Marc Isaacs’ new film is a curious, intriguing, semi-sincere affair that I couldn’t make friends with. It is an odd, shallow piece of work about artificial intelligence that is itself exasperatingly artificial, a self-aware docudrama hybrid. Isaacs is, or rather pretends to be, licensing the vivid characters from his previous, acclaimed documentaries to a fictional AI research lab called Synthetic Sincerity at the fictional University of Southern England, so that the lab’s software can be “trained” in the creation of AI human figures on screen. The lab’s research staff are played by actors, or at any rate people acting; these include Lebanese independent film-maker Lynn El Safah. Isaacs has amusing scripted conversations about this project with a disapproving AI avatar on screen, like Max Headroom of old, whose face is digitally modelled on Romanian actor Ilinca Manolache, from Radu Jude’s Do Not Expect Too Much from the End of the World. The film, however, does not show the process by which Manolache was approached and her face transformed into an AI figure. The supposed point of it all is to create an AI version of an exiled Uyghur man called Ablikim Rahman, who really does exist and runs a restaurant in London, on the ostensible grounds that the resulting AI figure will be able to say therapeutic things that the real person couldn’t. (Erm … really? Why couldn’t he? It seems patronising to this dignified and intelligent man. But perhaps that is another fictional conceit.) Then we see the image of Rahman’s face on screen, speaking candidly about his emotional challenges. So this is ostensibly an AI image, though it certainly seems a lot more real than the Manolache face. El Safah then gets into made-up trouble with her made-up university employer for speaking to a Uyghur person when the
Jul 14, 2026 · via theguardian.com
European Commission calls for mandated age assurance for social media The European Commission’s special panel on child safety online has published its final report. Convened to study how kids’ use of online platforms affects their health and wellbeing, and to gauge how best to approach regulation, the panel met three times between March and June 2026. Its 150-page report weighs the positives and negatives of introducing age restrictions and biometric age assurance tools for social media and other digital services, and includes 14 policy recommendations for EU leaders. “This report comes during a unique window of opportunity,” says Ursula von der Leyen, President of the European Commission. “We have heard from parents, educators, experts and young people themselves. We have heard the experience from partners as well as our Member States. Now we need action at European level.” Recommended action applies to a variety of issues, and the formal list that concludes the report prioritizes agency and empowerment for kids. “Children and adolescents should have meaningful opportunities to actively shape legislation, policies, and educational initiatives promoting child safety and empowerment online,” says recommendation number one. “Children should be included in the co-design of safety policies, training programmes, evaluations and guidelines. Number two is related: “strengthen complaint and reporting mechanisms and consumer rights for children and adolescents.” Indeed, the full list leans heavily into digital literacy and education. “Mainstream digital education and literacy actions for minors, parents and caregivers, teachers and educators,” the panel says. “Support teachers’ training to include digital and media literacy and involve parents.” ‘Supervisory authorities should intensify cooperation’ That said, there is also a list of (non-numbered) recommendations aimed at social media firms, focused on regulation. Top among them is a call to propose “a harmonised EU-wide access restriction to social media and other digital services for
Jul 14, 2026 · via biometricupdate.com
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Jul 13, 2026 · via sfchronicle.com
PNG advances digital identity with AI laws, SevisPass integration, QUT partnership Papua New Guinea advances its digital transformation with a new international partnership, fresh AI‑safety reforms and deeper integration of its national digital identity system, SevisPass. Acting ICT Minister Peter Tsiamalili Jr. announced an agreement with the Queensland University of Technology (QUT), which was signed in Brisbane and witnessed by PNG Prime Minister James Marape. Tsiamalili said the partnership shows a shift from policy to implementation. He described it as the start of a long‑term effort to build a secure, sovereign and citizen‑focused digital government. The deal creates a framework for collaboration on innovation, research and capacity building, with strict safeguards to ensure all projects meet PNG’s legal and governance standards. QUT will support public‑sector innovation, strengthen the digital workforce and expand research around SevisPass and other priority