Even as the Milwaukee Police Department backed away from facial recognition technology earlier this year after strong public criticism, records obtained by NNS show the department’s overall surveillance infrastructure continues to grow. The number of cameras, license plate readers and other surveillance technologies available to the department has increased in recent years, along with the amount of money spent on these systems. As was the case with facial recognition technology, MPD leaders say the tools help solve crimes and can make investigations more efficient Critics, however, warn that an expanding surveillance infrastructure could make future monitoring technologies more powerful and raise concerns about privacy, oversight and civil rights violations – the same concerns raised over facial recognition technology. Vaun Mayes, community activist and violence interrupter for the city’s Department of Community Wellness and Safety, says the cameras are a problem. “People don’t want their rights or privacy threatened or infringed on,” Mayes said. Critics are also questioning whether the department has produced evidence showing the surveillance expansion has led to measurable improvements in crime-solving or public safety. More cameras, more systems As of June 2026, MPD had access to 1,749 cameras, according to department records. Included are 1,200 body-worn cameras, 390 squad-car cameras and 159 fixed cameras. While many of the cameras serve general surveillance purposes, a growing number are part of automatic license plate reader, or ALPR, systems. MPD risk manager James Lewis said the department currently uses three separate ALPR platforms operated by different vendors: Flock Safety, Genetec and Axon. The Genetec system includes 39 fixed ALPR cameras. Officers can also access a cloud-based Flock Safety network consisting of 35 ALPR cameras. Axon ALPR technology is also installed on 80 MPD patrol vehicles, allowing officers to capture and compare license plate data while on patrol. Records show MPD’s
Jun 8, 2026 · via milwaukeenns.org
Meta reportedly tucked facial-recognition code into the app for its camera glasses, then got very upset when journalists treated that like something the public might want to know. While not yet active or available to consumers, Wired reports that Meta has installed facial recognition software that plugs into a client-side database housed in Meta's O&O app, matching people to their faces. Certainly, that could not be abused. The report made clear that NameTag isn't activated yet, nor is it accessible to consumers in its current form. But the prospect of consumer-facing smart glasses equipped with facial recognition tech has long had privacy advocates on edge — and with public adoption of Meta's smart glasses on the rise, privacy concerns have become more pertinent than ever. If a company has gone about building out the infrastructure to roll out a wildly controversial feature of this kind, consumers might want to know about that, even if the feature currently remains inaccessible. And yet, according to the company's executives, it's "dishonest" to inform the public about a piece of unreleased tech that Meta has chosen to incorporate into a consumer product. In its initial response to Wired, Meta referred to the discovery as "sensational" and characterized NameTag as exploratory. "We've said before we're exploring these types of features, and what you're seeing is just evidence of that exploration," the company said in a statement to Wired. "Nothing has shipped to consumers and no final decision has been made on what to do here, if anything. If we do decide to roll something out, we will take a thoughtful approach and do so with full transparency. One decision we can be clear about—we are not building a central face database." Futurism The kraken is unreleased because it is still in development. Previously: • When
Jun 8, 2026 · via boingboing.net
AI brings order to label diversity Vision AI Label Reader automates label capture and ensures process reliability Goods-in operations in the electronics industry are under increasing pressure. Countless components from a wide range of manufacturers arrive with constantly changing label layouts, multilingual markings and ever shorter throughput times. What could once be managed manually has now become a bottleneck. Damaged barcodes or reflective packaging further increase effort and make processes more error-prone The Vision AI Label Reader from collective mind GmbH (COMI) demonstrates how this complexity can be managed. The AI-based image processing system automates the capture and interpretation of item information in goods-in and logistics - regardless of layout, language or code type. Designed for industrial use, the solution improves process reliability, enhances data quality and streamlines workflows. A uEye CP industrial camera from IDS Imaging Development Systems GmbH provides the image data required for analysis. Fully automated capture instead of manual inspection The Vision AI Label Reader is designed for applications where a wide variety of items, labels and packaging are processed on a daily basis. This makes it particularly suitable for electronics manufacturing service providers as well as companies with complex logistics processes and extensive inventories. One concrete example is Rutronik Elektronische Bauelemente GmbH, a globally leading broad-line distributor of electronic components, where the system is already in successful operation. The goal is to automatically capture all relevant item information and make it available in a structured format. To achieve this, the system recognizes all labels on an object, reads printed text as well as 1D and 2D codes, and then interprets the content using artificial intelligence. Handwritten entries can also be processed if required. Crucially, recognition does not rely on predefined label standards. New layouts, languages or code formats can be handled without retraining - a
