ICE Plans to Deploy 1,570 Additional Iris Scanners Nationwide Under No-Bid Contract Federal records show the agency is purchasing expanded access to a private biometric database built largely through sheriff partnerships. The Department of Homeland Security plans to deploy 1,570 iris scanning devices to Immigration and Customs Enforcement locations across the country within 30 days of finalizing a no-bid contract with the Massachusetts technology company Bi2 Technologies, according to a Statement of Objectives posted to SAM.gov on May 8. The proposed sole-source contract would give ICE agents the ability to scan a person’s iris using a smartphone and compare the result against a database containing more than five million criminal booking records. According to the procurement document, the system is designed to return matches in real time during field operations. The May procurement appears to expand an existing ICE-Bi2 capability rather than create it from scratch. Federal spending records show DHS awarded Bi2 a $4.6 million contract in September 2025 for iris biometric recognition technology and access to a biometric information system. That earlier award required Bi2 to provide 200 biometric devices. The new Statement of Objectives calls for 1,570 devices, suggesting ICE is seeking to scale up the system’s physical deployment while maintaining nationwide access to Bi2’s IRIS and MORIS tools. What Iris Scanning Is, and How It Works Biometric identification uses a physical characteristic of the body to confirm a person’s identity. Fingerprints are the most familiar example. Facial recognition, which compares photographs against databases of known images, has become increasingly common in law enforcement over the past decade. Iris recognition works on the same principle, but analyzes the colored ring surrounding the pupil of the eye. Bi2 Technologies describes the iris as “the most anatomically unique biometric visible on the human body” and says its IRIS system
May 11, 2026 · via projectsaltbox.com
Codebreaker #30 — a new week, two new codes, same six tries.
Here's a quick refresher if you need it: If you love Wordle, Connections, or any daily puzzle, Codebreaker is your new obsession. It's a daily logic game where you get 6 attempts to crack a secret 5-digit code. After each guess, you'll find out which digits are correct and in the right spot, which are in the code but in the wrong place, and which aren't in the code at all. Solve the first puzzle, and a harder bonus code unlocks. Two puzzles. One daily session. It sounds simple. It is not simple.
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May 11, 2026 · via buzzfeed.com
Figures Abstract Convolutional Neural Networks (CNNs) require a larger amount of input samples and computing resources to learn discriminative features for accurate identification of multiple retinal conditions, making the development and deployment of such models challenging on limited computing resources. This study presents a robust CNN (called LiteFeatNet) that requires fewer trainable parameters, computational resources, and processing time for accurate prediction. To enhance robustness and reduce computational time, a pre-trained NASNetMobile backbone is employed, and a method for time-efficient discriminative feature extraction from deep intermediate layers is proposed. The extracted features are refined using a spatially-aware feature map reduction module and classified using a custom classification module with fewer number of trainable parameters, reduced computational resource requirements, and computational time. Experiments are conducted using 1824 images from three distinct class labels in the Retinal Fundus Multi-Disease Image Dataset (RFMiD), with a 60:20:20 train-validation-test split. The LiteFeatNet architecture has a compact size (19.87 MB) and was trained using a standard pre-processing pipeline and training configurations. It outperformed twelve state-of-the-art models, achieving the highest testing accuracy of 90.33%, precision of 90.69%, recall of 90.33%, and F1-score of 90.27%, with a fast, impressive inference time of 4 milliseconds per image. Further, a generalizability study was also conducted using an external dataset, RFMiD 2.0, and the LiteFeatNet achieved competitive performance with quicker testing time compared to other architectures. To evaluate the scalability and adaptability of our proposed integrated framework for larger multi-class problems, we assessed the scalability by increasing class label complexity using two additional disease categories. Results validated the effectiveness and computational efficiency of this integrated framework compared with 9 baseline architectures. An ablation study was also conducted using the LiteFeatNet and two top-performing transfer learning architectures to validate that the synergistic combination of deep feature extraction and feature map refinement is the
May 11, 2026 · via journals.plos.org
Dr. Harshil Dave, a research scientist in the Optical Sciences Division of the U.S. Naval Research Laboratory, demonstrates to Christian Tofteroo how beam shaping can increase the effective range of laser weapon systems during the Department of War’s annual Lab Day at the Pentagon in Arlington, Virginia, May 6, 2026. (U.S. Army photo by Bernardo Fuller) (This photo was altered for security purposes by blurring out identification badges.)
| Date Taken: | 05.06.2026 |
| Date Posted: | 05.11.2026 10:24 |
| Photo ID: | 9675791 |
| VIRIN: | 260506-A-AJ780-1007 |
| Resolution: | 7791x5194 |
| Size: | 9.39 MB |
| Location: | ARLINGTON, VIRGINIA, US |
| Web Views: | 6 |
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This work, Pentagon Lab Day [Image 23 of 23], by Bernardo Fuller, identified by DVIDS, must comply with the restrictions shown on https://www.dvidshub.net/about/copyright.