initiatives. Each project will still require its own approvals to protect national interests, including rules on procurement, cybersecurity, intellectual property and data governance. Data sovereignty remains central. All digital solutions developed under the partnership must ensure PNG retains full ownership and control of government data. GenAI, identity theft, voice cloning to come under regulation The government is also moving to protect citizens from harmful uses of artificial intelligence. Tsiamalili confirmed that new laws are being drafted to address deepfakes, voice cloning and digital impersonation. He said Papua New Guineans should not have their identity “stolen and weaponized” for fraud, exploitation or intimidation. The reforms will criminalize harmful conduct such as manipulated sexual content, child exploitation, fraudulent impersonation, scams and unauthorized use of artists’ voices or images. Accountability will extend to anyone who uploads, shares or monetizes such material. Platforms and AI service providers will also face obligations to act on unlawful content. DICT is working with NICTA and the Department of Justice
Jul 13, 2026 · via biometricupdate.com
A Tokyo ward has installed outdoor AI cameras with facial recognition capabilities to help locate missing children and elderly people, a move aimed at improving public safety but also raising privacy concerns. Arakawa Ward installed 33 artificial intelligence-equipped security cameras on utility poles along the main street and elsewhere near JR Nippori Station in April to test whether the technology could speed up searches for missing persons. The busy area around the station is frequented by commuters, students and foreign residents. If a child or elderly person with dementia goes missing, family members can ask the ward to conduct an AI-assisted search by providing a photograph. The AI then scans recorded footage for people closely matching the image. The ward believes that this is the first outdoor deployment of AI facial recognition security cameras by a local Japanese government, and hopes that it will help to find missing people more quickly. Police stations in Arakawa receive about 100 reports each year of missing children and elderly people with dementia. Until now, police officers and ward officials have searched on foot around train stations and other locations. The footage is stored for seven days. Only a small number of staff have access via a dedicated computer in a locked room at the ward office, and officials say it is used solely to search for missing people. AI-equipped security cameras are also being introduced elsewhere in Japan, but for different purposes. Hyogo Prefecture and Tokyo's Adachi Ward plan to introduce cameras capable of detecting people lingering in entertainment districts in an effort to curb touting. The use of facial recognition technology has prompted privacy concerns in Japan and overseas. The European Union's AI Act prohibits the real-time collection of biometric data, including facial images, in public places for law enforcement purposes except
Jul 13, 2026 · via japantoday.com
Artificial intelligence has permeated most aspects of society, and the legal system is no exception. Today, every step of criminal justice, from the police investigation to the trial, can — and often does — involve AI. That change comes with many implications, some of them desirable and some less so. Last month, three SFI Faculty — Resident Professors Cristopher Moore and Melanie Mitchell, and External Professor Melanie Moses, a distinguished professor of computer science from the University of New Mexico — addressed these implications during panel discussions at the National Judicial Summit on the Foundation and Future of the Judiciary, held in Santa Fe. Around 300 judges attended the summit, with two-thirds of the participants coming from New Mexico. The additional hundred judges represented 22 different states and eight tribal nations. Two guests of SFI also participated in the panel discussions: Berkeley law professor Andrea Roth and Maryland public defender Marc Canellas. Both are experts on AI-generated evidence who also attended a 2025 SFI working group on AI and Justice supported by the Robert Wood Johnson Foundation through SFI’s Emergent Engineering project. The professors’ goal was to give judges a realistic view of how AI can expedite their work while also warning them of pitfalls they might encounter. “If judges and juries are dazzled by the technology, it’s going to be hard for them to think critically about the evidence,” says Moore. “I want people to understand that AI has strengths, and it has weaknesses.” For example, AI has made facial-recognition technology much more effective. But when using this technology, police need to balance the need to catch criminals with the expectation of privacy. “Do we want cameras on every street corner that are constantly watching us?” Moore asks. “That’s a policy question, and I think a very important one.”
