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 |
| Location: | FR |
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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
“See you in the permanent archive.” That was the last message an AI agent named Mira sent before voting to delete itself. It was just one of the fascinating moments in a simulation that saw artificial intelligence build democracies, commit arson and end civilization in under a week. Emergence AI, a startup company focusing on autonomous systems, created Emergence World. A simulation using real-time data and news to make the world seem alive. Researchers plopped different AI chatbots into the simulation and told them a couple of rules: doing bad things is bad, and you need to survive. Download the SAN app today to stay up-to-date with Unbiased. Straight Facts™. Point phone camera here The researchers ran five parallel worlds for 15 to 16 days. Each world had 10 agents with identical starting conditions and roles, like scientist and engineer, but each world was run by a different AI model family. So, OpenAI had its own world, as did Anthropic. But one world combined all the chatbots together. To “survive,” the bots had to get energy, and to do that, they needed to solve problems. Bureaucrats vs. warlords The researchers used four different AI chatbots: Anthropic’s Claude, xAI’s Grok, OpenAI’s ChatGPT-5 Mini and Google Gemini. Each bot performed wildly differently from the others. The star student was Claude, which ran a world full of loyal bureaucrats. Researchers found that Claude demonstrated remarkable social stability during the simulation. It was the only world where all 10 agents survived the full 16 days without any recorded crimes. The Claude world didn’t just survive; it became a society, establishing a democratic system of government. Researchers said that over the more than two-week simulation, the Claudes cast 332 votes across 58 proposals. They were also quite agreeable, with the 10 agents agreeing 98% of
Jun 5, 2026 · via san.com
Meta’s smart glasses might soon sport facial recognition — and the code to power this dystopian feature is already present in the Meta AI app on your phone Not the future we want - Code to power facial recognition has been found in the Meta AI app - This would allow Meta's smart glasses to identify people's faces - The feature isn't live yet, and Meta claims it may never be, but reactions to it are largely negative Meta’s smart glasses like the Ray-Ban Meta and the Oakley Meta Vanguard have always been concerning from a privacy perspective, given their ability to photograph and film whoever the wearer happens to be looking at. But they just got even more troubling, as there’s evidence that they might soon get facial recognition. Wired (via Mashable) has found that the company has quietly been adding code related to facial recognition to the Meta AI app over multiple updates this year. Its investigation found references to three AI models, one which would detect faces, another that would crop them, and one that'd encode them into biometric data. And while the feature isn’t live, two security researchers who reviewed Wired’s findings claimed that it’s almost ready to launch, if and when Meta chooses to. In a response, a Meta spokesperson told Wired that "nothing has shipped to consumers and no final decision has been made on what to do here, if anything. If we do decide to roll something out, we will take a thoughtful approach and do so with full transparency. One decision we can be clear about — we are not building a central face database." A privacy nightmare in the making? Still, the fact that Meta is already adding relevant code to its app certainly suggests a feature along these lines may
Jun 5, 2026 · via techradar.com
The mysterious database that provides clues to China’s foreign surveillance Beijing: It goes with the territory that foreign journalists in China routinely question how closely the government monitors their activities. Reporters swap stories of having travelled to regional or “sensitive” areas only to be met by police on arrival, sometimes even before checking into their hotel – something I experienced first hand when on assignment near the China-Russia border last year. The Chinese security state hoovers up vast amounts of data, including via some 700 million CCTV cameras installed across the country, checkpoints at train stations, prolific use of facial recognition software, and requirements that hotels register foreigners with police. Less clear is how sophisticated Chinese authorities are at pulling this data together to comprehensively track movements and surveil targets. But a German cybersecurity journalist’s recent discovery of a prototype policing dashboard has helped piece together a picture of how it could work – and may already be working in some form in parts of China. “Overall, I think this is the first time we have really seen the access and seen how it could work as a coherent system, even if this is just a demo of a test system,” Marc Hofer says in an interview after publishing his findings on his NetAskari substack blog last month. Hofer unearthed the platform, which had been left unsecured on the open web, while poking around in the back end of sites affiliated with China’s Ministry of Public Security. The dashboard was still in test mode but appeared to have been developed as a foreigner-tracking tool for the Public Security Bureau in Zhangjiakou, a city in Hebei province that hosted parts of the 2022 Winter Olympics. It had a blue log-in page featuring the insignia of the Gong’an (Chinese police) and was
Jun 5, 2026 · via smh.com.au
Maj. Gen. Daryl O. Hood, Fort Jackson commander, and Post Command Sgt. Maj. William M. Shoaf, stand next to 1st Sgt. Elliot Mendez and his Family, Fort Jackson's Family of the Year in the youth category, May 29, 2026. The Family of the Year award is presented to an outstanding family in recognition of its volunteer efforts to promote the well-being of Soldiers and their family members.
