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Arrests made and weapon found thanks to <b>facial recognition</b> | Bradford Telegraph and Argus

Police have made several arrests and recovered a weapon following the latest deployment of live facial recognition cameras in Bradford city centre. West Yorkshire Police used the technology over three days from August 19 to 21, with support from the Bradford City Neighbourhood Policing Team. The cameras helped locate a missing person and identified four individuals wanted for criminal offences, all of whom were arrested. One of those detained, a man wanted for theft, was also arrested on suspicion of possessing an offensive weapon after officers found a knuckle duster. Inspector Justin Adams of the Bradford City Neighbourhood Policing Team said: "The Live Facial Recognition Team have again supported City NPT not only in helping us locate wanted individuals but also in supporting our commitment to protecting vulnerable people. "It has helped officers find a high-risk missing person quickly, ensuring they received the support and safeguarding they needed at the earliest possible opportunity. "The technology also helps us identify those on orders who may cause harm, enabling officers to intervene, engage and take action to keep our communities safe. "Local businesses and traders have broadly welcomed the use of LFR over the last six months, which is now supported by funding from the Knife Crime Concentration Fund." Since February 2026, live facial recognition camera vans have been deployed in Bradford 17 times, resulting in 29 arrests. The system also flagged 21 individuals subject to Sexual Harm Prevention Orders, who were then checked for compliance. Live facial recognition works by comparing real-time camera images to a watchlist of wanted individuals and suspects. Alerts generated by the system are reviewed by trained officers before any action is taken. Images that do not match anyone on the watchlist are deleted within seconds. West Yorkshire Police said the technology is helping to keep communities

Idemia PS face biometrics score high accuracy at scale in latest NIST FRTE

Idemia PS face biometrics score high accuracy at scale in latest NIST FRTE Idemia Public Security’s latest facial recognition algorithm has ranked first across the tested gallery sizes in the frontal mugshot-to-mugshot scenario of NIST’s latest FRTE 1:N Identification evaluation. The submission recorded a 1.11 percent FNIR against the 12 million-person gallery, with an FPIR of 0.1 percent. Its ability to maintain low error rates as the enrolled population increased shows the strongest result. Scale provides useful context for differentiating leading biometric algorithms as they converge on controlled image comparisons. Idemia’s (013) face biometerics algorithm delivered an FNIR of 0.05 percent in mugshot identification with databases of 1.6 million and 12 million images, and 0.55 percent on webcam-to-mugshot comparisons, all best in the field. The company’s results remain close to those of the leaders in other controlled or near-frontal scenarios. In visa-to-border comparisons, Idemia scored a 0.13 percent FNIR, third overall but also tops so far in the “by developer” rankings, because NEC’s (010) submission scored better overall than its (011) and (012) algorithms, which have lowest two FNIRs to date. Idemia ranks ninth for visa-to-kiosk identification at 4.74 percent and twenty-sixth when matching profile images against a mugshot gallery with an FNIR of 7.50 percent. The company also finished in the top tier for demographic fairness by FPIR, according to an announcement celebrating its results. The new submission shows a substantial improvement over its algorithm evaluated last year. In visa-to-border identification, FNIR declined from 0.22 percent to 0.13 percent at the same 0.3 percent FPIR operating point. Progress also extends to several other tests, with border-to-border FNIR falling from 2.42 percent to 1.21 percent and mugshot-to-webcam FNIR declining from 0.82 percent to 0.55 percent. The visa-to-kiosk performance improved from 5.16 percent to 4.74 percent. “These results validate our investments

