a social media post: “To receive this recognition from such a respected organization is incredibly humbling. Every dish we serve is rooted in tradition, passion, quality ingredients, and the spirit of Naples that inspires everything we do.” The pizzeria has maintained strong placements on the list in prior years, consistently ranking in the top 20. It remains the only Texas entry for 2026. Other Top Dallas-Area Pizzerias Several other Dallas-area spots earned recognition in local and national guides in 2026. Motor City Pizza in Lewisville ranked as the “Best Pizza in Texas” for the second year in a row according to 5 Reasons to Visit. It also placed in the top 6 for best pizza in Dallas per D Magazine. D Magazine’s May 2026 guide to the best pizza in Dallas highlighted several standouts alongside Partenope, including Zoli’s NY
Jun 2, 2026 · via dallasexpress.com
Amazon, Ring sued for alleged privacy violations from facial recognition tools The Familiar Faces feature may store biometrics without people’s knowledge. The hits keep coming for Amazon's smart home brand Ring. The company has been sued on claims that a feature on its doorbells and cameras allows the devices to collect and store photos of passers by without their consent. The Familiar Faces capability at the center of the lawsuit is an optional one that leverages AI to recognize and remember people who frequent a location. Plaintiff Charles Sigwalt is seeking class action certification for the suit, and is looking for at least $5 million in damages. "Millions of other Americans passed by a Ring security camera and unknowingly had their facial recognition information collected," the suit states. It also referenced a letter to Amazon from US Senator Edward Markey, who wrote, "Amazon's system forces non-consenting bystanders into a biometric database without their knowledge or consent. This is an unacceptable privacy violation." Since Amazon acquired Ring in 2018, it has faced lawsuits about hacked devices and privacy concerns. It also saw backlash over a feature it advertised during the 2026 Super Bowl that seemed more like a mass surveillance tool than a way to find lost pets.
Jun 2, 2026 · via engadget.com
Figures Abstract Lung diseases, particularly lung cancer, remain a leading cause of mortality worldwide, accounting for approximately 1.8 million deaths annually. Early and accurate diagnosis is critical for improving patient outcomes. This study also introduces a unified platform for evaluating multiple convolutional neural network architectures and comparing them to a Vision Transformer model while utilizing a common tensor-based preprocessing pipeline for classifying lung cancer with CT/PET-CT imaging. To enhance model adaptability, all input images were initially converted into tensors prior to training, enabling implicit fine-tuning without altering the original architecture. The YOLOTransfer dataset, comprising diverse and annotated medical images, was used to benchmark model performance. Classical CNN models such as AlexNet, VGG-16, ResNet-50, DenseNet, and EfficientNet were compared against ViT in terms of accuracy, sensitivity, specificity, F1-score, and AUC-ROC. Among all models, ResNet-50 and EfficientNet achieved the highest accuracy, while the Vision Transformer showed competitive results in capturing complex global patterns. The findings highlight the complementary strengths of convolutional and transformer-based architectures for medical image analysis and demonstrate the feasibility of deep learning approaches for lung cancer detection. Citation: Asim N, Sirshar M, Khan MZ, Ejaz S, Khalid S, Aljubayri I, et al. (2026) Tensor enhanced chest cancer classification via CNN and Vision Transformer models. PLoS One 21(6): e0348863. https://doi.org/10.1371/journal.pone.0348863 Editor: Muhammad Mateen, Soochow University, CHINA Received: September 3, 2025; Accepted: April 22, 2026; Published: June 2, 2026 Copyright: © 2026 Asim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All image files are available from the database Roboflow, “YoloTransfer Dataset,” It is available at: https://universe.roboflow.com/mehmet-fatih-akca/yolotransfer, 2024. Funding: The author(s) received no specific funding for this work. Competing interests: The
Jun 2, 2026 · via journals.plos.org
Amazon's Ring sued over facial recognition feature, latest privacy concern for doorbell maker SAN FRANCISCO, June 2 : Amazon was sued on Monday by a Virginia resident over what he said were privacy violations after the company's Ring doorbell cameras at friends and family members' homes collected and stored images of his face using facial recognition software. The plaintiff, Charles Sigwalt, who is seeking class-action status, sued Amazon in federal court in Seattle alleging a feature known as “Familiar Faces” retains images of passersby without their consent. He is seeking at least $5 million in damages for the class. Familiar Faces, which is optional, uses artificial intelligence to identify and remember people so that when they return to a home or a business, notifications can include specific names. Those affected “did not consent to have their privacy rights violated at the entrance way,” according to the suit. “Millions of other Americans passed by a Ring security camera and unknowingly had their facial recognition information collected.” Amazon declined to comment. The suit, which seeks unspecified damages for those impacted, is just the latest in a string of controversies around Amazon’s Ring, the unit that makes the eponymous smart doorbells and security systems. Ring, which Amazon bought in 2018 for $1 billion, in February faced a backlash over a service that it advertised during the Super Bowl that it said helps people find lost dogs by activating its neighborhood network of cameras. Users and privacy advocates were concerned the cameras could be deployed to surveil whole neighborhoods or areas. Following the criticism, Ring in February ended an unrelated partnership with Flock Safety, which deploys license plate readers and cameras for law enforcement use. In 2023, the U.S. Federal Trade Commission reached a $5.8 million settlement with Ring over privacy allegations that it
Jun 2, 2026 · via channelnewsasia.com
Operations too frequent.
