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The result concerns encoding, not adult recall A 2025 paper in Science found that activity in the infant hippocampus predicted which pictures babies would appear to recognize about a minute later. Tristan Yates and colleagues collected usable functional MRI scans from 26 awake infants aged 4.2 to 24.9 months. This is one study, not settled consensus. It provides evidence that parts of the memory-making machinery are working during infancy, especially from around the first birthday. It did not follow a memory from infancy into adulthood or demonstrate that forgotten childhood experiences remain stored for decades. That distinction narrows the old explanation for infantile amnesia. A young hippocampus may be capable of encoding at least some individual experiences well before people can later describe an autobiographical memory from the same period. The experiment turned looking into a memory test Testing episodic memory in a preverbal infant requires an indirect measure. The team showed each baby a new photograph of a face, object or scene for two seconds inside the scanner. After 20 to 100 seconds, with three to 12 other items intervening, the old picture reappeared beside a new one from the same category. If a baby looked longer at the old picture, the researchers treated that as a familiarity preference. They then sorted the original viewing trials according to what happened at test. The question was whether brain activity during the first encounter differed for items that later received the longer look. It did. Blood oxygen level-dependent activity, the indirect signal measured by fMRI, was higher in the hippocampus during encoding of pictures followed by a familiarity preference. The effect was clearest in the posterior hippocampus, a region involved in episodic encoding in adults, and among the 13 infants older than 12 months. The babies did not, as a group,
A filing submitted by Meta to the US Patent and Trademark Office shows that Meta has continued developing features that use facial recognition in conjunction with its controversial smart glasses. The application for the patent was submitted in February and made public this week. There’s no indication of how long the approval process could take or whether the patent will ultimately be granted. The application, which CNET has viewed, includes 44 pages of drawings and descriptions of a system that would use Meta’s smart glasses technology in conjunction with AI facial recognition. The system would use AI facial recognition to identify people and create media, including videos, that contain that information. It could also use inputs such as location data or information from online services to create those media files, support a wide range of content-creation activities or interact with Meta services, according to the lengthy filing. A representative for Meta did not immediately respond to a request for comment. The patent filing comes at a sensitive time for Meta and this product category. Privacy advocates have heavily criticized the company’s lack of privacy protections, while the nickname “pervert glasses” has been used more frequently in media stories about its smart glasses. That sentiment was expanded upon by the nonprofit Center for Democracy and Technology, which issued a statement about the patent application, calling it an escalation of privacy harms. “Meta’s patent application includes relatively innocuous use cases, but, if deployed, this tech can and will be used for much more nefarious purposes and will put marginalized people like victims of stalking and domestic violence at risk,” wrote Ariana Aboulafia, project lead for disability rights in technology policy for the center. “No one should be forced to constantly question whether the person next to them at a party, in the
OCR Studio updates AI MRZ scanning for difficult remote ID captures OCR Studio has updated its on-device software to scan the machine readable zones (MRZs) of passports, identity cards, visas, and other documents captured during remote onboarding. The company says its AI-enabled OCR MRZ scan technology can extract identity and document information from low-resolution images and documents photographed at difficult angles or under conditions involving shadows and blur. The update is designed to improve first-pass document capture, reducing repeated scans and manual reviews during remote identity verification. OCR Studio targets banks, fintech companies and retailers that verify identity documents captured on customer-operated devices. Remote identity verification shifts the capture process to customers, who often use personal phones and webcams under varying image processing software and operating conditions. In some instances, a document may be worn or poorly illuminated, while the user may hold it at an angle or move the camera during capture. Those conditions often prevent software from reliably locating and reading the machine readable zone (MRZ). The commercial challenge is achieving accurate first-pass extraction across different document types, devices and lighting conditions. Recognition occurs locally within mobile apps and web browsers, allowing document data to be extracted without sending images to the cloud. However, on-device processing alone does not determine privacy compliance. Organizations deploying the software may still transmit, store or process document images and extracted data as part of their identity verification workflows Demand for reliable document capture extends beyond commercial onboarding. U.S. Citizenship and Immigration Services recently requested remote identity verification technology that could operate across major phones and browsers through image capture and identify quality problems. Article Topics digital ID | document verification | OCR Studio | remote identity proofing Comments
