Vadzo Imaging Announces Global Shutter Camera Portfolio for Smart City Vision: Why Embedded Vision Engineers Choose Global Shutter for Traffic Monitoring, Public Safety, and Urban Analytics Applications Vadzo Imaging highlights the Falcon-234CGS Global Shutter USB camera, Falcon-234MGS Mono USB camera, Falcon-235CGS Color USB camera, Falcon-235MGS Monochrome USB camera, and Falcon-900MGS Global Shutter Mono USB camera, five USB 3.0 global shutter camera products built on onsemi AR0234, AR0235, and Sony IMX900 sensors, engineered to address the motion distortion, synchronization, and image consistency challenges that make rolling shutter sensors unreliable for smart city deployments including traffic analytics, license plate capture, public safety surveillance, and edge AI inference. FORT WORTH, Texas, June 5, 2026 (Newswire.com) - Vadzo Imaging, a provider of embedded vision camera solutions, is addressing a fundamental image quality challenge in smart city vision system deployments: what happens to detection accuracy and system reliability when rolling shutter cameras are used to capture fast-moving vehicles, pedestrians, and urban activity. For engineers and system integrators building traffic monitoring infrastructure, license plate recognition systems, public safety networks, and edge AI platforms, rolling shutter distortion produces skewed frames, geometric inconsistency, and misread data the moment subjects move at any meaningful velocity. When downstream algorithms depend on accurate vehicle classification, plate character recognition, or pedestrian tracking, that distortion translates directly to missed detections, false positives, and infrastructure that fails to meet its design specification. Vadzo's answer is the Falcon series which includes five USB 3.0 global shutter camera products purpose-built for urban and infrastructure environments, powered by onsemi AR0234, Onsemi AR0235, and Sony Pregius S IMX900 sensors. Together they cover monochrome precision analytics, color vehicle and pedestrian detection, high-resolution wide-area surveillance, and NIR-capable low-light operation across a single, consistent USB 3.0 camera platform with OEM customization support at every level. Why Global Shutter is the Right
Jun 5, 2026 · via newswire.com
BuzzFeed GamesOnly People With A Very, Very, Very, Very, Very, Very, Very, Very, Very, Very, Very, Very Good Eye For Color Can Solve Today’s HuedokuHuedoku #38! It’s Friday. Go out on a colorful high note. 🌈🎉Posted 6 hours agocommentFacebookPinterestLinkby Crystal RoBuzzFeedBuzzFeed StaffHi, I’m Crystal, a Senior Editor based in Los Angeles and creator of BuzzFeed’s “That Got Dark” newsletter. Huedoku is a daily color puzzle with simple rules and a satisfying solve. It's like sudoku, but with colors instead of numbers. Sign up here to get notified every time we publish a new Huedoku! Have a great weekend — Huedoku #39 drops Monday! 🌈 🌈 New Huedoku drops every weekday at 4:00 a.m. PT / 7:00 a.m. ET. 🧩🗂️ Huedoku Archive — Every past puzzle, all in one place! Comments
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Jun 5, 2026 · via buzzfeed.com
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Jun 4, 2026 · via youtube.com
Figures Abstract Life on Earth has evolved into a staggering diversity of species, most of which still remain undiscovered, unrecognized, or unmonitored. As our ocean’s richest biodiversity hotspot, coral reefs harbor more than one third of marine biodiversity, but many reef species are small and cryptic and, therefore, difficult to identify and study. Among these, tiny bottom-dwelling (‘cryptobenthic’) fishes have been highlighted as a highly diverse (>3,000 species), understudied, and ecologically important group. However, the classification and monitoring of these fishes depend almost exclusively on the knowledge of few expert scientists, which has resulted in limited knowledge concerning the taxonomy, distribution, and population trends of these fishes. Deep learning-driven image classification—known for its ability to learn complex patterns in visual data—is an ideal candidate for automating taxonomic image classification and therefore broaden participation in ecological monitoring and biodiversity science. We developed CryptoVision, a new taxonomy-aware convolutional neural network with three output heads that explicitly considers taxonomic hierarchies (family, genus, species) and their