Jun 8, 2026 · via roboticstomorrow.com
Meta has quietly scrubbed facial recognition code from its smart glasses companion app following a WIRED investigation that exposed the feature's existence. The code, discovered in Meta AI—the app that powers the company's Ray-Ban smart glasses—vanished in the latest update, but Meta refuses to say whether it was experimental, accidental, or planned for future deployment. The move raises fresh questions about what AI capabilities tech giants are building into wearable devices that can record people without obvious consent indicators. Meta just got caught with its hand in the biometric cookie jar—and the company's silence is almost as revealing as the code itself. Investigative reporters at WIRED discovered facial recognition capabilities embedded in Meta AI, the companion app that controls Meta's Ray-Ban smart glasses. The code suggested Meta was either testing or preparing to deploy face-matching technology that could identify people captured through the glasses' cameras. Within days of WIRED's inquiry, that code disappeared from the app entirely. Meta's response? Radio silence on the specifics. The company won't confirm whether the feature was an abandoned experiment, an accidental inclusion, or a planned capability that got exposed too early. That opacity is triggering alarm bells among privacy advocates who've long warned about the dangers of normalizing always-on recording devices. The timing couldn't be more awkward for Meta. The company has spent years rebuilding trust after the Cambridge Analytica scandal and has repeatedly promised transparency around its AI development. CEO Mark Zuckerberg has positioned Meta's smart glasses as a privacy-conscious alternative to more intrusive wearables, emphasizing that a recording light alerts bystanders when the camera is active. But facial recognition changes the equation entirely. While recording someone in public might be legal, automatically identifying and cataloging faces crosses into surveillance territory that makes even tech-friendly regulators nervous. The EU's AI Act specifically classifies
Jun 8, 2026 · via techbuzz.ai
Butler County prosecutors say a multi-agency investigation that used facial recognition technology and distinctive tattoos led to the arrest, prosecution and sentencing of a Hamilton man who stole credit cards from older victims. Trevon Lee Adams, 30, was sentenced June 4 to 30 months in prison after pleading guilty to theft from a person in a protected class, a third-degree felony. In a related case filed by Fairfield police, Adams received an additional 18-month sentence on a fourth-degree felony charge of the same offense. The sentences will run concurrently. Garrett Baker, chief prosecutor of the county’s economic crimes division, said Hamilton and Fairfield police worked on the investigation, which relied in part on facial recognition technology to identify Adams. “The detectives did a fantastic job,” Baker said. “Especially when an individual is coming in wearing a ski mask trying to conceal every aspect of their identity.” Det. Ryan Beckelhymer led the Hamilton investigation, while Det. Brian Wells led the Fairfield case. Baker said Beckelhymer relied in part on facial recognition technology. “It was able to give them a lead,” Baker said. According to court records, the first incident occurred in mid-January 2026 at the Planet Fitness in Fairfield. Adams entered the gym using another person’s membership and went directly to the locker room, where he stole a wallet containing debit, credit and HSA cards from a 65-year-old man. Surveillance footage shows Adams leaving the locker room at 3:18 p.m. Police were called at 4:01 p.m., and shortly after, Adams was captured on camera at a Meijer on South Gilmore Road wearing different clothing and a ski-style mask. He used the stolen cards to make $1,078.02 in purchases, including five $100 Mastercard gift cards and a soda. In a separate incident later that month, Adams stole cards from a 70-year-old man
Jun 8, 2026 · via journal-news.com
