(The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.) (THE CONVERSATION) A woman strolls into a grocery store, thinking about grabbing some apples. Before she even reaches the produce aisle, a security camera has scanned her face. Whether the system is checking for shoplifters or simply logging her arrival, her face has joined a digital ledger, a trace she can’t easily erase. Retailers, banks, airports, stadiums and office buildings are doing the same. But what if the woman’s facial information is stolen or misused? If a cybercriminal steals her password, she can change it. If they acquire her credit card number, she can cancel the card. But she can’t reset or revoke the appearance of her cheekbones. Facial recognition systems don’t keep actual images. They convert a face into a mathematical template that maps the positions and proportions of the face’s features. When another camera scans a person later, the system checks their live face against these templates to confirm an identity. In my work as a cybersecurity professor at Rochester Institute of Technology, I have found that even though templates are more secure than photos – which anyone online can capture and manipulate – templates, too, can be stolen. Once that happens, these digital keys create a lifelong vulnerability. If a facial recognition database is breached, the “locks” that a template opens – accessing a bank app, getting through security at an airport, entering an office building – can’t be reset. A person’s face is permanent, and so is the threat. The threat isn’t theoretical. Biometric data has been stolen in data breaches. In 2024, biometric data from a facial recognition system used at bars and clubs in Australia was hacked. And in 2019, biometric data from a pilot facial recognition system
Apr 28, 2026 · via bozemandailychronicle.com
A recent Pentagon contract just revealed the smartest artificial intelligence strategy in business. While some builders and investors chase fully autonomous everything – self-driving cars, pilotless airplanes, human-less factories – the real winners are asking: How much autonomy should we actually give our AI systems? The answer is reshaping how companies deploy AI across every industry. It's not what the hype machine is selling. Beacon AI, a California-based aviation software company, recently signed a four-year contract with US Special Operations Command (USSOCOM) worth up to $49.5 million. The deal is not for a fully autonomous, pilotless aircraft or for sci-fi autonomous fighters. Instead, it's for AI-powered pilot assistance software that cuts cockpit workload and speeds mission-critical decisions in high-risk operations. Think R2-D2, not HAL 9000. The system integrates flight data, weather, routing, and pilot inputs into real-time decision support. This is exactly what human pilots need when operating in contested, time-sensitive environments. Here is the strategic insight. Beacon deliberately chose limited AI autonomy over full automation, and the Pentagon validated this choice with a large check. The autonomy levels every executive needs to understand Most boardrooms discuss AI as if it's binary. You either have it or you don't. This is simplistic. AI autonomy operates on a spectrum, and understanding these levels is critical for any leadership team deploying AI systems. The framework comes from autonomous vehicles, but applies to AI systems in any industry: - Level 0 (no automation): Humans do everything. Traditional tools with no AI assistance. - Level 1 (driver assistance): AI provides information and basic support. Think spelling-check or fraud alerts that humans must act upon. The AI has no decision-making power. - Level 2 (partial automation): AI can execute specific tasks while humans maintain oversight and control. Examples: AI scheduling systems that suggest meetings but
Apr 28, 2026 · via jpost.com
Pet Health and AI News from NYC's Top Digital Cybersecurity Master's Program Artificial Intelligence Biotechnology Computer Science Cybersecurity Data Analytics and Visualization Digital Marketing and Media Mathematics Nursing Occupational Therapy Physician Assistant Physics Speech-Language Pathology How AI Is Revolutionizing Pet HealthJust as healthcare is becoming increasingly personalized for humans, AI is bringing similar advances to pet care. By using machine learning and predictive analytics, AI can analyze a pet’s unique characteristics – including breed, age, lifestyle, and genetics – to help inform customized wellness plans. These expanding capabilities are showing strong potential to improve how animals are examined, diagnosed, and treated.In veterinary clinics, research labs, and pet tech startups, AI is creating