Allies and partners gather for an Allied ceremony in recognition of the efforts of all Allied participants in Operation Overlord during the 82nd anniversary of D-Day in Bayeux, France, June 3, 2026. U.S. service members assigned to units throughout Europe and multinational partners will participate in events and ceremonies June 2-7, to commemorate the anniversary of D-Day in the Normandy region of France. (U.S. Army photo by Sgt. 1st Class Brenden Delgado)
| Date Taken: | 06.04.2026 |
| Date Posted: | 06.06.2026 15:06 |
| Photo ID: | 9730193 |
| VIRIN: | 260604-A-NF551-1758 |
| Resolution: | 6720x4480 |
| Size: | 9.13 MB |
| Location: | FR |
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Jun 7, 2026 · via dvidshub.net
New Zealand has no laws governing the use of artificial intelligence in creative and commercial work, and the copyright sector is warning the gap is leaving individuals with little recourse when their image, voice or work is used without consent. Copyright Licensing NZ chief executive Sam Irvine said the country was behind and the situation was urgent. "At the moment, they're not — but they are now talking about it, which I think is a really good step forward." He was blunt about what was needed. "We don't have any AI law and we need it now." New Zealand warned it's falling behind as calls grow for AI regulation - Watch on TVNZ+ 1News reported on Friday on models who complained clothing company Huffer had used AI-generated images of them without their permission — a claim Huffer denied. Irvine said the case illustrated a broader problem, and called for a low-cost complaints mechanism so individuals could seek redress without going to court. "It could be like a small claims tribunal — so that it's able, particularly for an individual, to take action when their work is used without their consent, without credit or any compensation." More than 100 cases involving complaints against AI companies are now before US courts, including a high-profile dispute in which Hollywood actress Scarlett Johansson threatened legal action after ChatGPT used a voice she said resembled hers without permission. Marketing expert Bodo Lang said the speed of AI made the lack of rules particularly dangerous. He demonstrated by mocking up an advertisement for chewing gum using an image of the late Queen in around three minutes. "This is taking me three minutes. It would have probably taken in the old days before AI three days or three weeks." He said the lack of clear rules was
Jun 6, 2026 · via 1news.co.nz
Researchers are replacing rigid silicon-based AI hardware with stretchable, neuromorphic electronics that mimic how the brain processes information, opening new possibilities for long-term human-machine integration. Modern artificial intelligence can outperform humans in tasks ranging from image recognition to medical data analysis, but there is one environment where today’s hardware still struggles: the human body. The problem is surprisingly simple. Human tissues are soft, flexible, and constantly moving. Conventional electronics are not. Even the most advanced silicon chips remain rigid, making long-term integration with organs, muscles, and skin extremely difficult. Devices attached to a beating heart, expanding lungs, or bending joints can irritate tissue, lose contact, and eventually fail. Researchers are now pursuing a radically different approach. Instead of forcing the body to adapt to electronics, they are redesigning electronics to behave more like the body itself. A review published in the International Journal of Extreme Manufacturing highlights the rise of soft neuromorphic electronics, a new class of devices that combine sensing, memory, and computing in materials that can stretch, bend, and conform to living tissue. The technology draws inspiration from the brain, not only in how it processes information but also in how it physically interacts with its environment. Electronics Inspired by the Brain Unlike traditional circuits that rely exclusively on electrons moving through metal pathways, these systems use soft materials such as flexible polymers and gel-like ionogels that transport both electrons and ions. This mechanism, known as organic mixed ionic-electronic conduction, more closely resembles the electrochemical signaling used by the nervous system. The active materials can absorb and release ions from their surroundings, continuously altering their internal electrical state. As a result, a single soft transistor can mimic synaptic plasticity, the biological process that allows brain cells to strengthen or weaken connections over time. In effect, the hardware itself
Jun 6, 2026 · via scitechdaily.com
The Velato app is an image recognition technology tool that takes aim at personal bookshelves as a way to help consumers quickly and easily add them to their digital catalogue. The app works by simply being aimed at a bookshelf and starting the scanning process, which will capture information from the spines of the various books instead of requiring them to be manually scanned via barcodes or manual entry. This will work particularly well for users with vast libraries of books to help them keep things organized and to never forget that they have a title. The Velato app will also work to deliver additional information on a user's library including insights and recommendations that are based specifically on established interests. Personal Library-Cataloguing Apps The Velato App Catalogues Tens of Books in Seconds Trend Themes - Shelf-scanning Catalogues — Image recognition transforms personal book organization by turning entire shelves into searchable digital inventories without barcode-by-barcode input. - Interest-based Reading Insights — Personal libraries become richer data sources as catalogue apps analyze owned titles to surface tailored recommendations and collection patterns. - Frictionless Home Archiving — Automated capture tools reduce the effort of documenting physical possessions, creating new value around household inventory intelligence and memory preservation. Industry Implications - Book Technology — Digital tools for readers are expanding beyond discovery and e-commerce into ownership management, collection analytics, and personalized literary services. - Computer Vision — Consumer-facing recognition systems gain practical relevance when applied to everyday objects like book spines, enabling faster digitization of physical environments. - Personal Productivity — Organization platforms can differentiate through passive cataloguing features that convert cluttered home assets into structured, searchable information.