Try again later
Page not found, please try again later.
Take me home
Jun 2, 2026 · via moomoo.com
Amazon was sued on Monday over alleged privacy violations from its Ring doorbell cameras. The class action lawsuit, filed in Seattle by Virginia resident Charles Sigwalt, claims that Ring’s Familiar Faces feature stores images of passersby without consent. Ring announced the Familiar Faces feature last September and faced pushback from consumer protection organizations like the EFF, as well as Senator Ed Markey (D-MA). But the company moved forward with its plans to launch the feature in December. Familiar Faces lets Ring users identify people who regularly come to their home through AI facial recognition. That way, if a regular guest, like a family member, mail carrier, or neighbor, comes to the door, the device will be able to recognize them and deliver more specific notifications like “Dad is at the door,” rather than “A person is at the door.” Ring users have to opt in to this feature, but privacy advocates noted that the people who walk past these Ring doorbells have not consented to these facial-recognition scans. That same concern is at the center of this class action lawsuit. According to the lawsuit, “Millions of other Americans passed by a Ring security camera and unknowingly had their facial recognition information collected.” Amazon did not immediately respond to a request for comment. At the time the feature was released, the company stated that face data is encrypted and never shared; unidentified faces are automatically removed after 30 days. Amazon’s Ring has a record of concerning behaviors regarding user privacy. In 2023, Amazon settled with the Federal Trade Commission (FTC) and paid a $5.8 million fine over allegations that the company’s staff and contractors had improperly accessed private videos from women customers; the FTC’s complaint said that every employee had full access to every customer video, even if the worker had
Jun 2, 2026 · via techcrunch.com
Students erupt as San Diego State University brings in massive surveillance system See more of our coverage in your search results. Add The California Post on GoogleMore than 1,300 AI-powered cameras have been installed across San Diego State University, monitoring students in dorms, classrooms, gyms and dining halls as part of a sprawling surveillance network that has sparked outrage on campus. The cameras were added as part of a more than $1.3 million upgrade completed by university police in 2024, according to records obtained by student journalists at the Daily Aztec. The system stretches across campus from Montezuma Road to Montezuma Mesa and includes cameras in academic buildings, bookstores, parking structures, recreation centers, and residence halls. But while SDSU says students are informed about the presence of security cameras, neither the school’s housing website nor its Guide to Community Living handbook mentions the system’s artificial intelligence capabilities, according to the student newspaper. The lack of transparency has left some students furious. “I think that this monitoring is a heinous violation of students’ privacy,” second-year business major Sophia Pomponio, who lives in Zacatepec, told the outlet before slamming the surveillance system. Sign up for the California Morning Report newsletter California's top news, sports and entertainment delivered to your inbox every day. Thanks for signing up! “Technology such as this spits in the face of students’ rights to privacy and freedom, and shows exactly how SDSU values their students, as currency.” Public records reveal that over 330 cameras are located in student housing alone, making up nearly 28% of the campus’s total surveillance devices. Huaxyacac, the university’s largest first-year residence hall, has 79 cameras installed as part of the surveillance upgrade. Tenochca has 36, and Chapultepec has 33. In total, 18 out of 24 residential buildings are under surveillance. Despite their widespread
Jun 2, 2026 · via nypost.com
