OkCupid and its owner Match Group reached a settlement with the Trump administration for not telling dating-app customers that nearly 3 million user photos were shared with a company making a facial recognition system. OkCupid also gave the facial recognition firm access to user location information and other details without customers’ consent, the Federal Trade Commission said. OkCupid and Match do not have to pay a financial penalty in a deal made with the FTC over an incident from 2014. OkCupid and Match did not admit or deny the allegations but agreed to a permanent prohibition barring them from misrepresenting how they use and share personal data, the FTC said yesterday. The FTC has been run entirely by Republicans since President Trump fired both Democratic commissioners. The proposed settlement requires approval from a judge and was submitted in US District Court for the Northern District of Texas. The dating-site company said it’s pleased to settle the matter without paying any fine. “While we do not admit any wrongdoing, we have settled this matter with the FTC with no monetary penalty to resolve an issue from 2014 and move forward,” an OkCupid spokesperson said in a statement provided to Ars today. “The alleged conduct at issue does not reflect how OkCupid operates today. Over the years, we have further strengthened our privacy practices and data governance to ensure we meet the expectations of our users.” Although a recent court ruling imposes limits on the FTC’s enforcement powers, that ruling applied only to the FTC’s in-house administrative process. The FTC can still pursue deceptive advertising claims in courts and seek financial penalties through court orders or settlements.
Mar 31, 2026 · via arstechnica.com
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Mar 31, 2026 · via media.stellantis.com
Maj. Anthony Munoz, center, receives recognition during the 153rd Logistics Readiness Squadron change of command ceremony at the Wyoming Air National Guard Base in Cheyenne, March 13, 2026. Munoz was recognized for his leadership and service as squadron commander. (U.S. Air National Guard photo by Maj. James Fisher)
| Date Taken: | 03.13.2026 |
| Date Posted: | 03.31.2026 12:30 |
| Photo ID: | 9592083 |
| VIRIN: | 260313-Z-WY307-1004 |
| Resolution: | 4380x3285 |
| Size: | 3.59 MB |
| Location: | CHEYENNE, WYOMING, US |
| Hometown: | LARAMIE, WYOMING, US |
| Web Views: | 2 |
| Downloads: | 2 |
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Mar 31, 2026 · via dvidshub.net
Loughborough University physicists have developed a device that can process data that changes over time directly in hardware, rather than relying on software running on conventional computers. Their latest research, published in Advanced Intelligent Systems, suggests the approach could be up to around 2000 times more energy efficient than conventional software-based methods. “This is exciting because it shows we can rethink how AI systems are built,” said Senior Lecturer in Physics, Dr Pavel Borisov, who led the research team funded by the Engineering and Physical Sciences Research Council (EPSRC). “By using physical processes instead of relying entirely on software, we can dramatically reduce the energy needed for these kinds of tasks.” How it works Many real-world AI problems involve processing data that changes over time and identifying patterns to predict what happens next – for example in weather systems, biological processes or sensor data. A technique called reservoir computing is often used for this, where incoming data is transformed into a form that makes patterns easier to detect and predict, typically using software. The Loughborough device performs this type of computation directly in hardware. It is a type of memristor – an electronic component that can store information about past inputs – made of nanoporous oxide. It contains random nanopores that create multiple electrical pathways, and these pathways act like the hidden processing layer of a neural network, allowing the material itself to carry out part of the computation. Study findings In their study, the researchers showed that the device can process time-dependent data and, when its output is fed into a linear computer model, can be used to identify patterns and make short-term predictions. They tested the system using the Lorenz-63 system – a well-known mathematical model of chaos linked to the “butterfly effect”, where small changes can lead
Mar 31, 2026 · via lboro.ac.uk
Abstract Gait disorders can be caused by various reasons including cerebral palsy and neuromuscular diseases. 