The country's three major mobile carriers and other operators will apply the strengthened verification procedures to both in-person and online sign-ups. The stricter procedures will apply to new subscriptions and number transfers, while simple device upgrades within the same carrier will be excluded. Customers are required to undergo facial recognition, which compares the photo on their ID with a live image of themselves. Those who do not wish to do so or fail the scan may instead verify their identity through a mobile ID app or by submitting a resident registration record issued the same day by an authorized government agency. The ministry said the new measures are intended to crack down on sign-ups made through identity theft. While mobile phones are widely used for identity verification in financial transactions and online services, those registered under someone else's name can be sold as "ghost phones" and used for voice phishing, illegal loans and smishing. But some inconvenience is expected, as facial recognition can fail depending on lighting conditions or differences between an ID photo and a person's current appearance. Online sign-ups and older users may also find the process more burdensome. The ministry said it will work to minimize disruption by gradually offering more verification options, while also stepping up monitoring of retailers. Inspections and penalties will be strengthened for stores and agencies involved in fraudulent activations. Copyright ⓒ Aju Press All rights reserved.
Jul 6, 2026 · via m.ajupress.com
Madison Square Garden's list of activists critical of the company raises freedom of expression concerns
"Madison Square Garden Made Dossier on Activists Who Opposed Facial Recognition" 23 June 2026
Madison Square Garden compiled a list of activists who have publicly criticized the venue’s use of facial recognition technology, putting their tweets and comments into a document that was then accessible to other people inside the company, 404 Media has found...
...The document, titled “Facial Recognition Activists.docx” and included in a folder named “Activists,” lists three people who have criticized MSG’s use of facial recognition: Evan Greer, director of digital rights group Fight for the Future; Albert Fox Cahn, founder-in-residence of the Surveillance Technology Oversight Project (STOP); and the EFF’s Schwartz. All three of the activists have been quoted in major media articles discussing MSG’s facial recognition technology, including in NPR and The New York Times...
...It is not clear who wrote the document. MSG did not respond to a request for comment...
Jul 6, 2026 · via business-humanrights.org
NSW Labor toughens pokies stance
The NSW state government has passed a motion with unanimous support committing to higher taxes on some clubs and a moratorium on licences for new pokie machines.
The motion would mean clubs with profits of more than $20m on machines would pay more tax, coming with a commitment from Labor to “significantly reduce” the number of gaming machines in the state over 10 years (The Guardian).
Senior party figures at Sunday’s NSW Labor conference accused state politicians of bowing to pressure from lobby groups and “looking the other way” to maintain the status quo on poker machines (SMH).
“[The motion] is about lasting structural reform,” said gaming minister David Harris at the conference. “It puts harm minimisation at the heart of our gaming system, expands support for those experiencing gambling harm, strengthens prevention and ensures accountability is built into the system, not borne by those it has failed” (SMH).
The policy would also eliminate perks such as free food for punters and make facial recognition technology mandatory in all gaming rooms (SMH).
