Flock Safety is pitching its police-style drone program to private businesses. It could bring aerial surveillance to shopping centers, warehouses, and hospitals. Flock Safety, whose drones were once reserved for police departments, is now offering them for private-sector security, the company announced today, with potential customers including including businesses intent on curbing shoplifting. Companies in the US can now place Flock's drone docking stations on their premises. If the company has a waiver from the Federal Aviation Administration to fly beyond visual line of sight (these are becoming easier to get), its security team can fly the drones within a certain radius, often a few miles. "Instead of a 911 call [that triggers the drone], it's an alarm call," says Keith Kauffman, a former police chief who now directs Flock's drone program.

Suit alleges OpenAI has a'troubling pattern' of hiring former xAI workers to access secrets about the Grok chatbot Elon Musk's artificial intelligence startup xAI has accused rival OpenAI of stealing its trade secrets in a new lawsuit, the latest in Musk's legal assault on his former business partner, Sam Altman. The lawsuit, filed on Wednesday in California federal court, alleged that OpenAI was engaged in a "deeply troubling pattern" of hiring away former xAI employees to gain access to trade secrets related to its AI chatbot Grok . The company says OpenAI is pursuing unfair advantages in the race to develop AI technology. "OpenAI is targeting those individuals with knowledge of xAI's key technologies and business plans, including xAI's source code and its operational advantages in launching data centers, then inducing those employees to breach their confidentiality and other obligations to xAI through unlawful means," the lawsuit states. Musk and xAI have launched numerous lawsuits against OpenAI in recent years as part of a longstanding feud between Altman and Musk.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Millions of Californians are getting a refund on their electric bill. Credits for Californians on October electric bills are set to go up in the coming years, according to the governor's office. This is read by an automated voice. Please report any issues or inconsistencies here .

Things to Do in L.A. Tap to enable a layout that focuses on the article. A Woodland Hills nursery is turning into a cemetery. Aerial view of where groves will turn to graves in Woodland Hills, where a developer has plans to redevelop Boething Treeland Nursery into a cemetery. This is read by an automated voice. Please report any issues or inconsistencies here .

The new president of the Los Angeles Police Commission, Teresa Sánchez-Gordon, is a former L.A. County judge who says her upbringing in an undocumented family gives her a unique perspective on the current challenges facing the LAPD.

In the current media landscape, it's a wonder it even got made. In a world where hallowed news organizations transform into conservative mouthpieces and milquetoast late-night jokes are grounds for suspension, satirical headlines from the Onion can feel closer to real life than parody. Now the site is taking on one of the most taboo subjects of all--disgraced sex offender and financier Jeffrey Epstein--in mockumentary form. It launches in theaters in New York City, Minneapolis, Los Angeles, and San Francisco for one day only on October 2; after that it will be available online. You can watch the trailer right here.

Accurate text recognition for historical documents can greatly advance the study and preservation of cultural heritage. Existing vision-language models (VLMs), however, are designed for modern, standardized texts and are not equipped to read the diverse languages and scripts, irregular layouts, and frequent degradation found in historical materials. This paper presents CHURRO, a 3B-parameter open-weight VLM specialized for historical text recognition. The model is trained on CHURRO-DS, the largest historical text recognition dataset to date. CHURRO-DS unifies 155 historical corpora comprising 99,491 pages, spanning 22 centuries of textual heritage across 46 language clusters, including historical variants and dead languages. We evaluate several open-weight and closed VLMs and optical character recognition (OCR) systems on CHURRO-DS and find that CHURRO outperforms all other VLMs. On the CHURRO-DS test set, CHURRO achieves 82.3% (printed) and 70.1% (handwritten) normalized Levenshtein similarity, surpassing the second-best model, Gemini 2.5 Pro, by 1.4% and 6.5%, respectively, while being 15.5 times more cost-effective. By releasing the model and dataset, we aim to enable community-driven research to improve the readability of historical texts and accelerate scholarship.

Infertility, a pressing global health concern, affects a substantial proportion of individuals worldwide. While advancements in assisted reproductive technology (ART) have offered effective interventions, conventional in vitro fertilization-embryo transfer (IVF-ET) procedures still encounter significant hurdles in enhancing pregnancy success rates. Key challenges include the inherent subjectivity in embryo grading and the inefficiency of multi-modal data integration. Against this backdrop, the adoption of AI-driven technologies has emerged as a pivotal strategy to address these issues. This article presents a comprehensive review of the progress in AI applications for embryo grading and pregnancy prediction from a novel perspective, with a specific focus on the utilization of different modal data, such as static images, time-lapse videos, and structured tabular data. The reason for this perspective is that reorganizing tasks based on data sources can not only more accurately depict the essence of the problem but also help clarify the rationality and limitations of model design. Furthermore, this review critically examines the core challenges in contemporary research, encompassing the intricacies of multi-modal feature fusion, constraints imposed by data scarcity, limitations in model generalization capabilities, and the dynamically evolving legal and regulatory frameworks. On this basis, it explicitly identifies potential avenues for future research, aiming to provide actionable guidance for advancing the application of multi-modal AI in the field of ART.

Multilingual Large Language Models (LLMs) are increasingly used worldwide, making it essential to ensure they are free from gender bias to prevent representational harm. While prior studies have examined such biases in high-resource languages, low-resource languages remain understudied. In this paper, we propose a template-based probing methodology, validated against real-world data, to uncover gender stereotypes in LLMs. As part of this framework, we introduce the Domain-Specific Gender Skew Index (DS-GSI), a metric that quantifies deviations from gender parity. We evaluate four prominent models, GPT-4o mini, DeepSeek R1, Gemini 2.0 Flash, and Qwen QwQ 32B, across four semantic domains, focusing on Persian, a low-resource language with distinct linguistic features. Our results show that all models exhibit gender stereotypes, with greater disparities in Persian than in English across all domains. Among these, sports reflect the most rigid gender biases. This study underscores the need for inclusive NLP practices and provides a framework for assessing bias in other low-resource languages.