Donald Trump Jr.'s Private DC Club Has Mysterious Ties to an Ex-Cop With a Controversial Past The Executive Branch has a reported membership list that includes Trumpworld elites like David Sacks. A WIRED review of corporate filings reveals an under-the-radar player: a notorious former DC police officer. When the Executive Branch soft-launched in Washington, DC, last spring, the private club's initial buzz centered on its starry roster of backers and founding members. The president's eldest son, Donald Trump Jr., is one of the club's several co-owners, according to previous reporting. Founding members reportedly include Trump administration AI czar David Sacks and his podcast cohost Chamath Palihapitiya, as well as crypto bigwigs Tyler and Cameron Winklevoss.

Deep networks achieve impressive accuracy and wide adoption in computer vision [17], speech recognition [14], and natural language processing [21].

Finally,an EEG forward model based onapre-estimated lead field matrix wasconstructed together with the ROIsource model togenerate scalp EEG observations.

Lasker, the Times explained, was the "intermediary" who tipped off the publication about Mamdani's application, which was included in a larger hack of Columbia's computer systems. After the Times published its story, Lasker celebrated on X. "I break-uh dah news," he wrote to his more than 260,000 followers. On both X and Substack, where he also has a large following, Lasker is best-known for compiling charts on the "Black-White IQ gap" and otherwise linking race to real-world outcomes. He seems convinced that any differences are the result of biology, and has shot down other possible explanations. He has suggested that crime is genetic.

This essay was adapted from Phil Christman's newsletter, the Tourist. Before 2023, my teaching year used to follow a predictable emotional arc. In September, I was always excited, not only about meeting a new crop of first-year writing students but even about the prep work. My lesson-planning sessions would take longer than intended and yet leave me feeling energized. I'd look forward to conference week--the one-on-one meetings I try to hold with every student, every term, at least once--and even to the first stack of papers.

Collaborative assistants, or chatbots, are data-driven decision support systems that enable natural interaction for task completion. While they can meet critical needs in modern society, concerns about their reliability and trustworthiness persist. In particular, Large Language Model (LLM)-based chatbots like ChatGPT, Gemini, and DeepSeek are becoming more accessible. However, such chatbots have limitations, including their inability to explain response generation, the risk of generating problematic content, the lack of standardized testing for reliability, and the need for deep AI expertise and extended development times. These issues make chatbots unsuitable for trust-sensitive applications like elections or healthcare. To address these concerns, we introduce SafeChat, a general architecture for building safe and trustworthy chatbots, with a focus on information retrieval use cases. Key features of SafeChat include: (a) safety, with a domain-agnostic design where responses are grounded and traceable to approved sources (provenance), and 'do-not-respond' strategies to prevent harmful answers; (b) usability, with automatic extractive summarization of long responses, traceable to their sources, and automated trust assessments to communicate expected chatbot behavior, such as sentiment; and (c) fast, scalable development, including a CSV-driven workflow, automated testing, and integration with various devices. We implemented SafeChat in an executable framework using the open-source chatbot platform Rasa. A case study demonstrates its application in building ElectionBot-SC, a chatbot designed to safely disseminate official election information. SafeChat is being used in many domains, validating its potential, and is available at: https://github.com/ai4society/trustworthy-chatbot.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Two winters ago, more than a year after my old college roommate and dear friend Paul was diagnosed with ALS, he started making pictures. By then, he was gradually losing the ability to do almost everything else. He could still walk at that point, often through the leafy corner of his Boston neighborhood, Jamaica Plain, where the old tree limbs cradled the houses and the streets were barely wide enough for a car, but only with the help of a cane. A condition of the disease called bulbar palsy slowed his tongue to the point his words wobbled enough that he sounded as if he were drunk. He could eat solid foods, albeit with some trouble, and could drink the Relyvrio medication powder he swirled with a spoon into a glass of water twice daily--a prescription for ALS that last year clinical trials suggested was ineffective, and a cocktail so bitter it made him physically wince--but he began coughing more and more as he labored to swallow anything at all.

Cardiovascular disease (CVD) persists as a primary cause of death on a global scale, which requires more effective and timely detection methods. Traditional supervised learning approaches for CVD detection rely heavily on large-labeled datasets, which are often difficult to obtain. This paper employs semi-supervised learning models to boost efficiency and accuracy of CVD detection when there are few labeled samples. By leveraging both labeled and vast amounts of unlabeled data, our approach demonstrates improvements in prediction performance, while reducing the dependency on labeled data. Experimental results in a publicly available dataset show that semi-supervised models outperform traditional supervised learning techniques, providing an intriguing approach for the initial identification of cardiovascular disease within clinical environments.