Each edition of Research Bytes showcases the scholarly and professional achievements of Drexel School of Computer and Information Sciences (SCIS) faculty and students. Research Accomplishments Recent Grant Awards Computer Science Assistant Professor Feng Liu, PhD received an in-kind gift of $32,000 in GPU hours from the NVIDIA Academic Grant Program to support autonomous driving AI research. Learn more. Liu is also a Co-PI on two recently awarded 2026 Longsview Fellowships. The first, awarded to his colleague, Drexel School of Engineering professor Fernanda Campos da Cruz Rios, PhD (PI), will support their research on automated detection of façade-level material to advance urban material stock and flow analysis. The second, awarded to the PI, Drexel professor Matthew McDonald, PhD (chemical and biological engineering), will be focused on using generative AI for organic crystal structure prediction. Computer Science Assistant Professor Li “Harry” Zhang, PhD is a Co-PI on a recent 2026 Grimes Family Faculty Award with School of Engineering colleague Zhiwei Chen, PhD (PI). The award will support their research on neurosymbolic learning for safe autonomous driving under ambiguous trust. Computer Science Associate Professor Edward Kim, PhD received a grant of $11,025 from trading firm Hard Eight Trading, LLC that will support PhD research on comparative analysis of machine learning models for gold futures forecasting. Honors & Recognition Professor and Information Science Department Head Helena Mentis, PhD received the Association of Computing Machinery Special Interest Group on Computer-Human Interaction (ACM SIGCHI) Lifetime Service Award. This award highlights Mentis’s remarkable contributions to the growth and success of SIGCHI over many years. Learn more. First-year information science PhD student Tzu-Yu Weng and Information Science Assistant Professor Karthik S. Bhat, PhD received an Honorable Mention Award for their paper “A Blessing and a Challenge: Unpacking Boundary Ambiguities Experienced by Caregivers of Older Adults” that will be
Apr 29, 2026 · via drexel.edu
Image: Cottonbro studio - Pexels A woman strolls into a grocery store, thinking about grabbing some apples. Before she even reaches the produce aisle, a security camera has scanned her face. Whether the system is checking for shoplifters or simply logging her arrival, her face has joined a digital ledger, a trace she can’t easily erase. Retailers, banks, airports, stadiums and office buildings are doing the same. But what if the woman’s facial information is stolen or misused? If a cybercriminal steals her password, she can change it. If they acquire her credit card number, she can cancel the card. But she can’t reset or revoke the appearance of her cheekbones. Facial recognition systems don’t keep actual images. They convert a face into a mathematical template that maps the positions and proportions of the face’s features. When another camera scans a person later, the system checks their live face against these templates to confirm an identity. In my work as a cybersecurity professor at Rochester Institute of Technology, I have found that even though templates are more secure than photos – which anyone online can capture and manipulate – templates, too, can be stolen. Once that happens, these digital keys create a lifelong vulnerability. If a facial recognition database is breached, the “locks” that a template opens – accessing a bank app, getting through security at an airport, entering an office building – can’t be reset. A person’s face is permanent, and so is the threat. The threat isn’t theoretical. Biometric data has been stolen in data breaches. In 2024, biometric data from a facial recognition system used at bars and clubs in Australia was hacked. And in 2019, biometric data from a pilot facial recognition system set up by U.S. Customs and Border Protection was breached in an attack
Apr 29, 2026 · via digitalinformationworld.com
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Apr 29, 2026 · via scottcoop.com