AMD Ryzen 7 5800X3D 10th Anniversary Review

AI Inference — GPT2 Artificial Intelligence and Machine Learning have enabled us to create applications that are almost magical in their abilities. GPT-2 is a powerful language model developed by OpenAI, the makers of ChatGPT, that excels at generating human-quality text. It's a key AI workload because it tests the efficiency and capability of AI systems in handling complex language tasks, which is crucial for many real-world applications. We are measuring the time it takes to generate 100 stories starting with the "Once upon a time, there was a" prompt. AI Inference — Stable Diffusion Stable Diffusion stands out as the second rock star AI workload. This cutting-edge image generation model creates high-quality, detailed visuals from textual descriptions. Its ability to produce realistic images has wide-ranging implications for industries such as art, design, advertising, and media, enabling innovative content creation and enhancing visual storytelling. We are timing how long it takes to generate a single image for the prompt "a photo of an astronaut riding a horse on Mars." AI / Inference — Image Upscaling Topaz Photo AI is a premier tool for AI-driven image processing. It enhances image resolution and quality by intelligently increasing detail and reducing noise. This advanced capability improves the clarity and sharpness of assets, making it a valuable tool for anyone needing high-resolution images. Our test reports how long it takes to upscale a 1.5 megapixel image to 22 megapixels. AI Training — Natural Language Processing Natural Language Processing (NLP) involves training AI models in understanding and generating human language. By applying algorithms and machine learning techniques to analyze and interpret text data, NLP enables tasks like translation, sentiment analysis, and text generation. We are measuring how long it takes to train a BERT language model with a collection of movie critic reviews. AI Training

EFF and Civil Society Groups Call on Nottinghamshire Police to Halt Live Face <b>Recognition</b>

This week, EFF, along with Big Brother Watch, Defend Digital Me, Liberty, Open Rights Group, Race Equality First, Statewatch, and Stopwatch, wrote to Nottinghamshire Police Force in the UK raising concern about the proposed roll-out of live facial recognition technology (LFR), and called for its immediate halt. In particular, the letter highlights six concerns: LFR Is Not "Just Another Tool" Nottinghamshire Police has stated that “facial recognition is just another tool to fight crime.” But LFR used in public spaces is an incredibly intrusive biometric mass surveillance technology that scans the faces of everyone who walks past the camera and takes biometric face prints. This is not just another tool, but a major escalation of surveillance that treats everyone as a suspect by default. People Having "Nothing to Worry About" Does Not Hold to Scrutiny According to Nottinghamshire Police, “if you aren’t entering the city or county to commit crime then you have nothing to worry about.” However, many people have legitimate concerns about the normalisation of invasive technologies. So a public that cannot move around their towns and cities without being subjected to a biometric identity check may be less willing to seek medical care or legal advice, speak with journalists, act in a union, vote, protest, or express their gender, sexual or religious identity. Disproportionate Targeting With LFR We are particularly concerned to learn that Nottinghamshire Police could deploy LFR to tackle low level crimes, such as youth behavior deemed anti-social, as part of Operation View. Reporting suggests that the force already possesses “a watchlist of young people believed to be causing the most problems,” including children as young as 11 years old. It would be highly disproportionate to deploy live facial recognition to tackle this behaviour. Many of these children are reportedly known to the police, and

Aging shifts memories from vivid details to bigger <b>picture</b>

Research from the University of East Anglia shows how our memories change with age and why specific details associated with past events can fade over time. A new study reveals that as people age, they not only remember fewer details from their past - but also struggle more when switching between different types of memories. It points to a shift in how we remember things as we grow old - where vivid and moment-specific recollections gradually give way to broader, more generalised knowledge. Rather than portraying memory decline as a simple loss, the study offers a nuanced picture of aging cognition and sheds light on how memory evolves over a lifetime. The team hope their findings could help guide new approaches to support healthy cognitive aging. Lead researcher Prof Louis Renoult, from UEA's School of Psychology, said: "As we get older, our memories tend to become less detailed and more general, focusing on the overall story rather than specific moments. "This shift is linked to changes in how the brain recalls information, meaning older people often remember the big picture but with fewer vivid details. "We wanted to better understand how young and older adults retrieve autobiographical memories - the personal experiences that form the story of our lives. "These sorts of memories can range from specific one-time events, such as a birthday party, to more general or repeated experiences like commuting to work. "We focused on how flexible people are when asked to shift between these types of memories, and what happens when that flexibility is put under pressure." How the research happened A total of 37 students, representing young minds, and 37 older adults from the community took part in the study. They were asked to recall two kinds of memories in response to cue words such as

NIST picks 11 contactless fingerprint biometrics providers for certification testing