Jun 6, 2026 · via trendhunter.com
Meta's Ray-Ban smart glasses are facing renewed scrutiny after a WIRED investigation revealed that the Meta AI companion app contains code for an unreleased facial recognition feature capable of identifying people captured through the device's camera. Researchers examining recent versions of the app uncovered references to an internal system known as "NameTag," which appears designed to recognise faces, convert them into biometric data, and alert users when familiar individuals are detected. The findings suggest Meta has been developing the technology for several months, raising fresh questions about privacy, biometric data collection, and the future of AI-powered wearables. How nametag works According to WIRED's analysis, the feature relies on three AI models. One detects a face in an image, another aligns and processes the image, while a third converts facial characteristics into biometric data that can be used for identification. Researchers also found evidence suggesting recognised facial data may be stored locally on user devices after facial "prints" are retrieved from Meta's servers. Although the feature is not currently available to consumers, its presence within the app indicates Meta has been actively exploring facial recognition capabilities for its smart glasses ecosystem. The discovery has reignited concerns about facial recognition in wearable devices. Unlike smartphones, smart glasses can capture images and video in a more discreet manner, raising questions about consent, surveillance, and the collection of biometric information in public spaces. Privacy advocates argue that real-time identification could make facial recognition more pervasive in everyday life, particularly if individuals are identified without their knowledge. The findings are likely to attract attention from regulators already examining how technology companies collect, store, and process sensitive biometric data. Meta says the feature remains under development and has not been released. "Nothing has shipped to consumers, and no final decision has been made on what to
Jun 6, 2026 · via eastleighvoice.co.ke
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Jun 6, 2026 · via youtube.com
Exclusive:Police Scotland could be cut off from 'essential' databases under biometrics shake up Police Scotland may be unable to access “essential” information on UK national databases due to proposed legislative changes around biometric data such as facial recognition scans, with citizens facing the prospect of “different protections and rights,” according to a senior watchdog. Professor William Webster, the UK Biometrics and Surveillance Camera Commissioner, said that while plans to overhaul the way biometrics are regulated in England and Wales would provide “legal certainty” in those nations, it would complicate matters in Scotland, meaning that the use of “contentious” technologies could be “governed to different standards.” Advertisement Hide AdAdvertisement Hide AdHe warned that the complex regulatory landscape would have a direct impact on criminal justice organisations who routinely share biometric records like DNA and fingerprints at a UK level, with Scotland’s national police force potentially facing the scenario where it cannot integrate existing data. But Dr Brian Plastow, the Scottish Biometrics Commissioner, told The Scotsman the “subtle nuances” in rules around biometric data as a result of devolution were not a “new phenomenon,” and stressed that UK policing bodies had dealt with differences for decades without any “substantive complications.” The UK government has said the new Police Reform Bill will introduce a new legal framework underpinning the use of facial recognition and similar technologies by police and other law enforcement agencies. The legislation, announced in the King’s Speech last month, is expected to create a new regulatory oversight body, combining Mr Webster’s office and that of the UK Forensic Science Regulator, granting it powers around audits, inspections, and compliance, and allowing it to make it clear when use of technologies like live facial recognition (LFR) can be justified. Advertisement Hide AdAdvertisement Hide AdHowever, Mr Webster pointed out that the introduction of
Jun 6, 2026 · via scotsman.com