May 11, 2026 · via dvidshub.net
bygenesys@genesys
13-year engineer shipping production ML and computer-vision systems with AI coding agents.
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13-year engineer shipping production ML and computer-vision systems with AI coding agents.
13-year engineer shipping production ML and computer-vision systems with AI coding agents.
13-year engineer shipping production ML and computer-vision systems with AI coding agents.
May 11, 2026 · via hackernoon.com
Image by Oselote - Getty Images Using traffic cameras for mass surveillance concept image It is no surprise that Milwaukee is in the midst of a surveillance crisis. It was clear through the year-long push against Facial Recognition Technology (FRT) with the City and County, that residents don’t want to be surveilled by law enforcement and military contractors through weird public/private business relationships. What we are hearing about FLOCK is more of the same. FLOCK is an Automated License Plate Recognition (ALPR) system that Milwaukee’s Police and Sheriff Departments currently use to surveil us. This week, the City of Appleton voted to end their usage of Flock. Just this month, Oshkosh and Sturgeon Bay refused to renew their contracts. Verona will end theirs also. Dane County will terminate its contract in May, and the City of Madison has ordinances protecting their residents from ALPRs being deployed. They’re doing this for many reasons. First and foremost, there are no guardrails that can guarantee our safety, nor meaningful arguments for such invasive, large-scale surveillance that feeds into an already expansive web of digital stalking. ICE and other federal agencies have been shown to directly or sneakily access this information from law enforcement agencies all across the country. It Goes On and On My Friends… Right now, there is a web of over 92,000 FLOCK cameras and counting across the nation. This creates a dragnet surveillance of all people whether or not they are suspected of a crime or wanted for any particular reason. What many don’t realize is that the cameras pick up everything in the range of their view. Not just license plates. They use AI to hone-in on the licenses, but everything is covered. This has been shown extensively and regardless of the data accessible through the agreement with the
May 11, 2026 · via shepherdexpress.com
As expected, Sony Music Publishing has announced an agreement to acquire the complete music rights portfolio of Recognition Music Group from funds managed by Blackstone, subject to customary closing conditions. Terms of the deal were not announced, but sources and Billboard have valued the catalog in the $2 billion range, although estimates vary widely. The deal will see Sony Music Publishing acquiring Recognition’s entire catalog of works, which includes shares in more than 45,000 songs, among them Journey’s “Don’t Stop Believin’,” Red Hot Chili Peppers’ “Under the Bridge,” Fleetwood Mac’s “Go Your Own Way,” Beyoncé’s “Single Ladies (Put A Ring On It),” Bruno Mars’ “Locked Out of Heaven,” Leonard Cohen’s “Hallelujah,” Chic’s “Good Times,” Soundgarden’s “Black Hole Sun,” Lady Gaga’s “Bad Romance,” Bon Jovi’s “Livin’ On A Prayer,” Rihanna’s “Umbrella,” Eurythmics’ “Sweet Dreams (Are Made Of This),” Shakira’s “Whenever, Wherever,” Steve Winwood’s “Higher Love” and Mariah Carey’s “All I Want For Christmas Is You,” among many others. Popular on Variety The deal is in partnership with the investment venture announced earlier this year between GIC and Sony Music Group, focused on acquiring music catalog assets across genres and international markets. The transaction follows Sony Music Publishing’s previous acquisition of Hipgnosis Songs Group in 2025, reflecting its continued collaboration with Recognition and Blackstone. Beginning in the mid-2010s, companies under the Hipgnosis umbrella went on a buying spree, paying top dollar for catalogs and other assets, including many of the above-listed songs. It spent more than $3 billiion and drove up the value of music catalogs considerably. However, the company became unstable and beginning in 2021, Blackstone acquired Hipgnosis Songs Capital for around $800 million, then bought the administration division of Hipgnosis Songs Management, before acquiring the publicly traded catalog assets of Hipgnosis Songs Fund for $1.6 billion in April 2024 Rob
May 11, 2026 · via variety.com