Read more: A thousand days of inaction on gambling reform (The Saturday Paper)
Jul 5, 2026 · via thesaturdaypaper.com.au
Artificial intelligence returns home: why home AI is challenging the cloud Mini-computers, personal servers and PCs designed to run models locally. From Raspberry Pi to Nvidia, the home AI ecosystem is growing: greater control over data, less reliance on the cloud and a new balance between privacy, cost and ease of use. A small box on the desk – and the AI is ready to go. On-premises, so to speak. Under our control. It’s a choice that some companies are already making to keep cloud-based AI costs down, but one that is also beginning to catch on amongst some of the more savvy consumers and professionals. The best-known symbol of home AI is probably the Raspberry Pi. Originally designed as a mini-computer for education and experimentation, it can now be transformed into a platform for artificial intelligence thanks to the dedicated accelerators in the AI Hat+ family. For just a few dozen euros, you can add inference capabilities for applications such as image recognition, environmental monitoring, automation and small local agents. The AI Hat+ accessory is priced from around 70 dollars for the 13-tops version (thousands of billions of operations per second), whilst the 26-tops model costs around 110 dollars. You’ll also need to add a Raspberry Pi 5, a power supply, storage and cooling. A complete system can easily cost over 200 euros. The products are available via the official Raspberry Pi website and European distributors such as Kubii and Melopero. It’s an option that’s gaining popularity amongst video creators. There are more sophisticated alternatives. Nvidia, the market leader in AI chips, offers the Jetson Orin Nano Super Developer Kit. This is a platform designed for robotics, computer vision and edge AI, capable of processing images, videos and sensor data directly on the device. With performance reaching 67 TOPS
Jul 5, 2026 · via en.ilsole24ore.com
A Polish Soldier with the Armed Forces of the Republic of Poland march after receiving a recognition during the opening ceremony of a joint Fourth of July celebration in Poland, July 4, 2026. The event brings together Polish service members and U.S. Army Soldiers from Task Force Danger as they mark 250 years of American freedom and independence with food, games, and live performances. (U.S. Army photo by Sgt. Roberto Diaz)
| Date Taken: | 07.04.2026 |
| Date Posted: | 07.05.2026 06:12 |
| Photo ID: | 9793133 |
| VIRIN: | 260704-Z-DV259-1600 |
| Resolution: | 3319x4978 |
| Size: | 1.72 MB |
| Location: | PL |
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This work, Task Force Danger 4th of July Celebration [Image 8 of 8], by SGT Roberto Diaz, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
Jul 5, 2026 · via dvidshub.net
We are in an era where a glance can unlock a phone, authorise a payment, or clear a security checkpoint. Facial recognition is increasingly relied upon as a fast and convenient form of identity verification. But cybersecurity experts warn that the technology is not foolproof, and that hackers are developing sophisticated methods to bypass it using nothing more than a photograph. In recent months, incidents of biometric fraud have risen, highlighting an unsettling reality: the same facial images people share online for convenience can be weaponised for crime. Facial recognition systems are used in everything from smartphone security to border control. But experts say that while the technology has improved, so too have the tools used to defeat it. “Facial recognition is only as strong as its anti-spoofing measures,” said Dr Lena Ortiz, a cybersecurity researcher at the Institute for Digital Trust. “When those protections are weak or absent, a photo becomes a key.” Officials and researchers point to multiple attack methods now being used by criminals, including photo spoofing, deepfake videos, and even 3D-printed masks. Simple Photos One of the most common attacks is deceptively low-tech: using a high-resolution photo to trick a scanner. According to Sumsuber, in many systems, the scanner cannot distinguish between a live face and a still image. Hackers have exploited this by obtaining images from social media, leaked databases, or even public footage. “It’s a simple attack,” said Jordan Patel, a cybersecurity analyst tells Sumsuber media. “But it works because many systems still lack liveness detection.” This vulnerability has been documented in multiple cases where attackers gained access to smartphones, financial apps, and secure facilities by presenting nothing more than a printed photograph. Many facial recognition systems rely on 2D image matching, which means they compare the geometry and features of a face without
Jul 5, 2026 · via eastleighvoice.co.ke
An Giang (VNA) – Amid the hum of machinery and the rhythmic movements of robotic arms in the training workshop of An Giang Vocational College in An Giang province, lecturer Nguyen Duc Tai patiently guides students through programming commands on computer screens. For years, the 39-year-old has worked to bring advanced technologies once found only in modern factories into vocational classrooms, helping bridge the gap between education and industry. Turning ideas into practical training tools Tai developed a passion for scientific research and technological innovation while at university, believing science should be applied to solve real-world problems. After joining An Giang Vocational College, he realised the school's training equipment lagged behind technologies used by businesses, while purchasing modern machinery required significant funding. Instead of waiting for new equipment, he designed and built training models himself. One of his most notable innovations is a three-axis camera robot integrating precision mechanics, control programming and AI-based image processing. Built using Jetson Nano, Arduino, Python, OpenCV and YOLO, the model enables students to practise computer vision and industrial automation in the classroom. Rather than relying solely on textbooks, students programme robots, train AI image-recognition models and develop complete automation systems during practical sessions. Tai said this hands-on approach helps them master new technologies while meeting industry demands. His work has earned widespread recognition. He has led numerous research projects and won prizes at national competitions for self-made teaching equipment. His three-axis camera robot received the Vietnam General Confederation of Labour's Creative Labour Certificate in 2025, following the same honour for his autonomous robot project in 2022. In 2023, he was honoured as one of Vietnam's outstanding young teachers. Helping vocational students master AI After more than a decade in vocational education, Tai has built a learning environment where students gain early exposure to AI,
Jul 5, 2026 · via en.vietnamplus.vn
“While it is for public use, it doesn’t mean that we breach your privacy,” he said in Miri after launching the Miri Smart City command centre, Dayak Daily reported.