Meta quietly adds facial recognition ‘NameTag’ to smart glasses, raising privacy concerns 'I don't know how Meta can responsibly deploy a technology like this,' privacy advocates says Meta is once again in the spotlight as the tech giant has reportedly been installing facial recognition software in its smart glasses, including Oakley and Ray-Ban smart glasses for the last few months. According to an analysis conducted by The Wired, Meta has quietly integrated code for face recognition, named NameTag, into its Meta AI companion app which is required to use Meta glasses. Through this feature, people wearing these glasses can easily identify other people in the surroundings captured by the glasses’ cameras. How this NameTag feature works The NameTag system consists of important components, including three AI models designed for detecting, cropping and encoding faces into biometric faceprints. Once activated, it converts every face the glasses see into a biometric signature and checks it against a database on the phone, one built to receive updates from Meta's servers. Although the feature is not yet active for users,the code embedded in the NameTag has been distributed through updates to millions of phones. Previously, Meta stated that it would take a “thoughtful approach” when it comes to rolling out the feature for the public and the company has not decided yet whether to introduce this feature or not. However, the security researchers’ claims contradict Meta’s statement. They confirmed that code is almost ready for deployment, citing “they're one switch away.” They also tested the matching system using a sample image and researchers got back two words: "Person recognized." According to Cooper Quintin, a security researcher and senior public interest technologist with the nonprofit Electronic Frontier Foundation’s Threat Lab, “The feature is not yet exposed to consumers but seems nearly ready to go. Despite
Jun 6, 2026 · via thenews.com.pk
Meta has quietly embedded face-recognition technology into the software platform that supports its smart glasses, according to a report that analysed the Meta AI companion app. The feature, internally referred to as ‘NameTag’, is reportedly designed to identify people captured by the camera on Meta’s smart glasses and notify users when a recognised individual is detected. The technology has not been publicly released and remains inactive, but researchers cited in the report claim that key components are already present within the app. According to the report, traces of the system began appearing in updates to the Meta AI app as early as January 2026. The application is required for several features on Meta’s Ray-Ban and Oakley smart glasses and has reportedly been downloaded more than 50 million times. The investigation found that three artificial intelligence models linked to the feature have already been deployed to users’ devices. One model detects faces, another crops facial images, and a third converts them into biometric signatures, commonly known as faceprints. These faceprints could then be compared with biometric data stored locally on a user’s phone. Researchers, who reviewed the code, said the system appears capable of recognising individuals and generating notifications when a match is found. However, it remains unclear whose faces would be included in the recognition database, how those profiles would be created, or how many people could ultimately be identified through the technology. The findings have renewed concerns among privacy advocates and digital rights groups. More than 70 advocacy organisations, including the American Civil Liberties Union and the Electronic Privacy Information Centre, have previously urged Meta not to deploy facial-recognition capabilities in smart glasses, arguing that the technology could enable covert identification of people in public spaces. Meta has disputed suggestions that the feature has been launched. In a statement
Jun 6, 2026 · via indianexpress.com