Asia-Pacific banks are pairing AI innovation with stronger fraud controls and tokenized finance. AI commanded most of the attention among innovators in the Asia-Pacific region in 2025, but looking ahead, fraud prevention, open banking, cross-border payments, and tokenization could also figure prominently. “The banks winning right now are the ones deploying AI where it actually generates revenue,” Zennon Kapron, founder and director at GL Insight, a global fintech consultancy firm, told Global Finance. “APAC’s leaders have moved into generative AI for wealth management and AI-driven credit scoring for thin-file customers, and the gap between early movers and laggards is widening fast,” he added. Nehal Vora, CEO of India’s CDSL, told Global Finance: “The coming year in Asia-Pacific finance, in our view, is likely to be shaped by a powerful trinity: intelligent data, intelligent protection, and intelligent access.” Vora anticipates further advances in anomaly detection and behavioral analytics that enable financial firms to detect manipulation and scams earlier in the business cycle. “AI-based fraud detection is now the price of admission in APAC,” added Kapron. As scam losses mount across Singapore, Australia, and Hong Kong, regulators are forcing banks to absorb more liability, reshaping the economics of fraud investment far more than any technology shift. It should help that the region enjoys some of the world’s most innovation-friendly regulatory frameworks. “This has been the most consequential year for APAC fintech regulation in a decade,” said Kapron. “Hong Kong, Singapore, Japan, and South Korea are all advancing stablecoin and digital asset frameworks in parallel, and APAC is now setting the global pace while the US and Europe play catch-up.” Most Innovative Bank in Asia-Pacific CTBC When it comes to thwarting scam attacks, industry collaboration can make a difference. That was CTBC Bank’s reasoning when it launched Taiwan’s first AI fraud alliance last
Jun 8, 2026 · via gfmag.com
BuzzFeed GamesOnly People With Elite Pattern Recognition Can Solve This Color Puzzle Called HuedokuHuedoku #39! New week, fresh start — the colors are waiting. 🌈✨Posted 17 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 #40 — 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
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Jun 8, 2026 · via buzzfeed.com
Vadzo Imaging Explains Drone Camera Optics for UAV Embedded Vision Applications: FOV, Distortion, and Vibration Tolerance When a UAV payload delivers distorted aerial maps, missed coverage between flight passes, or defocused frames after the second flight, the failure is rarely traced back to optics selection. It is attributed to processing, post-flight software, or weather. Vadzo Imaging examines how field of view, lens distortion, and vibration tolerance determine aerial image accuracy and deployment outcome across UAV mapping, aerial inspection, precision agriculture, search and rescue, and smart city surveillance applications. FORT WORTH, Texas, June 8, 2026 (Newswire.com) - Vadzo Imaging, a globally trusted provider of embedded vision camera products, today publishes a definitive technical guide on drone camera optics selection for UAV embedded vision applications. For UAV system designers, payload engineers, and embedded vision integrators, the selection of field of view, lens distortion profile, M12 lens mount, and vibration tolerance determines aerial map accuracy, inspection coverage, and image quality before the first flight. Vadzo Imaging builds its UAV camera module portfolio across 20MP monochrome, 8MP 4K HDR, and 13MP 4K color sensor architectures on the M12 S-Mount compact drone camera lens interface and explains precisely where each belongs. Why FOV Selection Determines Coverage and Ground Resolution Before the Mission Launches Field of view is the first drone lens selection decision for any UAV embedded vision payload, and selecting the wrong FOV produces one of two failures before the mission data is usable. A field of view that is too wide for the survey altitude introduces barrel distortion at the image periphery that degrades photogrammetry accuracy and requires software correction before aerial maps can be used for dimensional analysis. A field of view that is too narrow reduces ground coverage per frame, increases the number of flight passes required, and reduces area coverage
Jun 8, 2026 · via newswire.com
Bristol researchers working on animal biometrics and using AI for conservation have been key contributors to the SA-FARI (Segment Anything in Footage of Animals for Recognition and Identification) project. SA-FARI has been developed by an international consortium led by ConservationX Labs (CXL) and META. The project builds on META’s latest Segment Anything Model 3 (SAM3), a foundational and cutting-edge Vision-Language Model that is designed to use text and visual prompts to precisely identify, segment, and follow objects in images or videos. SA-FARI enables researchers to track animals in footage using ‘masklets’ which represent the exact outline of an animal in a video from frame-to-frame through time. It means the animal can be accurately separated from its background and form the basis of individual and behavioural analysis. This method has the potential to save thousands of hours for researchers using camera trap surveys in terms of viewing content manually. The project trained and benchmarked an AI system which can automatically detect, name and track animals of around 100 species pixel-accurately in footage. To do this, a vast dataset of more than 