NIST picks 11 contactless fingerprint biometrics providers for certification testing NIST has completed the first phase of its vendor engagements under the Fast Capture (FastCap) program for evaluating contactless fingerprint technologies. Eleven biometrics providers have joined the program under a Cooperative Research and Development Agreement (CRADA) following successful qualification testing. The testing stage included cooperative evaluations of the vendor technologies’ biometric acquisition capabilities, refinement of performance metrics and prototype testing for contactless rapid tenprint scanners. The 11 vendors that satisfied the qualification test criteria are: U.S.-based AA Technology, Gambit ID, which has offices in Canada and the U.S., Idemia, Identy, IDloop, Mentalix, Sciometrics, Synolo, Thales, Tech5 and Veridium. The qualification holds up these biometrics developers as global leaders in contactless fingerprints. The qualification testing was carried out based on NIST Special Publication 500-339, “Specification for Certification Testing of Contactless Fingerprint Acquisition Devices, 1.0,” published in April, 2023. The specification emphasizes the “fundamental departure” that contactless fingerprint biometrics represent from legacy contact-based capture technologies. The steps, procedures and software for the tests, along with the properties to be measured, are defined in NIST SP 500-336. SP 500-336, “Specification for Interoperability Testing of Contactless Fingerprint Acquisition Devices, v1.0,” was published in 2022. The same NIST document explains the creation of standardized test fingers for use in comparing the 2D images created by contact-based fingerprint scanners and the 3D images captured by contactless biometric scans. Properties to be measured (measurands) include image entropy, signal to noise ratio, scale factor computations and NFIQ image quality assessment. The next step is for NIST to review the qualification testing outcomes. Phase Two of the FastCap initiative will involve more vendor engagement as well as government bodies to encourage the adoption of contactless biometrics. The qualification testing model will also be expanded into a framework for ongoing

AI and Machine Learning in Biology: Applications, Benefits &amp; Challenges

The unprecedented convergence of AI and Machine Learning in Biology with the life sciences, often described as AI in biology, represents a milestone in scientific progress. AI/ML, once the exclusive tools of computer scientists, is now being incorporated into several scientific disciplines, with biology at the forefront. At the vanguard of a new era of biomedical discovery and innovation, these technologies empower researchers to address an expanding spectrum of scientific questions, ranging from the intricate biochemical mechanisms that govern cellular functions to the urgent challenges in modern healthcare, opening new avenues for understanding the fundamental processes of life. Artificial intelligence is the broader field of developing systems capable of performing tasks generally associated with human intelligence. Machine learning is a subset of AI in which algorithms learn patterns from data and use them to generate predictions, classifications or recommendations. Table of Contents Why Is AI and Machine Learning in Biology Important for Research? Modern biology generates enormous and diverse datasets across the domains of genomics, proteomics, imaging, and clinical records. The need to process this information to extract meaningful patterns and insights often proves to be a formidable challenge, requiring substantial analytical effort by researchers. This is where the unique strengths and capabilities of AI/ML come to the forefront, enabling researchers to perform tasks such as: - Pattern Recognition in Large, Complex Datasets AI and ML algorithms are uniquely capable of detecting subtle patterns and associations that might be challenging or impossible for humans to discern, therefore often guiding the research process. - Predictive Modelling AI/ML can identify trends and patterns that can be used to build predictive models capable of rapidly analyzing complex biological systems and processes, such as disease progression or molecular interactions. These include, but are not limited to, modelling disease processes, predicting drug-target interactions, and identifying

Exclusive: Trump DOJ appears to have hired lawyer convicted of hacking election sites to ...

Exclusive: Trump DOJ appears to have hired lawyer convicted of hacking election sites to check ‘integrity’ A Florida lawyer who pleaded guilty after illegally accessing government election websites appears to have joined President Donald Trump’s Department of Justice (DOJ) — and taken part in a recent DOJ election monitoring operation in Minnesota. The apparent hiring of David Michael Levin would be among the most troubling example yet of the department’s Civil Rights Division bringing on lawyers — including the acting chief of the voting section — who have taken extreme steps to undermine fair elections, as it rushes to put together a team to carry out Trump’s anti-voting agenda. And Levin’s involvement in election monitoring could offer a hint of the kind of personnel who will staff the operation this fall, when the DOJ has said it will send 1,000 monitors to the polls. Get updates straight to your inbox — for free Join 350,000 readers who rely on our daily and weekly newsletters for the latest in voting, elections and democracy. Levin was charged with three felonies in 2016 after Florida investigators said he used a cyberattack to obtain credentials, then entered restricted portions of a county elections website, and separately accessed the state Division of Elections website. Levin later said he had wanted to look into “the integrity of elections.” The charges were later reduced to two misdemeanors. Levin pleaded guilty, served 20 days in jail and received two years of probation. Now, images and video posted online suggest Levin has joined the DOJ’s Civil Rights Division — the federal office charged with enforcing many of the nation’s voting rights laws and that is now leading the Trump administration’s nationwide grab for sensitive voter data. Democracy Docket initially identified Levin using publicly available facial-recognition reverse image search tools.

