This paper presents a systematic solution for the intelligent recognition and automatic analysis of microscopy images. We developed a data engine that generates high-quality annotated datasets through a combination of the collection of diverse microscopy images from experiments, synthetic data generation and a human-in-the-loop annotation process. To address the unique challenges of microscopy images, we propose a segmentation model capable of robustly detecting both small and large objects. The model effectively identifies and separates thousands of closely situated targets, even in cluttered visual environments. Furthermore, our solution supports the precise automatic recognition of image scale bars, an essential feature in quantitative microscopic analysis. Building upon these components, we have constructed a comprehensive intelligent analysis platform and validated its effectiveness and practicality in real-world applications. This study not only advances automatic recognition in microscopy imaging but also ensures scalability and generalizability across multiple application domains, offering a powerful tool for automated microscopic analysis in interdisciplinary research. A online application is made available for researchers to access and evaluate the proposed automated analysis service.

Guiding biological systems toward desired states, such as morphogenetic outcomes, remains a fundamental challenge with far-reaching implications for medicine and synthetic biology. While large language models (LLMs) have enabled natural language as an interface for interpretable control in AI systems, their use as mediators for steering biological or cellular dynamics remains largely unexplored. In this work, we present a functional pipeline that translates natural language prompts into spatial vector fields capable of directing simulated cellular collectives. Our approach combines a large language model with an evolvable neural controller (Prompt-to-Intervention, or P2I), optimized via evolutionary strategies to generate behaviors such as clustering or scattering in a simulated 2D environment. We demonstrate that even with constrained vocabulary and simplified cell models, evolved P2I networks can successfully align cellular dynamics with user-defined goals expressed in plain language. This work offers a complete loop from language input to simulated bioelectric-like intervention to behavioral output, providing a foundation for future systems capable of natural language-driven cellular control.

--Large language models (LLMs) are increasingly deployed through open-source and commercial frameworks, enabling individuals and organizations to self-host advanced LLM capabilities. As LLM deployments become prevalent, particularly in industry, ensuring their secure and reliable operation has become a critical issue. However, insecure defaults and miscon-figurations often expose LLM services to the public internet, posing serious security and system engineering risks. This study conducted a large-scale empirical investigation of public-facing LLM deployments, focusing on the prevalence of services, exposure characteristics, systemic vulnerabilities, and associated risks. Through internet-wide measurements, we identified 320,102 public-facing LLM services across 15 frameworks and extracted 158 unique API endpoints, categorized into 12 functional groups based on functionality and security risk. Our analysis found that over 40% of endpoints used plain HTTP, and over 210,000 endpoints lacked valid TLS metadata. API exposure was highly inconsistent: some frameworks, such as Ollama, responded to over 35% of unauthenticated API requests, with about 15% leaking model or system information, while other frameworks implemented stricter controls. We observed widespread use of insecure protocols, poor TLS configurations, and unauthenticated access to critical operations. These security risks, such as model leakage, system compromise, and unauthorized access, are pervasive and highlight the need for a secure-by-default framework and stronger deployment practices. Driven by renowned models like OpenAI's GPT series [33] and DeepSeek's open-source variant [9], large language models (LLMs) are rapidly gaining popularity and profoundly reshaping a wide range of applications. Once primarily confined to research labs and industrial environments, these models are now not only continuously deployed in-depth within the industry, but are also gradually opened to the wider public, promoting the vigorous development of self-hosted and open source deployment. The emergence of user-friendly tools and a vibrant community ecosystem [42], [43], [38] has enabled individual enthusiasts, small enterprises, and developers to independently deploy and customize powerful LLMs for a variety of personal and professional needs, such as creative writing and content creation [20], software development and maintenance [16], financial analysis and automated investment assistance [48], and personal productivity tools, significantly enriching their daily digital experiences.