U.S. Army Lt. Gen. Joe Hilbert, Eighth Army commanding general, presents a certificate to Hui Chong Pak during the Eighth Army Civilian of the Quarter ceremony at Camp Humphreys, South Korea, May 29, 2026. The recognition demonstrated Eighth Army’s commitment to honoring civilian excellence that directly supports the command’s readiness, resilience and mission accomplishment. (U.S. Army photo by Pv2 Yunseong Jang)
| Date Taken: | 05.28.2026 |
| Date Posted: | 06.01.2026 19:04 |
| Photo ID: | 9713741 |
| VIRIN: | 260529-A-JR370-9593 |
| Resolution: | 4735x3788 |
| Size: | 6.74 MB |
| Location: | CAMP HUMPHREYS, KR |
| Web Views: | 23 |
| Downloads: | 0 |
This work, Eighth Army Civilian of the Quarter [Image 6 of 6], by SGT Alexcia Rupert, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Jun 1, 2026 · via dvidshub.net
Figures Abstract Scene Text Recognition (STR) is a fundamental component of intelligent perception systems and plays a crucial role in a wide range of real-world applications such as autonomous driving, document understanding, and human–computer interaction. STR still faces several challenges in practical applications, including high sensitivity to spatial perturbations, limited representational capacity of lightweight Connectionist Temporal Classification(CTC)-based models, and the difficulty of handling diverse text styles within a single unified architecture. Although SVTRv2 enhances the recognition ability of CTC models through a combination of local and global mixing mechanisms, its robustness and generalization capability remain insufficient when dealing with geometric distortions, complex backgrounds, or text with large stylistic variations. To address these issues, we propose SVTRv2X, an enhanced STR framework built upon SVTRv2 that integrates three complementary improvement modules. The Jumble Module strategically rearranges input patches before the patch embedding stage, fundamentally reducing the model’s reliance on fixed spatial structures and significantly improving robustness to rotated, misaligned, and irregular text. The Self-Distillation Module transfers deep-layer knowledge to shallow features, effectively strengthening early-stage representations while maintaining lightweight inference. The Mixture-of-Experts (MoE) Module expands model capacity through sparsely activated expert networks, allowing specialized processing of different text styles without introducing substantial computational overhead. Extensive experiments demonstrate that SVTRv2X achieves state-of-the-art performance on multiple STR benchmarks, substantially advancing the model’s recognition capability in real-world scene text scenarios. Citation: Guo J, Cui H, Tang W, Zhou X, Xu X, Cheng Q (2026) SVTRv2X: Enhanced scene text recognition via self-distilled mixture-of-experts. PLoS One 21(6): e0349085. https://doi.org/10.1371/journal.pone.0349085 Editor: Hikmat Ullah Khan, University of Sargodha, PAKISTAN Received: January 20, 2026; Accepted: April 25, 2026; Published: June 1, 2026 Copyright: © 2026 Guo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction
Jun 1, 2026 · via journals.plos.org
Figures Abstract Identifying spatial domains is crucial in spatial transcriptomics, yet effectively integrating gene expression, spatial location, and histology remains challenging. We present STESH, a Spatial Transcriptomics clustering method that combines Expression, Spatial information and Histology. STESH extracts histological features using a convolutional neural network and generates expression, histology, spatial, and collaborative convolution modules for a multi-view graph convolutional network with a decoder and attention mechanism. We evaluated STESH on multiple tissue types and technology platforms. STESH consistently outperformed ten state-of-the-art methods, achieving superior clustering accuracy with the highest scores in adjusted Rand index, normalized mutual information, and Fowlkes-Mallows index. Author summary Identifying spatial domains in spatial transcriptomics is key to understanding tissue structure and gene expression patterns, yet existing approaches struggle to fully and effectively