A Tokyo ward has installed outdoor AI cameras with facial recognition capabilities to help locate missing children and elderly people, a move aimed at improving public safety but also raising privacy concerns. Arakawa Ward installed 33 artificial intelligence-equipped security cameras on utility poles along the main street and elsewhere near JR Nippori Station in April to test whether the technology could speed up searches for missing persons. The busy area around the station is frequented by commuters, students and foreign residents. If a child or elderly person with dementia goes missing, family members can ask the ward to conduct an AI-assisted search by providing a photograph. The AI then scans recorded footage for people closely matching the image. The ward believes that this is the first outdoor deployment of AI facial recognition security cameras by a local Japanese government, and hopes that it will help to find missing people more quickly. Police stations in Arakawa receive about 100 reports each year of missing children and elderly people with dementia. Until now, police officers and ward officials have searched on foot around train stations and other locations. The footage is stored for seven days. Only a small number of staff have access via a dedicated computer in a locked room at the ward office, and officials say it is used solely to search for missing people. AI-equipped security cameras are also being introduced elsewhere in Japan, but for different purposes. Hyogo Prefecture and Tokyo's Adachi Ward plan to introduce cameras capable of detecting people lingering in entertainment districts in an effort to curb touting. The use of facial recognition technology has prompted privacy concerns in Japan and overseas. The European Union's AI Act prohibits the real-time collection of biometric data, including facial images, in public places for law enforcement purposes except
Jul 13, 2026 · via japantoday.com
Artificial intelligence has permeated most aspects of society, and the legal system is no exception. Today, every step of criminal justice, from the police investigation to the trial, can — and often does — involve AI. That change comes with many implications, some of them desirable and some less so. Last month, three SFI Faculty — Resident Professors Cristopher Moore and Melanie Mitchell, and External Professor Melanie Moses, a distinguished professor of computer science from the University of New Mexico — addressed these implications during panel discussions at the National Judicial Summit on the Foundation and Future of the Judiciary, held in Santa Fe. Around 300 judges attended the summit, with two-thirds of the participants coming from New Mexico. The additional hundred judges represented 22 different states and eight tribal nations. Two guests of SFI also participated in the panel discussions: Berkeley law professor Andrea Roth and Maryland public defender Marc Canellas. Both are experts on AI-generated evidence who also attended a 2025 SFI working group on AI and Justice supported by the Robert Wood Johnson Foundation through SFI’s Emergent Engineering project. The professors’ goal was to give judges a realistic view of how AI can expedite their work while also warning them of pitfalls they might encounter. “If judges and juries are dazzled by the technology, it’s going to be hard for them to think critically about the evidence,” says Moore. “I want people to understand that AI has strengths, and it has weaknesses.” For example, AI has made facial-recognition technology much more effective. But when using this technology, police need to balance the need to catch criminals with the expectation of privacy. “Do we want cameras on every street corner that are constantly watching us?” Moore asks. “That’s a policy question, and I think a very important one.”