States Swarajya Staff Jun 06, 2026 | Updated 01:39 PM GMT+5:30 Save & read from anywhere! Bookmark stories for easy access on any device or the Swarajya app. Security arrangements for the annual Amarnath Yatra have been significantly strengthened with the introduction of QR code-based verification for service providers and mandatory fire safety audits at base camps. Anantnag Police launched the 'Pahchan App', a QR-based mobile application designed to register and verify service providers including pony handlers, drivers and photographers ahead of the 57-day pilgrimage commencing on 3 July. All service providers associated with the yatra, including pony handlers, pithoos and taxi operators, have been verified through the app and issued QR codes by police, allowing pilgrims and security personnel to scan these codes to verify credentials. Authorities set a 30 May deadline for RFID cards for 28,000 service providers, creating a comprehensive digital tracking system for the pilgrimage. Fire safety has emerged as a critical focus following recent incidents. The Fire and Emergency Services Department was directed to conduct comprehensive fire safety audits at base camps and lodgement centres, while authorities reviewed security deployment plans across key transit points. Central Armed Police Force companies have arrived in Kashmir, with Srinagar Police extending elaborate arrangements including accommodation, medical support and communication facilities. The security grid incorporates multiple technological measures. Police have strengthened security by increasing CCTV surveillance, installing elevated watchtowers, introducing AI-based facial recognition systems and enhancing RFID-based monitoring. Officials were directed to intensify drone surveillance and deploy dog squads for anti-sabotage checks at vital installations, langars and lodgement centres. The pilgrimage will proceed along two routes: the traditional 48-kilometre Nunwan-Pahalgam track in Anantnag district and the 14-kilometre shorter but steeper Baltal route in Ganderbal district, concluding on 28 August. The Pahchan app enables instant verification of service providers and
Jun 6, 2026 · via swarajyamag.com
Barbara Gavin, daughter of the Brig. Gen. James Gavin, delivers remarks at Sainte Mere Eglise, France, on June 5, 2026. This memorial is in recognition of Brig. Gen. James M. Gavin, the youngest general to command an American division in World War ll. The statue marks the location where Gavin landed during the airborne assault on Normandy. Eighty-two years after the Paratroopers of the 82nd Airborne Division jumped into Normandy on D-Day and helped change the course of history, their legacy keep living on through every All American who proudly wears the AA patch and carries forward the same spirit of courage, sacrifice, and commitment. (U.S. Army photo by Sgt. Jayreliz Batista Prado)
| Date Taken: | 06.04.2026 |
| Date Posted: | 06.06.2026 04:08 |
| Photo ID: | 9729688 |
| VIRIN: | 260604-A-JA130-5332 |
| Resolution: | 7524x5182 |
| Size: | 6.57 MB |
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Jun 6, 2026 · via dvidshub.net
If you are fortunate enough to have a ticket to an event at Madison Square Garden in New York – say, an NBA Finals game – one aspect of your visit will be having your face scanned by a facial recognition system. Major event venues are increasingly using the technology. Some, like Madison Square Garden, use it for surveillance purposes, and some, like Citizens Bank Park in Philadelphia, to offer visitors optional ticketless admission. Adoption of facial recognition technology is increasing, becoming more prevalent in daily life, from public buses to public buildings. The Transportation Security Administration has deployed the latest facial recognition technology at security checkpoints at numerous airports. The agency says the new system will be used in cities across the U.S. that are hosting World Cup 2026 soccer matches. The growing use of facial recognition has broadened concerns about accuracy and bias. But in my research studying facial recognition technology in the Vision Lab at the University of Dayton, I’ve found that advanced deep learning models have made face recognition systems more accurate and reliable. The AI models, trained on hundreds of millions of face images, are more than 99% accurate in controlled environments – settings such as cellphones, airports and border checkpoints. Facial recognition basics Facial recognition involves three steps: locate a face in an image or video frame, create a faceprint that catalogues salient features – including the shape of the face and landmark points such as eyes, nose and mouth – and record the texture of the skin. Then it compares the faceprint to those in a database, which may be inside a smartphone or at a bank or hospital, to verify a person’s identity or allow access. In the physical world, these systems are faster and simpler than requiring people to show IDs.