Abstract The United Nations’ Sustainable Development Goals, SDG 12: Responsible Consumption and Production, and SDG 13: Climate Action highlight the importance of environmental conservation and reducing pesticide use. Early and accurate pest identification is essential for implementing targeted pest control measures, which helps reduce unnecessary and incorrect pesticide use. While effective pest recognition and classification are crucial for ecological research and biodiversity conservation, traditional methods remain labor-intensive, time-consuming, and dependent on experts. Several deep learning techniques have been introduced in recent years, leading to more efficient and accurate identification and classification of crop pests. This research presents a structurally adapted DenseNet model for multi-class pest image classification based on dense connections. The model is fine-tuned through hyperparameters involving dense blocks and transition layers to perform consistently across three different datasets, including the IP102 dataset, which contains over 75,000 images of 102 pest species. The study also addresses dataset imbalance to prevent biased outcomes by deep learning models. The proposed structurally adapted model for fine-grained classification achieves 82.69% accuracy and 81.45% F1 score on the IP102 dataset, complementing existing advanced methods. Similar content being viewed by others Funding Open access funding provided by Manipal University Jaipur. The authors received no funding for this work. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or
Apr 29, 2026 · via nature.com
SenseTime, a Chinese AI company best known for its facial recognition technology, released a new open source model on Tuesday that it claims can both generate and interpret images far faster than top models developed by US competitors. SenseNova U1 could help the company reclaim lost ground after it slipped from its place among the leading players in China’s AI development race. The model’s secret sauce is its ability to “read” images without translating them to text first, speeding up the process and reducing the amount of computing power required. “The model’s entire reasoning process is no longer limited to text. It can reason with images as well,” Dahua Lin, cofounder and chief scientist at SenseTime, said in an interview with WIRED. Lin, who is also a professor of information engineering at the Chinese University of Hong Kong, says that models capable of processing images directly will enable robots to better understand the physical world in the future. Like DeepSeek's latest flagship model, SenseTime says U1 can be powered by Chinese-made chips. “Several Chinese domestic chipmakers have finished optimizing compatibility with our new model,” Lin says. On release day, 10 Chinese chip designers, including Cambricon and Biren Technology, announced their hardware supports U1. That flexibility matters because US export controls restrict Chinese firms from accessing the world's most advanced AI chips, particularly those used for training, which at this point are primarily developed by Western companies like Nvidia. “We will continue to push for training on more different chips,” Lin says. But he also acknowledges that SenseTime “may still need to use the best chips to ensure the speed of our iteration.” SenseTime released U1 for free on Hugging Face and GitHub, another sign of how Chinese companies are becoming some of the most active contributors to open source AI.
Apr 29, 2026 · via wired.com
If you want to visit the “Happiest Place on Earth,” you’ll go through a new gatekeeper first: facial recognition. The Anaheim resort has expanded facial-recognition technology at entrances to Disneyland Park and Disney California Adventure after months of limited testing, reads Disney’s privacy notice, in which the company states the intention is to make reentry easier and to prevent fraud. Disney says the system is optional: Guests who do not want to use it can enter through non-facial-recognition lanes, where a cast member manually validates their ticket. However, those guests may still have their photos taken, even as Disney says biometric technology is not used on those images. According to Disney’s privacy notice, the system compares a camera image taken at the entrance with the image saved when a guest first used a ticket or pass, converting the images into unique numerical values to look for a match. Disney says it deletes the numbers within 30 days, unless they must be kept for legal or fraud-prevention purposes. And for kids under 18, they can use the system only with parent or guardian consent. Disney didn’t immediately responded to Fortune’s requests for comment. “The security, integrity, and confidentiality of your information are extremely important to us,” reads the company’s privacy notice announcing the technology. “We have implemented technical, administrative, and physical security measures that are designed to protect Guest information from unauthorized access, disclosure, use and modification.” “From time to time, we review our security procedures to consider new technology and methods, as appropriate,” it continued. “Please be aware that, despite our best efforts, no security measures are perfect or impenetrable.” By Friday, the tech was being used in most entrance lines at the two parks, with the Los Angeles Times finding only four lines were spared. Signs near the gates