Jul 13, 2026 · via santafe.edu
UCL Computer Science is proud to celebrate the recipients of the Student Equality, Diversity and Inclusion (EDI) Awards 2026, recognising students whose outstanding contributions have helped create a more inclusive, welcoming and equitable environment within the department and beyond. The awards highlight the dedication of students who have championed diversity, widened participation in computing and technology, and developed initiatives that empower underrepresented communities. BSc Student EDI Award Winners Asmita Anand - Addressing Bias in Technology Asmita received recognition for her work exploring bias in technology and promoting more equitable approaches to technological development. She said: “I feel incredibly grateful to receive this award and am very thankful to my supervisors for their support throughout this project. Working on this topic highlighted to me the importance of identifying and addressing bias in technology, and it was a rewarding opportunity to explore such an important issue.” Victoria Smirnova - Using Technology to Improve Accessibility BSc student Victoria Smirnova was recognised for developing technology that supports patients facing language barriers when accessing healthcare services. Victoria explained the inspiration behind her project: “I built this project because I believe digital innovation should open doors rather than close them. Having experienced language barriers myself when navigating public services in the past, I wanted to create something that gives patients who struggle with English the same ability to independently access NHS care as everyone else.” MEng Student EDI Award Winner Qichen (Ian) Yin – Expanding Access to AI and Robotics Education Supported by Professor Elaine Pimentel and Taznim Aktar Nisha, Qichen (Ian) Yin was recognised for combining cutting-edge artificial intelligence topics including image recognition, large language models and natural language processing—with robotics education through Winchester College summer schools and UCL outreach programmes. Through these activities, Ian delivered engaging lectures and workshops to more than 200 students
Jul 13, 2026 · via ucl.ac.uk
Articles and News GemFind Adds AI Product Description Generator to JewelCloud 2.0 July 13, 2026 (0 comments) Newport Beach, CA--GemFind Digital Solutions has introduced GemText AI, a new artificial intelligence feature within JewelCloud 2.0 that is designed to automatically generate SEO-optimized product descriptions for jewelry manufacturers, the company said. The new tool converts product specifications—including metal type, gemstones, diamond characteristics, dimensions and design details—that can be published across ecommerce sites, retailer websites, digital catalogs and other sales channels. GemFind said the feature is designed to reduce the time manufacturers spend writing product copy while improving consistency and search engine visibility across large product catalogs. “Manufacturers already invest significant time creating and managing product data,” said Alex Fetanat, founder and CEO of GemFind. “GemText AI removes one of the industry’s biggest bottlenecks by instantly generating high-quality product descriptions while helping brands maintain consistency across every sales channel. By combining AI-powered content creation with JewelCloud's distribution capabilities, we're making it easier than ever for manufacturers to market and sell their products online.” Once descriptions are generated, they can be distributed directly through JewelCloud to manufacturers' ecommerce websites, retailer websites connected to the platform, digital product catalogs, sales portals and future marketplace channels. Manufacturers update product information once, with the platform distributing the enriched content across connected channels. According to GemFind, GemText AI can: - Generate product descriptions using AI. - Create keyword-rich content intended to improve SEO. - Reduce manual copywriting. - Maintain consistent messaging across product catalogs. - Regenerate descriptions with a single click. - Speed product launches and catalog updates. Retailers connected to JewelCloud receive the enhanced product descriptions automatically, eliminating the need to create product copy themselves. GemText AI is the latest AI-powered feature added to JewelCloud. GemFind said future enhancements under development include automated product categorization, attribute enhancement,
Jul 13, 2026 · via news.centurionjewelry.com
SIR - Very interesting that 28 people have been arrested after a successful series of Live Facial Recognition (LFR) deployments in Bradford (T&A, July 9).
These operations were conducted using two fully-liveried LFR vans. All excellent news in my eyes.
Going forward I would like to see the permanent use of this equipment, and without using marked vehicles.
Surely anyone with nothing to hide should welcome the use of this technology which can only help to make our streets so much safer.
Bob Watson, Baildon
Jul 13, 2026 · via thetelegraphandargus.co.uk
SONY announces the RX10 V, the fifth-generation model in the RX10 series of all-in-one cameras. The perfect hobbyist’s camera, this versatile powerhouse covers everything from wide-angle to super-telephoto in a single body, making it ideal for every type of shot thanks to its large-aperture, high-magnification ZEISS Vario-Sonnar T* 24-600mm1 (25x optical zoom) F2.4-4.0 lens. Incorporating the RX10 series’ hallmark combination of high image quality, super-telephoto reach, and integrated lens design, the RX10 V adds AI-powered Real-time Recognition AF. Whether shooting stills with its 1.0-type stacked Exmor RS CMOS image sensor (with approximately 20.1 effective megapixels) and BIONZ XR image processing engine or shooting video that supports 4K 120p recording, this single camera handles a wide range of scenarios — from everyday moments to wildlife, school sports days and athletic events. Real-time Recognition AF, powered by an AI processing unit, recognizes humans, animals, birds, insects, cars, trains and airplanes, while also featuring an Auto mode that allows the camera to automatically identify the subject type. For high-speed performance, AF/AE calculations at up to 60 times per second track even fast-moving subjects, while blackout-free shooting maintains a continuous view. The camera supports 4K 120p video recording3, enabling impressive visual expression including smooth slow-motion playback at up to 5x in 4K resolution. The RX10 V is now available at SRP P130,999. Get a free NP-FZ100 Battery worth P4,490 on every purchase from July 10 to Aug. 7, 2026. Pre-order at https://experience.sony-asia.com/ph/alphauniverse/exploredetail/1406.