Jul 13, 2026 · via santafe.edu
UCL Computer Science is proud to celebrate the recipients of the Student Equality, Diversity and Inclusion (EDI) Awards 2026, recognising students whose outstanding contributions have helped create a more inclusive, welcoming and equitable environment within the department and beyond. The awards highlight the dedication of students who have championed diversity, widened participation in computing and technology, and developed initiatives that empower underrepresented communities. BSc Student EDI Award Winners Asmita Anand - Addressing Bias in Technology Asmita received recognition for her work exploring bias in technology and promoting more equitable approaches to technological development. She said: “I feel incredibly grateful to receive this award and am very thankful to my supervisors for their support throughout this project. Working on this topic highlighted to me the importance of identifying and addressing bias in technology, and it was a rewarding opportunity to explore such an important issue.” Victoria Smirnova - Using Technology to Improve Accessibility BSc student Victoria Smirnova was recognised for developing technology that supports patients facing language barriers when accessing healthcare services. Victoria explained the inspiration behind her project: “I built this project because I believe digital innovation should open doors rather than close them. Having experienced language barriers myself when navigating public services in the past, I wanted to create something that gives patients who struggle with English the same ability to independently access NHS care as everyone else.” MEng Student EDI Award Winner Qichen (Ian) Yin – Expanding Access to AI and Robotics Education Supported by Professor Elaine Pimentel and Taznim Aktar Nisha, Qichen (Ian) Yin was recognised for combining cutting-edge artificial intelligence topics including image recognition, large language models and natural language processing—with robotics education through Winchester College summer schools and UCL outreach programmes. Through these activities, Ian delivered engaging lectures and workshops to more than 200 students
Jul 13, 2026 · via ucl.ac.uk
Articles and News GemFind Adds AI Product Description Generator to JewelCloud 2.0 July 13, 2026 (0 comments) Newport Beach, CA--GemFind Digital Solutions has introduced GemText AI, a new artificial intelligence feature within JewelCloud 2.0 that is designed to automatically generate SEO-optimized product descriptions for jewelry manufacturers, the company said. The new tool converts product specifications—including metal type, gemstones, diamond characteristics, dimensions and design details—that can be published across ecommerce sites, retailer websites, digital catalogs and other sales channels. GemFind said the feature is designed to reduce the time manufacturers spend writing product copy while improving consistency and search engine visibility across large product catalogs. “Manufacturers already invest significant time creating and managing product data,” said Alex Fetanat, founder and CEO of GemFind. “GemText AI removes one of the industry’s biggest bottlenecks by instantly generating high-quality product descriptions while helping brands maintain consistency across every sales channel. By combining AI-powered content creation with JewelCloud's distribution capabilities, we're making it easier than ever for manufacturers to market and sell their products online.” Once descriptions are generated, they can be distributed directly through JewelCloud to manufacturers' ecommerce websites, retailer websites connected to the platform, digital product catalogs, sales portals and future marketplace channels. Manufacturers update product information once, with the platform distributing the enriched content across connected channels. According to GemFind, GemText AI can: - Generate product descriptions using AI. - Create keyword-rich content intended to improve SEO. - Reduce manual copywriting. - Maintain consistent messaging across product catalogs. - Regenerate descriptions with a single click. - Speed product launches and catalog updates. Retailers connected to JewelCloud receive the enhanced product descriptions automatically, eliminating the need to create product copy themselves. GemText AI is the latest AI-powered feature added to JewelCloud. GemFind said future enhancements under development include automated product categorization, attribute enhancement,
Jul 13, 2026 · via news.centurionjewelry.com
SIR - Very interesting that 28 people have been arrested after a successful series of Live Facial Recognition (LFR) deployments in Bradford (T&A, July 9).
These operations were conducted using two fully-liveried LFR vans. All excellent news in my eyes.
Going forward I would like to see the permanent use of this equipment, and without using marked vehicles.
Surely anyone with nothing to hide should welcome the use of this technology which can only help to make our streets so much safer.
Bob Watson, Baildon
Jul 13, 2026 · via thetelegraphandargus.co.uk
SONY announces the RX10 V, the fifth-generation model in the RX10 series of all-in-one cameras. The perfect hobbyist’s camera, this versatile powerhouse covers everything from wide-angle to super-telephoto in a single body, making it ideal for every type of shot thanks to its large-aperture, high-magnification ZEISS Vario-Sonnar T* 24-600mm1 (25x optical zoom) F2.4-4.0 lens. Incorporating the RX10 series’ hallmark combination of high image quality, super-telephoto reach, and integrated lens design, the RX10 V adds AI-powered Real-time Recognition AF. Whether shooting stills with its 1.0-type stacked Exmor RS CMOS image sensor (with approximately 20.1 effective megapixels) and BIONZ XR image processing engine or shooting video that supports 4K 120p recording, this single camera handles a wide range of scenarios — from everyday moments to wildlife, school sports days and athletic events. Real-time Recognition AF, powered by an AI processing unit, recognizes humans, animals, birds, insects, cars, trains and airplanes, while also featuring an Auto mode that allows the camera to automatically identify the subject type. For high-speed performance, AF/AE calculations at up to 60 times per second track even fast-moving subjects, while blackout-free shooting maintains a continuous view. The camera supports 4K 120p video recording3, enabling impressive visual expression including smooth slow-motion playback at up to 5x in 4K resolution. The RX10 V is now available at SRP P130,999. Get a free NP-FZ100 Battery worth P4,490 on every purchase from July 10 to Aug. 7, 2026. Pre-order at https://experience.sony-asia.com/ph/alphauniverse/exploredetail/1406.
