The company is bringing its Private Processing encryption service to its much-maligned smart glasses. Meta knows you probably have some privacy concerns about its smart glasses . At its annual Meta Connect event today in Menlo Park, California, the company announced plans to incorporate its siloed AI processing scheme known as Private Processing into its Ray-Ban Meta smart glasses. The feature, designed for WhatsApp last year, allows the glasses' AI assistant to get to know you and fulfill your requests without Meta accessing any of your data. "Most of the time, glasses are helping you see well, protecting your eyes and complementing your look, and that's it," Meta wrote in its press release, perhaps somewhat underselling the monumental historic significance of vision-correcting lenses as a technology.

A grassroots live tour by YouTuber Louis Rossmann highlights growing resistance to companies that keep people from controlling their devices. The train blasts through right as Louis Rossmann starts to speak. This has happened several times already at the event he's hosting on a Friday night in San Jose, California. All the speakers before him patiently paused when the train on the nearby tracks roared by, its loud horn blaring, then resumed talking. But Rossmann is mid-sentence in his rapid-fire delivery about the topic he's fiercely passionate about, and he doesn't stop. He shouts over the train.

Pentagon orders 30 U.S. universities to audit foreign ties to China, Russia, and Iran or risk losing federal funding over national security concerns.

'I wish I could push ChatGPT off a cliff': professors scramble to save critical thinking in an age of AI Lea Pao, a professor of literature at Stanford University, has been experimenting with ways to get her students to learn offline. She has them memorize poems, perform at recitation events, look at art in the real world. It's an effort to reconnect them to the bodily experience of learning, she said, and to keep them from turning to artificial intelligence to do the work for them. "There's no AI-proof anything," Pao said. "Rather than policing it, I hope that their overall experiences in this class will show them that there's a way out."

North Dakota colleges three-year bachelor's degree programs approved by State Board of Higher Education on Jan. 29, allowing students to graduate a year earlier starting fall 2026.

The line between human and machine authorship is blurring, particularly as it's become increasingly difficult to tell whether something was written by a person or AI. Now, in what may seem like a tipping point, the digital marketing firm Graphite recently published a study showing that more than 50% of articles on the web are being generated by artificial intelligence. As a scholar who explores how AI is built, how people are using it in their everyday lives, and how it's affecting culture, I've thought a lot about what this technology can do and where it falls short. If you're more likely to read something written by AI than by a human on the internet, is it only a matter of time before human writing becomes obsolete? Or is this simply another technological development that humans will adapt to?

The adoption of large language models (LLMs) is transforming the peer review process, from assisting reviewers in writing more detailed evaluations to generating entire reviews automatically. While these capabilities offer exciting opportunities, they also raise critical concerns about fairness and reliability. In this paper, we investigate bias in LLM-generated peer reviews by conducting controlled experiments on sensitive metadata, including author affiliation and gender. Our analysis consistently shows affiliation bias favoring institutions highly ranked on common academic rankings. Additionally, we find some gender preferences, which, even though subtle in magnitude, have the potential to compound over time. Notably, we uncover implicit biases that become more evident with token-based soft ratings.

Timely assessment of integrity of structures after seismic events is crucial for public safety and emergency response. This study focuses on assessing the structural damage conditions using deep learning methods to detect exposed steel reinforcement in concrete buildings and bridges after large earthquakes. Steel bars are typically exposed after concrete spalling or large flexural or shear cracks. The amount and distribution of exposed steel reinforcement is an indication of structural damage and degradation. To automatically detect exposed steel bars, new datasets of images collected after the 2023 Turkey Earthquakes were labeled to represent a wide variety of damaged concrete structures. The proposed method builds upon a deep learning framework, enhanced with fine-tuning, data augmentation, and testing on public datasets. An automated classification framework is developed that can be used to identify inside/outside buildings and structural components. Then, a YOLOv11 (You Only Look Once) model is trained to detect cracking and spalling damage and exposed bars. Another YOLO model is finetuned to distinguish different categories of structural damage levels. All these trained models are used to create a hybrid framework to automatically and reliably determine the damage levels from input images. This research demonstrates that rapid and automated damage detection following disasters is achievable across diverse damage contexts by utilizing image data collection, annotation, and deep learning approaches.

A new program trained on iPhone photos could curb food waste. Avocados have a carbon footprint that is three times higher than bananas. Breakthroughs, discoveries, and DIY tips sent every weekday. The days of buying a rock-tough avocado in the hopes of avoiding mushy food waste may soon be over. Machine learning researchers at Oregon State University (OSU) recently designed an artificial intelligence program that visually assesses avocado quality and ripeness .