11,000 wildlife videos taken in natural habitats was curated and annotated. SA-FARI offers this data freely downloadable for biologists, researchers, and conservationists to boost ecological projects worldwide with cutting-edge AI powers. A paper about the project was presented last weekend [Saturday 6 June] at the Conference for Computer Vision and Pattern Recognition (CVPR) in Denver, USA, widely regarded as the leading conference for visual AI. The paper was selected an award candidate by CVPR. For the Bristol team working on animal biometrics and AI conservation, this is the second consecutive year to be nominated. Tilo Burghardt, Professor of Computer Vision and Animal Biometrics from the University of Bristol’s School of Computer Science, and a co-author, said: “Global problems require global
Jun 8, 2026 · via bristol.ac.uk
Code uncovered by journalists revealed that Meta quietly embedded facial recognition tech into its AI-enabled smart glasses — and top Meta executives are fuming. Last week, Wired reported that Meta discreetly moved to infuse facial recognition tech into its popular smart glasses, as evidenced by a piece of code discovered in the Meta AI app by the magazine’s journalists. The unreleased feature, internally dubbed “NameTag,” would “transform faces captured by Meta’s glasses into unique biometric signatures, commonly known as faceprints, and check each one against faceprints stored on the user’s phone — a database that’s currently configured to receive updates from Meta,” as Wired put it. The report made clear that NameTag isn’t activated yet, nor is it accessible to consumers in its current form. But the prospect of consumer-facing smart glasses equipped with facial recognition tech has long had privacy advocates on edge — and with public adoption of Meta’s smart glasses on the rise, privacy concerns have become more pertinent than ever. If a company has gone about building out the infrastructure to roll out a wildly controversial feature of this kind, consumers might want to know about that, even if the feature currently remains inaccessible. And yet, according to the company’s executives, it’s “dishonest” to inform the public about a piece of unreleased tech that Meta has chosen to incorporate into a consumer product. In its initial response to Wired, Meta referred to the discovery as “sensational” and characterized NameTag as exploratory. “We’ve said before we’re exploring these types of features, and what you’re seeing is just evidence of that exploration,” the company said in a statement to Wired. “Nothing has shipped to consumers and no final decision has been made on what to do here, if anything. If we do decide to roll something out, we
Jun 8, 2026 · via futurism.com
Where AI-Enhanced Data Analytics Is Headed Next - By Omri Kohl, Pyramid Analytics - June 08, 2026 Among the many business use cases that AI is shaking up, data analytics stands out. It’s an area that has changed immensely with the advent of conversational AI, ML, and automation, yet still has massive potential for additional transformation. The real story isn’t that analytics got smarter; it’s that decision-making is getting instrumented end-to-end. In the last two years, we’ve seen the emergence of NLP-based analysis that allows non-tech LOB users to query data using natural-language text or voice prompts. AI-powered BI tools can automatically select the right analysis methods for each situation, choose easy-to-consume visualizations, guide users to spot insights and draw conclusions, and highlight relevant datasets for closer study. AI has also transformed data analytics from the bottom up by boosting data gathering. AI processors can incorporate more data by extracting meaning from unstructured assets like videos, images, and audio clips; enhance data semantics to derive value from sources like social media posts and consumer sentiment; and handle enormous datasets that are unusable with manual methods. AI data integration brings together data that’s siloed in otherwise inaccessible locations and catalogs and categorizes it for a more unified data view. AI can also generate synthetic data to fill gaps in existing datasets or create new data for training. Additionally, AI streamlines data preparation and preprocessing by automating data cleaning, merging, validation and augmentation using NLP and pattern recognition. It groups similar assets to enhance data classification and retrieval; automates feature engineering and statistical techniques that improve data modeling; and drives data semantics that adapt models to different user needs. Because AI tools learn on their own, they ensure that data collection and preparation remain effective and trustworthy. In a nutshell, this means
Jun 8, 2026 · via cdotrends.com