Jun 6, 2026 · via stuff.co.za
Abstract Medical vision-language models (MVLMs) offer promise in clinical practice but face limitations in generalizability, data quality, and clinically meaningful evaluation. We propose RadiSim-CL, an MVLM trained via curriculum learning by simulating the three-phase pathway of a radiologist: foundational knowledge understanding, anatomical knowledge, and advanced diagnostic reasoning. To support this, we curate RadiSim, a 12-million image-text pair dataset aligned to these phases. We evaluate the model using a five-stage coarse-to-fine validation framework: (1) modality recognition, (2) anatomical recognition, (3) anatomical localization, (4) abnormality and disease diagnosis, and (5) disease differentiation and grading. This framework spans 24 zero-shot subtasks across MR, CT, and DR imaging. RadiSim-CL achieves comparable performance to state-of-the-art baselines in both foundational and anatomical tasks, and demonstrates superior capabilities in complex reasoning (e.g., an AUC of 0.953 for brain tumor diagnosis and an accuracy of 0.764 for meningioma grading). Ablation studies further confirm the curriculum’s effectiveness. RadiSim-CL thus offers a scalable, clinically aligned solution to enhance diagnostic precision. Similar content being viewed by others Acknowledgements This work was supported in part by National Natural Science Foundation of China (grant numbers 82441023, U23A20295, 62131015), National Key Research and Development Program of China (No. 2022YFE0205700), Beijing Natural Science Foundation (IS24053), and HPC Platform of ShanghaiTech University and Shanghai United Imaging Intelligence Co., Ltd. Author information Authors and Affiliations Corresponding authors Ethics declarations Competing interests M.T. is an intern at Shanghai United Imaging Intelligence Co., Ltd. B.Z., G.R., J.N., Z.X., Y.Z., S.Z., X.C., and D.S. are employees of Shanghai United Imaging Intelligence Co., Ltd. The companies have no role in designing and performing the surveillance and analyzing and interpreting the data. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information Rights and permissions Open Access This article is licensed
Jun 6, 2026 · via nature.com
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Jun 6, 2026 · via instagram.com
The continuous assimilation of knowledge by artificial intelligence systems relies on a delicate compromise between their tendency to forget old knowledge and their rigidity when incorporating new data. In a study published in Nature Communications, scientists used Bayesian approaches inspired by biological synapses, to introduce uncertainty and better balance memory and adaptation. The human brain continuously learns while preserving acquired knowledge, a balance that artificial intelligence (AI) systems still struggle to reproduce. When an AI model assimilates new information, it often tends to erase previously acquired knowledge (catastrophic forgetting) or, conversely, become too rigid to integrate new data (catastrophic recall). Continuous learning corresponds to a sequential training situation, in which several datasets are presented successively. Within MESU, the weights of the neural network follow a probability distribution that allows approximating a formulation that harmoniously reconciles learning and forgetting, unlike previous methods. © Damien Querlioz, C2N To overcome this challenge, scientists from the Center for Nanosciences and Nanotechnologies ( C2N, CNRS/Université Paris-Saclay), the CEA-Leti and the CEA-List drew inspiration from neuroscience, where recent work suggests that biological synapses follow Bayesian principles: they adjust their representations of the world by weighting new observations against prior knowledge, while taking into account their degree of uncertainty. On this basis, the team proposed a new continuous learning framework, called Metaplasticity from Synaptic Uncertainty (MESU). In MESU, each connection in the network acts as a Bayesian synapse, maintaining its own uncertainty estimate. It thus adapts its learning speed according to the confidence placed in new information, while incorporating a progressive forgetting mechanism for data deemed less relevant. MESU therefore translates certain neuroscientific hypotheses about how the brain reconciles memory stability and cognitive flexibility. The experiments conducted showed that MESU achieves a solid balance between memorization and adaptation. On several datasets, including animal image classification, permuted digit
Jun 6, 2026 · via m.techno-science.net
Meta has embedded facial recognition code into software used by its smart glasses, according to an investigation by Wired, which was confirmed by the Electronic Frontier Foundation's Threat Lab on Thursday. Though the feature isn't yet turned on for consumers, it's sitting in the Meta AI smartphone app. Wired reports that Meta quietly added the facial-recognition components as early as January over multiple updates to its Meta AI companion app -- which has been downloaded more than 50 million times. The feature, under the internal designation "NameTag," would let the Meta smart glasses biometrically identify anyone in view and notify the wearer with information about that person. When the feature is activated, Wired reports, "it will 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." In other words, NameTag would store biometric face data in an embedded database architecture that can compare new faceprints to existing ones. The database is designed to live on a user's phone but is configured to receive updates from Meta. The EFF says the code was verified through static analysis and argues that Meta is moving ahead with surveillance-capable glasses in a way that normalizes biometric tracking without people's consent. "Despite the billions of reasons not to, Meta seems to have created the capacity to turn their customers into a distributed surveillance machine," EFF's senior staff technologist Cooper Quintin said in its article. "This is just one more reason to think twice before buying or using Meta's surveillance glasses." Earlier this year, The New York Times reported that Meta was working on these types of features but had not officially announced plans to roll them out. At the time, CNET's smart glasses and XR expert Scott Stein wrote about