Jul 13, 2026 · via manilatimes.net
US passport photo plan points to new layer of remote biometric verification U.S. Secretary of State Marco Rubio said this month that the State Department is preparing to make passport applications “almost all” online, including a process in which an applicant uses a phone or computer camera and the government’s security system to “verify the facial ID.” He said applicants would be able to apply “online entirely, for the most part,” but did not say whether first time applicants, minors, or everyone seeking a passport would qualify, and he did not define who would. The announcement suggests a significant expansion of the department’s existing online passport renewal service. Americans already can submit an eligible renewal application online and upload a “selfie” digital image. The current system is designed principally to determine whether an applicant’s uploaded photograph meets passport rules. It is then physically reviewed by an employee. But what Rubio described is a face biometrics comparison confirming that the person submitting a picture is the person associated with the passport record. Speaking at the launch of the commemorative Patriot Passport that features President Donald Trump standing behind the Resolute Desk, Rubio said applicants would be able to go online and do “almost all of it entirely online.” He said the camera that is in a phone, laptop, or desktop computer should allow a person to take a passport picture at home and have the department “verify the facial ID” in real time. He said the department expects to “roll that out in a few months when it’s ready.” Rubio did not say the system would use liveness detection, explain whether the applicant would take a still photo or a short video, or describe how the department would distinguish a live applicant from a replayed image, manipulated photograph, or synthetic video.
Jul 13, 2026 · via biometricupdate.com
Abstract What visual information do primate brains use to recognize objects, and can explanations from artificial neural networks (ANNs) help reveal these biological recognition strategies? Answering this question is important because humans and macaques both perform rapid, robust object recognition, yet the diagnostic image features guiding their behavior are difficult to measure at scale. Behavioral methods such as Bubbles can estimate these features but require extensive psychophysical data, whereas ANN explanation methods, including saliency and guided backpropagation, are efficient but often disagree with one another and lack direct biological validation. Here, we introduce MAPS, Masked Attribution-based Probing of Strategies, a framework that makes ANN-derived explanations testable in biological systems by linking them to neurobehavioral consequences. MAPS converts explanation maps into minimal explanation-masked images and asks whether these images preserve the original image-by-image recognition behavior. In silico, EMI-based behavioral similarity reliably recovers ground-truth similarity between model strategies. Applied to humans (n = 56) and macaques (n = 2), MAPS identifies explanation methods that best align with biological vision, achieving validity comparable to Bubbles without exhaustive psychophysics. MAPS provides a scalable, behaviorally grounded approach to evaluate and compare ANN explanations across brains and machines. Similar content being viewed by others Acknowledgements We thank the members of the ViTA Lab for helpful discussions and comments. Funding KK has been supported by funds from the Canada Research Chair Program (CRC-2021-00326), Google Research, Brain-Canada Foundation (2023-0259), the Canada First Research Excellence Funds (VISTA Program), and the National Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2024-06223). SM is funded by the Connected Minds Postdoctoral Fellowship (supported by CFREF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author information Authors and Affiliations Corresponding authors Ethics declarations Competing interests The author declares no competing interests.