How hundreds of mysterious Nasca lines have been uncovered—with the help of AI A century ago, scientists began to study enigmatic Nasca geoglyphs in Peru. Thanks to AI, the known number has nearly doubled, and these discoveries are providing new insights into what their messages meant. The discovery of geoglyphs in southern Peru has been supercharged by artificial intelligence (AI). In just six months, more than 300 new figures were identified, almost doubling the previously known number. Etched into the desert, the relief-type glyphs depict domesticated animals, anthropomorphic figures, and human heads. A team from Yamagata University’s Nasca Institute, led by archaeologist Masato Sakai and backed by the IBM Research center, trained an object-detection AI model with high-resolution imagery to refine their investigation into the distribution of geoglyphs in the Nasca desert. The newly identified glyphs are relatively small, and may have fulfilled roles quite different from those of the vast, sprawling geoglyphs that the Nasca desert is best known for. The latest findings are not only a striking addition to the catalog but also a vital aid in helping reveal part of the mystery surrounding these enigmatic designs. (Where should archaeologists dig next? The winners of this OpenAI contest can tell them.) A game changer The Nasca Lines are massive geoglyphs of animals, people, plants, and geometric designs that adorn the high desert of southern Peru. Examples of note include the famous depictions of a hummingbird, a spider, and a monkey with a long, spiraling tail. Thought to date to as early as 100 B.C., the well-known giant figurative geoglyphs average almost 300 feet in length, with some recognizable even from ground level. The latest discoveries are thought to date to roughly the same period and are, by contrast, about 30 feet in size, harder to detect, and tucked
May 11, 2026 · via nationalgeographic.com
Zhipu GLM-5V-Turbo sparks a "misfire," and the war of domestic multimodal agents is on the verge of breaking out. In the fierce competition among domestic large models, Zhipu's GLM series has always held a trump card of great commercial value: extremely strong coding ability. As the main form of AI shifts from large language models to agents, the industry competition has entered the second half. Developers and development ecosystems are the groups with the strongest willingness to pay. However, the expectations of industry giants for AI are obviously not limited to an "outsourced programmer." Only by becoming an all - around agent that can truly take over the system workflow can AI enter the lives of ordinary people. Therefore, a powerful AI is far from enough just by typing on the keyboard. It must have "eyes" to examine web page layouts, understand posters and charts, and even comprehend various non - text complex information on the GUI. A few days ago, DeepSeek's gray - scale test of the "image recognition mode" fired the first shot. Now, Zhipu is also closely following and officially launching a new exploration in the multimodal field. In the technical report of the latest model GLM - 5V - Turbo, we can clearly see that this is Zhipu's new charge towards the native multimodal agent, and also a confession full of technological brute force, engineering compromises, and business considerations. 01 The Violent Aesthetics and Micro - operation Art of the Visual Foundation The idea of adding visual capabilities to large language models has been frequently attempted in the past few years. However, the resulting visual language models (VLM) are often just spliced products. The language model is the absolute "brain," and the visual module is just an external camera. That is to say, the model simply
May 11, 2026 · via eu.36kr.com
Sony Semiconductor Solutions and TSMC have signed a preliminary agreement to form a strategic partnership for the development and manufacturing of next-generation image sensors. The move could mark a major shift in the future of imaging, as Sony aims to combine its sensor design leadership with TSMC’s process technology and manufacturing expertise. The target is clear. Future image sensors will need to serve cameras, smartphones, cars, robots, and AI-driven machines that rely on vision as their primary sensing tool. TSMC’s manufacturing expertise could help Sony push more advanced stacked sensor designs, faster readout circuits, improved efficiency, and more sophisticated processing layers. This does not mean that a specific Alpha, FX, VENICE, or smartphone camera is around the corner. The agreement is still preliminary. However, it signals where Sony believes the image sensor industry is heading. A new joint venture in Japan According to the announcement, Sony Semiconductor Solutions and Taiwan Semiconductor Manufacturing Company have signed a non-binding memorandum of understanding to establish a joint venture. Sony is expected to be the majority and controlling shareholder. The JV would set up development and production lines inside Sony’s newly constructed fab in Koshi City, Kumamoto Prefecture, Japan. That