Why the most important question isn’t whether AI can create — it’s understanding what it actually creates Something remarkable happened in early 2026. A massive study pitting the latest AI systems against more than 100,000 human participants on standardized creativity tests found that generative AI can now beat the average human on certain measures of original thinking and idea generation. That headline traveled fast. The alarm bells rang. Think pieces multiplied. But here is what that headline missed entirely: the most creative humans — the top 10% — still left AI well behind, particularly on richer work like poetry, storytelling, and the kind of meaning-laden expression that tends to define what we actually call great art. The study did not settle the debate. It opened a much more interesting one. We are at an inflection point where the question “can AI be creative?” has been effectively answered with a qualified yes. The better question — the one that will shape how we use, value, and think about creativity for the next century — is: what kind of creativity are we actually talking about? Two Engines Running on Different Fuel Human creativity and AI creativity are not two versions of the same process. They are fundamentally different engines, running on completely different fuel. Human creativity runs on lived experience. On grief, joy, embarrassment, obsession, and the slow accumulation of a life actually being lived. Vincent van Gogh did not paint the way he painted because he processed a dataset of Post-Impressionist techniques. He painted out of emotional and existential turmoil, a desperate need to find beauty inside a life filled with suffering. Frida Kahlo’s self-portraits were not exercises in visual novelty. They were intimate explorations of pain and resilience, processed through a body that had survived a near-fatal bus crash at
Jun 8, 2026 · via futuristspeaker.com
Abstract The acquisition of piano performance skills relies on continuous practice and precise feedback, yet traditional manual evaluation is constrained by time costs and subjective variations, making it difficult to meet the demands of large-scale music education. This study proposes a self-supervised multimodal Transformer framework whose core contribution is the fusion across audio spectral features, symbolic MIDI representations, and a MIDI-derived spatial/kinematic proxy, demonstrating cross-modal attention’s ability to exploit heterogeneous representations under controlled conditions through adaptive fusion mechanisms. Since the MAESTRO dataset lacks video recordings, hand posture features are synthetically derived from MIDI parameters rather than captured from independent visual sensors, representing a kinematic proxy for validating multimodal fusion concepts under controlled conditions. The two-stage training strategy employs contrastive learning, masked prediction, and temporal reconstruction objectives to learn general-purpose music representations during the pretraining phase, and optimizes fine-grained detection capabilities for five error categories of pitch, timing, dynamics, touch, and pedal during the fine-tuning phase, significantly reducing dependence on large-scale annotated data. Experiments on the public MAESTRO dataset validated the substantial advantages of multimodal fusion over unimodal approaches, with the self-supervised pretraining strategy demonstrating stronger generalization capabilities under limited annotation scenarios. Difficulty-level comparison experiments confirmed the model’s robustness in complex performance contexts. The core contribution lies in demonstrating cross-modal attention’s ability to fuse heterogeneous representations across audio, symbolic MIDI, and a MIDI-derived spatial/kinematic proxy under controlled conditions; these findings do not imply that video-based hand pose observation would necessarily yield similar gains, which remains future work. Similar content being viewed by others Introduction The acquisition of piano performance skills requires persistent training and accurate feedback. Manual evaluation of these skills has some limitations in terms of the available time and the subjective nature of the assessment process, making it less feasible in meeting the demands of a broader musical education
Jun 8, 2026 · via nature.com
By borrowing a trick from tiny jumping spiders, Northwestern University engineers have developed an extremely energy-efficient 3D camera. Called SpiderCam, the new device senses depth the same way that jumping spiders judge distances before making a high precision hop. To estimate depth, the system captures two images of the same scene with slightly different focus settings and measures subtle differences in blurriness between the two images. With this strategy, the camera produces real-time 3D maps while consuming less than a watt of power. That’s less energy than used by a standard nightlight. The innovation could enable a new generation of battery-powered devices that need to gauge their surroundings, like wearable technologies, assistive devices, robots and drones. The study’s co-first authors Marcos Ferreira and Tianao Li presented the work on June 7 at the Computer Vision Foundation’s Conference on Computer Vision and Pattern Recognition in Denver “Jumping spiders jump to catch prey, to avoid predators and to get around, and that requires excellent vision,” said Northwestern’s Emma Alexander, the study’s corresponding author. “But their brains are very small — the size of a poppy seed — so they have to compute these distances in a highly efficient way. We wanted to understand whether we could borrow some of the same principles to create an extremely energy efficient depth sensor that could be used in resource-constrained situations where users don’t have unlimited access to power.” The innovation could enable a new generation of devices that need to gauge their surroundings, like wearable technologies, robots and drones. An expert in bio-inspired computer vision, Alexander is an assistant professor of computer science at Northwestern’s McCormick School of Engineering. Most 3D cameras estimate depth either by comparing images from multiple viewpoints or by projecting and measuring light. While these approaches work well, they can require