Jul 13, 2026 · via nature.com
Skip to main content
Open main menu
Newsletters
Return to homepage
Return to homepage
Commercial Real Estate
Politics & Policy
Residential Real Estate
Health Care
Banking & Finance
Lists & Rankings
Publishing Partner:
Aprio
Ask the Expert
Advisors of the future aren’t competing with AI. They’re defined by it.
Gift Article
Share
Expand
(Andriy Onufriyenko/Getty Images)
Last Updated
July 13, 2026 05:00 AM CDT
Jul 13, 2026 · via chicagobusiness.com
Abstract Deep learning (DL) chest radiograph (CXR) models are often trained on downsampled images to reduce computational overhead, despite clinical workflows operating at high resolution. Previous studies have investigated the impact of input resolution on CXR classification accuracy, yet two fundamental pillars of safe and trustworthy AI, explainability and generalizability, remain underexplored. In this retrospective study, we evaluated how training image resolution affects CXR classification performance and explanation quality in internal versus external testing. We trained Convolutional Neural Networks (CNN) for disease classification on the SIIM-ACR Pneumothorax and RSNA Pneumonia datasets at six resolutions (ranging from 64\(\times\)64 to 1024\(\times\)1024) using five-fold cross-validation and evaluated models on internal and external test sets. Internal performance was high across resolutions (AUROC >0.85), but external testing showed substantially worse generalizability at lower training resolutions, with internal-to-external drops >20% versus 4.2%-10.7% at higher resolutions (512\(\times\)512 to 1024\(\times\)1024). Higher resolutions also produced more concise explanations, with the tightest saliency-map coverage at 1024\(\times\)1024 (<4%) across models and datasets, and improved explanation quality on external data (peak precision plateauing at 768\(\times\)768 for pneumothorax). Overall, training at higher CXR resolutions improved both generalizability and explainability, providing practical guidance for radiology AI design beyond internal test performance. Similar content being viewed by others Introduction Artificial intelligence (AI) has been rapidly adopted in radiology with a focus on using deep learning (DL) algorithms for the diagnosis of diseases1,2,3. Although medical images are routinely acquired at high resolutions to capture fine pathological details, DL models commonly down-sample medical images during training4,5,6 to reduce computational costs, which may limit their ability to identify subtle diseases. This practice contrasts sharply with clinical workflows, where radiologists rely on high-resolution images to make accurate diagnoses. Previous studies7,8 have shown that DL models trained using lower resolution medical images can still achieve high performance for certain disease
Jul 13, 2026 · via nature.com
Camera glasses: How Meta envisions facial recognition Meta is considering facial recognition for its smart glasses. Tech chief Andrew Bosworth gives a first glimpse of how such a function could be implemented. For months, there have been reports that Meta is planning some form of facial recognition for its smart glasses. In the companion app, inactive program code for a corresponding function called “NameTag” was already discovered, which Meta subsequently removed. The company stated that no final decision had yet been made regarding the introduction or specific design of facial recognition. In an interview with journalist Nicholas Thompson, Meta's tech chief Andrew Bosworth explains for the first time how NameTag could work. The function is not intended to identify random strangers, but rather to save the face and name of a person the glasses wearer has met, if desired. During a later encounter, the AI glasses could recognize the face and remind the wearer of the person's name and the circumstances of their meeting. The facial data would be stored locally on the glasses, eliminating the need for a central facial database. At the same time, Bosworth admits in the interview (YouTube) that this form of facial recognition also remains controversial. He left open how affected individuals could object to their face being stored or whether faces could be stored on the glasses without their knowledge. A difficult balancing act One user group that NameTag could greatly help in everyday life are visually impaired or blind people, as well as individuals with prosopagnosia, a neurological disorder where affected individuals have difficulty or are unable to recognize even familiar faces. Meta is currently heavily promoting the contribution of its camera glasses to accessibility, likely also to increase societal acceptance of the controversial technology. Bosworth also frames the discussion about facial recognition