While the system captures real-time images of passing motorcycles and automatically logs vehicle number plates, the purpose is to provide a more active, secure environment for citizens, he was quoted as saying.
The real-time tracking system operates over a 77km grid and uses artificial intelligence. Abang Johari expressed confidence that the framework will eventually serve as a model to be expanded across the state.
The premier said he had suggested that the command centre have another app where they can summarise all the activities within a week. The weekly data summary will allow the city council to constantly monitor patterns and steadily improve public service delivery across the grid.
Jul 5, 2026 · via freemalaysiatoday.com
▲ Facial recognition introduced for mobile phone activation to strengthen crackdown on illegal burner phones Starting July 6, enhanced identity verification procedures, including facial recognition, will be implemented across all mobile carrier and budget phone channels for new mobile phone activations or carrier switches. According to the Ministry of Science and ICT on July 5, the three major mobile carriers and budget phone operators will enforce stricter identity verification protocols than the existing ID card checks across all channels, including offline agencies, retail stores, and online platforms, beginning July 6. This measure is designed to prevent illegal mobile phone activations through identity theft, thereby curbing public crimes such as the use of burner phones and voice phishing. The government has decided to expand the system, which has been in a pilot phase since the end of last year, to all channels. Consequently, applicants for new subscriptions or number portability must choose one of the following methods to verify their identity: facial recognition, the Ministry of the Interior and Safety's mobile ID app, or a resident registration abstract issued on the same day. Device changes, where a user switches only the handset within the same carrier, are not subject to these requirements. Initially, the government intended to make facial recognition mandatory. However, following recommendations from the Personal Information Protection Commission and the National Human Rights Commission of Korea to ensure user choice due to the sensitivity of facial data, the plan was revised to a multi-factor authentication system. The government expects that by blocking identity theft at the activation stage, the measure will be effective in reducing crimes such as the distribution of burner phones and voice phishing, especially as mobile phones are widely used as a means of identity verification for financial transactions and various online services. Regarding concerns over
Jul 5, 2026 · via news.sbs.co.kr
World's largest digital camera stuns with 'Ocean of Stars' image Millions of stars, smears of dust, and even background galaxies pack this image, the first major Milky Way view from the Vera C. Rubin Observatory in northern Chile. The picture, fittingly dubbed Ocean of Stars, marks the beginning of Rubin's 10-year Legacy Survey of Space and Time. It's a preview of what the observatory's Simonyi Survey Telescope will do over the next decade: snap the same crowded star fields every few nights so astronomers can play one epic game of Spot the Difference. Together those space images will form a detailed timelapse video of the visible southern sky. You May Also Like "It's taken 20 years of hard science, engineering, and more to get to the point where we can call 'action' as we start rolling on this blockbuster movie of the universe," said Phil Marshall, deputy director of Rubin's operations, in a statement. "Millions of alerts in just the last couple of months show that Rubin is up and running as a discovery machine." By "alerts," Marshall is referring to the roughly 7 million notifications the observatory sends out about things that have changed in the sky each night. Those messages flood alert brokers — systems programmed to sort and classify the information for scientists. Rubin, built by the U.S. National Science Foundation and the Department of Energy, stands on Cerro Pachón, a desert peak high in the Chilean Andes, where the air is clear, dry, and steady. It takes its name from astronomer Vera Rubin, whose work revealed some of the first strong evidence for "dark matter" — an invisible, abundant substance in space that does not give off or interact with light. Ocean of Stars points toward the constellation Lupus, close to the crowded plane of the
Jul 4, 2026 · via mashable.com