combine gene expression data, spatial location information, and histological images—three critical pieces of spatial transcriptomics data. To address this gap, we developed a new method that integrates all three types of information to accurately detect spatial domains. We used deep learning to extract detailed features from histological images, built separate analytical frameworks for each data type, and fused these frameworks with an attention mechanism to capture the intrinsic links between different data modalities. We tested this method on multiple tissue types and technical platforms, comparing it against ten leading approaches, and found it consistently achieved higher clustering accuracy and could precisely reconstruct complex biological tissue structures, even uncovering fine details of tumor heterogeneity. This method enhances downstream spatial transcriptomics analyses and provides a more reliable tool for researchers, helping to deepen our understanding of the spatial organization of tissues and the relationship between gene expression and histological morphology. Citation: Zhang H, Chang J, Li Z, Sun Y, Hu P, Wang H, et al. (2026) Histology-informed spatial domain identification through multi-view graph convolutional networks. PLoS
Jun 1, 2026 · via journals.plos.org
Robot dogs deployed around World Cup venues in North Texas are being used for security inspections and hazardous-material investigations, not for facial recognition or ticket verification, according to the company behind the technology. The clarification comes after a viral social media video claimed that “face-scanning robots” were being used in Dallas to verify World Cup ticket holders. The video showed a robotic dog bearing the logos of South Korean automaker Hyundai and robotics company Boston Dynamics. The post alleged the machines were scanning faces as part of World Cup security operations. Boston Dynamics disputed that claim. “Boston Dynamics’ Spot robots are being deployed at designated World Cup venues to perform perimeter security inspections and will be used to assist security personnel with investigating things like suspicious packages or other potentially hazardous materials. The robots do not have facial recognition capabilities,” a company spokesperson said, WFAA reported. The robotic dogs, known as Spot robots, are part of security preparations surrounding FIFA World Cup events in North Texas. While some social media users expressed concerns about surveillance, the companies involved said the machines are intended to support security personnel rather than identify spectators. According to Hyundai, the robots are designed to assist with on-site security operations during the tournament. Similar robotic systems have been used in crowded public settings, search-and-rescue missions, hazardous-material assessments, and bomb-detection operations. The robots have also appeared at other sporting events, including cricket matches in England, professional football games in Atlanta, and the Indianapolis 500. Officials in Mexico are also using robotic canines as part of World Cup security efforts, where they help scout potentially dangerous areas and transmit live video to security teams. The viral claims surfaced as officials continue preparing for the tournament across North Texas. In March, the Dallas Police Department announced it had received
Jun 1, 2026 · via dallasexpress.com
BuzzFeed GamesOnly People With Elite Pattern Recognition Can Solve This Color Puzzle Called HuedokuHuedoku #34! Welcome to June — kick off the month with a clean solve. 🌈☀️Posted 7 hours agocommentFacebookPinterestLinkby Crystal RoBuzzFeedBuzzFeed StaffHi, I’m Crystal, a Senior Editor based in Los Angeles and creator of BuzzFeed’s “That Got Dark” newsletter. Huedoku is a daily color puzzle with simple rules and a satisfying solve. It's like sudoku, but with colors instead of numbers. Sign up here to get notified every time we publish a new Huedoku! Come back tomorrow for Huedoku #35 — and share your score to challenge a friend! 🌈 🌈 New Huedoku drops every weekday at 4:00 a.m. PT / 7:00 a.m. ET. 🧩🗂️ Huedoku Archive — Every past puzzle, all in one place! Comments