In brief - Meta filed a patent for cameras that use facial recognition to tag people by name and auto-generate clips around their detected actions. - The system is assistant-driven: it serves pre-sorted "who did what, when" highlights to a phone or headset on request. - It's a continuation of a 2022 parent application and sits at "Ready for Examination"—not granted, and far from a shipping feature. Meta Platforms Technologies, the entity that holds Meta's R&D and hardware intellectual property, published a patent for a camera system that identifies people by face and logs what they do. The filing describes cameras feeding footage into an AI that tags individuals in frame, detects their actions, and bundles both into media clips served on demand to a phone or headset. This isn't a bolt-from-the-blue idea. Meta's camera hardware has been on faces for years: the Ray-Ban smart glasses, now not so affectionately referred to as “pervert glasses” by much of the internet, built with EssilorLuxottica and first shipped in 2023, record first-person video and let the wearer ask an AI about what it sees. The same Meta entity behind those frames and the Quest headsets owns this filing, and the drawings show a glasses wearer observing a room of people. Facial recognition would take that hardware from "records what I point at" to "knows and names who's in the room." How the pipeline runs The patent lays out a four-step loop. Cameras capture live or recorded footage and pass it to the system. A facial-recognition layer scans the frames to flag who is present—not just that a person is there, but which specific person. A separate action-detection layer runs on top of that, reading movements like picking something up, leaving a room, or a group gathering. Finally, the system bundles its findings
Flock cameras help North Palm Beach police track down suspected boat burglar NORTH PALM BEACH, Fla. (CBS12) — A network of Flock license plate cameras helped investigators track a suspected North Palm Beach boat burglar across multiple counties, connecting him to a series of Garmin GPS thefts, according to police records. Detectives with the North Palm Beach Police Department began investigating after a trail camera captured a man aboard a boat early Dec. 31, 2025. The man appeared to be photographing the vessel and nearby boats while speaking with someone in Spanish. Investigators later identified 26-year-old Brian Sebastian Miranda as the suspected serial Garmin GPS thief in the video. The key break came from a silver Mercedes bearing Florida dealer tag PNO44U. Naples police had linked the vehicle to another attempted Garmin GPS theft. North Palm Beach investigators entered the tag into the Flock license plate reader system. The data showed the Mercedes near the North Palm Beach boat during the Dec. 31 incident. See also: Palm Beach County property tax campaign hits the road on fire trucks Flock cameras also tracked the Mercedes through Collier County on Jan. 2, when investigators say another Garmin GPS was stolen from a boat. The vehicle was recorded entering Port of the Islands at 10:08 a.m., around the estimated time of the theft, then leaving at 10:43 a.m. A traffic camera later captured it entering a nearby 7-Eleven. The stolen Garmin was valued at about $3,100, per police. Investigators also used facial-recognition analysis on surveillance images, which produced an investigative lead identifying Miranda. Cellphone records obtained through a search warrant further placed Miranda's phone in North Palm Beach during the first incident and in Collier County during the Jan. 2 thefts. Police say the same dealer tag was later seen on a Lamborghini,
Simple Object Recognition with the UNIHIKER K10 This project demonstrates that practical object recognition does not always require complex machine learning frameworks. Using only the built-in camera, display, and processing power of the UNIHIKER K10, it is possible to create a simple, fast, and effective object recognition system that can be trained directly by the user. The UNIHIKER K10 is a compact ESP32-S3 based development platform that combines several useful components into a single device: - ESP32-S3 microcontroller - Integrated color display - Built-in camera - Speaker and microphone - Wi-Fi and Bluetooth connectivity - Multiple AI and multimedia capabilities - and Large PSRAM memory for image processing These features make the K10 an excellent platform for experimenting with computer vision, artificial intelligence, robotics, and educational projects. - ESP32-S3 microcontroller - Integrated color display - Built-in camera - Speaker and microphone - Wi-Fi and Bluetooth connectivity - Multiple AI and multimedia capabilities - and Large PSRAM memory for image processing These features make the K10 an excellent platform for experimenting with computer vision, artificial intelligence, robotics, and educational projects. The second experiment used the built-in AI library supplied with the UNIHIKER K10. This demonstrated the ability of the K10 to perform real-time vision tasks using the integrated camera and display. Although these factory AI examples are useful demonstrations, the objective of this project was to create a completely different object recognition method that could be trained directly by the user. I should mention that the Arduino codes for all the examples in this project are given at the end of the text. Now let's move on to the idea from the beginning. The final project introduces a very simple but surprisingly effective object recognition technique. Instead of using neural networks or machine learning models, the system creates a visual signature