new opportunities to better understand and enhance animal health. The following discussion explores how AI is reshaping pet care and driving the next generation of veterinary innovation.AI Tools for Pet Health DiagnosticsArtificial intelligence is reshaping veterinary diagnostics by adding speed, consistency, and pattern recognition to the clinical process. While traditional diagnostics rely heavily on training, experience, and visual interpretation, AI helps surface insights that may be difficult to detect during routine exams, especially in early or complex cases.Rather than replacing veterinarians, AI acts as a support layer. It processes large volumes of data in seconds, compares findings against thousands of previous cases, and highlights areas that may require closer attention. The result is earlier detection, more confident diagnoses, and better-informed treatment decisions for pets.Imaging AnalysisMedical imaging plays a central role in veterinary diagnostics, but interpreting X-rays, MRIs, and ultrasounds can be time-consuming and subjective. AI-powered imaging tools improve this process by scanning images for subtle patterns that may not be immediately visible to the human eye.Trained on large datasets of veterinary images, these systems can identify early signs of disease, structural abnormalities, and progressive conditions. They flag areas
Apr 28, 2026 · via yu.edu
Inside Munger Elementary-Middle School on Martin Street on Detroit's southwest side, what looks like a horizontal iPad is installed just inside the school's front entrance. Every visitor to the school must present a valid form of identification, such as a driver's license, to have it scanned by the iPad and then have their face scanned. When the two images are cross-referenced and verified, the visitor's information is saved in the school's system for future visits. The system is part of a new facial recognition software system, called Visitor Aware, now in place at every Detroit Public Schools Community District school. The system matches visitors' faces against a valid form of identification before they're allowed into the school. When visitors are flagged, they may not be allowed in the building. Munger Elementary-Middle School Principal Donnell Burroughs said the software, which was officially rolled out earlier this school year, was needed for security. It's "more secure and safe" than a traditional sign-in sheet for visitors, Burroughs said. But critics have concerns about how facial recognition is being used in schools. Some cited privacy concerns and worried about inaccurate identifications, meaning someone is flagged when he or she shouldn't be. Others said they worry about the technology having a chilling effect, possibly discouraging some parents from visiting their children's schools. “What these systems are trying to do is to automate student security without human involvement," said Molly Kleinman, managing director of the University of Michigan's Science, Technology and Public Policy program. Detroit is one of 12 districts in Michigan that use Visitor Aware, according to its parent company Singlewire. Bloomfield Hills Schools also launched Visitor Aware last month. Both the Detroit school district and Bloomfield Hills Schools said it streamlines the process for allowing visitors into buildings and that keeping students and staff
Apr 27, 2026 · via govtech.com
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Apr 27, 2026 · via detroitnews.com
Artificial intelligence has moved well beyond the pilot stage. One survey found that 88% of organizations have implemented AI in at least 1 business function. And engineering is no exception. Already, AI has reshaped how engineers work. Its applications range from design optimization to automation and beyond. It’s expanding the scope of existing jobs and raising the bar on the skills engineers need to stay competitive. Learn more about the impact of AI on engineering jobs, emerging career opportunities, and ways to thrive in a changing field. Key Points - Artificial intelligence has become fundamental enough to shift traditional engineering roles, changing how engineers work and the work itself. 