Jul 13, 2026 · via manilatimes.net
US passport photo plan points to new layer of remote biometric verification U.S. Secretary of State Marco Rubio said this month that the State Department is preparing to make passport applications “almost all” online, including a process in which an applicant uses a phone or computer camera and the government’s security system to “verify the facial ID.” He said applicants would be able to apply “online entirely, for the most part,” but did not say whether first time applicants, minors, or everyone seeking a passport would qualify, and he did not define who would. The announcement suggests a significant expansion of the department’s existing online passport renewal service. Americans already can submit an eligible renewal application online and upload a “selfie” digital image. The current system is designed principally to determine whether an applicant’s uploaded photograph meets passport rules. It is then physically reviewed by an employee. But what Rubio described is a face biometrics comparison confirming that the person submitting a picture is the person associated with the passport record. Speaking at the launch of the commemorative Patriot Passport that features President Donald Trump standing behind the Resolute Desk, Rubio said applicants would be able to go online and do “almost all of it entirely online.” He said the camera that is in a phone, laptop, or desktop computer should allow a person to take a passport picture at home and have the department “verify the facial ID” in real time. He said the department expects to “roll that out in a few months when it’s ready.” Rubio did not say the system would use liveness detection, explain whether the applicant would take a still photo or a short video, or describe how the department would distinguish a live applicant from a replayed image, manipulated photograph, or synthetic video.
Jul 13, 2026 · via biometricupdate.com
Abstract What visual information do primate brains use to recognize objects, and can explanations from artificial neural networks (ANNs) help reveal these biological recognition strategies? Answering this question is important because humans and macaques both perform rapid, robust object recognition, yet the diagnostic image features guiding their behavior are difficult to measure at scale. Behavioral methods such as Bubbles can estimate these features but require extensive psychophysical data, whereas ANN explanation methods, including saliency and guided backpropagation, are efficient but often disagree with one another and lack direct biological validation. Here, we introduce MAPS, Masked Attribution-based Probing of Strategies, a framework that makes ANN-derived explanations testable in biological systems by linking them to neurobehavioral consequences. MAPS converts explanation maps into minimal explanation-masked images and asks whether these images preserve the original image-by-image recognition behavior. In silico, EMI-based behavioral similarity reliably recovers ground-truth similarity between model strategies. Applied to humans (n = 56) and macaques (n = 2), MAPS identifies explanation methods that best align with biological vision, achieving validity comparable to Bubbles without exhaustive psychophysics. MAPS provides a scalable, behaviorally grounded approach to evaluate and compare ANN explanations across brains and machines. Similar content being viewed by others Acknowledgements We thank the members of the ViTA Lab for helpful discussions and comments. Funding KK has been supported by funds from the Canada Research Chair Program (CRC-2021-00326), Google Research, Brain-Canada Foundation (2023-0259), the Canada First Research Excellence Funds (VISTA Program), and the National Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2024-06223). SM is funded by the Connected Minds Postdoctoral Fellowship (supported by CFREF). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author information Authors and Affiliations Corresponding authors Ethics declarations Competing interests The author declares no competing interests.