detail is important. This is a Sony-led image sensor initiative, with TSMC joining as a strategic manufacturing and process technology partner. Sony remains in control of the sensor business direction, while TSMC contributes the semiconductor manufacturing capabilities that are becoming increasingly critical for advanced sensing architectures. The companies are also discussing potential investments by the JV, along with additional capital investment by Sony in its existing plant in Nagasaki. These investments are expected to be implemented in phases, based on market demand and Japanese government support. Image sensors are becoming semiconductor platforms For decades, image sensor progress was mostly discussed through familiar terms such
May 11, 2026 · via ymcinema.com
Estates, gated communities and office parks face sweeping changes to security procedures as the Information Regulator calls for access-controlled areas to collect minimal visitor information and protect privacy. The regulator has now published the “Own-Initiative Code of Conduct for Gated Access Areas” that will govern how gated-access environments handle personal information to conform to the prescripts of the Protection of Personal Information Act (Popia). The code applies to residential and commercial premises with access control. It is not a guidance note but a code of conduct, which carries much heftier weight than the former. The regulator in the code of conduct says members of the public have raised concerns that the collection of personal information at gated access entry points is excessive. “The regulator undertook research into the utilisation of closed-circuit camera (CCTV) surveillance and, in addition, considered complaints received in this regard. These collectively revealed certain access control practices of an intrusive nature, including the processing of biometric information such as the use of facial recognition systems for the purpose of positive identification of data subjects,” the draft code of conduct reads. “Furthermore, the deployment of CCTV surveillance at access control points results in the capture of facial images without the consent of data subjects and, at times, without their knowledge or awareness. “Such processing may constitute excessive collection and processing of personal information in so far as it is not relevant and limited to what is necessary for the legitimate purpose for which it is collected and accordingly warrants the imposition of appropriate regulatory measures to ensure compliance with provisions of Popia.” Under the proposed code of conduct, visitor books must not be visible to others in a queue, and digital visitor management systems must encrypt data. The code also prohibits indiscriminate copying of IDs and driver’s licences
May 11, 2026 · via businessday.co.za
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May 10, 2026 · via hackernoon.com
New Zealand-founded retail crime intelligence company Auror has grown to more than 85,000 stores globally and increased annual revenue by 60% as retailers increasingly turn to intelligence and surveillance software to combat organised retail crime. The Auckland-founded company, which provides software used by retailers to record theft, violence and repeat offending across store networks, says much of its recent growth has come from existing customers expanding Auror into overseas markets. Watch BNZ Business Breakfast with Mei Heron live from 6am on TVNZ1 or TVNZ+ Founder and chief executive officer Phil Thomson told BNZ Business Breakfast the company's growth was increasingly being driven by existing customers taking the platform into new markets. "What's really cool is that we're being pulled by our customers - they're seeing the value in what we do, and so they're pulling us into markets that they've got stores in as well," he said. Despite the company's global growth, Thomson said Auror remained rooted in New Zealand. "While only 10% of our revenue probably comes from this market now... we overinvest, over-optimise here. It's a great place to start a company and as a test market." Auror raised $82 million in late 2024 in a Series C funding round led by US security company Axon, valuing the company at more than $500 million. Thomson said breaking into overseas markets had required rebuilding credibility from scratch. "If you look at the US, probably a great example for us... the three questions that we often got were: 'Are you a US company? Who are your US customers? And where is New Zealand?'" he said. "Even though we had great credibility down here in New Zealand and Australia, we had to start again." Auror's rapid expansion has also brought increased attention to privacy and the use of facial recognition technology.