Jun 8, 2026 · via news.northwestern.edu
MONTGOMERY, Ala. – AARP Alabama is alerting residents across the state to a troubling new scam in which criminals impersonate representatives of “Publishers Clearing House” and use FaceTime calls to exploit victims through facial recognition technology. According to reports received by AARP’s Fraud Watch Network, scammers are contacting individuals – often older adults – claiming they have won a large cash prize or sweepstakes. The criminals then persuade victims to engage in a FaceTime video call under the guise of verifying their identity or confirming prize details. During these calls, scammers capture images or video of the victim’s face, which they may attempt to use to access banking or financial accounts that rely on facial recognition security features. “This is a particularly alarming twist on a long-running scam,” said AARP Alabama State Director Candi Williams. “Criminals are combining familiar tactics with newer technologies to gain access to people’s personal and financial information. We urge all Alabamians to remain vigilant and skeptical of unsolicited prize notifications.” Warning signs of the scam - Unsolicited calls, texts or messages claiming you’ve won a prize - Requests to join a FaceTime or video call for “verification” - Pressure to act quickly or keep the winnings confidential - Requests for personal, financial or account information How to protect yourself - Do not engage with unexpected prize notifications. - Never share personal or financial information with unknown contacts. - Avoid video calls with unsolicited callers, especially if they request identity verification. - Secure your devices by limiting or disabling facial recognition for financial apps if possible. - Report suspicious activity immediately to your financial institution and local law enforcement. “If it sounds too good to be true, it is,” added Williams. “No legitimate organization will ask you to verify a prize through a video call or
Jun 7, 2026 · via cullmantribune.com
‘Access to education across different cultures and languages needs to be improved’: IEEE award winner’s best work came from identifying issues, and fixing them IEEE award winner sees technology as an enabler, not a driver Lately, it seems that all the focus has been on artificial intelligence, but let us not forget that some of the most important technological breakthroughs have actually come from people who are really good at what they do. IEEE Fellow, professor and researcher Karen Panetta has spent decades applying engineering techniques to problems that extend far beyond computing to solve some of the world’s most pressing issues. Her work has ranged from underwater imaging systems for search-and-rescue to wildlife monitoring systems and low-cost methods for detecting harmful pathogens – thus her work has had a really profound effect on life on Earth. Many of Panetta’s projects actually emerged from identifying real-world problems, rather than pursuing technology for technology’s sake, and that’s a clear differentiator between the people who have the biggest impact on scientific development, and those that don’t. STEM education and AI literacy should be a priority Today, research is one of the sectors most impacted by AI, and the likes of Panetta are now able to push their work even further with projects like computer and human vision. She is also the 2026 winner of the IEEE Mildred Dresselhaus Medal, acknowledged for her “contributions to computer vision and simulation algorithms, and leadership in developing programs to promote STEM careers.” Now, Panetta is a prominent voice on AI literacy and access to STEM education – as virtually every sector is rushing to deploy AI, she argues that public understanding of the technology has failed to keep pace, and the risks could soon emerge. Sign up to the TechRadar Pro newsletter to get all the
Jun 7, 2026 · via techradar.com