There’s an ongoing arms race to protect identities and privacy in the age of facial recognition, biometric scanning and data collection. Now, some of those concerns are coming from the influx of wearables, namely, smart glasses, and how to go about protecting oneself from being filmed in public. Some joke about singing Disney songs, others use software, and some have even resorted to physical tricks. And the concern is warranted: Meta’s Ray-Ban Meta Glasses, for example, have been coined “pervert glasses” for recording people undressing, and the company is even being sued after a study revealed Meta’s subcontractors are viewing your most intimate moments. This is all culminating into a real privacy concern as not only is your right to privacy (or lack thereof) in the public realm coming into question, but so is how to stop what you do in public from getting stored on a company’s servers. “We are living in weird times,” Jim Waldo, a professor of computer science who teaches several technological privacy courses at Harvard, told Fortune. “The technology is changing. It’s the combination of the Meta Glasses with facial recognition, AI, and a number of other sorts of technologies that are all coming together and putting us in an environment that we just aren’t prepared to deal with yet.” The privacy concerns are real—from the suit to data collection, and even the social media content made with the tech has left people chalking up Meta Glasses as a form of surveillance. Even Meta’s Instagram has had to act: the platform disabled several accounts thanks to violations of content usage after those accounts amassed millions of followers by streaming live feeds from Meta glasses. “We don’t want harassing content on our platforms and take it down when we find it,” a spokesperson for Instagram told
Moon phase today: What the Moon will look like on August 14 The New Moon has passed, but the Moon hasn't returned to full visibility quite yet. In fact, it will take a few more days before we can spot anything on its surface again. What is today’s Moon phase? As of Friday, Aug. 14, NASA's Daily Moon Guide tracker tells us the Moon phase is in Waxing Crescent phase, with 3% of its surface visible. When is the next Full Moon? The next Full Moon will take place on August 28. You May Also Like What are Moon phases? The Moon doesn't look the same from Earth every night. As it travels around our planet, it moves through a lunar cycle that takes roughly 29.5 days to complete, according to NASA. The changing positions of the Sun, Earth, and Moon affect how much of the Moon's sunlit side we can see, creating the different phases. Over the course of the cycle, the Moon progresses from a narrow crescent to a half Moon and eventually a Full Moon, before starting the sequence over again. The eight phases are: 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 eye). Waxing Crescent - A small sliver of light appears on the right side (Northern Hemisphere). First Quarter - Half of the Moon is lit on the right side. It looks like a half-Moon. Waxing Gibbous - More than half is lit up, but it’s not quite full yet. Full Moon - The whole face of the Moon is illuminated and fully visible. Waning Gibbous - The Moon starts losing light on the right side. (Northern Hemisphere) Third Quarter (or Last Quarter) - Another half-Moon, but now
Abstract During abrasive flap wheel grinding, flaps may fail and tear prematurely. Such flap tears increase local tool flexibility and can impair the uniformity of material removal in automated grinding processes. To ensure process reliability, this paper proposes a novel computer vision approach for the automated detection and quantification of flap tears using deep learning. The processing pipeline is based on images showing the radial tool structure, followed by a geometric normalization that transforms the flap arrangement into a linear coordinate system. A YOLOv8-based object detection model is employed to localize abrasive flaps. To overcome class imbalances, a hybrid approach is implemented where the neural network performs a robust one-class detection, while the final distinction between intact flaps and stubs is achieved through deterministic, scale-invariant post-processing filters. The system was validated on a dataset including various tool diameters, grit sizes, and wear conditions. For tools within the trained distribution, the system achieved 96.5% classification accuracy and a Mean Absolute Error of 0.14 and 0.12 for flap and stub counts. However, performance is limited when encountering significant geometric domain shifts, such as larger tool diameters. The results demonstrate that the combination of deep learning and geometric heuristics provides a robust basis for automated flap tear detection in industrial flap wheel grinding applications. 1 Introduction Abrasive flap wheels are widely used for repair tasks or finishing of complex workpieces due to their high compliance [1, 2]. However, this compliance comes at the cost of high mechanical stress on the individual flaps, making tool wear a critical factor for process stability. In addition to grit wear and geometric change of the flap profile due to flap load, flaps may tear and detach prematurely. Ongoing investigations indicate that flap tears increase vibrations and localized loss of tool stiffness due to missing mutual support between