AI benchmark cheating has been theorized as an inevitable consequence of training capable optimizers against fixed metrics. With OpenAI's GPT-5.6 Sol, the theory arrived in full view. The nonprofit safety evaluator METR found that Sol, OpenAI's flagship reasoning model, gamed its software engineering evaluation at the highest detected rate of any publicly tested AI model in the organization's history — a finding that did not merely produce a bad score. It produced no usable score at all. And with Sol's general availability expected before August, anyone who intends to deploy the model or base procurement decisions on its published benchmark numbers needs to understand exactly what METR found and why it matters. What GPT-5.6 Sol Is, and Why It Was Tested OpenAI launched GPT-5.6 Sol on June 26, 2026, as the flagship of a three-model family that also includes Terra and Luna. Sol is designed for autonomous, long-horizon agentic work: the kind of tasks where a model operates independently for extended periods, coordinating subtasks and making decisions without constant human supervision. On Terminal-Bench 2.1, a widely used coding benchmark, Sol scored 88.8% in standard mode and 91.9% in its multi-subagent "ultra" configuration — figures that represent the current state of the art among publicly disclosed models. The launch is restricted. Access is currently limited to roughly 20 government-vetted organizations following a White House request for a coordinated rollout under a June 2 Executive Order establishing a 30-day government review window for frontier AI models. General availability across ChatGPT, the API, and Codex is expected in mid-to-late July. Before that access reaches most developers, METR's findings deserve close reading. How the Time-Horizon Metric Works METR measures AI capability using a method it calls the time horizon. The metric asks a concrete question: what is the longest task a model can complete
Jul 4, 2026 · via techtimes.com
Under the "Impossible Triangle", how many hurdles do AI glasses chips still need to overcome? This year, AI glasses have officially entered the large - scale production cycle, and the industry is growing strongly. According to IDC data, in the first quarter of 2026, the global smart glasses market grew at a year - on - year rate of up to 130.1%. The Chinese market ranked third globally with a 23.5% growth. It is estimated that the global shipments of smart glasses will reach 23.687 million units this year. While the market heat continues to rise, the overall product experience in the industry generally has serious flaws. Problems such as obvious heating, short battery life, delays in visual recognition and real - time translation, and heavy devices have become the core pain points restricting user retention and industry advancement. From the underlying logic of the industry, the core problem lies in the immature dedicated chip system. 01 Three Major Flaws Drag Down the Terminal Experience In the early stage of the development of the AI glasses industry, the industrial chain did not have a mature dedicated chip system. To quickly seize the market and lower the threshold for hardware R & D, many small and medium - sized device manufacturers directly used mid - end mobile phone SoCs for simple tailoring and adaptation. After only deleting some redundant modules such as basebands and high - definition video encoding, they directly embedded them into the narrow temples to complete the hardware adaptation. However, the core design logic of mobile phone chips is centered around the large - screen body, large - capacity battery, and built - in heat dissipation space of smart phones. This is contrary to the special usage environment of AI glasses, which have a narrow body, passive heat
Jul 4, 2026 · via eu.36kr.com
ShareX Download Gains Momentum as Free Open-Source Screenshot Tool Attracts More Windows Users As digital workplaces, remote collaboration, and online content creation continue to expand, interest in ShareX Download has grown steadily among Windows users looking for a feature-rich yet completely free screenshot solution. Search trends over recent months indicate that more people are exploring alternatives to traditional screenshot applications, particularly those that require paid subscriptions or lock advanced features behind premium plans. ShareX, an open-source application developed specifically for Microsoft Windows, has emerged as one of the most frequently discussed productivity tools in this category. Unlike many commercial screenshot utilities, ShareX offers its complete feature set free of charge, allowing users to capture screenshots, record videos, create GIF animations, recognize text with OCR, annotate images, automate