Apr 28, 2026 · via fortune.com
The suspect in a gruesome double murder that went unsolved for decades died in jail just weeks after he was arrested. Floyd William Parrott, 64, was arrested in Lincoln, Nebraska, on March 25 by officers with the Houston Police Department and the FBI, according to the Harris County District Attorney’s Office. He was charged with capital murder in connection with the killings of 21-year-old Andy Atkinson and 22-year-old Cheryl Henry, who were found dead inside a car parked in a cul-de-sac with injuries to their necks on Aug. 23, 1990, authorities said. The cold case, known as the “Lovers’ Lane Murders,” went unsolved for nearly 36 years. Parrott was found unresponsive in his Nebraska jail cell on Tuesday before his extradition to Texas to face trial, according to the Lancaster County Board of Commissioners. “We ache for Andy’s and Cheryl’s families who were denied their day in court. Our anger for what Parrott took from them is matched only by our determination to keep going,” the DA’s office said in a statement. “His survivors deserve accountability.” ADVERTISEMENT - 1‘Lovers’ Lane Murders’ Suspect Dies in Jail After ArrestUNRESPONSIVEThe suspect in a three-decade-old cold case died before he could face trial. - 2$500M Russian Superyacht Makes Mystery Hormuz Blockade RunCOMING THROUGHNeither Iran nor the U.S. interfered with the mega yacht’s movement. Shop with Scouted This Growth Factor Serum Targets Jowls in Just One WeekLIFT ME UPA next-generation growth factor serum formulated to visibly lift, firm, and smooth—fast and sans irritation.- 3'Stranger Things' Spinoff Renewed for Second SeasonSTAY TUNEDJust last week, it landed in Netflix’s weekly top 10 English-language TV shows. - 4Former NFL Player Dies at 35'DEVASTATING LOSS'The defensive end played in 80 games in the NFL throughout his career. Shop with Scouted Score Up to 70% Off Sex Toys During Lovehoney’s
Apr 28, 2026 · via thedailybeast.com
Figures Abstract Graph Neural Network (GNN) faces limitations in few-shot image classification due to insufficient adaptive feature extraction and limited long-range dependency modeling. To address these challenges, this study proposes an Improved Graph Neural Network (IGNN) integrating two key innovations. Firstly, we design an Attention-Enhanced Feature Extraction module, which combines Efficient Channel Attention (ECA) and self-attention mechanisms, enabling the model to dynamically focus on discriminative intra-image details and inter-image contextual relationships, thereby improving feature representation robustness. Secondly, we introduce a gated recurrent unit (GRU)-based Pre-message-passing mechanism, which establishes cross-sample associations between support and query sets before message propagation, effectively capturing long-range dependencies and mitigating information smoothing. The experimental results of three public datasets demonstrate that our proposed framework outperforms the existing methods and shows significant potential. It offers a pragmatic tool for applications requiring rapid adaptation to limited data, such as remote sensing and medical image analysis. Citation: Chen J, Fu B, Zou L (2026) IGNN: An improved graph neufral network with integrated attention and pre-message-passing for few-shot image classification. PLoS One 21(4): e0348057. https://doi.org/10.1371/journal.pone.0348057 Editor: Nagaraju Y, Dayananda Sagar College of Engineering, INDIA Received: June 14, 2025; Accepted: April 5, 2026; Published: April 28, 2026 Copyright: © 2026 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The authors used the publicly available datasets Omniglot dataset, MiniImageNet dataset, and CUB-200-2011 for the experiments. The Omniglot dataset can be accessed at https://github.com/brendenlake/omniglot. The MiniImageNet dataset can be accessed at https://image-net.org/update-mar-11-2021.php. The CUB-200-2011 dataset can be accessed at https://www.vision.caltech.edu/datasets/cub_200_2011/. Funding: This research was funded by the Sichuan Science and Technology Program, grant number 2025YFHZ0007, 2024JDHJ0015 and the Fundamental Research Funds for