May 10, 2026 · via 1news.co.nz
The assumption most people carry into their own minds is that the noise is the problem. That the loop of small anxieties replaying at 11pm, the way a single critical comment can surface six times before lunch, the creeping sense that something is about to go wrong even when nothing is — all of it is evidence of a mind that has turned against them. A glitch. A malfunction. Something to be quieted, medicated, or apologized for in therapy. And that assumption is almost entirely wrong. What psychology has long observed about the brain’s pattern-recognition system isn’t a story of malfunction. It’s a story of a scanner doing exactly what it was designed to do — continuously, efficiently, without ever waiting to be asked. The problem isn’t that the scanner is broken. The problem is that most people have never been told they can change what it’s scanning for. That distinction sounds small. It isn’t. - The Scanner’s Purpose: Your brain’s negativity bias isn’t broken — it’s a survival system that kept humans alive for millennia. - The Redirection Principle: You can’t turn off anxious scanning, but you can train it to notice different signals with consistent practice. - The Competence Hidden: People with overactive mental scanners often develop exceptional emotional intelligence and environmental awareness. The channel nobody chose Here is what almost nobody outside this experience understands: the brain doesn’t tune to negativity because it prefers suffering. It tunes to threat because, for most of human history, missing a threat cost more than missing a comfort. A scanner calibrated toward danger kept people alive. That same scanner, running in a body sitting at a kitchen table at 7am with a cup of tea going cold, is still faithfully doing its ancient job — flagging the unresolved email, the tone
May 10, 2026 · via leravi.org
Last week, at 4am, 19-year-old Sammy Amz was scrolling through X when something caught his eye: a popular Twitch streamer was competing in a 1v1 “mog-off” with a stranger, and losing. The next day he opened the Omoggle gaming website and began to play. Quickly he matched with another user – green dots appeared on their faces onscreen, as the website began to compare their measurements: canthal tilt, palpebral fissure ratio, nose-to-face width ratio and so on. Omoggle enables one stranger to “dominate” another in a contest of looks, which in online slang, is called mogging. It uses facial recognition to analyse and score the faces of competitors between one and 10. Omoggle’s ecosystem is based on Omegle, a now defunct site that randomly matched strangers for video-based online chats. “It’s not [scored] by looks, but it’s like, how your head is shaped, how your face is shaped,” said Amz. A week later, Amz had already competed in hundreds of mog-offs, along with some of the biggest UK streamers, emulating a trend that began in the US. On Tuesday, the Amazon-owned live-streaming platform Twitch got onboard, changing their rules to allow for “participation in current trends”, such as Omoggle. Previously, its community guidelines had prohibited the use of websites that connect a streamer to a stranger’s video feed, because of the risks of accidentally exposing its users to harmful content. To decide on a mog-off winner, Omoggle uses something called the PSL scale. The letters stand for “Perceived Sexual Market Value,” but originally, they represented three incel sites: PUAhate.com, Sluthate.com and Lookism.net. These online forums encouraged young men to develop an obsession with their physical appearance. For some it was nihilistic, and seemed to promote resentment against women who were perceived to only value physical attractiveness in men. For others,
May 10, 2026 · via theguardian.com
Hands-on building beats passive learning. Real progress comes when you train models, debug errors, and see results. Start small, finish fast. Short projects help you stay consistent and build real momentum. Reusable code accelerates learning. You spend less time setting up and more time understanding how models work. PyTorch provides a strong framework for building and training deep learning models. Project-based learning improves clarity on how systems handle data and learning steps. Real projects demonstrate how inputs move through networks and produce outputs. Practical implementation exposes training errors and their solutions. Consistent project work builds confidence and strengthens technical understanding. Let’s take a look at how these projects can shape real skills. Designed for text generation using PyTorch and poetry datasets. A neural network learns patterns in words, sentence structure, and sequence flow. The training process feeds structured text data into the engine . It predicts the next word based on earlier context. The system produces poems that match selected themes or emotions. This demonstrates how sequence models process language data. Also Read: Best 10 Tableau Projects to Try in 2026 with Source Code A tool that suggests music based on user data. Listening history is analyzed to identify patterns. User preferences are mapped to song features to improve recommendations. Suitable songs are predicted based on these patterns. The training process improves accuracy through feedback data. This shows how recommendation engines work in real platforms. Using image data to detect plant diseases. Leaf images are processed using a convolutional neural network. Visual patterns linked to diseases are identified from these inputs. Images are then classified as healthy or infected. Image preprocessing improves input quality and model performance and demonstrates practical use of computer vision in agriculture. This project connects image processing with text generation. It extracts features from images using
May 10, 2026 · via analyticsinsight.net
