The recent request goes against decades of precedent and puts noncitizens at further risk of immigration enforcement actions. Workers at the Social Security Administration have been told to share information about in-person appointments with agents of Immigration and Customs Enforcement, WIRED has learned. "If ICE comes in and asks if someone has an upcoming appointment, we will let them know the date and time," an employee with direct knowledge of the directive says. They spoke on the condition of anonymity for fear of retaliation. While the majority of appointments with SSA take place over the phone, some appointments still happen in person.

Among those Robert F. Kennedy Jr. recently named to a federal autism committee are people who tout dangerous treatments and say vaccine manufacturers are "poisoning children." US health secretary Robert F. Kennedy Jr. has filled an autism committee with friends, associates, and former colleagues who believe that autism is caused by vaccines. Autism advocates are now worried the group could pave the way for dangerous pseudoscientific treatments going mainstream. Last week, Kennedy announced an entirely new lineup for the Interagency Autism Coordinating Committee (IACC), a group that recommends what types of autism research the government should fund and provides guidance on the services the autism community requires. The group is typically composed of experts in the area of autism research, along with policy experts and autistic people advocating for their own community.

Here's What You Should Know About Launching an AI Startup AI startups say the promise of turning dazzling models into useful products is harder than anyone expected. Three founders discuss what it takes. Julie Bornstein thought it would be a cinch to implement her idea for an AI startup . Her résumé in digital commerce is impeccable: VP of ecommerce at Nordstrom, COO of the startup Stitch Fix, and founder of a personalized shopping platform acquired by Pinterest . Fashion has been her obsession since she was a Syracuse high schooler inhaling spreads in Seventeen and hanging out in local malls.

The computer interrupted while Pamela was still speaking. I had accompanied her - my dear friend - to a recent doctor's appointment. She is in her 70s, lives alone while navigating multiple chronic health issues, and has been getting short of breath climbing the front stairs to her apartment. In the exam room, she spoke slowly and self-consciously, the way people often do when they are trying to describe their bodies and anxieties to strangers. Midway through her description of how she had been feeling, the doctor clicked his mouse and a block of text began to bloom across the computer monitor. The clinic had adopted an artificial-intelligence scribe, and it was transcribing and summarizing the conversation in real time.

Robby Starbuck speaks in an interview in New York in March. Robby Starbuck speaks in an interview in New York in March. Critics condemn Robby Starbuck, appointed in lawsuit settlement, for'peddling lies and pushing extremism' A prominent anti-DEI campaigner appointed by Meta in August as an adviser on AI bias has spent the weeks since his appointment spreading disinformation about shootings, transgender people, vaccines, crime, and protests. Robby Starbuck, 36, of Nashville, was appointed in August as an adviser by Meta - owner of Facebook, Instagram, WhatsApp, and other tech platforms - in an August lawsuit settlement. Since his appointment, Starbuck has baselessly claimed that individual shooters in the US were motivated by leftist ideology, described faith-based protest groups as communists, and without evidence tied Democratic lawmakers to murders.

The promise of AI in medicine depends on learning from data that reflect what matters to patients and clinicians. Most existing models are trained on electronic health records (EHRs), which capture biological measures but rarely patient-clinician interactions. These relationships, central to care, unfold across voice, text, and video, yet remain absent from datasets. As a result, AI systems trained solely on EHRs risk perpetuating a narrow biomedical view of medicine and overlooking the lived exchanges that define clinical encounters. Our objective is to design, implement, and evaluate the feasibility of a longitudinal, multimodal system for capturing patient-clinician encounters, linking 360 degree video/audio recordings with surveys and EHR data to create a dataset for AI research. This single site study is in an academic outpatient endocrinology clinic at Mayo Clinic. Adult patients with in-person visits to participating clinicians are invited to enroll. Encounters are recorded with a 360 degree video camera. After each visit, patients complete a survey on empathy, satisfaction, pace, and treatment burden. Demographic and clinical data are extracted from the EHR. Feasibility is assessed using five endpoints: clinician consent, patient consent, recording success, survey completion, and data linkage across modalities. Recruitment began in January 2025. By August 2025, 35 of 36 eligible clinicians (97%) and 212 of 281 approached patients (75%) had consented. Of consented encounters, 162 (76%) had complete recordings and 204 (96%) completed the survey. This study aims to demonstrate the feasibility of a replicable framework for capturing the multimodal dynamics of patient-clinician encounters. By detailing workflows, endpoints, and ethical safeguards, it provides a template for longitudinal datasets and lays the foundation for AI models that incorporate the complexity of care.