Apr 28, 2026 · via journals.plos.org
Abstract Chronological age predicts cancer survival but does not capture differences in biological aging rates. We apply FaceAge, an artificial intelligence algorithm that predicts biological age from a facial photograph, to serial clinical facial photographs to calculate the Face Aging Rate (FAR; change in FaceAge divided by the time between photographs). We analyze data from 2276 cancer patients receiving radiation therapy, using photographs captured during routine care. Higher FAR is associated with worse overall survival in stratified analyses of cohorts with the following intervals between photographs: short 10-365 days (adjusted hazard ratio [aHR] and 95% confidence interval: 1.25 [1.03-1.51]), mid 366–730 days (aHR: 1.37 [1.00-1.86]), and long 731-1,460 days (aHR: 1.65 [1.22-2.22]) after adjustment for time between photographs, sex, race, and diagnosis. FAR provides additional prognostic information beyond single time-point measures of FaceAge. FAR is a non-invasive prognostic biomarker that captures dynamic changes in biological aging. Similar content being viewed by others Introduction Aging is a multifaceted biological process characterized by the decline of physiological functions and increased vulnerability to disease and death1. It affects nearly all living organisms and is intricately linked with various diseases, particularly cancer, as both result from the accumulation of cellular damage over time2,3,4,5. Chronological age has long been recognized as a predictor of survival; this relationship is particularly evident in cancer patients6,7,8,9,10,11,12. However, it treats all individuals within an age group identically, disregarding variations in biological aging rates13. Recognizing these limitations, it becomes essential to explore biological age indicators that can be readily implemented into clinical practice, especially those that quantify the rate of aging and are easily accessible, for personalized risk assessment. Recent longitudinal evidence demonstrates that significant variation in biological aging trajectories can already be quantified in young adulthood, underscoring the potential for early interventions before disease manifestation14. This study aims to
Apr 28, 2026 · via nature.com
Fans erupt as Disneyland rolls out facial recognition technology across park entrances The Happiest Place on Earth is rolling out facial recognition technology to expedite park entrance and catch out fraudsters. Guests “may choose to use entrance lanes equipped with facial recognition technology” at both Disneyland Park and Disney California Adventure, where cameras capture an image and convert it into “unique numerical values” to verify identity, according to the company. The system compares those values for a match and, in most cases, deletes them within 30 days. Disneyland officials say the optional technology is designed to streamline entry while preventing fraud. The entertainment giant says participation is voluntary, noting that traditional entry lanes without biometric scanning remain available. In those lines, cast members manually verify tickets instead of relying on facial recognition. The company also emphasized that “the security, integrity and confidentiality” of guest data is a priority, though it acknowledged that “no security measures are perfect or impenetrable.” At the parks, reactions from visitors were mixed. “Pretty much every other place is doing the same thing, John LeSchofs, 73, a frequent parkgoer who comes every six weeks, told the Los Angeles Times. “The police, the government, they’re all using facial recognition. I don’t think it’s going to stop.” Others expressed hesitation, particularly around transparency and consent. Robert Howell, 30, visiting from Virginia, said he was unaware of the technology until arriving at the park. “It’s a little scary because it’s not clear how it’s going to be used,” Howell said. “With TSA I know that’s an option that you can opt out, but I didn’t realize you could here so I just did it.” For some families, concerns center on how the technology affects children. Sandra Contreras said she felt uneasy when it came to her young daughter. “When
Apr 28, 2026 · via nypost.com