biological constraints. Built on ResNet50v2 and enhanced with Squeeze-and-Excitation modules, CryptoVision employs a custom taxonomy-focal cross-entropy loss and four hierarchical fusion strategies (standard, concatenation, gating, attention) to assess the algorithm’s performance. Trained on a unique dataset of ~7,600 laboratory-standard and ~18,800 web-sourced images covering 113 species of small reef fishes, our tool highlights the power of integrating deep learning with innovative, taxonomically-informed design and high-resolution imagery. Indeed, CryptoVision achieved a ~ 25% improvement across all metrics when lab-standard imagery was incorporated and among the fusion variants, the gating approach delivered the best calibration (expected calibration error ≈ 0.01) and 90.5% average precision. Finally, guided saliency map analyses of species in the dwarfgoby genus Eviota illustrate that model attention can align with expert-defined morphological traits that represent critical features for species delimitation. Our results demonstrate that taxonomy-aware, multi-output deep
Jun 4, 2026 · via journals.plos.org
Gandhinagar, December 8-10, 2022
The Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) is Indiaâs premier conference in Computer Vision, Graphics, Image Processing and related fields. Starting in 1998, it was a biennial international conference till 2021, providing a forum for the presentation of technological advances and research findings in these areas. ICVGIP 2022, the 13th conference in this series, is being organized by IIT Gandhinagar in association with the Indian Unit for Pattern Recognition and Artificial Intelligence (IUPRAI), an affiliate of the International Association for Pattern Recognition (IAPR). ICVGIP will be held annually from 2022 due to the huge growth in the community.
ICVGIP is dedicated to fostering the community of computer vision, graphics and image processing researchers and enthusiasts in India and abroad. We strive to live up to this goal at every occurrence of this annual conference.
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🔗 Best Paper Awards
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(Date: 9 Dec 2022, Time: 7PM-8PM, Venue: Jasubhai Memorial Auditorium)
Jun 4, 2026 · via events.iitgn.ac.in
JACKSONVILLE, Fla. — New details are emerging in the case of a North Carolina man who spent more than 50 days in jail after authorities identified him as a suspect in a Jacksonville crime he did not commit. The case began with a report of a stolen vehicle and surveillance video captured at a Publix on Baymeadows Road. Jalil Richardson said investigators never properly verified whether he was even in Florida before identifying him as a suspect. “There was no proper investigation done, um, to even reach out to me or to see if I was even in Florida,” Richardson said. >>> STREAM ACTION NEWS JAX LIVE <<< The State Attorney’s Office said an 85% facial recognition match and two eyewitness identifications were enough to establish probable cause against Richardson. However, prosecutors later dropped the case after evidence showed he was hundreds of miles away in North Carolina at the time of the crime. “And I sat in there for over 50 days in the most worst jail ever,” Richardson said. The State Attorney’s Office told Action News Jax it has identified two cases in which AI facial recognition technology used by the Jacksonville Sheriff’s Office helped develop the wrong suspects. One was Richardson’s case. The other involved Robert Dillon, whose case was also dropped for similar reasons. “The technology is simply too dangerous for law enforcement to be using at all,” said Adam Schwartz, privacy litigation director for the Electronic Frontier Foundation. Schwartz, who has practiced law for 30 years, pointed to a list compiled by the American Civil Liberties Union documenting wrongful arrests nationwide linked to facial recognition errors. [DOWNLOAD: Free Action News Jax app for alerts as news breaks] “More than a dozen innocent people have been arrested by police because of errors with face recognition,” Schwartz
Jun 4, 2026 · via actionnewsjax.com