A 55-year-old voter, Sharafa Lasisi, has appealed to the Independent National Electoral Commission to resolve the failure of the Bimodal Voter Accreditation System to recognise his face, saying he should not be disenfranchised. Lasisi made the appeal on Saturday while attempting to vote at Polling Unit 006, Ward 04, in the Isale/Oke-Afo area of Ikirun, Ifelodun Local Government Area of Osun State. The voter said he had made several attempts to have his face verified by the BVAS machine but was unsuccessful. He said election officials attributed the difficulty to network problems and advised him to return later. Lasisi said he followed the advice and returned to the polling unit, only to encounter the same problem. He said, “My name is Sharafa Lasisi. I am from Alamisi Unit 6. I am 55 years old. I have been to where they are supposed to verify my face but they said the network isn’t letting the verification go through. “They said I should go and eat at home. I went and came back and it still didn’t go through. I want them to help me so I will be able to cast my vote also.” The development came as voters continued to turn out across polling units in the ongoing Osun State governorship election. Lasisi urged the electoral authorities to intervene and ensure that he was allowed to exercise his franchise.:::
Dolby Vision 2 HDR May Be Available on Current 4K Blu-ray Players The introduction of Dolby Vision 2, a new high dynamic range (HDR) technology, has raised questions regarding its compatibility with existing 4K Blu-ray players. While the system may be activated via firmware updates, each manufacturer must decide whether to implement this feature. Reports from TechRadar have revealed that so far, only Sony has confirmed that it will not be updating its Blu-ray player lineup to include support for Dolby Vision 2. In a statement, Sony said, "We do not plan to provide a software/firmware update to support Dolby Vision 2 on the current Blu-ray player lineup." This stance is noteworthy, particularly as Sony has not released a new standalone Blu-ray player in recent years, apart from a minor update. The upcoming PlayStation 6 is also expected to lack a 4K Blu-ray drive, casting further doubt on the future of the 4K Blu-ray format. Other manufacturers, such as Panasonic and Magnetar, have not responded to inquiries regarding their plans for Dolby Vision 2 support. Dolby Vision 2 technology is optimised for specific televisions that are equipped with modern processors. However, 4K Blu-ray players can theoretically support this new HDR format through a firmware update that allows them to transmit the appropriate data to compatible TVs. It is important to note that the content itself must also be encoded in Dolby Vision 2 to take full advantage of its features, meaning existing discs will not benefit unless new editions are released. Nonetheless, improvements may still be noticeable on Dolby Vision 2-ready televisions, even without a firmware update. The new technology promises enhanced visual experiences, including new image processing capabilities and a content recognition system aimed at delivering a more authentic representation of the creatorâs vision. While Dolby Vision 2 is
Separating fact from fiction on automatic license plate readers | Opinion Sidney Wordell Guest columnist Aug. 15, 2026, 6:05 a.m. ET Every Rhode Island police chief has the same mission: keeping their community safe and giving victims the justice they deserve. To do that, our departments must have access to modern tools that help solve crimes, locate dangerous offenders and find endangered people quickly. One of those tools is automated license plate reader (ALPR) technology.
Abstract Tongue images are increasingly employed as diagnostic data in intelligent clinical systems. However, the storage and bandwidth costs of high-resolution image data can reduce diagnostic efficiency, making image compression a critical tool for balancing data fidelity, transmission cost, and retrieval latency. General-purpose methods fail to fully exploit the cross-channel correlations and smooth textures of tongue images, leading this study to propose a lossless compression algorithm based on a residual prediction framework. The compression pipeline comprises four stages: First, integer lifting performs reversible color transformation, eliminating interchannel redundancy. Second, an enhanced LOCO-I model incorporating gradient detection and “Left-fused” mode captures smooth tongue textures. Third, an S-shaped scan reorders residuals to preserve spatial locality. Finally, cross-channel differential type mapping, dynamic bit-width packing, and hybrid entropy coding mitigate statistical redundancy. Experimental results across three tongue datasets demonstrate that the proposed algorithm exceeds the compression ratios of standard methods by 6%, 9% and 1%, respectively. It significantly outperforms standard PNG and JPEG 2000 formats while maintaining a decoding latency comparable to QOI. Furthermore, generalization tests on endoscopic images of colonic polyps, retinal fundus images, and dermatological images show compression improvements of 7%, 5% and 6%, respectively. The proposed method achieves a robust balance between high compression efficiency and low-latency decoding while ensuring faithful reconstruction within practical engineering constraints. Funding This work was supported by the National Science and Technology Innovation 2030 Major Program (Grant No. 2025ZD0544900), entitled “Research on Early Warning Technology for Colorectal Cancer Based on Multi-dimensional Analysis System of Traditional Chinese Medicine Tongue”; and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX25_2274), entitled “Research on Model Compression Algorithms for Medical Image Recognition”. Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