Large Language Model-based agents(LLM-based agents) are increasingly deployed in customer service, yet they often forget across sessions, repeat errors, and lack mechanisms for continual self-improvement. This makes them unreliable in dynamic settings where stability and consistency are critical. To address the limitations of existing approaches, we propose MemOrb, a lightweight and plug-and-play verbal reinforcement memory layer that distills multi-turn interactions into compact strategy reflections. These reflections are stored in a shared memory bank and retrieved to guide decision-making, without requiring any fine-tuning. Experiments show that MemOrb significantly improves both success rate and stability, achieving up to a 63 percentage-point gain in multi-turn success rate and delivering more consistent performance across repeated trials. Our results demonstrate that structured reflection is a powerful mechanism for enhancing long-term reliability of frozen LLM agents in customer service scenarios. Large Language Model-based agents (LLM-based agents) are increasingly adopted in large-scale customer service systems, where they act as interactive assistants for diverse users (Brown et al., 2020). Despite their rapid deployment, these agents face persistent challenges: they often lose critical information across sessions, repeat errors without systematic correction, and struggle to adapt to rapidly changing product catalogs. Such limitations undermine their reliability in dynamic environments such as e-commerce. Existing memory solutions typically rely on short-term caching or user-specific profiles (Chhikara et al., 2025; Zhong et al., 2023). Consequently, purely per-user or short-horizon memories are insufficient for robust long-term improvement.

"Our focus is really on what we can do to pull the doctor out of the visit," says Akido's CTO. Imagine this: You've been feeling unwell, so you call up your doctor's office to make an appointment. At the appointment, you aren't rushed through describing your health concerns; instead, you have a full half hour to share your symptoms and worries and the exhaustive details of your health history with someone who listens attentively and asks thoughtful follow-up questions. You leave with a diagnosis, a treatment plan, and the sense that, for once, you've been able to discuss your health with the care that it merits. AI companies have stopped warning you that their chatbots aren't doctors Once cautious, OpenAI, Grok, and others will now dive into giving unverified medical advice with virtually no disclaimers. You might not have spoken to a doctor, or other licensed medical practitioner, at all.

We study a sequential resource allocation problem where a decision maker selects subsets of agents at each period to maximize overall outcomes without prior knowledge of individual-level effects. Our framework applies to settings such as community health interventions, targeted digital advertising, and workforce retention programs, where intervention effects evolve dynamically. Agents may exhibit habituation (diminished response from frequent selection) or recovery (enhanced response from infrequent selection). The technical challenge centers on nonstationary reward distributions that lead to changing intervention effects over time. The problem requires balancing two key competing objectives: heterogeneous individual rewards and the exploration-exploitation tradeoff in terms of learning for improved future decisions as opposed to maximizing immediate outcomes. Our contribution introduces the first framework incorporating this form of nonstationary rewards in the combinatorial multi-armed bandit literature. We develop algorithms with theoretical guarantees on dynamic regret and demonstrate practical efficacy through a diabetes intervention case study. Our personalized community intervention algorithm achieved up to three times as much improvement in program enrollment compared to baseline approaches, validating the framework's potential for real-world applications. This work bridges theoretical advances in adaptive learning with practical challenges in population-level behavioral change interventions.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Men, you may have heard, are in crisis. The causes are many, and the left and right identify different villains (toxic masculinity, according to the former; feminism and gynecocracy, says the latter). But there seems to be a growing consensus that something is rotten in man-land. And across the political spectrum there is at least a consensus that male despair and disconnect is fueled in large part by dramatic changes in society, the economy, and the family--all of which have left many men feeling dangerously unmoored, isolated, and purposeless.