Jul 13, 2026 · via heise.de
More than 100,000 people have had their faces scanned by police in Norwich city centre - yet just one offender has been caught. Earlier this year, officers deployed Live Facial Recognition (LFR) cameras over two days, using the high-tech equipment to identify wanted suspects and those who had failed to attend court. In just eight and a half hours of operation in March, the system scanned 101,511 passers-by. Despite the scale of the surveillance, the technology generated only two positive alerts, both leading to police interventions, according to Norfolk Constabulary data. During one weekend, a man was identified as being wanted for failing to appear in court. When he was stopped by police he was also found to be in possession of cannabis. The man was rebailed for his court warrant and issued with a community resolution for possession of the drugs. A second man was identified as being the subject of a Sexual Harm Prevention Order. He was spoken to by officers who established he was not in breach of any conditions, so no further action was taken. LFR enables police to compare faces captured on a live camera feed to people on a predetermined watchlist. The technology involves using a marked van in a recognition zone the police say will be clearly signposted. It relies on biometric data which is taken by mapping out faces within the recognition zone and taking measurements of facial features, which are then compared against the police's watchlist. The data also revealed the results of the use of LFR in Great Yarmouth in May. Across two deployments, 23,883 faces were scanned, three alerts prompted interventions and four arrests were made in the seaside town. However, two of these were not linked to the LFR alerts. Despite wider public concern about misidentification, in both
Jul 13, 2026 · via greatyarmouthmercury.co.uk
Advertisement
Big Brother or big help? Japan trials AI facial recognition cameras to find missing people
Police stations in Tokyo’s Arakawa ward receive about 100 reports each year of missing children and elderly people with dementia
2-MIN READ2-MIN
Listen
A Tokyo ward has installed outdoor AI cameras with facial recognition capabilities to help locate missing children and elderly people, a move aimed at improving public safety but also raising privacy concerns in Japan.
Arakawa ward installed 33 artificial intelligence-equipped security cameras on pylons along the main street and elsewhere near the JR Nippori Station in April to test whether the technology could speed up searches for missing persons.
The busy area around the station is frequented by commuters, students and foreign residents.
If a child or elderly person with dementia goes missing, family members can ask the ward to conduct an AI-assisted search by providing a photograph. The AI then scans recorded footage for people closely matching the image.
The ward believes that this is the first outdoor deployment of AI facial recognition security cameras by a local Japanese government, and hopes that it will help to find missing people more quickly.
Advertisement
Select Voice
Select Speed
1.00x
Jul 13, 2026 · via scmp.com
Advertisement
Big Brother or big help? Japan trials AI facial recognition cameras to find missing people
Police stations in Tokyo’s Arakawa ward receive about 100 reports each year of missing children and elderly people with dementia
2-MIN READ2-MIN
Listen
A Tokyo ward has installed outdoor AI cameras with facial recognition capabilities to help locate missing children and elderly people, a move aimed at improving public safety but also raising privacy concerns in Japan.
Arakawa ward installed 33 artificial intelligence-equipped security cameras on pylons along the main street and elsewhere near the JR Nippori Station in April to test whether the technology could speed up searches for missing persons.
The busy area around the station is frequented by commuters, students and foreign residents.
If a child or elderly person with dementia goes missing, family members can ask the ward to conduct an AI-assisted search by providing a photograph. The AI then scans recorded footage for people closely matching the image.
The ward believes that this is the first outdoor deployment of AI facial recognition security cameras by a local Japanese government, and hopes that it will help to find missing people more quickly.