JACKSONVILLE, Fla. — New details are emerging in the case of a North Carolina man who spent more than 50 days in jail after authorities identified him as a suspect in a Jacksonville crime he did not commit. The case began with a report of a stolen vehicle and surveillance video captured at a Publix on Baymeadows Road. Jalil Richardson said investigators never properly verified whether he was even in Florida before identifying him as a suspect. “There was no proper investigation done, um, to even reach out to me or to see if I was even in Florida,” Richardson said. >>> STREAM ACTION NEWS JAX LIVE <<< The State Attorney’s Office said an 85% facial recognition match and two eyewitness identifications were enough to establish probable cause against Richardson. However, prosecutors later dropped the case after evidence showed he was hundreds of miles away in North Carolina at the time of the crime. “And I sat in there for over 50 days in the most worst jail ever,” Richardson said. The State Attorney’s Office told Action News Jax it has identified two cases in which AI facial recognition technology used by the Jacksonville Sheriff’s Office helped develop the wrong suspects. One was Richardson’s case. The other involved Robert Dillon, whose case was also dropped for similar reasons. “The technology is simply too dangerous for law enforcement to be using at all,” said Adam Schwartz, privacy litigation director for the Electronic Frontier Foundation. Schwartz, who has practiced law for 30 years, pointed to a list compiled by the American Civil Liberties Union documenting wrongful arrests nationwide linked to facial recognition errors. [DOWNLOAD: Free Action News Jax app for alerts as news breaks] “More than a dozen innocent people have been arrested by police because of errors with face recognition,” Schwartz
Jun 4, 2026 · via actionnewsjax.com
SANTIAGO, Chile, and BOSTON, June 04, 2026 (GLOBE NEWSWIRE) -- Classiq and Pontificia Universidad Católica de Chile (UC Chile) today announced a joint research project to develop hybrid quantum algorithms for biomedical image analysis, assisted by classical machine learning and the NVIDIA CUDA-Q platform for quantum-classical computing. The 12-month engagement, titled “Enhancing Pathology through Quantum Computing,” is funded through Avanza UC 2025, the Internal Research and Creation Competition of UC Chile. To the collaborators’ knowledge, it is the first announced consortium in Latin America to combine quantum computing, machine learning and computational pathology. The engagement marks quantum computing’s and Classiq’s growing presence in Latin America and reflects the company’s expanding work with academic, research and public-sector institutions, including in health innovation. It also reinforces Chile’s emerging role in quantum computing, AI and advanced technology development. A Media Snippet accompanying this announcement is available by clicking on this link. Quantum machine learning applies quantum computing methods to machine learning problems, including classification, pattern recognition and complex data analysis. The initial project focus is on renal pathology, an area of growing public health importance in Chile and across Latin America. This includes applying quantum machine learning to computational pathology, with an initial emphasis on kidney lesion classification, automated glomerular segmentation and semantic pattern search across full histological slides. The work will be conducted in collaboration with Dr. Luciano Rebouças and Dr. Washington Conrado, researchers at Fundação Oswaldo Cruz (FIOCRUZ) and professors/researchers at Universidade Federal da Bahia (UFBA) in Brazil, combining expertise in digital pathology, computer vision and biomedical data analysis using curated histopathology datasets, provided by the Brazilian institutions. The research will leverage the Classiq quantum computing software platform and the NVIDIA CUDA-Q platform to leverage a seamless workflow from algorithm development through to simulation and execution. “Latin America has the scientific
Jun 4, 2026 · via markets.businessinsider.com