Sony has some of the best mirrorless offerings in the world. The company’s decision to pivot to a different format in 2012 has been their best, and it appears that it is paying off really well for many photographers. Portrait image-makers get some impressive options to choose from as well, but there is one camera that easily takes precedence over others. The camera is the Sony a1 II and features the same sensor as the a1, but it offers a new BIONZ XR. The device also has a new dedicated AI processing chip, for autofocus and image processing abilities. One also gets Pre-Capture, a burst rate of 30 fps, with full AF/AE tracking, and 8.5-stop in-body image stabilization. While the a1 II offers some amazing specs, it also has an amazing autofocus system. The a1 II has Real-time Recognition AF+ and Preset Focus, and has better tracking ability. When used with Sigma 60-600mm f4.5-6.3, it was able to track birds really well. Then with 24-105mm f4 also locks focus on with ease. It also did a fabulous job of detecting people of color in low light. When focusing on people of color in low light, this is the best Sony camera by far. Though the Nikon Z9 does a better job still and the Leica SL3 can hold its own, the Sony a1 II was able to focus on people of color very well in extremely low light. Like the autofocus, the images are pretty fantastic. The camera helps to capture details and one can use JPEGs with ease. The RAW files are versatile as always, one can retrieve with ease. The colors are nice, and one can always bump it up on RAW files. As we said in our review, “Well, the Sony a1 II is worth the upgrade
May 10, 2026 · via thephoblographer.com
When the smart battlefield turns blind: AI’s snag in the Iran war The seduction of speed The Iran war may come to be remembered as the conflict in which artificial intelligence (AI) crossed a decisive threshold, if not the Rubicon of post-modern warfare itself, moving from an auxiliary analytical tool to the pulsating nerve centre of the kill chain. What once required teams of human analysts labouring for hours over satellite imagery, drone feeds, signals intercepts, battlefield maps, and strike-option comparisons was suddenly compressed into fractions of seconds through an integrated AI-assisted architecture: Palantir’s Maven for intelligence fusion, frontier large language models (LLMs) such as Anthropic’s Claude for interpretive summarisation, and the Rapid Strike Interface (RSI) as the operational layer through which ranked outputs were rendered immediately actionable. As The Washington Post reported on March 4, 2026, Claude “generated approximately 1,000 prioritised targets on the first day of operations alone”, synthesising satellite imagery, signals intelligence, and surveillance feeds in real time and feeding these outputs into machine-structured decision sequences that drastically reduced the interval between analysis and strike authorisation. The significance of this shift lies not only in the unprecedented acceleration of data processing within contemporary military systems, but also in the transformation of the very rhythm of judgement that defines modern warfare. Across recent conflicts and advanced military simulations, AI-assisted command architectures have increasingly enabled analysts and operators to move from raw sensor inputs to synthesised battlefield interpretations at speeds previously unattainable through human-only processing. Systems such as Palantir’s Maven Smart System, deployed within US defence infrastructures, exemplify this shift towards integrated intelligence fusion, where satellite imagery, drone feeds, and signals intelligence are consolidated into unified operational displays. In some reported configurations, LLMs and generative AI tools are being explored or tested as interpretive layers to assist in summarising
May 10, 2026 · via thedailystar.net
The SCOPLE wearable AI device is an advanced piece of technology engineered to leverage advanced capabilities to help users better understand people they encounter on a daily basis. The device works by being worn and will go to work capturing information regarding a person's facial expressions, attention and more. The system will then utilize its artificial intelligence (AI) algorithm to interpret the data and provide users with information on how people react to their presence. This could work well for helping users to better understand how others perceive them or perhaps even gain more insight into conversations. The SCOPLE wearable AI device has a privacy-first design that doesn't storage any photos or videos with all data anonymized to increase peace of mind. Communication-Analyzing AI Wearables The SCOPLE Wearable AI Device Determines Emotions and More Trend Themes - Emotion-detection Wearables — Devices that infer emotional states from facial and behavioral cues enable new forms of interpersonal analytics that could reshape customer interactions and social dynamics. - Privacy-first AI Interfaces — Systems designed to anonymize and avoid storing raw sensor data are creating trust-focused AI products that balance personalization with regulatory and ethical constraints. - Real-time Social Feedback — Instant interpretation of attention and reactions during conversations is generating opportunities for context-aware augmentation of communication and presentation effectiveness. Industry Implications - Retail Customer Experience — In-store and omnichannel retailers could integrate wearable-derived emotional insights to refine merchandising, layout and personalized service strategies. - Corporate Training and HR — Learning and development programs may incorporate moment-to-moment feedback on engagement and emotional response to evaluate and tailor coaching, interviewing and leadership training. - Healthcare and Mental Health — Clinical and wellness providers stand to gain continuous, objective markers of mood and social functioning that could inform diagnosis, monitoring and treatment personalization.
May 10, 2026 · via trendhunter.com
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May 10, 2026 · via youtube.com