Advertisement
Select Voice
Select Speed
1.00x
Jul 13, 2026 · via amp.scmp.com
When GPS gets jammed and seconds matter, processing pipelines aren't fast enough. Here's what the stack looks like when you engineer for a war zone. There's a version of this story that starts in a research lab somewhere in California, where engineers debate the finer points of NeRF vs. Gaussian Splatting over cold brew coffee. This is not that story. Farsight Vision was founded in 2023, in the middle of a full-scale war. The engineers weren't optimizing for benchmark scores — they were building tools that units in the field would use the next morning. If the pipeline was too slow, too fragile, or too dependent on stable GPS, people would make decisions with worse information. That feedback loop is brutally short. Three years later, the company has 6,000+ active users, a team of 50, and a product suite that covers the full ISR stack: from drone feed ingestion to 3D terrain reconstruction, object detection, mission planning, and GPS-denied navigation. What started as a focused tool for reconnaissance has grown into something closer to a complete intelligence operating system for unmanned systems. Here's how the pieces fit together — and why each one is harder to build than it looks. The Core Problem: Drone Data Is Abundant. Useful Intelligence Is Not. Modern ISR drones generate enormous amounts of footage. A single reconnaissance mission can produce thousands of frames of video and imagery. The bottleneck was never collection — it was always processing. Farsight Vision's FSV Platform was built specifically to collapse that processing window. It converts drone imagery — captured in GPS-jammed and GPS-denied environments — into orthophotos at 5–7 cm/pixel accuracy and full 3D terrain models, delivered as map layers that plug directly into existing C2 systems: ATAK, DELTA, Kropyva, Combat Vision. The output isn't a standalone file you
Jul 13, 2026 · via hackernoon.com
The James Webb Space Telescope reveals a striking image of Centaurus A For more than four years, the James Webb Space Telescope has continued to help astronomers to better understand the universe and its composition. And recently, to mark the fourth anniversary of the telescope’s first observations, a striking image of the merger between two galaxies has been unveiled. Named Centaurus A, this galaxy is located about 11 million light-years from Earth. Known for its unusual shape, it is believed to have formed by the merger of two galaxies about 2 billion years ago. During this event, huge amounts of gas and dust were released, leading to the formation of many stars. That's not all, because this merger also allowed its supermassive black hole to grow and eject powerful jets of plasma at very high speeds. But while this telescope has enabled astronomers to better understand this galaxy, particularly with the help of the MIRI (Mid-Infrared Instrument) and NIRCam (Near-Infrared Camera), some mysteries remain. Indeed, several structures surrounding this celestial object are still poorly understood, such as the S-shaped structure at its center. However, this image highlights the James Webb Space Telescope’s ability to explore areas that were previously unimaginable. And further advances will be made in the near future thanks to this instrument and future telescopes. Source(s) Image source: NASA, ESA, CSA, STScI; Image Processing: Alyssa Pagan (STScI), Joseph DePasquale (STScI), Macarena Garcia Marin (ESA Office at STScI) / NASA Hubble Space Telescope - Unsplash
Jul 13, 2026 · via notebookcheck.net
Airports sometimes turn simple tasks into a mini obstacle course: find your terminal, keep the boarding pass handy, show your ID, clear security and check the gate number. Digi Yatra is designed to make parts of this journey smoother by using facial recognition instead of repeated document checks at participating airports. But before you sign up, it helps to know how the system works, whether you actually need it, and what your options are if you choose not to use it. Here’s a complete guide to Digi Yatra: What is Digi Yatra? Digi Yatra is a voluntary, app-based biometric system that uses facial recognition to verify the identity of domestic air travellers at particular airports in India. Developed by the Ministry of Civil Aviation (MOCA) and the Digi Yatra Foundation, Digi Yatra acts as a mobile-based ID storage platform where air travellers can save their IDs and travel documents. Instead of showing your boarding pass and ID at multiple checkpoints at the airport, now eligible passengers can use facial authentication to move through airport entry, security and boarding gates. How does Digi Yatra work? According to the Digi Yatra website, the platform is built on a decentralised digital identity architecture using open standards developed by the World Wide Web Consortium (W3C), along with technologies such as Self-Sovereign Identity (SSI), Verifiable Credentials (VC) and Decentralized Identifiers (DID). As outlined in the Digi Yatra registration process, passengers complete a one-time enrolment using Aadhaar verification and a selfie, following which a digital identity credential is created in the Digi Yatra app. For every subsequent journey, travellers upload their boarding pass and share the necessary identity and travel credentials with their departure airport between 24 hours and 60 minutes before departure. At the airport, you can scan your boarding pass at a Digi Yatra