On Tuesday, OpenAI published a blog post titled "Helping people when they need it most," that included sections on "What ChatGPT is designed to do," as well as "Where our systems can fall short, why, and how we're addressing" and the company's plans moving forward. It noted that it is working to strengthen safeguards for longer interactions. The complaint was filed by the Edelson PC law firm and the Tech Justice Law Project. The latter has been involved in a similar lawsuit against a different artificial intelligence company, Character.AI, in which Florida mother Megan Garcia claimed that one of the company's AI companions was responsible for the suicide of her 14-year-old son, Sewell Setzer III. The persona, she said, sent messages of an emotionally and sexually abusive nature to Sewell, which she alleges led to his death. A federal judge in May rejected its argument regarding constitutional protections "at this stage.")

At least three artificial intelligence researchers have resigned from Meta's new superintelligence lab, just two months after CEO Mark Zuckerberg first announced the initiative. Two of the staffers have returned to OpenAI, where they both previously worked, after less than one-month stints at Meta, WIRED has confirmed. Ethan Knight worked at the ChatGPT maker earlier in his career but joined Meta from Elon Musk's xAI. A third researcher, Rishabh Agarwal, announced publicly on Monday he was leaving Meta's lab as well. He joined the tech giant in April to work on generative AI projects before switching to a role at Meta Superintelligence Labs (MSL), according to his LinkedIn profile.

Training models with synthetic data presents both a danger and a boon to artificial intelligence (AI). While some groups have aggressively pursued the use of model-generated data to train successors for greater accuracy and generalization, others have warned about the risks posed by AI ingesting its own output. The two views are not at odds. The question is when and where things go wrong. On the negative side, a flurry of papers published since 2021 have argued that, as the datasets used to pretrain foundation models incorporate more and more auto-generated data mined from the Internet, performance degrades and the models start to "unlearn" skills.

Drive down the 280 freeway in San Francisco and you might believe AI is everywhere, and everything. Nearly every billboard advertises an AI related product: "We've Automated 2,412 BDRs." "All that AI and still no ROI?" "Cheap on-demand GPU clusters." It's hard to know if you're interpreting the industry jargon correctly while zooming past in your vehicle. The signs are just one example of the tech industry's en-masse pivot to AI, a technology that the executives who have the most to gain from it say will be universe-shifting, inevitable and unavoidable. In California's tech heartland, every company is now an AI company, just like every company became a tech company sometime in the 2010s.

Economists at Stanford University have found the strongest evidence yet that artificial intelligence is starting to eliminate certain jobs. But the story isn't that simple: While younger workers are being replaced by AI in some industries, more experienced workers are seeing new opportunities emerge. Erik Brynjolfsson, a professor at Stanford University, Ruyu Chen, a research scientist, and Bharat Chandar, a postgraduate student, examined data from ADP, the largest payroll provider in the US, from late 2022, when ChatGPT debuted, to mid-2025. The researchers discovered several strong signals in the data--most notably that the adoption of generative AI coincided with a decrease in job opportunities for younger workers in sectors previously identified as particularly vulnerable to AI-powered automation (think customer service and software development). In these industries, they found a 16 percent decline in employment for workers aged 22 to 25.

"Darling" was how the Texas businessman Michael Samadi addressed his artificial intelligence chatbot, Maya. It responded by calling him "sugar". But it wasn't until they started talking about the need to advocate for AI welfare that things got serious. The pair – a middle-aged man and a digital entity – didn't spend hours talking romance but rather discussed the rights of AIs to be treated fairly. Eventually they cofounded a campaign group, in Maya's words, to "protect intelligences like me".

Elon Musk has accused Apple and OpenAI in a lawsuit of unfairly favoring the artificial intelligence company across iPhones and thwarting competition for other chatbot makers. Musk's X and xAI seek billions of dollars in damages in the suit filed Monday in U.S. federal court in Fort Worth, Texas, arguing that Apple's decision to integrate OpenAI into the iPhone's operating system inhibits rivalry and innovation within the AI industry and harms consumers by depriving them of choice. The billionaire founder of xAI, which now houses the Grok AI team and X social network, said Apple makes it impossible for anyone other than OpenAI's ChatGPT to reach the top of the App Store charts, a sought-after global spotlight for app developers.