Wired found code for an unreleased facial recognition feature in Meta's AI app Meta was previously reported to be exploring facial recognition for its smart glasses. Code for a facial recognition feature that can run on Meta smart glasses is buried in the company's Meta AI app, according to a new report from Wired. While not currently enabled, accessible to customers or part of a formerly announced feature, the code appears to be further evidence that Meta is considering how facial recognition could work with its smart glasses, as The New York Times first reported in February. The feature, called "NameTag" in the code Wired found, is reportedly capable of capturing people's faces using the company's smart glasses and later notifying the wearer when it recognizes a previously captured face. No part of NameTag is currently running or sending biometric data to Meta's servers today, according to a security researcher who reviewed the code Wired found, but past versions of the Meta AI app have included interface elements for the feature, like a "Connections" menu that suggests users "remember the people you met." Anonymous Meta sources who spoke to The New York Times similarly referred to the company's facial recognition tool as "Name Tag." Per a memo reviewed during reporting, Meta was interested in launching the feature during a "dynamic political environment" in the US because "civil society groups that we would expect to attack us would have their resources focused on other concerns." While there are potential accessibility benefits to a pair of smart glasses that can identify faces for users with visual impairments, the feature poses serious ethical concerns, too. "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,"
Jun 4, 2026 · via engadget.com
Meta has deployed facial recognition code to millions of their always-on surveillance glasses, according to new reporting by Wired. EFF’s Threat Lab was able to confirm that the facial recognition code is present through static analysis of the application. This dangerous new Meta functionality stores faceprints as a series of 2,048 numbers uniquely representing the positioning of a person’s facial features. When this feature is activated, it will convert every new face in the sightlines of the surveillance glasses into a series of numbers, and compare it to all the existing faceprints in the user’s database. Wired and EFF confirmed that the code is present and active, though not yet exposed to consumers. Another researcher confirmed that when they manually added a face to the app database by connecting the phone to a computer in debug mode and issuing a few commands, the glasses would subsequently detect that face when it came into view. Meta has already paid $650 million to settle a BIPA lawsuit challenging mass facial recognition of every photo posted to its platform, a feature which it has since shut down. Despite the billions of reasons not to, Meta seems to have created the capacity to turn their customers into a distributed surveillance machine. This is just one more reason to think twice before buying or using Meta’s surveillance glasses. Considering that Meta previously wrote in an internal document that they want to launch facial recognition “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," this invasive new feature doesn't come as a surprise. But Meta's surveillance plans won't escape public scrutiny that easily, and we'll be watching if this feature is rolled out to the public.
Jun 4, 2026 · via eff.org
The Lutheran Foundation awards Health Professions Scholarships to Trine University students The Lutheran Foundation is pleased to announce three Health Professions Scholarship recipients from Trine University. June 04, 2026 Spaces are still available in Trine University’s 2026 Allen School of Engineering and Computing summer camps, which will take place July 22-24. The Engineer Your Future and Tech Titans Computing camps provide high school students with the opportunity to gain hands-on experience and learn from Trine University’s expert faculty. Campers stay overnight in Trine’s apartment-style residential facilities and enjoy meals at the award-winning Whitney Commons Café. The Engineer Your Future camp exposes students to engineering fields such as biomedical, chemical, civil, electrical, design and mechanical engineering through hands-on activities. Past activities have included building an electronic cicada, assembling a 3D-printed prosthetic hand, designing and constructing a cantilevered bridge, making ice cream and building and launching a water-powered rocket. The Tech Titans Computing Camp introduces students to virtual reality, Python programming, robotics and embedded computers, AI image recognition and cybersecurity. The camps expose students to specific engineering and computing disciplines and examples of projects that engineering and computing majors at Trine University might design. Cost for the camps is $100. For more information, visit trine.edu/camps.