JLG to Advance Future Vision with Nextera Robotics Oshkosh Corp., a global industrial technology company that develops purpose-built vehicles, equipment and services, and parent company of JLG Industries, has made a strategic equity investment in Nextera Robotics. "Our investment in Nextera is a confirmation of our belief in their technology, and our partnership will help Nextera accelerate its mission to connect the construction job site,” said Mahesh Narang, executive vice president, Oshkosh Corp., and president, Oshkosh Access segment, which is primarily aerial equipment maker JLG. Nextera develops autonomous technologies that create updated digital maps of active construction sites using visual AI and autonomous robotics. These digital representations provide project teams with timely insights into project progress, quality and safety, helping identify issues earlier and improve decision-making throughout the construction process. "Nextera has developed unique technology across visual image recognition using AI, Bluetooth mesh technology and use cases for jobsite autonomy," said Shashank Bhatia, chief technology officer, Oshkosh Access segment. “We will explore using these technologies in future JLG products to advance our strategy of developing products for the job site of the future." As part of the relationship, JLG will support Nextera's commercialization efforts through customer engagement, field validation and application expertise. In turn, Nextera's technology platform complements JLG's technology roadmap and can expand how connected equipment creates value on the job site. Nextera Robotics is a Boston-based technology company developing autonomous robotics and AI solutions for the construction industry. For more information about JLG products and services, visit JLG.com. Source: JLG Industries About the Author Michael Roth Editor Michael Roth has covered the equipment rental industry full time for RER since 1989 and has served as the magazine’s editor in chief since 1994. He has nearly 30 years experience as a professional journalist. Roth has visited hundreds of rental
Tools to evade AI-powered surveillance: Are they effective? You might cover or obscure your face, but that can get you in trouble with the cops. - Privacy activists are developing tools to evade facial recognition as police surveillance networks expand across the United States. - Bill Swearingen says his noRecognition pattern blocked Flock cameras after 31 million tests and a Def Con demonstration. - Masks, infrared eyewear, makeup, and adversarial clothing can disrupt some systems, but each has limits and legal risks. - New systems using gait and body-shape analysis may identify people even when faces are hidden or unclear. Key Takeaways by nexos.ai, reviewed by Cybernews staff. With concern over ever-smarter yet often poorly regulated facial recognition software growing worldwide, a clan of privacy activists and researchers is building tools that can fool such invasive surveillance-enabling technology. Roughly 1 in every 3 American adults is indexed in police facial recognition networks. “Most never consented, were never informed, and have no way to opt out,” says Bill Swearingen, a cyber professional from Kansas City. “Their faces were scraped from driver's license photos, mugshot databases, and public records, then fed into AI systems that can identify them in real time from any camera feed.” Since AI is error-prone, the systems produce much higher match rates for darker-skinned individuals. Check if your data has been leaked This, of course, means that people who, at least in the US, already face the most aggressive policing, are “also the people most likely to be wrongly identified by the technology justifying that policing,” Swearingen points out. 31 million tests and a pattern Indeed, the world already looks pretty dystopian. The cameras built by ambitious cop-friendly companies like Flock Safety are everywhere now (and the CEO wants more), and the databases are vast: we didn’t opt-in,
A man who was wrongfully arrested and held in the St. Louis County Jail for two years sued Christopher Gatlin alleged Thursday in an amended complaint that his arrest and indictment were the product of shoddy police work, poor training, police misconduct, and the use of a defective facial recognition program developed by Amazon for law enforcement use, known as the Rekognition program. Gatlin originally filed the lawsuit in January 2025, but amended the complaint to name Amazon as a defendant ... Learn more about Bloomberg Law or Log In to keep reading: See Breaking News in Context Bloomberg Law provides trusted coverage of current events enhanced with legal analysis. Already a subscriber? Log in to keep reading or access research tools and resources.