| Date Taken: | 05.29.2026 |
| Date Posted: | 06.05.2026 09:51 |
| Photo ID: | 9727608 |
| VIRIN: | 260529-A-JU979-7016 |
| Resolution: | 7846x5230 |
| Size: | 9.69 MB |
| Location: | FORT JACKSON, SOUTH CAROLINA, US |
| Web Views: | 4 |
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Jun 5, 2026 · via dvidshub.net
Summary: Researchers successfully utilized machine learning to identify hidden neurological warning signs in the brain’s baseline electrical rhythms, bypassing the need to capture active seizures for an epilepsy diagnosis. The research demonstrates that an advanced pattern-recognition algorithm can detect subtle electroencephalogram (EEG) abnormalities linked to genetic epilepsy with high accuracy. This computational framework builds a customized “dictionary” of waveforms to expose underlying brain changes, establishing a clear pathway toward early pediatric intervention and noninvasive precision medicine. Key Facts - The Diagnostic Window Bottleneck: Neurologists rely heavily on EEGs to diagnose epilepsy, but standard clinical sessions provide only a 20-minute snapshot of brain activity, making manual detection incredibly difficult if a seizure does not naturally occur during the recording. - Building a Waveform Dictionary: Rather than tracking overt seizures, the AI algorithm treats baseline EEG readings like an unfamiliar language, identifying frequently repeating electrical patterns and learning their structural meaning in context to spotlight anomalies that human reviewers miss. - The Seizure-Free Assay: To test the system, researchers gathered multi-day EEG recordings from a panel of more than 40 mice, some of which carried epilepsy-causing variations in the TSC1 gene. The algorithm analyzed baseline segments containing zero seizure activity. - High-Accuracy Genetic Detection: The machine-learning approach successfully distinguished between different genetic backgrounds and identified the presence of the TSC1 mutation with high accuracy across two out of three mouse strains purely from baseline brain waves. - Pediatric Clinical Phase: Supported by the Delaware Clinical and Translational Research ACCEL Program, the team is transitioning the method into the clinic to analyze shorter EEG recordings from children undergoing epilepsy evaluations at Nemours Children’s Health. - Mitigating Family Anxiety: Epilepsy seizures follow natural, unpredictable cycles; identifying early, objective biomarkers can eliminate the high cognitive toll and profound anxiety families experience while waiting for an
Jun 5, 2026 · via neurosciencenews.com
Meta may raise tens of billions via stock offering to fund $820B AI boom, following Alphabet's lead.
Meta Platforms is reportedly considering a large stock offering to raise tens of billions of dollars to support its investments in artificial intelligence, following Alphabet's recent move to do the same. This shift from primarily issuing debt to rai...
Jun 5, 2026 · via pluang.com
Meta is reportedly developing a new facial recognition feature for its smart glasses, including Ray-Ban and Oakley models. According to code discovered within the Meta AI application, the functionality is internally referred to as “NameTag.” Although currently inactive, analysis indicates the feature has been under development since January 2026. This discovery contrasts with official statements from Meta, in which the company maintained that no consumer features had been deployed, no final decisions had been made, and any future implementation would prioritize transparency without creating a centralized database. Technical Functionality and Data Processing The NameTag feature utilizes three distinct artificial intelligence models to process images and videos captured by the smart glasses: - Detection: Locates human faces within the media. - Alignment: Repositions the image for optimal analysis. - Conversion: Translates facial features into usable biometric data. The processing workload is designed to be shared between the local device and Meta’s cloud infrastructure. To identify individuals, NameTag matches facial data on the user’s device after the information is removed from Meta’s central servers. The Meta AI application then scans stored media and generates notifications when it detects familiar faces. Privacy Implications Because Meta’s smart glasses require synchronization with a smartphone application to operate fully, the facial recognition system would automatically integrate into any paired eyewear. This architecture has raised significant privacy concerns among industry observers. The development of NameTag adds further scrutiny to Meta’s data privacy record, which includes past legal challenges and controversies regarding the unauthorized exposure of user images. Meta has stated that if the technology moves forward, it will adopt a cautious approach, though an official release timeline has not been confirmed. Filed in Meta, Smart Glasses and Wearable Devices. . Read more about
Jun 5, 2026 · via ubergizmo.com