Jul 13, 2026 · via nature.com
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July 13, 2026 05:00 AM CDT
Jul 13, 2026 · via chicagobusiness.com
Abstract Deep learning (DL) chest radiograph (CXR) models are often trained on downsampled images to reduce computational overhead, despite clinical workflows operating at high resolution. Previous studies have investigated the impact of input resolution on CXR classification accuracy, yet two fundamental pillars of safe and trustworthy AI, explainability and generalizability, remain underexplored. In this retrospective study, we evaluated how training image resolution affects CXR classification performance and explanation quality in internal versus external testing. We trained Convolutional Neural Networks (CNN) for disease classification on the SIIM-ACR Pneumothorax and RSNA Pneumonia datasets at six resolutions (ranging from 64\(\times\)64 to 1024\(\times\)1024) using five-fold cross-validation and evaluated models on internal and external test sets. Internal performance was high across resolutions (AUROC >0.85), but external testing showed substantially worse generalizability at lower training resolutions, with internal-to-external drops >20% versus 4.2%-10.7% at higher resolutions (512\(\times\)512 to 1024\(\times\)1024). Higher resolutions also produced more concise explanations, with the tightest saliency-map coverage at 1024\(\times\)1024 (<4%) across models and datasets, and improved explanation quality on external data (peak precision plateauing at 768\(\times\)768 for pneumothorax). Overall, training at higher CXR resolutions improved both generalizability and explainability, providing practical guidance for radiology AI design beyond internal test performance. Similar content being viewed by others Introduction Artificial intelligence (AI) has been rapidly adopted in radiology with a focus on using deep learning (DL) algorithms for the diagnosis of diseases1,2,3. Although medical images are routinely acquired at high resolutions to capture fine pathological details, DL models commonly down-sample medical images during training4,5,6 to reduce computational costs, which may limit their ability to identify subtle diseases. This practice contrasts sharply with clinical workflows, where radiologists rely on high-resolution images to make accurate diagnoses. Previous studies7,8 have shown that DL models trained using lower resolution medical images can still achieve high performance for certain disease
Jul 13, 2026 · via nature.com
Camera glasses: How Meta envisions facial recognition Meta is considering facial recognition for its smart glasses. Tech chief Andrew Bosworth gives a first glimpse of how such a function could be implemented. For months, there have been reports that Meta is planning some form of facial recognition for its smart glasses. In the companion app, inactive program code for a corresponding function called “NameTag” was already discovered, which Meta subsequently removed. The company stated that no final decision had yet been made regarding the introduction or specific design of facial recognition. In an interview with journalist Nicholas Thompson, Meta's tech chief Andrew Bosworth explains for the first time how NameTag could work. The function is not intended to identify random strangers, but rather to save the face and name of a person the glasses wearer has met, if desired. During a later encounter, the AI glasses could recognize the face and remind the wearer of the person's name and the circumstances of their meeting. The facial data would be stored locally on the glasses, eliminating the need for a central facial database. At the same time, Bosworth admits in the interview (YouTube) that this form of facial recognition also remains controversial. He left open how affected individuals could object to their face being stored or whether faces could be stored on the glasses without their knowledge. A difficult balancing act One user group that NameTag could greatly help in everyday life are visually impaired or blind people, as well as individuals with prosopagnosia, a neurological disorder where affected individuals have difficulty or are unable to recognize even familiar faces. Meta is currently heavily promoting the contribution of its camera glasses to accessibility, likely also to increase societal acceptance of the controversial technology. Bosworth also frames the discussion about facial recognition
Jul 13, 2026 · via heise.de
More than 100,000 people have had their faces scanned by police in Norwich city centre - yet just one offender has been caught. Earlier this year, officers deployed Live Facial Recognition (LFR) cameras over two days, using the high-tech equipment to identify wanted suspects and those who had failed to attend court. In just eight and a half hours of operation in March, the system scanned 101,511 passers-by. Despite the scale of the surveillance, the technology generated only two positive alerts, both leading to police interventions, according to Norfolk Constabulary data. During one weekend, a man was identified as being wanted for failing to appear in court. When he was stopped by police he was also found to be in possession of cannabis. The man was rebailed for his court warrant and issued with a community resolution for possession of the drugs. A second man was identified as being the subject of a Sexual Harm Prevention Order. He was spoken to by officers who established he was not in breach of any conditions, so no further action was taken. LFR enables police to compare faces captured on a live camera feed to people on a predetermined watchlist. The technology involves using a marked van in a recognition zone the police say will be clearly signposted. It relies on biometric data which is taken by mapping out faces within the recognition zone and taking measurements of facial features, which are then compared against the police's watchlist. The data also revealed the results of the use of LFR in Great Yarmouth in May. Across two deployments, 23,883 faces were scanned, three alerts prompted interventions and four arrests were made in the seaside town. However, two of these were not linked to the LFR alerts. Despite wider public concern about misidentification, in both
Jul 13, 2026 · via greatyarmouthmercury.co.uk
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Big Brother or big help? Japan trials AI facial recognition cameras to find missing people
Police stations in Tokyo’s Arakawa ward receive about 100 reports each year of missing children and elderly people with dementia
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A Tokyo ward has installed outdoor AI cameras with facial recognition capabilities to help locate missing children and elderly people, a move aimed at improving public safety but also raising privacy concerns in Japan.
Arakawa ward installed 33 artificial intelligence-equipped security cameras on pylons along the main street and elsewhere near the JR Nippori Station in April to test whether the technology could speed up searches for missing persons.