FORM Earns Two Golds at 2026 American Business Awards, Further Proving Industry Leadership in Mobile Task Management Best Mobile Operations Management Solution and Best Use of Augmented Reality awards underscore AI and AR innovation that is delivering real results for retailers and CPG brands BOSTON, April 28, 2026 /PRNewswire/ -- FORM, a leading provider of mobile task management and retail execution solutions, today announced it won two Gold Stevies® in the 2026 American Business Awards® for its GoSpotCheck image recognition platform. The awards recognize FORM's unparalleled technological leadership and the company's continued success in delivering measurable results on behalf of retail, grocery, distributor and CPG companies everywhere. FORM won Gold Stevies for its Mobile Operations Business Management Solution, GoSpotCheck, and the solution's augmented reality capabilities that help field teams improve on-shelf availability, ensure planogram compliance, and close the gap between corporate strategy and in-store execution. The awards come on the heels of another major recognition FORM received in March when GoSpotCheck earned a 2026 Artificial Intelligence Excellence Award from The Business Intelligence Group, one of the world's foremost independent AI recognition programs. "It's not only our customers noticing our innovation; the broader industry is paying attention to FORM's market dominance and technological leadership," said Matt Collins, Chief Marketing Officer, FORM. "These awards are gratifying because they prove the mettle of this overall team, which is working tirelessly to deliver real value to customers. And we're only getting better with our recent merger, which is leading to a bigger scale and reach than we've ever had." The American Business Awards are the U.S.A's premier business awards program. All organizations operating in the U.S.A. are eligible to submit nominations – public and private, for-profit and non-profit, large and small. More than 3,600 nominations from organizations of all sizes and in virtually every
Apr 28, 2026 · via prnewswire.com
Belt scale provides performance across applications Tecweigh’s HDS (heavy-duty S-type load cell) belt scale system is designed for high-capacity, high-impact applications. Tecweigh says the scale frame is built to withstand harsh operating conditions, while its load cell design ensures consistent readings across a range of belt speeds and material flows. The HDS is available in configurations ranging from one to four idlers and comes standard with onboard static calibration weights that can be raised and lowered during conveyor operation. The load cell can be installed on new conveyors or retrofitted onto existing systems. When paired with the Tecweigh WP50 processor, the company says operators gain real-time insights into throughput, totals and system performance. Scale tech allows for autonomous material recognition Wingfield Scale Co. launched iD Point image recognition technology for use on its WingScan Loadmaster. According to Wingfield Scale, iD Point uses advanced machine learning and AI to identify what material is being moved on trucks and railcars and attach a material code to each measurement. Users upload images of any material, then run a “learning” step so the system can recognize it in real time. Wingfield Scale says iD Point makes WingScan a fully autonomous, multidimensional measurement system. iD Point is currently in prerelease as a WingScan add-on feature.
Apr 28, 2026 · via pitandquarry.com
A woman strolls into a grocery store, thinking about grabbing some apples. Before she even reaches the produce aisle, a security camera has scanned her face. Whether the system is checking for shoplifters or simply logging her arrival, her face has joined a digital ledger, a trace she can’t easily erase. Retailers, banks, airports, stadiums and office buildings are doing the same. kAmqFE H92E :7 E96 H@>2?’D 724:2= :?7@C>2E:@? :D DE@=6? @C >:DFD65n x7 2 4J36C4C:>:?2= DE62=D 96C A2DDH@C5[ D96 42? 492?86 :E] x7 E96J 24BF:C6 96C 4C65:E 42C5 ?F>36C[ D96 42? 42?46= E96 42C5] qFE D96 42?’E C6D6E @C C6G@<6 E96 2AA62C2?46 @7 96C 4966<3@?6D]k^Am kAmu24:2= C64@8?:E:@? DJDE6>D 5@?’E <66A 24EF2= :>286D] %96J 4@?G6CE 2 7246 :?E@ 2 k2 9C67lQ9EEADi^^HHH]5:8:E2=D6?D6]2:^3=@8^9@H\5@6D\724:2=\C64@8?:E:@?\H@C<Qm>2E96>2E:42= E6>A=2E6k^2m E92E >2AD E96 A@D:E:@?D 2?5 AC@A@CE:@?D @7 E96 7246’D 762EFC6D] (96? 2?@E96C 42>6C2 D42?D 2 A6CD@? =2E6C[ E96 DJDE6> 4964<D E96:C =:G6 7246 282:?DE E96D6 E6>A=2E6D E@ 4@?7:C> 2? :56?E:EJ]k^Am kAmx? >J H@C< 2D 2 k2 9C67lQ9EEADi^^HHH]C:E]65F^5:C64E@CJ^;DH:4D\;@?2E92?