3D clinical gait analysis (3DGA) serves as a valuable clinical tool to assess gait abnormalities. Our previous research introduced a diagnostic tool that combines deep learning (DL) with 3DGA to evaluate childhood gait disorders. It achieved a promising diagnostic accuracy ranging from 0.77 to 0.99 across different pathologies. However, the lack of transparency limits their adoption. This research seeks to unveil the critical features that drive these models’ diagnoses, improving interpretability and building trust in their decision-making process. Four different explaining artificial intelligence (XAI) methods were applied: LIME, DeepLift, Integrated Gradients, and sequential feature selection. These methods were used on various network architectures applied to three separate datasets involving different gait disorders. The results show that the features highlighted by XAI methods are relevant and reliable for diagnostic purposes. Moreover, quantitative analysis indicated that Integrated Gradients is the most appropriate XAI method in this case. Further experiments demonstrate that using parts of the critical features can achieve better accuracy than using all of the features. In conclusion, this research identified the diagnostic basis of DL models through XAI methods, enhanced diagnostic accuracy by focusing on critical features, and improved clinicians’ understanding and trust in the DL diagnostic tool. Similar content being viewed by others Data availability The datasets used in this study are available from the corresponding author upon reasonable request. References Bax, M. et al. Proposed definition and classification of cerebral palsy, April 2005. Dev. Med. Child Neurol. 47, 571–576. https://doi.org/10.1017/S001216220500112X (2005). Kalia, L. V. & Lang, A. E. Parkinson’s disease. The lancet 386, 896–912 (2015). Deenen, J. C. W., Horlings, C. G. C., Verschuuren, J. J. G. M., Verbeek, A. L. M. & van Engelen, B. G. M. The epidemiology of neuromuscular disorders:
Mar 31, 2026 · via nature.com
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Mar 31, 2026 · via youtube.com
Angela Lipps, a 50-year-old grandmother from Tennessee, spent over five months in jail because an AI facial recognition tool told the police that she had committed crimes in North Dakota. When they realised their mistake, she was released over 1,600 kilometres away from her home with no money and no means to return. Lipps relied on charities to fly back to Tennessee, reports suggest. Police in Fargo, North Dakota, have admitted that an error happened, but when asked whether the department would issue an apology, officials did not give an affirmative answer. Lipps was first arrested in Tennessee on July 14, according to the Fargo Police Department and a verified GoFundMe page. She lost her home, her car, and even her dog because of the ordeal. Months ago, several cases of bank fraud had occurred in and around Fargo, according to police. They used an AI tool to scan surveillance footage, relying on their "partner agency’s facial recognition technology” and “additional investigative steps independent of AI to assist in identification”, CNN reported. The company was Clearview AI, a startup that had a database of billions of photos built by using images from all over the internet. The software incorrectly matched the suspect's face to Angela Lipps, even though she lived in another state and had never even been to North Dakota. An arrest warrant was issued, and Lipps was arrested in Tennessee while babysitting her grandchildren. She spent over three months in a Tennessee jail and was later extradited to North Dakota, according to Fargo police and her lawyers. Lipps recalled the extradition on her GoFundMe page, "It was the first time I had ever been on an aeroplane. I was terrified and exhausted and humiliated." It wasn't until December that her lawyer provided bank records and receipts showing she
Mar 31, 2026 · via wionews.com
What Wigs Can Teach Us About Irreversible Self Commodification Words: Nadia Trudel | Images collages from Tomihiro Kono It’s all relatively innocent, yet as facial recognition technology leads to criminal charges, secret public bathroom cameras torment, and the whims of strangers land civilians in deepfake pornography, anonymity finds a sense of urgency. Until I figure out how to become a lighthouse keeper, I can’t exactly scrub my extensive digital presence or abandon the city, really, I don’t want to. I must confess my reasons for writing professionally, posting thirst traps, or performing elaborate karaoke numbers are not purely artistic— so, fine, I’m an attention seeker! What’s a girl to do? ___STEADY_PAYWALL___ Well, what about wigs? There’s an inherent mystery to wigs and everything they hide, and they might be the answer to our current cultural identity crisis. Wigs can conceal balding, damage, vanity, a K-Pop idol’s comeback dye job, a popstar’s real self. No one understands that better than Tomihiro Kono, the iconic wig artist testing the possibilities of hair. The mystery of Kono’s wigs is less ‘Is that her real hair?’ and more ‘Who is that? What is that?’ This became especially evident to Kono’s frequent collaborator and partner, the visual artist Sayaka Maruyama. While editing photos for Kono’s latest book Space Creatures, she discovered that some of the wigs made faces undetectable to facial recognition systems. The book presents wigs created from 2022-2025 as selectable identities, alien organisms on display which blur the lines between human and inhuman, individual and system. “Classically, transformation is a before and after narrative. The ugly duckling becomes the swan. The end. This makes it difficult for many to understand folks whose gender identities or sexual orientations seem to be ever changing.” Kono explains in the book “This may be because the wig
Mar 31, 2026 · via polyesterzine.com