88% of organizations now use AI in at least one business function. - Engineers are using AI to tackle complex real-world problems, with applications spanning predictive maintenance, design optimization, and automation. - AI’s impact on engineering is just beginning. But it’s already creating new job opportunities and demanding new skills to stay relevant. - AI likely won’t replace engineers, but it will affect some roles more than others. That makes adaptability 1 of the most valuable traits in the field right now. What Is Artificial Intelligence in Engineering? AI enables machines to learn, adapt, and perform tasks that typically require reasoning. In engineering, that capability translates into more powerful tools for analyzing systems and solving complex problems. Common artificial intelligence methods and capabilities used in engineering include: - Machine learning (ML): These systems train on data to perform specific tasks with minimal human oversight, learning and adapting from that data along the way. In engineering, ML is also used in automation, predictive maintenance, and systems optimization. - Computer vision: This AI capability interprets and extracts meaning from visual data such as images and video. In engineering, it can support real-time inspection,
Apr 27, 2026 · via intuit.com
Visual AI Apps Compared: What Actually Works in 2026? Which Visual AI Apps Actually Work in 2026? Visual AI is no longer a futuristic concept. It is already part of how people interact with the world around them. From identifying objects to understanding complex scenes, visual AI apps are changing how we search, learn, and make decisions. But with so many options available in 2026, one question keeps coming up: what actually works? The answer is not as simple as picking the most popular app. Different tools are built for different purposes, and understanding those differences can save you a lot of time and frustration. What People Expect from Visual AI Today A few years ago, basic image recognition felt impressive. Now, expectations are much higher. People want more than just a label. They want meaningful answers. When someone points their camera at something, they expect: - Accurate identification - Clear explanations - Context that helps them act on the information For example, identifying a plant is useful. Explaining whether it is safe, common, or valuable is far more helpful. This shift from recognition to understanding is what separates average apps from truly useful ones. The Main Types of Visual AI Apps Most visual AI tools today fall into a few clear categories. 1. Context-Aware Visual AI (Chance AI) Tools like Chance AI focus on understanding what you are looking at, not just recognizing it. Instead of only matching images, they aim to explain objects with context. This approach is useful when: - You do not know what something is - You need more than just a label - You want quick, meaningful answers It works well across categories like plants, products, tools, and unfamiliar objects, making it closer to a real-world assistant than a traditional search tool. 2. General
Apr 27, 2026 · via vocal.media
Figures Abstract Medical image classification requires models that effectively capture both fine-grained local patterns and global anatomical structures while maintaining computational efficiency for clinical deployment. Although state-of-the-art models such as MedMamba utilize State-Space Models (SSMs) to balance accuracy and efficiency, their sequential operations limit parallelism and increase runtime. To overcome these limitations, we propose MedSpectralNet, a lightweight Convolutional Neural Network (CNN) architecture that approximates self-attention with linear complexity to efficiently extract multi-frequency features. The model introduces a dual-stream feature extractor that processes global and local information in parallel, and a ContextGate block that adaptively fuses multi-scale representations. MedSpectralNet is evaluated across six benchmark datasets from MedMNIST (including BloodMNIST, BreastMNIST, DermaMNIST, PneumoniaMNIST, OrganCMNIST, and OrganSMNIST), MedSpectralNet achieves an average accuracy of 93.7% on OrganCMNIST and 98.0% on BloodMNIST, showing 1–4.3% relative accuracy gains when compared to larger transformer-based models. Importantly, it delivers this performance with only 8.5 million parameters, representing approximately 60% fewer parameters than MedMamba-T, which requires 14.5 million parameters. MedSpectralNet has also achieved high AUC values up to 0.999 across multiple classes, demonstrating state-of-the-art accuracy with substantially reduced computational cost and improved parallelization, which makes MedSpectralNet well-suited for real-time and resource-constrained classification-based medical applications. Citation: Afrin N, Fahim MA-NI, Alamro W, Allawi YM, Abadleh A, Sultan SM, et al. (2026) MedSpectralNet: A lightweight convolutional neural network architecture for multi-modal image classification. PLoS One 21(4): e0346128. https://doi.org/10.1371/journal.pone.0346128 Editor: Ali Mohammad Alqudah, University of Manitoba, CANADA Received: October 26, 2025; Accepted: March 16, 2026; Published: April 27, 2026 Copyright: © 2026 Afrin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data underlying the results presented in the study are available