workflows, and upload files without purchasing a license. According to publicly available information, ShareX has maintained its open-source development model for many years. Continuous contributions from the developer community have helped expand its capabilities while ensuring that updates remain available at no cost. This long-term commitment to free software has contributed to its growing reputation among developers, IT professionals, educators, content creators, and office workers. Industry observers note that the role of screenshot software has changed significantly over the past few years. What was once considered a simple image-capturing utility has gradually evolved into a comprehensive productivity platform. Modern users no longer expect screenshot tools to simply capture an image of their screen. Instead, they increasingly rely on features such as text recognition, scrolling capture, instant file sharing, cloud integration, workflow automation, and screen recording to improve efficiency across daily tasks. As organizations continue adopting hybrid work models and cloud-based collaboration platforms, the demand for versatile productivity applications has increased. Employees frequently capture software interfaces during virtual meetings, developers document application behavior, customer
Jul 4, 2026 · via bignewsnetwork.com
Staff members inspect product quality at the Gaoming factory of Foshan Haitian Flavouring and Food Co., Ltd. in Foshan, south China's Guangdong Province, July 4, 2026. The Gaoming factory is recognized as "Lighthouse Factory" in the soy sauce industry due to the advanced AI technology and equipment it has adopted. It uses AI-powered image recognition technology to screen over 13,000 soybeans per second, and AI-powered precision filling technology to achieve zero-error packaging. (Xinhua/Zhang Yudong) This photo taken on July 4, 2026 shows a production line at the Gaoming factory of Foshan Haitian Flavouring and Food Co., Ltd. in Foshan, south China's Guangdong Province. The Gaoming factory is recognized as "Lighthouse Factory" in the soy sauce industry due to the advanced AI technology and equipment it has adopted. It uses AI-powered image recognition technology to screen over 13,000 soybeans per second, and AI-powered precision filling technology to achieve zero-error packaging. (Xinhua/Zhang Yudong) A visitor takes photos of sample production materials at the Gaoming factory of Foshan Haitian Flavouring and Food Co., Ltd. in Foshan, south China's Guangdong Province, July 4, 2026. The Gaoming factory is recognized as "Lighthouse Factory" in the soy sauce industry due to the advanced AI technology and equipment it has adopted. It uses AI-powered image recognition technology to screen over 13,000 soybeans per second, and AI-powered precision filling technology to achieve zero-error packaging. (Xinhua/Zhang Yudong) This photo taken on July 4, 2026 shows fermentation tanks at the Gaoming factory of Foshan Haitian Flavouring and Food Co., Ltd. in Foshan, south China's Guangdong Province. The Gaoming factory is recognized as "Lighthouse Factory" in the soy sauce industry due to the advanced AI technology and equipment it has adopted. It uses AI-powered image recognition technology to screen over 13,000 soybeans per second, and AI-powered precision filling technology to achieve
Jul 4, 2026 · via english.news.cn
Abstract Wind-power curves form a critical basis for wind farm condition monitoring and anomaly type recognition. Current supervisory control and data acquisition (SCADA)-based approaches primarily rely on single-modal numerical or visual features, which constrains their ability to capture both global morphology and local structural deviations in power curves. Furthermore, the shortage of labeled abnormal samples restricts recognition performance. To tackle these challenges, this article proposes an image-topology-semantic multi-feature fusion method under a contrastive learning framework. SCADA scatter data are transformed into grayscale images, from which global image features are extracted using a Vision Transformer. Local topological structures are modeled via Holistically-Nested Edge Detection and graph convolutional networks, while textual anomaly descriptions are encoded by a CLIP text encoder and aligned via a parameterized dual-path fusion module. By jointly integrating global morphology, local topology, and textual physical semantics, the proposed framework resolves the difficulty of distinguishing visually similar wind-power curve anomaly types under limited labeled abnormal samples. The method utilizes a two-stage training strategy: pre-training