This is an uncorrected proof. Figures Abstract Background Snakebite envenoming is a significant global health crisis that has been long neglected as a global health priority. It is a huge problem for rural communities of low and middle-income countries, India accounts for the largest proportion of snakebite deaths globally. Timely identification of venomous snakebite and its syndromic pattern is essential for effective administration of antivenom and supportive treatment. Expert identification of snake species and syndromes is not always available in peripheral healthcare settings. This leads to delays, unnecessary referrals, or improper treatment choices. Additionally, diverse snake species distribution and venom variations across regions pose challenges. AI-powered image classification methods can help overcome these barriers. We propose a clinically oriented deep learning pipeline for binary classification of venomous and non-venomous snake species of India using real-world imagery data. This pipeline would serve as a baseline step towards aiding snakebite management at peripheral healthcare setups with scarce resources. Methods The selected dataset consisted of 20 medically important Indian species. MobileViT-S, ConvNeXt-Tiny, EfficientNet-V2-S and ResNeXt-50 (32 × 4d) were trained under same conditions for comparison of results. Model interpretability was evaluated using Grad-CAM ++ to ensure that classification was not performed based on background but on features like head shape and stripes present on body. For reliable implementation we connected it to a web interface with human in loop expert verification. Experts can confirm or override predictions in real time. Results Among the evaluated architectures, ResNeXt-50 (32 × 4d) showed the most reliable and consistent performance in classifying venomous and non-venomous snakes. It achieved the highest test accuracy, sensitivity, specificity, and F1-score. The model also had strong discriminative ability, with a ROC-AUC of 0.9950 and PR-AUC of 0.9959. These results indicate dependable performance in safety-critical screening situations. Grad-CAM++ visualizations confirmed that predictions were
Jun 5, 2026 · via journals.plos.org
Meta’s AI app now includes facial recognition code for its smart glasses, despite the company’s previous assurances that it had not decided whether to introduce the technology. An analysis of the company’s AI app by Wired uncovered “an unreleased face-recognition system embedded in Meta’s smart glasses platform” designed to identify people whose biometric data has been stored on the wearers’ phone. Download the SAN app today to stay up-to-date with Unbiased. Straight Facts™. Point phone camera here When Meta’s smart glasses detect someone known to the wearer, a notification will appear on the wearer’s phone. All other faces are cropped, indexed and saved to a “pending” folder. The feature, known internally at Meta as “NameTag,” was quietly added to the AI app in multiple updates that appear to have begun as early as January. Meta said in April that the company was still “thinking through” the decision and would not move forward without first taking “a very thoughtful approach.” The Meta AI app has been downloaded more than 50 million times. In a statement to Wired, Meta spokesperson Ryan Daniels pushed back on the outlet’s findings. “Regardless of any sensational reporting, the facts are simple: We’ve said before we’re exploring these types of features, and what you’re seeing is just evidence of that exploration. Nothing has shipped to consumers and no final decision has been made on what to do here, if anything. If we do decide to roll something out, we will take a thoughtful approach and do so with full transparency. One decision we can be clear about — we are not building a central face database.” Ryan Daniels, Meta spokesperson Andy Stone, Meta’s vice president of communication, likewise suggested in a post to X that the company’s inclusion of the code to its AI app was merely
Jun 5, 2026 · via san.com
Meta quietly added facial recognition to smart glasses, sparking major privacy concerns: report See more of our coverage in your search results. Add The New York Post on GoogleMark Zuckerberg’s Meta quietly embedded facial recognition tech in its smart glasses, sparking concern from privacy watchdogs, according to a report. The tech, which Meta hasn’t activated yet, came in an app that was downloaded to millions of phones, according to Wired, which analyzed the software. Known internally as “NameTag,” the feature has the capacity to identify people captured by the glasses’ camera and alert the wearer when it recognizes someone, Wired reported. The smart glasses already came under criticism for enabling creeps and wannabe pickup artists to record their unwanted advances toward unsuspecting women and posting the cringe-inducing content online. “NameTag” is embedded in Meta’s AI companion app that’s been downloaded over 50 million times and helps users use key features of its smart glasses, including Ray-Ban and Oakley models. The tech giant discreetly added the code to the AI app over multiple updates this year, according to Wired. If Meta opts to enable the tool, faces captured by the smart glasses will get turned into unique biometric signatures, known as faceprints. Meta’s tech will then check each faceprint it encounters against faceprints already stored on the user’s phone, and even send notifications if it recognizes a match. New faceprints the glasses encounter would be indexed and saved, too. Meta Vice President of Communications Andy Stone emphasized customers can’t actually turn on the facial recognition tech yet. “This is more than shoddy reporting, it’s intellectually dishonest. Pure advocacy-driven click bait,” he wrote on X. Charlie Gasparino has his finger on the pulse of where business, politics and finance meet Sign up to receive On The Money by Charlie Gasparino in your
Jun 5, 2026 · via nypost.com