Comments
Jun 1, 2026 · via buzzfeed.com
FBI seeks industry input on biometric algorithms for NGI modernization The scale of the system is one of the most important details in the notice The Federal Bureau of Investigation (FBI) is surveying the biometrics industry for commercially available algorithms that could support or replace key matching capabilities within the bureau’s Next Generation Identification (NGI) system, one of the largest government repositories of biometric and criminal history information in the world. The Request for Information (RFI), issued by the FBI’s Criminal Justice Information Services (CJIS) division in Clarksburg, West Virginia, provides a detailed view of the bureau’s continuing effort to assess new biometric matching technology for fingerprints, latent prints, facial recognition, iris recognition, and tattoo recognition, as well as the systems needed to transition and maintain these capabilities inside FBI-approved cloud environments. CJIS operates NGI as a central biometric and criminal history platform used by federal, state, local, tribal and territorial law enforcement partners. The FBI says it is looking to scientific advances to improve the “range and quality” of its identification and investigative capabilities. For biometric vendors, the notice is an early signal that National Institute of Standards and Technology (NIST)-tested performance, secure domestic software handling, GovCloud deployment, FedRAMP High compliance, and the ability to manage massive biometric repositories will be central to any future FBI consideration. The RFI is the third in a series of FBI market research notices focused on biometric matching algorithms. This latest notice asks vendors to provide information on ownership structure, secure software build facilities, algorithm capabilities, transition plans, scalability, software maintenance, licensing, response times, and accuracy. A key threshold requirement is participation in National Institute of Standards and Technology (NIST) biometric testing. The FBI says published NIST results will be required for all vendors interested in supplying biometric algorithms to the bureau. A
Jun 1, 2026 · via biometricupdate.com
Massive Attack Is Taking Aim at Palantir ‘I find their declarations, objectives and moral framing pretty terrifying.’ by Gary McQuiggin 1 June 2026 Trip-hop legends Massive Attack are using an artistic rendering of notorious spy-tech firm Palantir’s own product to take aim at the company in their new live show. Custom-made facial recognition software will scan a 75,000-person crowd at Primavera Sound in Barcelona on Thursday, before cameras lock on to individuals, pull their faces up on screen and label them with “11 weeks no time off, burnout”, “unfinished books” and other satirical descriptors. Founded 20 years ago by arch-libertarian billionaire Peter Thiel, with financial backing from the CIA, Palantir has become one of the world’s most infamous surveillance tech companies, counting the US and Israeli militaries, ICE, the FBI and the NHS among its clients. Speaking exclusively to Novara Media in Helsinki, where the new show premiered, Massive Attack frontman Rob Del Naja explained that he wanted to draw the public’s attention to Palantir’s expansion from “kill chain tech” used in Gaza to accessing Britons’ medical records. “We really need a much wider debate on the suitability of a company like this having such capture of our societal infrastructure,” he said. The Bristol band’s intervention couldn’t be more timely. Just last month, London mayor Sadiq Khan blocked Palantir from a lucrative contract with the Metropolitan police, citing concerns over the company’s ethics. Its UK boss, Louis Mosley – a descendant of Britain’s most famous fascist – responded by claiming that Khan’s decision had put Londoners more at risk of rape and theft. Meanwhile, a debate is raging within NHS England. In 2023, Palantir won a £330 million contract to provide technology for a new platform designed to improve data sharing across the service. But senior health workers have described