Jun 6, 2026 · via cnet.com
On 4 June 2026, a security researcher publishing under the name Buchodi released a technical analysis of Stella, the companion app for Meta’s Ray-Ban and Oakley smart glasses. Inspecting version 273.0.0.21 of the Android build, the researcher found what they described as a complete, dormant facial recognition pipeline: three on-device AI models, a biometric database schema, a vector similarity index dimensioned to those models, a write path for unrecognised faces, and a hardcoded notification channel labelled “nametags_recognition.” The research was published alongside reporting in WIRED, which confirmed that code had been added to the app across multiple updates since January 2026. The app, which is required to use the glasses’ key features, had been downloaded more than 50 million times before any of this was disclosed. What the researcher found in the app The three models identified in the Stella build are SCRFD, a face detection model developed by InsightFace; KPSAligner, which crops and aligns detected faces using facial keypoints; and SFace, which converts an aligned face into a 2048-number biometric fingerprint. The SFace variant in Stella appears to be scaled larger than the public reference implementation: 96 megabytes versus approximately 40 megabytes in the open-source version, with a 2048-dimension output. These models arrive on the device via Meta’s asset delivery system. Alongside the models, the researcher found a SQLite database stored under Meta’s cross-device sync framework, RLDrive, in a namespace called person_profiles . The database schema holds named person records, face records linked to each person, and a vector table dimensioned at exactly 2048 floats to match the SFace embedder, using cosine-distance search. Each face row links back to a person name. Recognition, when it runs, is a cosine-similarity query against the stored faceprints, followed by a join to retrieve the person’s name for the notification text. The researcher
Jun 6, 2026 · via spacedaily.com
Foodstuffs South Island is expanding its facial recognition technology to a fourth Christchurch store. The supermarket giant says a trial from October last year to January was aimed at identifying and managing people with a history of serious and harmful in-store behaviour. There were 531 confirmed matches with people of interest, with no one mis-identified and no false positives recorded. The three Christchurch stores in the trial - New World St Martins, Pak'nSave Papanui and Pak'nSave Moorhouse - will continue using the technology, with New World Stanmore joining them. Interest has come from other stores although no further rollouts have been confirmed. Foodstuffs said staff reported that repeat offenders were less likely to return to the trial stores and incidents involving threatening or harmful behaviour had dropped. The results gave confidence the technology could be deployed carefully and responsibly, head of retail for Foodstuffs South Island Kent Mahon said. "The focus has always been on reducing harm. The trial showed we can do that while keeping accuracy high and respecting customer privacy," he said. Each store would have privacy, legal and risk assessments before implementation, and prominent signage would alert customers that the technology was in use. Foodstuffs said it would continue to monitor the system's performance and would update the list of stores on its website using facial recognition . Sign up for Ngā Pitopito Kōrero, a daily newsletter curated by our editors and delivered straight to your inbox every weekday.
Jun 6, 2026 · via rnz.co.nz
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Jun 6, 2026 · via sfgate.com
According to a report from Wired, Meta has been quietly installing facial recognition in its Ray-Ban Meta and Oakley Meta smart glasses for the last few months. Internally called "NameTag", the feature, if activated, will use AI to identify people captured by Ray-Ban Meta's camera, alert the wearer when it recognizes someone, and store faceprints on users' phones. How Meta's "NameTag" works The software has not been switched on, but if it is, it will use Meta's AI app to transform images of anyone photographed with Meta glasses into a biometric faceprint, and check against a database of faceprints stored locally on the user's Meta AI mobile app. If it finds a match, the user will be notified. If it doesn't, the faceprint will be indexed into a folder named "pending." So everyone who the wearer encounters in public could become an unidentified target waiting for a name in a stranger's private databases. “The feature is not yet exposed to consumers but seems nearly ready to go,” Cooper Quintin, a security researcher and senior public interest technologist with the nonprofit Electronic Frontier Foundation’s Threat Lab told Wired. “Despite the billions of reasons not to, Meta seems to have created the capacity to turn their customers into a distributed surveillance machine.” Back in February, documents obtained by the New York Times revealed Meta was weighing the “safety and privacy risks" of adding facial recognition to its smart glasses. In April, the company said it was taking a "a very thoughtful approach" to the technology. But the first component of facial recognition software was installed in January, without consumers being aware of it (which seems less than thoughtful to me). It goes deeper than that, though. According to the company memo leaked to the Times, Meta's potential strategy was to roll out
Jun 5, 2026 · via lifehacker.com