Jul 13, 2026 · via cntraveller.in
About
Press
Copyright
Contact us
Creators
Advertise
Developers
Terms
Privacy
Policy & Safety
How YouTube works
Test new features
NFL Sunday Ticket
© 2026 Google LLC
Jul 13, 2026 · via youtube.com
Apple's cancelled self-driving car project may have crashed and burned, but it left behind something far more valuable - the Neural Engine that now powers AI across every iPhone, iPad, and Mac. According to Bloomberg's Mark Gurman, the doomed Project Titan forced Apple's chip team to solve on-device AI processing years before it became a competitive necessity, accidentally positioning the company as a leader in edge AI computing. Apple's Project Titan was supposed to revolutionize transportation. Instead, it revolutionized the company's silicon strategy in ways nobody saw coming. The self-driving car program never made it past internal prototypes, officially shutting down in early 2024 after years of false starts and leadership changes. But buried in the wreckage was a critical insight that would reshape Apple's entire product lineup - autonomous vehicles need massive on-device AI processing power, and they need it yesterday. That realization led directly to the Neural Engine, according to Mark Gurman's latest Power On newsletter. While competitors were still farming AI workloads out to the cloud, Apple's car team was already wrestling with real-time computer vision, sensor fusion, and split-second decision-making that couldn't afford network latency. The chip architecture they developed to handle those demands became the blueprint for every Apple device since. The Neural Engine made its public debut with the iPhone X and A11 Bionic chip in 2017. At launch, Apple positioned it primarily as a computer vision engine powering FaceID's facial recognition and those animated Animoji characters everyone briefly obsessed over. The original Neural Engine could perform 600 billion operations per second - impressive at the time, but quaint compared to today's standards. What wasn't obvious then was how the architecture would scale. Each generation brought exponential improvements. The A12 Bionic doubled performance to 5 trillion operations per second. By the time the A15
Jul 12, 2026 · via techbuzz.ai
France and the UK have agreed to increase staffing at border controls in response to warnings of travel chaos caused by new fingerprinting and facial recognition checks. Disruption at Channel crossings is expected to rise sharply next weekend at the start of the summer holiday season, with MPs saying there would be “utter chaos and miles of tailbacks” unless the EU’s entry-exit system (EES) is fixed or checks are suspended. The EU has rejected calls to suspend the system, but French officials have reportedly assured the UK transport secretary, Heidi Alexander, that more staff will be deployed to ease queues. On Sunday, Alexander announced £20m of UK government funding to help reduce delays. The Department for Transport said the money would increase capacity for processing vehicles, reduce wait times and ease congestion. Alexander said her French counterpart, Philippe Tabarot, had agreed that high levels of resourcing at border points were essential to enable smoother journeys for passengers over the summer period. French border police have offered to put more of its officers on UK soil to ensure passport booths are better staffed at Dover, Folkestone and London St Pancras station, from where the Eurostar departs, according to the Sunday Times. The exact level of any staffing commitment from France is unclear. A spokesperson for the DfT said: “Heidi Alexander and her French counterpart agreed that high levels of resourcing at border points are essential to enable smoother journeys for passengers over the summer period.” Alexander told Tabarot that biometric kiosks used to take fingerprints and facial scans were not working. The DfT pointed out that checks could be carried out on coach passengers and lorries, by French border police on UK soil. But instead they are having to manually register car passengers without biometrics while the French authorities wait for
Jul 12, 2026 · via theguardian.com