Jun 4, 2026 · via trine.edu
Figures Abstract Background Postoperative cerebrovascular events, including transient ischemic attacks, infarctions, and hemorrhages, remain a significant concern in pediatric patients with Moyamoya disease (MMD)undergoing surgical revascularization. This study aimed to develop an explainable deep learning-based classification model using intraoperative arterial blood pressure (ABP) waveform analysis for postoperative cerebrovascular events in pediatric patients undergoing surgery for MMD, with exploratory analysis of associated waveform-derived physiologic features. Methods This retrospective study included 181 pediatric patients (≤18 years) who underwent revascularization surgery for MMD, with an independent temporal holdout cohort of 79 patients reserved for validation. ABP signals were preprocessed using detrending, pulse segmentation, and normalization, then converted into image representations for deep learning classification. Various convolutional neural network (CNN) models, including ResNet50, ResNet34, DenseNet121, VGG16, and VGG19, were evaluated against Vision Transformer (ViT) architectures. Multiple image transformation methods were tested, and Grad-CAM analysis and statistical comparisons of waveform-derived physiologic features were conducted between patients with and without postoperative cerebrovascular events. Results The optimal model configuration achieved the best performance using raw pulse waveforms with three consecutive pulses per image. CNN-based models outperformed ViT-based models, with the highest internal classification performance observed using raw pulse waveforms (AUROC = 0.772, SD = 0.070).In the independent temporal validation cohort, the model achieved an AUROC of 0.738 ± 0.011 at the patient level. Grad-CAM visualization highlighted the diastolic runoff phase as a region of interest for classification. Four waveform-derived features related to arterial compliance were significantly associated with postoperative cerebrovascular events (p < 0.05). Conclusions In this study, CNN-based deep learning models demonstrated the feasibility of predicting postoperative cerebrovascular events from intraoperative ABP waveforms, with diastolic runoff dynamics emerging as a potentially relevant physiologic pattern. These findings are exploratory and require prospective multi-center validation before clinical application. Citation: Park J-B, Shin Y, Kim J, Kim YJ, Lee
Jun 4, 2026 · via journals.plos.org
Meta has quietly embedded face-recognition technology for its smart glasses into an app downloaded to millions of phones, according to a WIRED analysis of the company's software. Code discreetly added to Meta’s AI app over multiple updates this year shows that the feature, internally called “NameTag,” identifies people captured by the glasses’ camera and, when activated, alerts the wearer when it recognizes someone. The discovery of NameTag in the live Meta AI app shows that Meta had begun shipping face-recognition code to users' phones while publicly describing it as something the company was still “thinking through.” In April, Meta said if it were to utilize face recognition, it wouldn't be rolled out without first taking "a very thoughtful approach." But WIRED found that as early as January, core components of the system had been integrated into software distributed to millions of people. Though not yet enabled, NameTag sits inside a Meta AI companion app that's been downloaded over 50 million times and is necessary for use of key features of its smart glasses, including Ray-Ban and Oakley models. If activated, 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—a database that’s currently configured to receive updates from Meta. Recognized faces will trigger notifications, while the rest are cropped, indexed, and saved to a folder marked “pending.” | Got a Tip? | |---| | Are you a current or former Meta employee who wants to talk about the company's technologies? We'd like to hear from you. Using a nonwork phone or computer, contact the reporter securely on Signal at dmehro.89 or dell.3030. | NameTag would revive a type of technology Meta said it had sunsetted in 2021, when the company announced it would
Jun 4, 2026 · via wired.com
Sponsored by Google Cloud Choosing Your First Generative AI Use Cases To get started with generative AI, first focus on areas that can improve human experiences with information. The systems, powered by Cosmos 3, are designed to accelerate development of autonomous vehicles, robots and vision AI systems. Nvidia has released a spate of new physical AI research tools, agent workflows and open source models to train more advanced AI systems for the real world. Unveiled this week at the Computer Vision and Pattern Recognition conference in Denver, the updates build on Nvidia's recently launched Cosmos 3 world foundation model and are designed to help researchers automate key stages of physical AI development, including simulation, synthetic data generation, policy training and evaluation. Physical AI refers to AI systems that interact with and operate in the physical world, including self-driving vehicles, industrial robots and embodied AI agents. The company said the new capabilities address a major challenge facing engineers in the industry: creating scalable workflows to train and test AI virtually before real-world deployment. “The core challenge in physical AI research isn’t simply developing stronger models. It’s building a full workflow around them,” Nvidia said in a blog post. “Today, these steps are fragmented across separate tools, slowing the pace of experimentation as researchers struggle to piece them together.” Among the announcements are new agent skills integrated across Nvidia Omniverse, Isaac Sim, Isaac Lab and Cosmos, enabling developers to automate tasks such as scene reconstruction, simulation setup, environment generation and reinforcement learning workflows. For autonomous vehicle development, Nvidia introduced tools to help researchers address the industry's “long-tail problem” --difficult-to-capture driving scenarios that are critical for training and validation. To bridge this gap, Nvidia said its AI agents can now automate the reconstruction of real-world driving environments from fleet data and generate synthetic
Jun 4, 2026 · via aibusiness.com
Amazon faces backlash for AI data center boom amid 30,000 job cuts, Seattle halts new data center projects.