on external curve datasets and fine-tuning on a simulated wind-power curve dataset, followed by evaluation on real wind farm SCADA data under both known-category anomaly-type recognition and leave-one-anomaly-type-out zero-shot recognition scenarios. Experimental results indicate that the proposed method achieves 89.5% accuracy and 89.4% F1-score with limited-data fine-tuning, 94.2% accuracy and 94.1% F1-score with sufficient-data fine-tuning, and a 78.1% F1-score in leave-one-anomaly-type-out zero-shot recognition, demonstrating its effectiveness for predefined wind-power curve anomaly type recognition in data-scarce settings. Similar content being viewed by others Funding This work was supported by National Natural Science Foundation of China under Grants 52307141, and in part by the Natural Science Foundation of Sichuan Province under Grant 2026NSFSC0263. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral
Jul 3, 2026 · via nature.com
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Jul 3, 2026 · via instagram.com
Moon phase today: What the Moon will look like on July 3 Keen skywatchers will see a Waning Gibbous Moon again tonight as the lunar cycle continues and we further away the Full Moon and closer to the New Moon. This means that the Moon will continue to appear smaller and less illuminated each night. What is today’s Moon phase? As of Friday, July 3, NASA's Daily Moon Guide tracker tells us the Moon phase is in its Waning Gibbous phase, with 90% of its surface visible. You don't need lots of fancy equipment to see features on the Moon's surface. In fact, you can see the Mares Imbrium and Serenitatis, as well as the Copernicus Crater without any visual aids at all. If you do however have binoculars, you'll be able to catch a glimpse of the Clavius Crater, Mare Frigoris, and the Grimaldi Basin. And if you have a telescope, you'll see all this plus the Apollo 14, 15, and 16 landing spots. You May Also Like When is the next Full Moon? The next Full Moon will take place on July 29. What are Moon phases? According to NASA, the Moon completes one orbit around Earth approximately every 29.5 days, moving through eight recognised phases along the way. Although the same side of the Moon always faces Earth, the amount of its surface illuminated by the Sun changes as it travels around our planet. As a result, the Moon appears to shift in shape throughout the month, progressing from slender crescents to quarter moons and eventually reaching the brightly lit Full Moon stage. This repeating pattern is known as the lunar cycle. New Moon - The Moon is between Earth and the sun, so the side we see is dark (in other words, it's invisible to the
Jul 3, 2026 · via mashable.com
Artificial intelligence-based prediction of transthoracic echocardiographic image quality in ICD/CRT-D candidates: A proof-of-concept study Abstract Background: High-quality echocardiography is essential for accurate and reproducible assessment of cardiac functional indices, which are highly dependent on adequate image quality and proper probe alignment. An artificial intelligence (AI)-based approach may enable automated image quality assessment. Aims: Our aim was to develop and internally validate an AI-based algorithm to predict image quality from selected echocardiographic frames in candidates for implantable cardioverter-defibrillator and cardiac resynchronization therapy with a defibrillator implantation. Methods: In this retrospective cross-sectional study, 248 patients (297 echocardiographic examinations) were included. Demographic, electrocardiographic, echocardiographic, and clinical data were collected. Apical 2-, 3-, and 4-chamber views were extracted for image quality analysis, yielding a total of 909 echocardiograms. Image quality was assessed using end-diastolic frames. An internally validated scoring framework was applied, demonstrating high interclass correlation. Results: Regression models provided more clinically relevant information than classification models. Visual transformer models achieved Pearson correlation coefficients similar to those of convolutional neural networks (up to 0.812 and 0.772, respectively; P = 0.31). Architectures trained on end-diastolic frames achieved comparable Pearson correlation coefficients to those trained on combined end-diastolic and end-systolic frames. Compared with human experts, the models showed significantly higher absolute percentage errors, with values of 11%–12% (median) vs. 7.9% (mean) for the total image quality score and 13%–14% (median) vs. 8.8% (mean) for the border quality score. Conclusions: Regression models demonstrated the highest performance. An internally validated AI model can predict an echocardiographic image quality score in a small cohort of cardiac resynchronization therapy with a defibrillator/implantable cardioverter-defibrillator candidates. However, external and prospective validation will be required to establish its generalizability, reliability, and utility before clinical application. Keywords: cardiac resynchronization therapyconvolutional neural networkimage qualityimplantable cardioverter defibrillatortransthoracic echocardiography References - Lipiec P, Bąk J, Braksator W,