Brea Rolls Out <b>Facial Recognition</b> System

The Brea Police Department will soon be able to use facial recognition software to identify potential suspects during investigations. On Tuesday, Brea City Council voted unanimously with no discussion to enter into an agreement with the Integrated Law and Justice Agency for Orange County (ILJAOC). This agreement will also give Brea PD access to a system able to analyze faces from surveillance camera footage, a photo supplied from a victim or witness and more. It comes as there’s growing concerns about increased police surveillance throughout Orange County, with some residents criticizing such systems. [Read: OC Cities Look to Beef Up Surveillance Amid Privacy Concerns] Brea is the 13th law enforcement agency in Orange County to enter into this agreement. “The PD regularly receives Ring video, photos, and CCTV footage from victims and witnesses related to crimes. Many of these have clear images of the suspect, but we still don’t know who they are,” wrote Professional Standards Lieutenant Chris Haddad in an email to Voice of OC. “This could give the investigator an additional lead to investigate if the system provides a potential match,” he wrote. The system works by analyzing images submitted by an authorized investigator. The software then creates a mathematical representation of facial characteristics seen in the photos and matches them with photographs in a jail booking database. The software produces candidate matches that a trained human investigator must evaluate using other evidence. Haddad explained how the system will only compare photos of unknown persons against booking photos of known people. “This is simply a tool that may create leads if the police department has a clear image of a suspect and that suspect has been booked into county jail at some point and has a booking photo in the system,” he wrote. According to the system’s policy,

Flock Safety Cameras Demystified: A Complete Guide

A Complete Guide to Flock Safety Cameras If you live in America, chances are you’ve driven past a Flock camera at some point over the past few years. Flock has become nearly ubiquitous thanks to its use by law enforcement, HOAs, and private property owners, with a footprint in over 5,000 communities across 49 states. Flock’s technology and hardware, including automated license plate recognition and gunfire locator systems, mostly flew under the radar since its inception in 2017. But in recent years, the company has seen increasing scrutiny, criticism, and litigation from private citizens, local governments, and civil liberties groups over concerns regarding their potential invasion of privacy and misuse. What are Flock cameras, and should you be concerned about their use in your community? What are Flock Cameras? Flock Safety, the company that builds and operates these cameras, says that they seek to deter crime, respond to emergencies, and investigate safety incidents. Its main products are automated license plate readers, or ALPRs, though they also offer traditional security cameras, mobile security trailers, drones, and security software. ALPRs capture identifying details of passing cars, including the license plate, make, model, color, and other visible characteristics. The cameras—which are located on public roads, in parking lots, and in front of private homes—use AI technology to analyze what it captures and upload that data to the company’s national surveillance platform. They do not use facial recognition technology, though images of pedestrians or bicyclists may be captured in the recordings. Flock is most notably used by law enforcement agencies, but the cameras are also heavily marketed to HOAs, schools, and retail businesses, which can then share their camera data to local police. This helps the company create an expansive surveillance network, with the stated goal of aiding criminal investigations. Flock says it captures

Brazil's data authority orders halt to <b>facial recognition</b> in Paraná schools | brief

Brazil's National Data Protection Authority (ANPD) has ordered the school system in the southern state of Paraná to immediately stop collecting biometric data of children and adolescents for attendance using facial recognition software, with further coverage provided by Biometric Update.The ANPD cited a failure by the Paraná State Department of Education to demonstrate an adequate legal basis for processing sensitive biometric data, nor sufficient guarantees of security and governance. The pilot project, which involved approximately one million students across 2,136 institutions, utilized facial recognition software from Innovatrics, subcontracted by Valid, and deployed through the Escola Paraná Biometria app. Teachers photographed classes, and images were sent to cloud servers for facial extraction and comparison.The teachers' union questioned the necessity of biometric data collection, stating traditional attendance methods are sufficient. The ANPD agreed, deeming facial recognition unnecessary and disproportionate, noting less invasive alternatives. Concerns were also raised about inadequate controls over image sharing, privacy, and data retention, potentially storing 14 years of biometric data per student. The ANPD has given Paraná 10 days to confirm the cessation of data processing across all involved systems and companies.Source: Biometric Update Get daily email updates SC Media's daily must-read of the most current and pressing daily news You can skip this ad in 5 seconds