The Consumer Federation of America along with Ultraviolet Action led the following coalition letter from 64 civil society organizations around the country. The letter was sent today to Meta, Rayban parent company EssorLuxottica, the White House, the Federal Trade Commission, the Department of Justice, several key state Attorneys General, and leadership of relevant Congressional committees. The letter reads: The 64 undersigned civil society organizations vehemently oppose the plans to integrate facial recognition features into Meta glasses. Despite Meta’s stated intention to release the feature during a “dynamic political environment where many civil society groups that we would expect to attack us would have their resources focused on other concerns,” we are committed to opposing this creepy and unacceptable escalation of surveillance on all fronts. Integrating facial recognition into Meta glasses is a dangerous and reckless plan that will harm both users and the entire public, regardless of whether they use Meta products, whether they consent, whether they are a public figure or layperson, and whether they even know about it. This move will endanger us all, and particularly give ammunition to scammers, blackmailers, stalkers, child abusers, and authoritarian regimes. It would also create acute and unnecessary national security risks. Even without this feature, Meta glasses create a world where people can be secretly recorded with both video and audio, with no control or awareness. Meta’s primary safety control, a barely noticeable LED that illuminates when users take photos or videos, is easily disabled and fails to provide reasonable transparency. Recent reporting has confirmed everything recorded with Meta glasses, often unknowingly even, could be seen by human moderators and data labelers throughout the world. Those recordings – many of which contain highly sensitive content – are at risk of being shared, sold, breached, and weaponized against people of all political parties,
Mar 31, 2026 · via consumerfed.org
How AI can help reduce food loss and waste in Nigeria’s tomato value chain Key takeaways - An AI image analysis system will evaluate tomato quality and quantify post‑harvest losses in Nigeria. - A pilot assessment in Jos, Nigeria, collected extensive visual and other data across multiple tomato varieties to train and validate the AI model. - The system will be scalable and applicable to other crops, with potential to reduce food waste, improve supply‑chain efficiency, and support smallholder farmers. Fruits and vegetables are essential to good nutrition, but in low- and middle-income countries (LMICs), people often do not consume enough of them, with most falling short of World Health Organization dietary recommendations. One key reason for this problem is supply: in many LMICs, a substantial amount of production is lost before it reaches consumers due to the widespread lack of appropriate post-harvest handling and cold chains, limiting produce availability and affordability. One challenge in reducing food loss and waste is understanding how quality deteriorates between farms and retail outlets. If weaknesses in specific links in the value chain can be identified, then solutions can be better targeted. To innovate on current, survey-based measures of food losses, we are developing an artificial intelligence-assisted, photo-based quality assessment in the tomato value chain in Nigeria. If successful, it will help us understand both the quality distribution (including the rate of spoilage) of tomatoes and the volume of loss in the tomato value chain through photos taken at wholesale and retail markets. The new approach will inform the selection of effective interventions to address food loss in several ways. First, it will improve the accuracy of food loss assessment. Second, it will be implemented in close to real time. Third, it is scalable, i.e., we can assess food loss simultaneously in many locations.
Mar 31, 2026 · via ssa.foodsecurityportal.org
Robotic microscope counting and photographing the smallest marine organisms The Askö laboratory is being equipped with an Imaging FlowCytobot, an autonomous, submersible microscope that can capture detailed images of the sea's phytoplankton communities. "When we’re done, you will be able to take it apart and put it back together, know when something is wrong, and, if necessary, repair it yourselves," says Thomas Fougere from McLane Research Laboratories, who will teach the research team from the Baltic Sea Centre how the new robotic microscope works. He removes the housing – a steel pressure chamber that allows the microscope to be submerged to a maximum depth of 40 metres. The hands-on learning begins with lifting the instrument. It is about one metre long and with the housing it weighs over 30 kilograms. An Imaging FlowCytobot (IFCB) is an automatic microscope designed to continuously sample seawater, detect and count plankton cells, and take high-resolution images. The instrument can be connected to machine learning–based software in order to automatically classify plankton images. The technology allows researchers to observe and quantify plankton communities with unprecedented temporal resolution. It is undeniably impressive, and the researchers are eager to see what this instrument will enable them to discover. Facts: Imaging FlowCytobot (IFCB) - The instrument is a submersible plankton microscope that can be configurated to autonomously draw in seawater, count and photograph cells. It can be connected to software that can be trained for machine learnt image recognition. - The microscope can take up to 30,000 images per hour. - Its optics are optimized for high-resolution images of particles sized 10-150 micrometres. - Particles are illuminated with a laser beam. Highly sensitive detectors measure fluorescence (from chlorophyll and other pigments) to distinguish living microorganisms from mineral particles, air bubbles, plastic fragments and other debris. If the signal