Jun 1, 2026 · via novaramedia.com
AI watermarking: Why Big Tech is betting on AI provenance, and losing kAm(96C6G6C :?E6C?6E FD6CD EFC?[ E96J 2C6 244@DE65 3J px\86?6C2E65 E6IE[ G:56@D[ 2?5 :>286D[ >2?J @7 H9:49 2C6 F?56E64E23=6] %96 D2?4E:EJ 2?5 ECFDE @7 @?=:?6 DA246D :D 4CF>3=:?8 F?56C E96 H6:89E @7 px\4@?E6?E] k2 9C67lQ9EEADi^^HHH]A2?8C2>]4@>^3=@8^2:\D276EJ\6I64FE:G6\@C56CQmq:56?’D px D276EJ 6I64FE:G6 @C56Ck^2m 2?5 E96 k2 9C67lQ9EEADi^^HHH]A2?8C2>]4@>^3=@8^2:\24EQmtFC@A62? &?:@?’D px p4Ek^2m 25G@42E65 AC@A6C =236=:?8 @7 px @FEAFE[ 2=D@ <?@H? 2D “H2E6C>2C<:?8[” 3FE E96D6 C68F=2E:@?D H6C6 F?5@?6 :? a_ad] s6DA:E6 ?@ ?2E:@?2= @C :?E6C?2E:@?2= DEC2E68J[ >2?J px 4@>A2?:6D 2C6 DE:== 56G6=@A:?8 2?5 56A=@J:?8 “H2E6C>2C<:?8” D:8?2=D :? E96:C 4@?E6?E] (9:=6 E9:D :D 2 DE6A :? E96 C:89E 5:C64E:@? E@ C682:? AF3=:4 ECFDE 2?5 AC@E64E 4@?DF>6CD[ H2E6C>2C<:?8 42? 62D:=J 36 3JA2DD65]k^Am kAmq6=@H[ k2 9C67lQ9EEADi^^HHH]A2?8C2>]4@>Qm!2?8C2> {23Dk^2m 6IA=@C6D 9@H H2E6C>2C<D H@C<[ H9J E96J 72:= 2?5 H9J px 56E64E:@? >6E9@5D E92E C6=J @? 2 DA64:7:4 EJA6 @7 A2EE6C? C64@8?:E:@? 2C6 C6=:23=6]k^Am k9bmpx H2E6C>2C<:?8 :D 56E64E65[ 3FE ?@E D66?k^9bm kAm%@ 9F>2?D[ px H2E6C>2C<:?8 :D :?G:D:3=6] x?DE625 @7 2 EC25:E:@?2= H2E6C>2C< 5:DA=2J:?8 2 4@>A2?J’D ?2>6 @C =@8@[ px H2E6C>2C<D 2C6 6>365565 :?E@ 2? px >@56=VD E6IE[ :>286[ 2?5 G:56@ @FEAFE :? 2? :>A6C46AE:3=6 H2J]k^Am kAmv@@8=6 FD6D k2 9C67lQ9EEADi^^566A>:?5]8@@8=6^>@56=D^DJ?E9:5^Qm$J?E9xsk^2m[ H9:49 6>365D DF3E=6 G2C:2E:@?D :? v6>:?:VD @FEAFE[ 56E64E23=6 3J v@@8=6VD @H? E649?@=@8J] u@C E6IE[ $J?E9xs 2=E6CD E96 AC65:4E23:=:EJ D4@C6D @7 46CE2:? H@C5D 244@C5:?8 E@ 2 AD6F5@C2?5@> 7F?4E:@?[ 8:G:?8 v6>:?: 2 AC676C6?46 7@C FD:?8 46CE2:? H@C5D] %9FD[ H96? E96 E6IE :D 765 324< :?E@ E96 >@56=[ :E 42? C64@8?:K6 :ED @H? 92?5:H@C< 32D65 @? H@C5 7C6BF6?4J 2?2=JD:D]k^Am kAmk2 9C67lQ9EEADi^^HHH]4@AJC:89E]8@G^2:^QmuF==J px\86?6C2E65 4@?E6?E 42??@E 36 4@AJC:89E65 F?56C &]$] =2Hk^2m[ D@ :EVD :? px 4@>A2?:6D’ :?E6C6DE E@ 56G6=@A 2 >6E9@5 7@C 56E6C>:?:?8 E96 AC@G6?2?46 @7 E96:C >@56=VD 4@?E6?E] %92E H2J[ @?46 E96 px\86?6C2E65 E6IE[ :>286 @C G:56@ 86ED 4:C4F=2E65 2C@F?5 E96 H63[ E96C6 :D 2 D:8?2EFC6 E@ :56?E:7J H96C6 :E 42>6 7C@>]k^Am kAmr=2:>:?8 DE2<6 E@ E96:C >@56=’D 4C62E:@?D
Jun 1, 2026 · via newspressnow.com
A contract has been awarded by the UK Home Office to implement AI facial recognition to support officers on the ground with verifying the ages of asylum seekers, where they can’t present documentation. The plans have sparked controversy amid tensions around the government’s approach to border security to tackle immigration. The tool, assisting immigration officers, will target adult migrants posing as unaccompanied children in a bid to gain asylum. They have issued guidance on how to use the AI algorithm, which has been trained on datasets of images of people The FAR is not currently in operational use by the Home Office during a testing phase, whilst there are plans to implement it in 2027. With potential limitations, the guidance states that the FAE system will be used to “provide additional information to help them make initial age decisions. It won’t be relied upon to make a definitive age decision or to replace holistic approaches such as Merton-compliant age assessments. […]” “An initial age assessment will always be made by a properly trained immigration officer who can also call on the advice of colleagues and social workers when needed.” As age algorithms cannot be proven 100% accurate, it raises concerns regarding performance bias according to factors including ethnicity, skin tone, gender, place of birth and image quality. “In its appraisal for using FAE for age assessments, the Home Office has tested industry leading algorithms on images across different ethnicities and genders. These algorithms will be further tested to assess their suitability for use within the immigration system.” The tender was awarded to Akhter Computers, a company based in Essex, to make the facial comparison during standard immigration checks.