MIDTOWN, Manhattan (WABC) -- A rally was held Friday outside Madison Square Garden over the venue's use of facial recognition technology. Demonstrators say the practice raises serious privacy concerns and question how the Garden uses the information it gathers about its guests. They are now urging lawmakers to take action and ban the venue from using the technology. City Council member Shahana Hanif and other advocates against the overuse of surveillance cameras in New York City say it's time to pass a law banning the technology in stores, restaurants and entertainment venues such as MSG and Radio City Music Hall - both run by James Dolan "For years, MSG owner James Dolan has doubled down on using facial recognition to scan anyone entering his venues and banning individuals that he dislikes," said Michelle Dahl with the Surveillance Technology Oversight Project. "In one widely reported case, a mother was turned away from Radio City Music Hall, simply because she worked at a law firm involved in litigation against Dolan. She was there chaperoning her Girl Scout troop and posed no security threat." Hanif is now sponsoring one law that would ban such biometric data collection. A second law would also prohibit landlords from using it. But both have faced pushback, especially from small businesses who say it's a security tool to prevent crime, and police often rely on surveillance videos to find dangerous criminals. But regarding the alleged overuse of scanning at MSG, supporters of a ban argue it's not only invasive but also selective. Surveillance cameras were turned off for Taylor Swift's wedding. "So clearly, this technology is not actually that necessary for security," Dahl said. The bill's sponsor says it's gaining momentum at City Hall. "You should not have to give up your sensitive biometric data to walk into
Canada’s place in the history of artificial intelligence runs deeper than the country’s recent investment announcements. Long before ChatGPT made AI a household subject, Geoffrey Hinton at the University of Toronto, Yoshua Bengio at the Université de Montréal and Richard Sutton at the University of Alberta were developing ideas that became foundations of the field. Hinton and Bengio shared the 2018 Turing Award for their breakthroughs in deep learning, while Sutton shared the 2024 award for his foundational work on reinforcement learning. Hinton’s Toronto team, including Alex Krizhevsky and Ilya Sutskever, also developed AlexNet, the 2012 system that demonstrated the potential of neural networks in image recognition and helped accelerate the current AI era. Canada followed this scientific leadership with an early institutional bet. In 2017, it launched what the Canadian Institute for Advanced Research (CIFAR) describes as the world’s first national AI strategy, supporting research institutes in Toronto, Montreal, and Edmonton and strengthening Canada’s ability to attract leading researchers. That inheritance still matters, although the character of the competition has changed. Advanced algorithms now depend on a physical foundation of specialized chips, enormous amounts of electricity, cooling systems, and the facilities that bring them together. Canada helped establish the intellectual foundations of modern AI. Its next challenge is converting that legacy into lasting technological and economic capacity. What a data centre actually does The physical capacity behind AI sits in data centres: highly engineered facilities containing rows of powerful servers, storage systems, and networking equipment. AI-focused centres connect thousands of specialized processors—most commonly graphics processing units, or GPUs—so they can divide a demanding calculation and work on it simultaneously. These machines carry out two broad stages of AI. During training, they process enormous datasets to build and refine a model. During inference, a trained model applies what it has
Ohio State football announced new in-stadium changes for home games ahead of the 2026 college football season, including the expansion of its facial recognition check-in software and the option for fans to rent single-game padded seatbacks. The facial recognition software, called Express Entry, was introduced during the 2025 season as an optional service that allowed students (and some fans in "premium" areas) to skip lines and use dedicated gates with facial scanners to enter. Ohio State described the 2025 rollout as "successful" and plans to expand Express Entry to certain public gates for the upcoming season. With last year's introduction, Ohio State became the second school in the country to use Express Entry after the University of Florida, which fully implemented the service throughout its stadium in August 2025. Motz Group contractors install a new playing surface at Ohio Stadium featuring Shaw Sports Turf’s Forge Series™ system on July 22, 2026. The new field will continue to be sponsored by Safelite and will be in place for the 2026 football season. (Doral Chenoweth/Columbus Dispatch) David Lenert (left) and Paul Malernee cut up souvenir pieces of turf as contractors install a new playing surface at Ohio Stadium featuring Shaw Sports Turf’s Forge Series system on July 22, 2026. The new field will continue to be sponsored by Safelite and will be in place for the 2026 football season. People who take a stadium tour are welcome to take home a piece of the turf while supplies last. (Doral Chenoweth/Columbus Dispatch) Motz Group contractors install a new playing surface at Ohio Stadium featuring Shaw Sports Turf’s Forge Series™ system on July 22, 2026. The new field will continue to be sponsored by Safelite and will be in place for the 2026 football season. (Doral Chenoweth/Columbus Dispatch) Motz Group contractors install a new