\H6:DD>2?Qm4J36CD64FC:EJ AC@76DD@Ck^2m 2E #@496DE6C x?DE:EFE6 @7 %649?@=@8J[ x 92G6 7@F?5 E92E 6G6? E9@F89 E6>A=2E6D 2C6 >@C6 D64FC6 E92? A9@E@D – H9:49 2?J@?6 @?=:?6 42? 42AEFC6 2?5 >2?:AF=2E6 – E6>A=2E6D[ E@@[ 42? 36 DE@=6?] ~?46 E92E 92AA6?D[ E96D6 5:8:E2= <6JD 4C62E6 2 =:76=@?8 GF=?6C23:=:EJ] x7 2 724:2= C64@8?:E:@? 52E232D6 :D 3C624965[ E96 “=@4<D” E92E 2 E6>A=2E6 @A6?D – 2446DD:?8 2 32?< 2AA[ 86EE:?8 E9C@F89 D64FC:EJ 2E 2? 2:CA@CE[ 6?E6C:?8 2? @77:46 3F:=5:?8 – 42?’E 36 C6D6E] p A6CD@?’D 7246 :D A6C>2?6?E[ 2?5 D@ :D E96 E9C62E]k^Am kAm%96 E9C62E :D?’E E96@C6E:42=] q:@>6EC:4 52E2 92D 366? DE@=6? :? 52E2 3C62496D] x? a_ac[ 3:@>6EC:4 52E2 7C@> 2 724:2= C64@8?:E:@? DJDE6> FD65 2E 32CD 2?5 4=F3D :? pFDEC2=:2 k2 9C67lQ9EEADi^^HHH]H:C65]4@>^DE@CJ^@FE23@I\724:2=\C64@8?:E:@?\3C6249^QmH2D 924<65k^2m] p?5 :? a_`h[ 3:@>6EC:4 52E2 7C@> 2 A:=@E 724:2= C64@8?:E:@? DJDE6> D6E FA 3J &]$] rFDE@>D 2?5 q@C56C !C@E64E:@? k2 9C67lQ9EEADi^^HHH]@:8]59D]8@G^C6A@CED^a_a_^C6G:6H\43AD\>2;@C\4J36CD64FC:EJ\:?4:56?E\5FC:?8\a_`h\3:@>6EC:4\A:=@E^@:8\a_\f`\D6Aa_QmH2D 3C624965k^2m :? 2? 2EE24< @?
Apr 28, 2026 · via caledonianrecord.com
Japan's APPI amendment bill would open narrow lane for some AI uses, tighten rules elsewhere A bill to amend Japan's APPI would codify a new concept of "statistical processing," while putting sharper boundaries around minors' data, facial feature data, opt-out sharing and enforcement. Contributors: Takashi Nakazaki Partner Anderson Mori & Tomotsune Japan's Personal Information Protection Commission announced 7 April that the Cabinet of Japan approved a bill to amend the Act on the Protection of Personal Information. The PPC frames the package as accomplishing two things at once: facilitating data use, including artificial intelligence-related data use, while strengthening protection and enforcement where data handling creates greater risks for individuals. The bill, which does move in both directions simultaneously, is not a wholesale rewrite of Japan's privacy law, enacted in 2003. It is a targeted reform package, and a meaningful one. Unlike the EU General Data Protection Regulation, which generally begins by asking what legal ground supports data processing, Japan's amendment bill does not try to import a new across-the-board lawful-basis model into the APPI. Instead, it works more surgically, by changing specific consent-sensitive parts of the APPI and building new rules around higher risk practices. Under Japan's private-sector framework, the more important questions are usually about purpose limitation, use beyond the stated purpose and rules on third-party sharing and overseas transfers. So, for most privacy teams, the more useful question is not whether Japan is moving closer to Europe. It is which APPI rules would be loosened, and which would get tougher under the amendment bill. A new statutory concept, not a free pass The most significant change is the introduction of a new statutory concept, "statistical processing," defined as the creation of statistics and similar analytical acts that derive trend- or characteristic-level information from large volumes of information, excluding
Apr 28, 2026 · via iapp.org
(The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.) (THE CONVERSATION) A woman strolls into a grocery store, thinking about grabbing some apples. Before she even reaches the produce aisle, a security camera has scanned her face. Whether the system is checking for shoplifters or simply logging her arrival, her face has joined a digital ledger, a trace she can’t easily erase. Retailers, banks, airports, stadiums and office buildings are doing the same. But what if the woman’s facial information is stolen or misused? If a cybercriminal steals her password, she can change it. If they acquire her credit card number, she can cancel the card. But she can’t reset or revoke the appearance of her cheekbones. Facial recognition systems don’t keep actual images. They convert a face into a mathematical template that maps the positions and proportions of the face’s features. When another camera scans a person later, the system checks their live face against these templates to confirm an identity. In my work as a cybersecurity professor at Rochester Institute of Technology, I have found that even though templates are more secure than photos – which anyone online can capture and manipulate – templates, too, can be stolen. Once that happens, these digital keys create a lifelong vulnerability. If a facial recognition database is breached, the “locks” that a template opens – accessing a bank app, getting through security at an airport, entering an office building – can’t be reset. A person’s face is permanent, and so is the threat. The threat isn’t theoretical. Biometric data has been stolen in data breaches. In 2024, biometric data from a facial recognition system used at bars and clubs in Australia was hacked. And in 2019, biometric data from a pilot facial recognition system