Jun 1, 2026 · via identityweek.net
Jiuzi Holdings Announces AI Intelligent Imaging Platform Achieves Milestone Progress and Advances Toward Commercial Deployment as Planned Rhea-AI Summary Jiuzi Holdings (Nasdaq:JZXN) reported milestone progress on its next-generation AI intelligent imaging and data platform and preparation for commercial deployment. The platform aims to help enterprises shift from traditional monitoring to real-time analytics by integrating AI recognition, multimodal data fusion, cloud architecture, and blockchain-based data notarization. According to the company, ongoing pilots and strategic initiatives are expected to validate technical capabilities, support large-scale deployment, and enable potential commercialization across multiple industry scenarios in North America and global markets. AI-generated analysis. Not financial advice. Positive - None. Negative - None. Key Figures Market Reality Check Peers on Argus JZXN was up 1.75% pre-news while only one peer (SDA) appeared in momentum scanners, moving -3.22%. Other peers showed mixed, mostly negative moves, pointing to stock-specific dynamics rather than a sector-wide auto retail shift. Previous AI Reports | Date | Event | Sentiment | Move | Catalyst | |---|---|---|---|---| | May 15 | AI partnership MoU | Positive | +14.4% | AI imaging and Web3 data MoU that previously triggered a strong positive move. | Previous AI-themed news produced a clearly positive price reaction of 14.41%, indicating the market has rewarded JZXN’s AI initiatives historically. Over the last six months, Jiuzi has combined capital markets activity with strategic pivots. A prior AI-focused MoU on May 15, 2026 drove a 14.41% move after outlining intelligent imaging and Web3 infrastructure plans. Earlier, a strategic investor committed $80 million on March 6, 2026, and the company announced electric truck deliveries to Vietnam. Today’s AI platform milestone fits the ongoing shift toward technology and data-centric solutions. Historical Comparison Past AI news for JZXN (1 event) saw an average move of 14.41%. This update extends the same AI imaging
Jun 1, 2026 · via stocktitan.net
Hangzhou, June 01, 2026 (GLOBE NEWSWIRE) -- Jiuzi Holdings, Inc. (Nasdaq: JZXN) (“Jiuzi” or the “Company”) today announced the latest developments regarding its AI intelligent imaging and data platform. The Company is preparing to advance the development of its next-generation AI intelligent imaging platform in accordance with its established strategic roadmap and is making systematic preparations for subsequent commercial deployment. The intelligent imaging platform is designed to enable enterprise customers to transition from traditional image monitoring and standalone data processing models to real-time intelligent analytics and data-driven decision-making. By integrating AI recognition capabilities, data processing architecture, and deployment systems, the Company is preparing to build a unified intelligent imaging and data solution. The Company believes that as digital infrastructure upgrades accelerate, enterprise demand for intelligent imaging systems is undergoing structural transformation. The market is shifting from validation of single algorithm functionalities toward demand for scalable, deployable, and compliant integrated platform architectures. As data volumes expand and scenario complexity increases, operators must continuously address data compliance requirements, algorithm accuracy competition, cross-scenario adaptability, and system stability challenges. Jiuzi believes that platform-level capability development may become a key competitive factor in the industry. In response to these trends, the Company is preparing to develop a unified AI intelligent imaging platform framework covering the full workflow from data acquisition and algorithm processing to scenario applications and business decision support, supporting the evolution of intelligent imaging systems toward infrastructure-level solutions. The Company believes that this integrated architecture differs from market solutions that provide only single recognition modules or isolated algorithm functions. By integrating multi-layer data structures and algorithm capabilities, the platform can transform complex image data into actionable insights, potentially enhancing customer operational efficiency and unlocking additional data asset value. Key features of the platform include: • Real-time facial recognition supporting large-scale multi-scenario identification and
Jun 1, 2026 · via markets.businessinsider.com
Measuring AI Adoption among Firms: How You Ask Matters KEY TAKEAWAYS - Worker-based surveys find higher rates of AI adoption in the U.S. than in Europe. Yet firm-level surveys suggest a higher adoption rate in Europe. - This U.S.-European difference in AI adoption among firms appears to stem from how surveys ask about AI. The main European survey asks broader questions, such as whether AI is used in marketing; the key U.S. survey had focused just on the technology’s use in production of goods and services. - A recent update to the U.S. survey question boosted the reported AI adoption rate among American firms, showing that how AI use is measured is key to understanding the technology’s economic impact. In our first blog post in this series, we documented that U.S. workers are more likely to use artificial intelligence (AI) on the job than workers in Europe. In our second post, we showed that differences in workforce composition and firm management practices go a long way toward explaining that gap. But alongside the worker surveys, we drew on firm-level data, and those data address a puzzle of their own—one that turns out to be less about economics and more about how surveys are designed. For the past two years, a number of researchers have noted a striking disconnect in the U.S. data: Worker surveys suggest that somewhere around 35% to 40% of workers use AI on the job, while the main U.S. firm survey put AI adoption among businesses at just 5% to 7%. That is a very large gap. Are workers using AI without their employers knowing? Are firms in denial about how widespread the technology has become? Or is something else going on? We think the answer is mostly the third option: measurement.This blog post and the previously
Jun 1, 2026 · via stlouisfed.org