Apr 27, 2026 · via journals.plos.org
Figures Abstract With the rapid development of wireless communication technologies, spectrum resources are becoming increasingly scarce, and spectrum monitoring technologies targeting control and interference suppression impose higher requirements on the real-time performance, reliability, and intelligence of signal detection, recognition, and key parameter extraction. Traditional signal processing methods heavily rely on operators’ prior knowledge, making it difficult to achieve intelligent spectrum monitoring, and often exhibit poor performance in complex electromagnetic environments with unknown signals or strong interference. Existing deep learning-based automatic modulation recognition techniques are more focused on signal recognition, with relatively limited research on detection and key parameter extraction. To address these challenges, this paper proposes a wideband signal processing frame based on semantic segmentation and signal spectrogram. The frame employs RepViT as the backbone network and achieves detection, recognition, and key parameter extraction of wideband signals through precise semantic segmentation of signal spectrogram. Experimental results on a large-scale synthetic dataset demonstrate that the proposed frame achieves a maximum signal recognition rate (mAcc) of 82.43% and an average signal recognition rate (aAcc) of 65.16% in multi-modulation scenarios and under different noise power levels. In terms of parameter extraction, the normalized root mean squared error (NRMSE) for time parameters (e.g., start time, and duration) is controlled within the ranges of 0.3%−2.8% and 0.4%−1.6%, respectively, while the NRMSE for frequency parameters (e.g., center frequency, and bandwidth) reaches 8.7% and 0.6% in multi-classification tasks, providing an effective reference solution for intelligent wireless signal analysis. Citation: Liu L, Zhu R, Chu P, Shi Z, Tian J, Zhang Y, et al. (2026) A frame of wideband wireless signal recognition and parameter extraction based on semantic segmentation. PLoS One 21(4): e0346685. https://doi.org/10.1371/journal.pone.0346685 Editor: Neng Ye, Beijing Institute of Technology, CHINA Received: December 15, 2025; Accepted: March 22, 2026; Published: April 27, 2026 Copyright: © 2026 Liu
Apr 27, 2026 · via journals.plos.org
JPLoft is recognized for delivering scalable, and enterprise-ready LLM solutions that empower organizations to turn data into intelligent business outcomes. DENVER, CO, UNITED STATES, February 27, 2026 /EINPresswire.com/ — As organizations worldwide accelerate digital transformation initiatives, JPLoft has announced a strategic expansion of its enterprise AI capabilities focused on advanced language intelligence and multimodal systems. The move reinforces the company’s commitment to building scalable, secure, and high-performance AI solutions tailored to complex business environments. Enterprises today are demanding smarter automation, deeper analytics, and more contextual intelligence to stay competitive. In response, JPLoft is reinforcing its technological foundation to build next-generation AI systems tailored to these evolving needs. These advanced solutions are designed to understand language, interpret visual data, and support high-value, data-driven decision-making across complex business environments. Meeting the Enterprise Demand for Language Intelligence The rapid growth of enterprise data—emails, documents, customer chats, reports, contracts, and multimedia content- has created both an opportunity and a challenge. Organizations need systems that can process, interpret, and extract insights from massive volumes of structured and unstructured information. To address this, JPLoft has positioned itself as a trusted LLM development company, helping enterprises design and deploy large language models tailored to their operational requirements. These advanced systems are built to summarize documents, generate reports, power intelligent chat interfaces, automate knowledge management, and enhance internal collaboration. Unlike generic AI tools, JPLoft focuses on domain-specific customization. Each model is trained and fine-tuned to align with industry language, regulatory requirements, and organizational workflows. This ensures relevance, accuracy, and long-term performance sustainability. A Structured Approach to AI Implementation Deploying large-scale AI systems requires more than model training. It demands structured planning, infrastructure readiness, and performance governance. JPLoft follows a phased implementation framework to ensure reliable enterprise adoption. The process includes: Business objective mapping Data readiness assessment Model selection