This article first appeared in Digital Edge, The Edge Malaysia Weekly on June 8, 2026 - June 14, 2026 In March, Kuala Lumpur City Hall (DBKL) announced that 10,000 closed-circuit television (CCTV) cameras, jointly operated by DBKL and the Royal Malaysia Police (PDRM), had been installed in public areas and integrated into DBKL’s Kuala Lumpur Command & Control Centre (KLCCC) as part of its smart city initiative. While the system promises enhanced security and improved city services, its implementation has raised concerns among activists about privacy and the extent of surveillance in public spaces. Such concerns have become more pressing as biometric identification and large-scale data collection are increasingly integrated into public surveillance systems in the name of safety and smart city infrastructure. Minister in the Prime Minister’s Department (Federal Territories) Hannah Yeoh tells Digital Edge in an email interview that the CCTVs are supported by 16 artificial intelligence (AI) analytics, designed to empower Kuala Lumpur’s smart city systems, including KLCCC and its function as a unified municipal command hub. Part of a broader RM500 million investment in AI-powered city surveillance since 2020, the system “does not merely record. It detects, analyses and triggers real-time actions, enabling faster and more coordinated responses between agencies”, Kuala Lumpur Mayor Datuk Seri Fadlun Mak Ujud was reported as saying. The adoption of AI surveillance tools is not new in Malaysia. Johor Bahru launched more than 1,500 AI-powered CCTVs as of January; Penang announced plans to have 5,000 cameras by 2030 last year; and Cyberjaya announced plans to deploy 375 automated number plate recognition devices over the next three years in May. What sets DBKL’s system apart is the sheer number of CCTVs. DBKL’s 10,000 CCTVs are supplied by ITMAX System Bhd (KL:ITMAX), as part of its 10-year contract starting in 2024, with recorded
Jun 7, 2026 · via theedgemalaysia.com
It may sound like yet another rollout of a dystopian surveillance state network of facial recognition cameras — but the New York Department of Transportation’s latest initiative has a far more tame goal in mind: tracking modes of transport to improve street design. According to the Gothamist, the New York Department of Transportation has added 100 roadside sensors across the city in order to pick up data on vehicle, bike, and pedestrian traffic. The effort is an expansion of a 2023 sensor pilot meant to gather data on city traffic, which saw 20 such devices installed on signposts in various locations. The machine-learning sensors — explained as a tool to help improve pedestrian crossings and bicycle infrastructure — are trained to anonymize faces and license plates, DOT deputy commissioner Eric Beaton told the Gothamist. An image shared by the DOT shows the sensor in action: a hazy blur obscures some details, but a machine learning algorithm ensures each pedestrian and vehicle is visually tracked in color-coded boxes, each with their own label. “There’s nothing that we ever touch or that anyone could ever touch that has anything identifying to any person or any vehicle,” the DOT’s Beaton claimed. “These sensors provide a much richer set of data for us to work with.” As part of the surveillance move, the DOT said it will share a portion of the data collected with the community. Accountability watchdogs, however, are demanding all of it. “If they’re collecting this data on behalf of the public, as a taxpayer-funded agency, we deserve to know what it says and so there should be regular reporting,” transit advocate and former policy director for the DOT Jon Orcutt told the Gothamist. Still, the situation highlights the tricky position city officials find themselves in, where collecting enough data to
Jun 7, 2026 · via futurism.com
Live Facial Recognition (LFR) vans will be deployed in Colchester as part of police efforts to tackle crime and improve public safety.
The technology will be used to identify individuals suspected of serious offences, including drug, violent, and sexual crimes, as well as theft on Friday, June 12.
An Essex Police spokesperson said: "As part of our work to keep you safe and tackle crime in Colchester, we’ll be visible in the city this week.
"That will include deploying our Live Facial Recognition vans on Friday.
"The van will be looking for people suspected of serious offences such as drug, violent and sexual offences, as well as thefts in addition to enforcing orders."
Officers said more than 160 arrests have been made using the technology, which can identify individuals even if their faces are partially covered.
The vans were used in the city centre last month to identify suspects, support law enforcement, and help safeguard vulnerable people and children at risk.
Essex Police has been deploying LFR technology since summer 2024, following a successful proof of concept exercise conducted in 2023.
Jun 7, 2026 · via essexcountystandard.co.uk
I’ve been struggling to get my head around the idea that a passkey, which can be a PIN on your phone, or facial recognition, can be safer than using a complicated password, and two factor authentication.
I get that having something unique to your device, not stored on a company’s server is unphishable, and less hackable by cybercrims, but what if your phone is nicked and someone guesses the password? And what if you lose your phone?
Sorry if that sounds simplistic, but I am genuinely stumped to understand why the UK’s National Cyber Security Centre and others who know about these things are so sold on passkeys. Can anyone who’s used them enlighten me? Martin Avis, Chester
Post your answers (and new questions) below or send them to nq@theguardian.com. A selection will be published next Sunday.
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Jun 7, 2026 · via theguardian.com