Apr 28, 2026 · via bozemandailychronicle.com
A recent Pentagon contract just revealed the smartest artificial intelligence strategy in business. While some builders and investors chase fully autonomous everything – self-driving cars, pilotless airplanes, human-less factories – the real winners are asking: How much autonomy should we actually give our AI systems? The answer is reshaping how companies deploy AI across every industry. It's not what the hype machine is selling. Beacon AI, a California-based aviation software company, recently signed a four-year contract with US Special Operations Command (USSOCOM) worth up to $49.5 million. The deal is not for a fully autonomous, pilotless aircraft or for sci-fi autonomous fighters. Instead, it's for AI-powered pilot assistance software that cuts cockpit workload and speeds mission-critical decisions in high-risk operations. Think R2-D2, not HAL 9000. The system integrates flight data, weather, routing, and pilot inputs into real-time decision support. This is exactly what human pilots need when operating in contested, time-sensitive environments. Here is the strategic insight. Beacon deliberately chose limited AI autonomy over full automation, and the Pentagon validated this choice with a large check. The autonomy levels every executive needs to understand Most boardrooms discuss AI as if it's binary. You either have it or you don't. This is simplistic. AI autonomy operates on a spectrum, and understanding these levels is critical for any leadership team deploying AI systems. The framework comes from autonomous vehicles, but applies to AI systems in any industry: - Level 0 (no automation): Humans do everything. Traditional tools with no AI assistance. - Level 1 (driver assistance): AI provides information and basic support. Think spelling-check or fraud alerts that humans must act upon. The AI has no decision-making power. - Level 2 (partial automation): AI can execute specific tasks while humans maintain oversight and control. Examples: AI scheduling systems that suggest meetings but
Apr 28, 2026 · via jpost.com
Pet Health and AI News from NYC's Top Digital Cybersecurity Master's Program Artificial Intelligence Biotechnology Computer Science Cybersecurity Data Analytics and Visualization Digital Marketing and Media Mathematics Nursing Occupational Therapy Physician Assistant Physics Speech-Language Pathology How AI Is Revolutionizing Pet HealthJust as healthcare is becoming increasingly personalized for humans, AI is bringing similar advances to pet care. By using machine learning and predictive analytics, AI can analyze a pet’s unique characteristics – including breed, age, lifestyle, and genetics – to help inform customized wellness plans. These expanding capabilities are showing strong potential to improve how animals are examined, diagnosed, and treated.In veterinary clinics, research labs, and pet tech startups, AI is creating new opportunities to better understand and enhance animal health. The following discussion explores how AI is reshaping pet care and driving the next generation of veterinary innovation.AI Tools for Pet Health DiagnosticsArtificial intelligence is reshaping veterinary diagnostics by adding speed, consistency, and pattern recognition to the clinical process. While traditional diagnostics rely heavily on training, experience, and visual interpretation, AI helps surface insights that may be difficult to detect during routine exams, especially in early or complex cases.Rather than replacing veterinarians, AI acts as a support layer. It processes large volumes of data in seconds, compares findings against thousands of previous cases, and highlights areas that may require closer attention. The result is earlier detection, more confident diagnoses, and better-informed treatment decisions for pets.Imaging AnalysisMedical imaging plays a central role in veterinary diagnostics, but interpreting X-rays, MRIs, and ultrasounds can be time-consuming and subjective. AI-powered imaging tools improve this process by scanning images for subtle patterns that may not be immediately visible to the human eye.Trained on large datasets of veterinary images, these systems can identify early signs of disease, structural abnormalities, and progressive conditions. They flag areas