Apr 27, 2026 · via blufftontoday.com
Australia plans biometric liveness detection refresh for national digital ID Australia plans to contract a biometric liveness detection capability to support the country’s national digital ID and protect it against advanced spoof attacks and emerging fraud threats. The Australian Taxation Office (ATO), which manages myID within the Australian Governments Digital ID System (AGDIS), has published an RFI for biometric liveness detection. The government is also in the process of opening up the AGDIS to private sector identity verification providers. The “Biometric Verification Capabilities to Support myID” must come as a SaaS solution, and support peak workloads of 10,000 verifications per hour with 95th percentile responses within one second. The myID system is working towards adding two new identity proofing levels. IP1+ involves the confirmation of the individual’s name and date of birth through identity document verification. IP2+ includes authentication with Australia’s Face Verification Service (FVS) plus liveness detection. ATO is hoping for responses from suppliers with identity verification expertise, particularly related to liveness detection and facial image capture, biometric matching and credential validation through technologies such as NFC. The agency specifically wants to address advancements in liveness detection since 2021, when its current capability was sourced from iProov. Scalable biometric authentication is also a concern, given that myID is up to 14 million users, and the ATO also wants a capability to assess non-Australian ID documents for Australians living overseas. Requirements include sufficient image quality capture for biometric comparison, based on ISO/IEC 29794-5. Biometric presentation attack detection (PAD) that “meets at least Evaluation Assurance Level 2 (Level B) as defined by ISO/IEC 30107-3:2023 and the Digital ID (Accreditation) Data Standards” must be present and integrated as part of a “single continuous process.” A qualified third party must attest to that level of PAD compliance. The face biometrics matching algorithm must
Apr 27, 2026 · via biometricupdate.com
Olga Cronin Monday 27 April — Last week the Independent Examiner of Security Legislation Judge George Birmingham published his first annual report. In it, he looked at three pieces of security legislation and made recommendations in respect of each: - Interception of Postal Packets and Telecommunications Messages (Regulation) Act 1993; - Criminal Justice (Surveillance) Act 2009; and - Communications (Retention of Data) Act 2011. Although many of his recommendations are to be welcomed, we are very concerned about Judge Birmingham’s position on encryption in particular. On encryption, he concedes that “very many people in many different walks of life on a daily basis use encryption and are encouraged to do so. It has brought significant additional security to financial transactions and communications.” This is correct. Journalists use encryption to protect their sources and patients use it to communicate with their doctors. Undoubtedly, our own politicians, their staff, garda, and intelligence officers themselves use encrypted messaging services because of its security. Encryption protects and secures the processing of our data when we are online banking, online shopping, accessing health data and carrying out our employment. In essence, it is essential for our collective cybersecurity. But Judge Birmingham recommends that legislation be passed to provide “for lawful access to all communications, including encrypted communications, incorporating appropriate safeguards”. ICCL understands that encryption presents a challenge for law enforcement but this position misunderstands the issue and what’s at stake. Creating “appropriate safeguards” won’t solve the fundamental problem - any piercing of encryption on the supposed basis of creating pathways solely for An Garda Síochána introduces vulnerabilities that would be exploited by others, ultimately undermining the security and privacy of everyone. As we’ve previously said, the issue at stake here is not a simple trade-off between individual freedoms, such as privacy and expression, and State
Apr 27, 2026 · via iccl.ie
Why Elon Musk and Sam Altman are fighting over OpenAI
Musk, who co-founded the company that created ChatGPT with Altman, wants more than $130 billion in damages in a lawsuit that could shakeup the artificial intelligence landscape. The BBC's Lily Jamali explains why the two tech giants are facing off in court.