Amazon engineers criticized the company for investing $200 billion in AI data centers while cutting 30,000 corporate jobs, raising concerns about resource use and workforce impact. Seattle City Council responded by approving a one-year moratorium on ...
Jun 4, 2026 · via pluang.com
BuzzFeed GamesIf You Can Solve This Color Puzzle In Less Than 3 Minutes, You Have Perfect Color VisionHuedoku #37! Thursday means the weekend is almost here — treat yourself. 🎨✨Posted 17 hours agocommentFacebookPinterestLinkby Crystal RoBuzzFeedBuzzFeed StaffHi, I’m Crystal, a Senior Editor based in Los Angeles and creator of BuzzFeed’s “That Got Dark” newsletter. Huedoku is a daily color puzzle with simple rules and a satisfying solve. It's like sudoku, but with colors instead of numbers. Sign up here to get notified every time we publish a new Huedoku! One more tomorrow — come back for Huedoku #38 and finish the week strong! 🌈 🌈 New Huedoku drops every weekday at 4:00 a.m. PT / 7:00 a.m. ET. 🧩🗂️ Huedoku Archive — Every past puzzle, all in one place! Comments
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Jun 4, 2026 · via buzzfeed.com
Abstract Deep diffractive neural networks have emerged as an all-optical computing paradigm that overcomes the bottlenecks of traditional electronic computing. However, traditional deep diffractive neural networks are typically limited to single-task processing. Here we demonstrate an orbital angular momentum multiplexing diffractive neural network (OAM-MDNN) that completes several recognition tasks simultaneously by exploiting orthogonal OAM modes to encode and decode information. We develop an end-to-end auto-optimizing strategy that improves mode utilization efficiency by approximately 14 times and the peak signal-to-noise ratio by approximately 14.4% compared with manual encoding. We experimentally demonstrate a 10-mode multiplexing recognition task for 40 object categories, including handwritten digits, letters and fashion items, with an average recognition accuracy of 86.8%. Further experiments validate the scalability and performance of the network for up to 100 object categories. Theoretically, we also show that OAM-MDNN can be scaled to up to 40 modes times 10 classes (400 classifications), showing potential for further extension. In addition, our OAM-MDNN supports simultaneous multitask input, enabling parallel recognition with high accuracy and minimal intermode crosstalk. We applied our architecture to real-world tasks, such as gesture recognition in numerical simulations, which demonstrated its practical application potential in biometric identification. OAM-MDNN paves the way for high-capacity and multi-dimensional parallel optical information processing based on OAM multiplexing. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription $32.99 / 30 days cancel any time Subscribe to this journal Receive 12 print issues and online access $259.00 per year only $21.58 per issue Buy this article - Purchase on SpringerLink - Instant access to the full article PDF. USD 39.95 Prices may be subject to local taxes which are calculated during checkout Similar content being viewed by others Data availability All
Jun 4, 2026 · via nature.com
Xiao-I Regains Full Nasdaq Compliance, Securing Continued Listing for ADSs Claim 55% Off TipRanks - Unlock trusted, data-backed investing tools with TipRanks Premium, from analyst ratings and forecasts to breaking news and portfolio analysis. - Discover high-conviction stock picks and new investing opportunities with the TipRanks Smart Investor Newsletter An announcement from Xiao-I Corp. ADR ( (AIXI) ) is now available. Xiao-I Corporation, a Shanghai-based cognitive intelligence specialist, develops enterprise AI products spanning natural language processing, voice and image recognition, machine learning, and affective computing for Chinese industries. Since 2001, it has built a broad portfolio designed to support digital transformation and intelligent upgrading across multiple commercial applications. On April 23, 2026, Xiao-I regained compliance with Nasdaq’s minimum market value requirement after its publicly held shares stayed above $15 million for ten consecutive business days. Subsequently, Nasdaq