AI and AV are the New Drivers of the Ever-Evolving Public Transit Future

AI and AV are the New Drivers of the Ever-Evolving Public Transit Future The history of public transportation is one of constant evolution and reinvention. Since the origin of the American Public Transportation Association (APTA) in 1882, transit providers have been at the center of continuous technology and institutional change. Through each ensuing decade, transit systems have been ever evolving. Over the past ten years, technologies enabling mobility-on-demand (TNCs, bike-share, micro transit) have become part of the urban mobility landscape. Today, automated vehicles (AV), artificial intelligence (AI), and machine learning (ML) are at the cutting edge of another wave of technology-driven innovation. Transit agencies are eager to better understand the potential for AI so APTA has been working to provide resources and opportunities for information sharing. The recent APTAtech Conference in St. Louis (August 9-12, 2026) provided the opportunity for a deep-dive into the various ways transit agencies are putting Artificial Intelligence to work today to boost efficiency, service delivery, security, and the overall rider experience. Prior to that, in May 2026, APTA released Artificial Intelligence (AI) and Machine Learning (ML) in Public Transit: A Primer, providing public transit agencies with a comprehensive resource for understanding, evaluating, and deploying AI and ML tools across their operations. Findings were based on a survey of transit agencies and staff interviews to capture current and planned AI applications. Real-world examples documented in the Primer include: - Metropolitan Transportation Authority (NY) increased maintenance productivity by 75 percent and decreased material costs by 24 percent in a test fleet, using an AI-powered predictive maintenance system for its bus fleet. - AC Transit (CA) used AI image recognition for bus lane enforcement, increasing violation citations from 22 to 787 over a comparable two-month period following implementation. - Riverside Transit Agency (CA) piloted a disruption management tool

Live <b>facial recognition</b> vans coming to Aylesbury town centre

Live facial recognition technology will be deployed in Aylesbury town centre next week as part of efforts to identify wanted suspects and deter crime. Thames Valley Police has announced that its specialist Live Facial Recognition (LFR) team will be operating in Market Square on Tuesday, August 25, alongside local officers. The force said the technology will be used to help identify known suspects, prevent offending and keep communities safe. Police routinely publicise planned deployments in advance, despite acknowledging that some individuals may seek to avoid the area as a result. Officers will be on hand throughout the day to answer questions from members of the public and explain how the technology works. Live Facial Recognition uses cameras to analyse facial features and compare them against a pre-determined watchlist of individuals wanted by police or sought in connection with investigations. The force also uses Retrospective Facial Recognition, which compares still images, such as those captured on CCTV, against the Police National Database to assist in identifying unknown suspects. Thames Valley Police said it recognises the need to balance public safety with privacy concerns and considers the legal and ethical implications whenever facial recognition technology is used. Residents who would like to learn more about the system have been encouraged to speak to officers during the deployment in Market Square. Share