Apr 27, 2026 · via bbc.com
Rather than type in a password or PIN countless times daily, smartphone users turn to biometrics for device unlock. Your face and fingerprint are unique to you and are generally difficult to replicate, making these kinds of biometric security both convenient and safe. However, biometric sensors aren't all equal — some are easier to trick than others, making them less secure. Facial recognition is notoriously hard for smartphone manufacturers to implement without a dedicated depth sensor. A single camera usually isn't enough, which is why Apple's camera cutout for the Face ID sensors on iPhones is shaped like an oval rather than a circle. Adding a secondary sensor, like an infrared camera, flood illuminator, dot projector, or iris scanner, can elevate a facial recognition suite's accuracy and make it harder to thwart. This is crucial on the Android side of things, because Google splits biometric sensors into three groups: Class 1 (formerly Convenience), Class 2 (formerly Weak), and Class 3 (formerly Strong). Only a Class 3 biometric can allow access to banking and payment apps, as well as other sensitive data. Today, the only phone brand in the U.S. with a Class 3 facial recognition system is Google Pixel. Samsung had, then removed, a Class 3 face unlock feature from Galaxy phones, and this is the reason why. These Android features make your phone a nightmare to steal — I enable them all Your phone is easier to steal than you think—these Android features quietly flip the odds. The three levels of Android biometrics security Most users don't know about this critical biometric standard Android phones commonly offer both a fingerprint and face unlock feature. Fingerprint sensors on Android phones are either capacitive, optical, or ultrasonic, and all three sensor types can meet the Class 3 biometric standard. So, you've
Apr 27, 2026 · via makeuseof.com
Accelerating the next-generation AR glasses experience with a gaze-controlled UI and ultra-lightweight form factor TOKYO, April 27, 2026 /PRNewswire/ -- Cellid Inc., a developer of displays and spatial recognition engines for next-generation AR glasses, today announced that it has supplied its latest waveguide (AR glass lens) for the next-generation AI smart glasses "J9," developed by Jorjin Technologies Inc., a premier AR/XR platform solution provider dedicated to wearable innovation for decades. This product is scheduled for public announcement in Q2 2026, and its reference design for evaluation is available now. In Japan, Cellid will serve as the authorized distributor. Cellid has established a strong track record through its collaboration with the Foxconn Group on the development of next-generation waveguides. For this model, the "J9," Jorjin and Cellid have combined their world-class development and manufacturing capabilities. Leveraging their experience in mass production, they aim to further accelerate the transition of AR glasses into the phase of widespread real-world adoption. An intuitive user experience enabled by the "J9" eye-tracking UI The J9 features a "Gaze-Controlled UI," enabling users to complete all interface operations using only their eyes. With a hands-free, voice-free "Look. Select. Done." Interaction model, users can easily perform a wide range of tasks, including taking photos, recording videos, navigating form screens, and adjusting brightness. Key Features of "J9" - Equipped with Cellid's ultra-thin waveguide: The ultra-thin, high-brightness glass waveguide achieves a thickness comparable to standard eyeglass lenses. - Gaze-controlled UI: Enables hands-free and voice-free operation—users can take photos and control the interface simply by looking. - Built-in high-precision eye tracking: Uses two compact cameras and infrared LEDs to detect gaze with approximately 3-degree accuracy. - Full-color display on the lens: Projects full-color images (500 × 380 pixels) directly into the user's field of view with a 25° FOV. - High-performance processor
Apr 27, 2026 · via prnewswire.com
Disneyland is allowing guests to opt out of its new facial recognition system when entering its theme parks in California. This week, the resort rolled out facial recognition software combined with biometric technology at the front entrances of Disneyland and Disney California Adventure following limited tests over the past few months. According to the Disney Experiences website, the facial recognition software uses images of guests' faces taken by at a camera at the entrance and the image of their faces that was saved when they first used the ticket or pass. The software utilises biometric technology to convert those images into unique numerical values, which are compared to find a match. Except in cases where data needs to be maintained for legal or fraud-prevention purposes, Disneyland deletes all numerical values within 30 days of creation, the website says. Alternative entrances for guests For guests who "do not wish to use this service", entrance lanes that do not employ facial recognition technology are available. Guests who would prefer not to participate can enter through the parks' main entrances located along the Esplanade and choose an entrance lane with overhead signage showing a person with a diagonal strikethrough. "When using these entrance lanes, you may still have your image taken. However, these lanes will not utilize biometric technology on your image. Instead, a cast member will manually validate your ticket," the website says. Regarding the new facial recognition software, it says: "The security, integrity and confidentiality of your information are extremely important to us. "We have implemented technical, administrative and physical security measures that are designed to protect guest information from unauthorized access, disclosure, use and modification." It adds: "From time to time, we review our security procedures to consider new technology and methods, as appropriate. "Please be aware that, despite our