Mar 31, 2026 · via su.se
Abstract Three-dimensional voxel reconstruction based on stereo vision is essential for environmental perception in autonomous robots. Existing pseudo-LiDAR methods recover voxel grids by estimating depth maps and projecting them pixel by pixel, leading to high computational cost and boundary over-smoothing. To overcome these issues, we model the inverse relationship between 2D pixels and 3D voxel grids and propose a Self-supervised 3D Voxel Reconstruction network from Stereo vision (SVRS). Specifically, we represent a given 3D scene as multi-scale uniform cubic voxel grids and introduce a novel Pixel-Voxel Projecting Module (PVPM). PVPM projects the 3D position of each voxel grid into index coordinates, which establishes implicit stereo–voxel correspondences and converts dense pixel features into sparse voxel representations. Furthermore, we explore an Octree-based Encoder-Decoder Architecture (OEDA) to reconstruct multi-scale voxel grids via hierarchical spatial partitioning, avoiding the influence of dense empty grids on sparse occupied grids via a coarse-to-fine manner. Finally, SVRS leverages off-the-shelf stereo matching methods within a self-supervised training framework. Experiments on the DrivingStereo dataset show that SVRS achieves competitive reconstruction accuracy while improving inference speed by up to 14\(\times\) over advanced pseudo-LiDAR approaches and 3\(\times\) over real-time approaches. Data availability The data supporting the findings of this study are available within the paper. The associated pre-processed raw data is available and can be shared with interested parties upon reasonable request. Please contact the corresponding author for more information. Code availability Our code is avaliable on https://github.com/zzy729425207/SVRS. Please contact the corresponding author for more information. References Menze, M., Heipke, C. & Geiger, A. Joint 3d estimation of vehicles and scene flow (ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015). Guo, Y. et al. Deep learning for 3d point clouds: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 43, 4338–4364 (2020). You, Y. et al. Pseudo-lidar++: Accurate depth for
Mar 31, 2026 · via nature.com
East TN grandmother mistakenly jailed for months after A.I. identified her as bank fraud suspect in North Dakota Angela Lipps said she was held in a Tennessee jail for 108 days before being extradited to North Dakota, where she was held until Christmas Eve. FARGO, N.D. (KVLY/WVLT) - An investigation is underway after an East Tennessee grandmother was identified as a bank fraud suspect in North Dakota by an artificial intelligence facial recognition system. Angela Lipps, of Elizabethton, said U.S. Marshals arrested her at gunpoint on July 14, 2025 while she was babysitting four children, adding that investigators used facial recognition software to match her to surveillance footage from a bank fraud case in Fargo, North Dakota, according to KVLY. Lipps said she was held in a Tennessee jail for 108 days before being extradited to North Dakota, where she was held until Christmas Eve. She was only released after her attorney pulled bank records showing she was in Tennessee at the time of the alleged crimes, resulting in the charges being dismissed. Since then, Fargo Police Chief Dave Zibolski has released several statements regarding Lipps’ arrest. He said West Fargo Police submitted a photo from a fake ID to the A.I. system that returned Lipps as a potential match and that he did not know she was in custody until Dec. 5. West Fargo disputed this, saying that while they submitted the photo, there wasn’t enough evidence to charge her. However, KVLY obtained an email showing that six detectives were notified of her arrest on July 14, the day of her arrest in Carter County. Lipps has hired two attorneys in the case, who said the department failed to conduct basic investigative steps before a warrant and charges were issued. They said their investigation remains ongoing and is focused
Mar 31, 2026 · via wvlt.tv
U.S. Air Force Senior Airman Jennifer Ramirez, 514th Aerospace Medicine Squadron dental technician, is recognized for outstanding contributions during the Lesser Antilles Medical Assistance Team (LAMAT) 2026 mission at Cheddi Jagan Dental School in Georgetown, Guyana, March 25, 2026. Recognition of individual performance highlights the skilled personnel who help sustain mission execution and medical readiness during global health engagements. (U.S. Air Force photo by Master Sgt. Albert Rullan)
| Date Taken: | 03.25.2026 |
| Date Posted: | 03.30.2026 13:21 |
| Photo ID: | 9590077 |
| VIRIN: | 260325-F-NA001-2542 |
| Resolution: | 4564x3043 |
| Size: | 2.32 MB |
| Location: | GY |
| Web Views: | 7 |
| Downloads: | 1 |
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Mar 31, 2026 · via dvidshub.net