Gulf Blue Navigator Earns Spot Among America's Best Accelerators

The inaugural recognition identifies leading incubators and accelerators across the United States based on alumni evaluations, peer recommendations, track records, and measurable founder outcomes. Operated through The University of Southern Mississippi Research Foundation, Gulf Blue Navigator is a late-stage accelerator supporting companies developing technologies across the Blue Economy, including maritime systems, aquaculture, coastal resilience, ocean observation, autonomous technologies, and advanced manufacturing. Natalie Guess, Assistant Director of Innovation and Impact at Southern Miss, said the recognition reinforces Mississippi’s growing innovation ecosystem and its national reach. “Being recognized by TIME and Statista as one of America’s best incubators and accelerators is an incredible honor and a meaningful validation of the work our team, partners, mentors and companies have built together. Gulf Blue Navigator was created to provide more than traditional accelerator programming. We connect companies with customers, research expertise, testing and demonstration capabilities, investors, manufacturers, government partners and the resources needed to move technologies from discovery to commercialization and deployment,” said Guess. The recognition was based on three primary components: Alumni Evaluations Analysis, which considered overall recommendations and six additional evaluation criteria; Track Record Analysis, which assessed information about leading companies that participated in each organization’s programs; and Expert Recommendation Analysis, which was based on recommendations from startup investors and experienced entrepreneurs. Jamie Miller, President and CEO of the Gulf Coast Business Council (GCBC), has supported Gulf Blue Navigator and its role along the Mississippi coast. The GCBC recognized Gulf Blue Navigator with the Rising Tide Award during the 2026 State of the Coast Symposium in May. “Gulf Blue Navigator’s recognition by TIME as one of the Top 100 accelerators is a tremendous validation of the innovative ecosystem being built here on the Mississippi Gulf Coast. By connecting entrepreneurs with world-class research, industry expertise, federal partners and resources to scale, Gulf Blue

Police urged to consider <b>facial recognition</b> to tackle crime and football violence

Police Scotland is being urged to adopt live facial recognition technology to crack down on violent crime and football disorder. In his latest annual report, His Majesty’s Chief Inspector of Constabulary in Scotland Craig Naylor has called on both the Scottish Government and policing to “be ambitious” in rolling out the tools. He said when used responsibly, drones and facial recognition technologies could “reduce the threats posed to the public from high-risk sex offenders, organised crime groups, violent offenders and others whose activities cause significant harm to communities”. He advised “the challenge” for public sector leaders was “not whether to adopt these technologies, but how quickly they can do so safely, ethically and effectively”. Mr Naylor said: “Police and law enforcement partners must seek to exploit, with appropriate safeguards in place, advancements in operational technology such as remotely piloted aircraft systems (drones) and facial recognition. “The public sector should be ambitious in harnessing innovation to improve services while preserving public trust and accountability by explaining the case for use of these new capabilities.” He stressed that football disorder, such as that seen when Rangers played Celtic in the Scottish Cup quarter-final in March, where rival fans clashed after a pitch invasion, “cannot continue to be tolerated”. Mr Naylor branded the incident “deeply concerning” and cited severe failures in planning and matchday safety. Warning of the “increasing threat of violence and disorder”, he said football clubs themselves, their governing bodies, local authorities and supporter groups must all “share responsibility with policing” in tackling the problem. He suggested an enhanced risk assessment process be put in place, along with mandatory multi-agency planning exercises for high-risk matches, stronger stewarding arrangements, improved segregation of rival supporters and “consistent use of evidence-led sanctions against those involved in violence or disorder”. Mr Naylor said: “Elements such

Why Human Informants Still Matter in the Age of AI Surveillance

Why Human Informants Still Matter in the Age of AI Surveillance The most frightening thing about surveillance is not always the camera on the wall or the intelligence officer behind a desk. Sometimes it is the colleague who repeats your private joke, the neighbour who watches who visits your home, or the friend who suddenly asks too many careful questions. Recent research on Iran has examined the everyday culture of informing, known in Persian as Zirabzani or Mokhberi. However, this phenomenon is not uniquely Iranian. Many political systems, especially authoritarian ones, survive not only by watching citizens from above but also by encouraging citizens to watch one another. Although every society has its own language for those who report on others, the phenomenon is remarkably familiar across cultures. In Iran, they are known as Zirabzan. Across much of the Arab world, the term Mukhbir immediately evokes the image of a state informant. In Türkiye, people speak of an İspiyoncu, while in Russia the deeply pejorative Stukach recalls both Soviet-era denunciations and contemporary political informing. In China, gàomìzhě (告密者) and gàomì (告密) refer to secret informers and the act of informing itself, terms shaped by decades of political campaigns and continuing systems of social monitoring. The names differ, but they all point to the same underlying social reality: ordinary citizens who become intermediaries between society and the state. This is the part of surveillance we often miss. Public debate is now dominated by artificial intelligence, facial recognition and digital monitoring. These technologies matter. But they work best when they are combined with something older and more intimate: fear inside society itself. Informant cultures emerge when people begin to believe that ordinary relationships are politically risky. A conversation at work, a comment in class, a joke at dinner, or a post on