Apr 28, 2026 · via yu.edu
Inside Munger Elementary-Middle School on Martin Street on Detroit's southwest side, what looks like a horizontal iPad is installed just inside the school's front entrance. Every visitor to the school must present a valid form of identification, such as a driver's license, to have it scanned by the iPad and then have their face scanned. When the two images are cross-referenced and verified, the visitor's information is saved in the school's system for future visits. The system is part of a new facial recognition software system, called Visitor Aware, now in place at every Detroit Public Schools Community District school. The system matches visitors' faces against a valid form of identification before they're allowed into the school. When visitors are flagged, they may not be allowed in the building. Munger Elementary-Middle School Principal Donnell Burroughs said the software, which was officially rolled out earlier this school year, was needed for security. It's "more secure and safe" than a traditional sign-in sheet for visitors, Burroughs said. But critics have concerns about how facial recognition is being used in schools. Some cited privacy concerns and worried about inaccurate identifications, meaning someone is flagged when he or she shouldn't be. Others said they worry about the technology having a chilling effect, possibly discouraging some parents from visiting their children's schools. “What these systems are trying to do is to automate student security without human involvement," said Molly Kleinman, managing director of the University of Michigan's Science, Technology and Public Policy program. Detroit is one of 12 districts in Michigan that use Visitor Aware, according to its parent company Singlewire. Bloomfield Hills Schools also launched Visitor Aware last month. Both the Detroit school district and Bloomfield Hills Schools said it streamlines the process for allowing visitors into buildings and that keeping students and staff
Apr 27, 2026 · via govtech.com
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Apr 27, 2026 · via detroitnews.com
Artificial intelligence has moved well beyond the pilot stage. One survey found that 88% of organizations have implemented AI in at least 1 business function. And engineering is no exception. Already, AI has reshaped how engineers work. Its applications range from design optimization to automation and beyond. It’s expanding the scope of existing jobs and raising the bar on the skills engineers need to stay competitive. Learn more about the impact of AI on engineering jobs, emerging career opportunities, and ways to thrive in a changing field. Key Points - Artificial intelligence has become fundamental enough to shift traditional engineering roles, changing how engineers work and the work itself. 88% of organizations now use AI in at least one business function. - Engineers are using AI to tackle complex real-world problems, with applications spanning predictive maintenance, design optimization, and automation. - AI’s impact on engineering is just beginning. But it’s already creating new job opportunities and demanding new skills to stay relevant. - AI likely won’t replace engineers, but it will affect some roles more than others. That makes adaptability 1 of the most valuable traits in the field right now. What Is Artificial Intelligence in Engineering? AI enables machines to learn, adapt, and perform tasks that typically require reasoning. In engineering, that capability translates into more powerful tools for analyzing systems and solving complex problems. Common artificial intelligence methods and capabilities used in engineering include: - Machine learning (ML): These systems train on data to perform specific tasks with minimal human oversight, learning and adapting from that data along the way. In engineering, ML is also used in automation, predictive maintenance, and systems optimization. - Computer vision: This AI capability interprets and extracts meaning from visual data such as images and video. In engineering, it can support real-time inspection,
Apr 27, 2026 · via intuit.com