Police release Tennessee grandmother after AI facial recognition led to her arrest 02:48 UP NEXT New documentary asks if we are doomed by AI 12:07 New Jersey teacher builds chatbot to help improve students writing 02:48 Viral robot appearances on the rise as White House hosts humanoid robot 02:56 AI generated fruit videos draw millions of views on TikTok 03:55 Company says they make IVF more accessible through AI-powered fertility care 03:26 Australian tech entrepreneur uses AI to create cancer treatment for his dog 06:22 Dancing robot goes rogue in hot pot restaurant 03:29 Students take advantage of artificial intelligence while studying for SAT 03:37 Meta buys AI social network Moltbook 06:03 Exclusive: Why Mark Cuban wants students to bet big on AI 03:40 Google sued over Gemini’s alleged role in man’s suicide 04:55 New app is using AI for matchmaking to help users find love 04:13 Tom Llamas meets humanoid robot 'Sprout.' How this technology could soon become a family fixture 12:08 Burger King's new AI chatbot to help employees with operations, improve ‘friendliness’ 02:56 Anthropic CEO says company cannot agree to Pentagon's AI usage demands 04:15 Concerns are growing over chatbots causing users to go into 'delusional spirals' 04:32 New app allows users a chance to date AI 04:58 Some companies using AI in their Super Bowl advertisements 05:17 Driverless Waymo strikes child in California 01:28 Top Story Police release Tennessee grandmother after AI facial recognition led to her arrest 02:48 Copied A Tennessee grandmother is demanding justice after spending months in jail. She says she was wrongfully arrested after an AI facial recognition tool falsely linked her to bank fraud. NBC News’ Kathy Park reports on the details of the arrest. March 31, 2026
Mar 31, 2026 · via nbcnews.com
West Fargo police detail how they use facial recognition software Department explains how Clearview AI tool works following national scrutiny over wrongful arrest WEST FARGO, N.D. (Valley News Live) - West Fargo police detailed how they use a facial recognition tool called Clearview AI. The tool has drawn scrutiny after a Tennessee grandmother spent five months in jail following a misidentification. The department has used Clearview AI since 2020. The software is used most often in theft and fraud cases. Capt. Burkhartsmeier said officers attempt to identify suspects through traditional methods before turning to the technology. “Our officers, detectives will try to identify the person just based off of whatever they got at the time. If they’re unable to come up with something for the suspect, then they’ll decide to run it through Clearview,” Burkhartsmeier said. “So, it has to meet those requirements first. It’s not like we use it on every single case.” The system runs a photo against a database of over 70 billion photos and returns potential suspects. Officers and detectives then verify whether the match is accurate. Burkhartsmeier said the software has limitations that factor into every search. “It does have limitations. So...hats, face coverings, stuff like that, depending on the angle that the person’s looking makes it more effective or less effective,” Burkhartsmeier said. The department said Clearview AI is one piece of a larger investigative process. Every potential match must be verified, a process that can involve multiple agencies. “A lot of times we get traveling fraud or theft groups that come from all over the country. So, if we get potential suspects on those, we’ll also send it to North Dakota SLIC and have them run it, see what they think, and then talk back and forth,” Burkhartsmeier said. “Still, that’s not an
Mar 31, 2026 · via kfyrtv.com
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Mar 30, 2026 · via youtube.com
Angela Lipps, a 50-year-old grandma from Tennessee, spent more than five months in jail after the AI facial recognition platform Clearview AI falsely matched the grandmother with a suspect of bank fraud more than 1,000 miles away in North Dakota. Fargo police chief Dave Zibolski admitted to CNN that there were a "couple of errors" in the investigation that led to Lipps' arrest. A "partner agency’s facial recognition technology” and “additional investigative steps independent of AI to assist in identification” led to a warrant being issued for Lipps, Zibolski said. The grandma was arrested on July 14 while looking after four children. Authorities in Tennessee held Lipps in county jail for 108 days before she was extradited to Fargo. Lipps says she had never even been to the state of North Dakota before her arrest. According to her GoFundMe page, Lipps found out that a woman in North Dakota stole tens of thousands of dollars from banks in Fargo using a fake military ID. Clearview AI matched the fake ID image with Lipps in Tennessee. The case against Lipps fell apart in December when the lawyer she was given in Fargo was able to produce bank records showing Lipps was at a gas station and ordering pizza in Tennessee at the time that authorities claimed she was in North Dakota. Lipps was released on Christmas Eve, after nearly 5 months in prison. Lipps says she lost her home, income, car, and health insurance as a result of her imprisonment. What is Clearview AI? Clearview AI is a tech company that has plenty of charges from critics on its rap sheet already. it has created massive facial-scan databases by scraping photos from social media platforms and other places on the internet, then training its machine learning algorithms on them. In 2020,
Mar 30, 2026 · via sea.mashable.com