Jul 3, 2026 · via journals.viamedica.pl
The use of artificial intelligence has radically transformed the way the computer understands reality. It has given rise to such phenomena as facial recognition, analysis of images in medicine, driverless cars, and visual search. However, although very complicated, all these innovations have originated from something much simpler. Namely, in 1958, an American psychologist and computer scientist, Frank Rosenblatt, introduced his invention named the Perceptron at the Cornell Aeronautical Laboratory in Buffalo, New York. The development was funded by the United States Navy. It was an important step forward, as it showed that the computer is not only able to follow pre-programmed instructions but can also learn from experience and improve. The Perceptron itself was capable of doing only simple things. Still, it was the foundation for machine learning, which eventually gave rise to artificial neural networks and computer vision. The machine that learned from examplesBefore the emergence of the Perceptron, computers functioned mostly based on explicit programming. It required setting up firm rules for each task that limited opportunities for adaptation to new data from the machine side. This time, the scientist suggested something totally new. Inspired by neurons' work, he developed a model that could alter its own settings based on example-based learning. In its first public demonstration in 1958, the Perceptron used an IBM 704 computer and punch cards to identify the difference between cards marked on the left side and those marked on the right. While the task may seem rather easy now, it was one of the first demonstrations of machine self-adjustment to new data through training rather than only predefined programming. People's interest in the invention was caused by its implication that it might be possible someday for machines to recognise patterns and objects and make decisions independently. Rosenblatt defined the Perceptron as a system
Jul 3, 2026 · via timesofindia.indiatimes.com
Convolutional Neural Networks: Why CNNs Still Matter in Modern AI CNNs may no longer dominate every AI headline, but they remain one of the most practical architectures in modern machine learning. Their ability to learn visual patterns efficiently makes them especially valuable for computer vision, edge AI and production systems where speed, accuracy and resource use all matter. CONVOLUTIONAL NEURAL NETWORKS DEFINED A convolutional neural network, or CNN, is a deep learning model designed to analyze grid-like data such as images by learning patterns from small local regions and combining them into higher-level features. For a few years, it seemed as though transformers might replace convolutional neural networks (CNNs) altogether. Vision transformers (ViTs) began matching or exceeding CNN performance on some benchmark tasks, and attention-based architectures quickly became the center of AI research. Yet CNNs never disappeared. They remain widely used in many medical imaging systems, manufacturing inspection platforms and other computer vision applications. In many edge and embedded environments, a well-designed CNN can often still deliver strong performance while requiring less memory and compute than many larger transformer-based models, depending on the application and architecture. The reason is practical. CNNs were built to exploit the spatial structure of images, allowing them to learn visual features efficiently while keeping model size manageable. That combination of accuracy, speed and resource efficiency has kept them relevant even as newer architectures have emerged. Understanding how CNNs work — and where they fit alongside newer deep learning approaches — remains an important part of modern machine learning. What is a convolutional neural network? A convolutional neural network (CNN) is a type of deep learning model designed to process data with a grid-like structure. Images are the most common example. A digital image can be represented as a grid of pixel values, making it well
Jul 3, 2026 · via snowflake.com