The busy area around the station is frequented by commuters, students and foreign residents.
If a child or elderly person with dementia goes missing, family members can ask the ward to conduct an AI-assisted search by providing a photograph. The AI then scans recorded footage for people closely matching the image.
The ward believes that this is the first outdoor deployment of AI facial recognition security cameras by a local Japanese government, and hopes that it will help to find missing people more quickly.
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Jul 13, 2026 · via scmp.com
Advertisement
Big Brother or big help? Japan trials AI facial recognition cameras to find missing people
Police stations in Tokyo’s Arakawa ward receive about 100 reports each year of missing children and elderly people with dementia
2-MIN READ2-MIN
Listen
A Tokyo ward has installed outdoor AI cameras with facial recognition capabilities to help locate missing children and elderly people, a move aimed at improving public safety but also raising privacy concerns in Japan.
Arakawa ward installed 33 artificial intelligence-equipped security cameras on pylons along the main street and elsewhere near the JR Nippori Station in April to test whether the technology could speed up searches for missing persons.
The busy area around the station is frequented by commuters, students and foreign residents.
If a child or elderly person with dementia goes missing, family members can ask the ward to conduct an AI-assisted search by providing a photograph. The AI then scans recorded footage for people closely matching the image.
The ward believes that this is the first outdoor deployment of AI facial recognition security cameras by a local Japanese government, and hopes that it will help to find missing people more quickly.
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Jul 13, 2026 · via amp.scmp.com
When GPS gets jammed and seconds matter, processing pipelines aren't fast enough. Here's what the stack looks like when you engineer for a war zone. There's a version of this story that starts in a research lab somewhere in California, where engineers debate the finer points of NeRF vs. Gaussian Splatting over cold brew coffee. This is not that story. Farsight Vision was founded in 2023, in the middle of a full-scale war. The engineers weren't optimizing for benchmark scores — they were building tools that units in the field would use the next morning. If the pipeline was too slow, too fragile, or too dependent on stable GPS, people would make decisions with worse information. That feedback loop is brutally short. Three years later, the company has 6,000+ active users, a team of 50, and a product suite that covers the full ISR stack: from drone feed ingestion to 3D terrain reconstruction, object detection, mission planning, and GPS-denied navigation. What started as a focused tool for reconnaissance has grown into something closer to a complete intelligence operating system for unmanned systems. Here's how the pieces fit together — and why each one is harder to build than it looks. The Core Problem: Drone Data Is Abundant. Useful Intelligence Is Not. Modern ISR drones generate enormous amounts of footage. A single reconnaissance mission can produce thousands of frames of video and imagery. The bottleneck was never collection — it was always processing. Farsight Vision's FSV Platform was built specifically to collapse that processing window. It converts drone imagery — captured in GPS-jammed and GPS-denied environments — into orthophotos at 5–7 cm/pixel accuracy and full 3D terrain models, delivered as map layers that plug directly into existing C2 systems: ATAK, DELTA, Kropyva, Combat Vision. The output isn't a standalone file you
Jul 13, 2026 · via hackernoon.com
The James Webb Space Telescope reveals a striking image of Centaurus A For more than four years, the James Webb Space Telescope has continued to help astronomers to better understand the universe and its composition. And recently, to mark the fourth anniversary of the telescope’s first observations, a striking image of the merger between two galaxies has been unveiled. Named Centaurus A, this galaxy is located about 11 million light-years from Earth. Known for its unusual shape, it is believed to have formed by the merger of two galaxies about 2 billion years ago. During this event, huge amounts of gas and dust were released, leading to the formation of many stars. That's not all, because this merger also allowed its supermassive black hole to grow and eject powerful jets of plasma at very high speeds. But while this telescope has enabled astronomers to better understand this galaxy, particularly with the help of the MIRI (Mid-Infrared Instrument) and NIRCam (Near-Infrared Camera), some mysteries remain. Indeed, several structures surrounding this celestial object are still poorly understood, such as the S-shaped structure at its center. However, this image highlights the James Webb Space Telescope’s ability to explore areas that were previously unimaginable. And further advances will be made in the near future thanks to this instrument and future telescopes. Source(s) Image source: NASA, ESA, CSA, STScI; Image Processing: Alyssa Pagan (STScI), Joseph DePasquale (STScI), Macarena Garcia Marin (ESA Office at STScI) / NASA Hubble Space Telescope - Unsplash
Jul 13, 2026 · via notebookcheck.net