Partnerships: the cornerstone of future-ready buildings | Facilities Dive

Susanne Seitz is CEO of Buildings Smart Infrastructure at Siemens. Opinions are the author’s own. Walk through almost any city in the world — from Madrid and Singapore to Chicago — and you will notice something remarkable: the vast majority of the buildings around us today will still be standing 25 years from now. That's both our industry's greatest challenge and one of its biggest opportunities. While technology continues to evolve, most existing buildings have yet to begin their digital transformation. Only 10-15% of medium-sized buildings and fewer than 5% of smaller buildings have a building management system installed. The opportunity before us isn't simply about creating smarter new buildings — it’s about transforming the millions of buildings that already exist. But no one organization can achieve that alone. The next competitive advantage in buildings won't come from technology alone — it will be the strength of the partner ecosystem behind it, anchored by the professionals who manage the buildings’ systems. If we're serious about creating buildings that are more intelligent, more efficient and more resilient, partnerships are no longer a nice-to-have. They are fundamental to how we will modernize the built environment and deliver meaningful outcomes for customers. Why partnerships matter now more than ever The conversation around buildings has changed. Customers are no longer asking for individual technologies — they are asking for solutions to increasingly complex business challenges. They want to reduce energy consumption while improving occupant comfort. They need to meet ambitious sustainability targets, manage aging infrastructure, address workforce shortages and prepare for a future where AI and automation play a much greater role in building operations. These aren't challenges that can be solved through technology alone. They require collaboration between technology providers, systems integrators, facilities managers and other partners who understand both the technology and

<b>Facial Recognition</b> Technology Coming into Wider Use in Japan

Facial Recognition Technology Coming into Wider Use in Japan Tokyo, Aug. 20 (Jiji Press)--Services using facial recognition technology are expanding in Japan, allowing, for example, touchless or ticketless entry through train station gates and payments at shops without the need to take out a wallet or a credit card. Expectations are growing that the evolution of artificial intelligence will improve accuracy of facial recognition systems and help reduce fraud. Meanwhile, concerns about privacy violations persist, making thorough user information management essential. Trial Holdings Inc., which runs discount stores and other operations, has introduced a payment service using NEC Corp.'s facial recognition technology at 15 stores in Tokyo and Fukuoka Prefecture, southwestern Japan. Advance registration allows customers to shop simply by scanning goods at the self-checkout counter without using cash or smartphone payment services. People can watch professional baseball games of the Yomiuri Giants at Tokyo Dome by entering the stadium through a Panasonic Connect Co. facial recognition system after obtaining an ID and pay at some shops without cash or credit cards. Hitachi Ltd. and Tobu Railway Co. have installed facial recognition train ticket gates at Ikebukuro Station on the railway operator's Tojo Line and other stations. In the future, the technology is expected to become available for payments at commercial facilities, including nearby convenience stores. (2026/08/20-17:28)

Manatee County Sheriff's Office gives a clear <b>picture</b> about Flock cameras | Your Observer

- August 20, 2026 Loading Between 2024 and 2026, Manatee County Sheriff’s Office deputies used license plate recognition cameras, or license plate readers, to arrest or recover 191 individuals. Not only are criminal suspects identified through the use of the technology, missing children and senior citizens with dementia and Alzheimer’s disease have been located using license plate readers. Flock is a brand name, and MCSO has 79 Flock cameras installed across the county. Including other brands, MCSO uses 134 license plate readers. Distrust of Flock cameras has been growing over the past year or two, but Manatee County Sheriff Rick Wells said his department has been using license plate readers for nearly six years. “There’s a lot of misinformation out there about license plate readers and how they’re being used,” Wells said. “The only thing that they’re taking a picture of is your vehicle and your license plate. We can’t tell who’s driving. You hear about facial recognition. That’s a lie.” The cameras read thousands of license plate numbers every day. Sheriff’s deputies are going into the system with a purpose. Wells used the example of a burglary. If a detective is given a vehicle description, he or she can search for that vehicle, but they have to enter a reason for the search, the case number associated with the investigation and exactly what they’re looking for, such as a red Toyota, before they can even enter the system. The system is audited every two weeks to ensure those procedures are being followed. The information is also deleted every 30 days. “In a perfect world, if everybody understood (how and why his office uses license plate readers), we could hold the information up to a year with the hopes that if we had a crime that maybe that vehicle was