Apr 27, 2026 · via blooloop.com
Meta's New Smart Glasses Feature Could Be A Privacy Nightmare, According To Advocates Meta has been trying to work facial recognition systems into its social media environment for what seems like forever. Not only is the company planning to add this feature to its line of smart glasses, but The New York Times got its hands on an internal memo about how the project carries "safety and privacy risks." On April 13, the ACLU sent a letter to Mark Zuckerberg, claiming the upcoming facial recognition system, dubbed "Name Tag," poses a threat to "vulnerable communities." These include religious minorities, people of color, LGBTQ+ people, and survivors of stalking and sexual harassment. In fact, anyone with an online presence (which is basically everyone) is vulnerable, from children to the CEOs of major companies. The ACLU is concerned that since the smart glasses look like your run-of-the-mill prescription glasses, users could "surveil and profile" everyone they see covertly to "identify and stalk" potential victims. The organization is also concerned that members of police forces could wear the smart glasses and use Name Tag to violate the Fourth Amendment. The letter cites a 2024 incident where Harvard students used smart glasses equipped with facial recognition software to "identify strangers on the Boston subway in real time," as well as studies that demonstrate attacks (physical and otherwise) against members of the LGBTQ+ community have increased in recent years. Name Tag is a definite concern. Luckily, there are glasses with specialized lenses that fool facial recognition software and apps that act as anti-smart glasses radar to help combat such features. Meta couldn't have picked a worse time, and it did so intentionally When a company discusses such matters as "safety and privacy risks," it's usually under the purview of how to minimize them. That's certainly
Apr 26, 2026 · via bgr.com
On first glance, scammy dating profiles have nothing but green flags. They're verified accounts, often posing as handsome men in their 40s. Their photos tell the story of a laid-back, yet exciting life — gym selfies, beach trips, a snowy hike in the Alps — except for the very last picture. It's not a photo at all, but an illustration, clearly AI-generated, depicting a totally different person's face pasted onto a billboard, a bobble head, an anime character, or an oil painting. One weird picture might not be enough to raise alarm bells (seasoned swipers have seen much worse on the apps), but, as journalist Christophe Haubursin uncovered, it is a surefire sign of a romance scammer. And if you're not careful, they can drain your wallet before you go on one coffee date (1). How the scam works Haubursin, a former Vox journalist and now independent YouTube creator, first heard about the issue when a friend showed him screenshots of strange Tinder matches. All the accounts were verified, and they all seemed totally normal — except for the AI images at the end. She wondered if this was some kind of subtle signal. Could these people be in a secret society? It was nothing so romantic as that. Haubursin figured out that, once you chat with one of these users, they quickly try to move the conversations to WhatsApp and steer the topic to cryptocurrency. They eventually pressure their would-be sweethearts to urgently send money. It's obvious scammer behavior. So what's with the odd images? They're a way to get around security features like Tinder's Face Check. To get a "verified" badge on a dating app, you need to take a video of your face from several angles, much like you would do to set up FaceID on an
Apr 26, 2026 · via moneywise.com
We’ve all been there — you’re patiently waiting for the people in front of you to hop on the ride, but then the seatbelt check takes forever! The good news is, Disney just filed a patent to fix that. In theory, they wouldn’t even need ride operators to check the seatbelts and lap bars in the first place! If it’s implemented, ride loading times and overall throughput could decrease dramatically. The patent is for a new AI system that uses cameras and machine learning to verify that ride restraints are properly secured before a vehicle dispatches. It works by capturing continuous video of each passenger seat during the loading process. A restraint verification platform analyzes footage using multiple machine learning models trained to detect the passenger’s body position and size, the restraint type, and whether the restraint is correctly securing the passenger. While the use of AI for something so safety-critical might sound scary, it could detect scenarios that human operators might miss. The patent mentions scenarios where a rider may be sitting on top of a seat belt instead of beneath it, or when the belt is intentionally extended beyond its proper length to create a false sense of it being tightly secured. The system combines video analytics with data from current sensors, notably seat sensors that detect whether a guest is seated, clasping sensors that confirm whether a restraint is locked, and rotary encoder devices that measure the physical length of the seat belt extended. Right now, ride operators rely on a combination of manual visual checks and basic sensor data, which can’t detect all improper restraint situations that the new system would. The patent notes that restraint inspection is “time-consuming due to the number of passengers and seat belts to inspect and/or due to the level of
Apr 26, 2026 · via allears.net
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Apr 26, 2026 · via swoknews.com