confirmed on May 29, 2026 that the company also met the minimum $1.00 bid price standard for ten straight trading days, restoring full compliance and ensuring its ADSs continue trading on the Nasdaq Global Market under the ticker AIXI. The resolution of both December 2025 deficiency notices removes an overhang on Xiao-I’s U.S. listing status and reduces near-term delisting risk for investors. By securing continued access to Nasdaq’s capital markets, the company preserves an important funding and visibility platform as it competes in the fast-evolving enterprise AI sector. Spark’s Take on AIXI Stock According to Spark, TipRanks’ AI Analyst, AIXI is a Neutral. The score is weighed down primarily by very weak financial performance (sharp 2025 revenue contraction, rapidly widening losses, negative equity, and ongoing cash burn). Technicals provide only modest support (near-term rebound vs. shorter moving averages and oversold signals, but weak momentum and below the 200-day trend). Valuation is also constrained by loss-making results (negative P/E) and no dividend yield data.
Jun 4, 2026 · via theglobeandmail.com
Hi. My name is Hoan Ton-That, and I’m addicted to Claude Code. I’m a lifelong programmer and the founder and former CEO of Clearview AI, the country’s first major facial recognition company that used AI, and where I wrote the first version of our software myself.
When I started Clearview, it ended up landing on the front page of The New York Times in January 2020 with the headline “The Secretive Company That Might End Privacy as We Know It.” We had built a groundbreaking facial recognition technology, which allowed law enforcement to search billions of public images just by uploading a photo of a face. The technology helped law enforcement solve crimes involving children, human trafficking, and financial fraud.
The reaction to our new technology raised concerns from privacy advocates and the public. Today’s simultaneous fear and excitement around AI coding tools remind me of what I went through with Clearview, and what any new technology goes through.
Two months ago I started using Claude Code, OpenAI’s Codex, and Cursor. I’m having the most fun I’ve ever had programming, and I can now build software that used to take months in a matter of days.
Jun 4, 2026 · via thefp.com
At the end of the day, an AI model is only as good as the data behind it. More examples, greater variety, and higher-quality inputs lead to better, more reliable results. But without the right training dataset, even the best architecture will fall short. In this article, we’ve rounded up five powerful sources for image datasets that are helping developers and researchers build smarter AI models in 2026. Whether you’re working on object detection, image classification, or facial recognition, you’ll find a dataset worth exploring. What is an image dataset? An image dataset is a structured collection of labeled images used to train, test, and evaluate computer vision models. By exposing AI to thousands or even millions of examples, these datasets help models learn to recognize patterns and identify objects, powering everything from facial recognition and object detection to image classification. Common use cases for image datasets - Reverse image search Image datasets are the foundation of reverse image search, a feature that allows users to find similar or identical images by uploading a picture or providing a URL instead of typing text. It’s widely used by stock content platforms like DepositPhotos, e-commerce apps such as Vinted, and search engines like Google. By training on large collections of labeled images, AI models learn to compare visual features and match an uploaded image with visually similar content across the web or within a platform. As a result, users can quickly identify a product in an image or find a higher-resolution version of a photo without typing a single word. - Behavior analysis Image datasets are also central to behavior analysis, where AI models are trained to detect and interpret human actions, facial expressions, and behavioral patterns. This can be used to monitor a driver’s attention on the road or measure student
Jun 4, 2026 · via markets.financialcontent.com