Automating structured clinical interviews could revolutionize mental healthcare accessibility, yet existing large language models (LLMs) approaches fail to align with psychiatric diagnostic protocols. We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational workflows through coordinated multi-agent collaboration. MAGI dynamically navigates clinical logic via four specialized agents: 1) an interview tree guided navigation agent adhering to the MINI's branching structure, 2) an adaptive question agent blending diagnostic probing, explaining, and empathy, 3) a judgment agent validating whether the response from participants meet the node, and 4) a diagnosis Agent generating Psychometric Chain-of- Thought (PsyCoT) traces that explicitly map symptoms to clinical criteria. Experimental results on 1,002 real-world participants covering depression, generalized anxiety, social anxiety and suicide shows that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.

Using LLMs in healthcare, Computer-Supported Cooperative Work, and Social Computing requires the examination of ethical and social norms to ensure safe incorporation into human life. We conducted a mixed-method study, including an online survey with 111 participants and an interview study with 38 experts, to investigate the AI ethics and social norms in ChatGPT as everyday life tools. This study aims to evaluate whether ChatGPT in an empirical context operates following ethics and social norms, which is critical for understanding actions in industrial and academic research and achieving machine ethics. The findings of this study provide initial insights into six important aspects of AI ethics, including bias, trustworthiness, security, toxicology, social norms, and ethical data. Significant obstacles related to transparency and bias in unsupervised data collection methods are identified as ChatGPT's ethical concerns.

Scientist and physicist Geoffrey Hinton believes there could be a one in five chance that humanity will eventually be taken over by artificial intelligence. Hinton, a Nobel laureate in physics who's been dubbed the'godfather of AI', made the startling prediction in an April 1 interview with CBS News that was aired on Saturday morning. 'I'm in the unfortunate position of happening to agree with Elon Musk on this, which is that there's a 10 to 20 percent chance that these things will take over, but that's just a wild guess,' Hinton said. Besides his cost-cutting responsibilities in the federal government, Musk is the chief executive of xAI, the company that made the AI chatbot Grok. Musk has said AI will become smarter than the entire human race by 2029.

Large language models (LLMs) are a transformational capability at the frontier of artificial intelligence and machine learning that can support decision-makers in addressing pressing societal challenges such as extreme natural hazard events. As generalized models, LLMs often struggle to provide context-specific information, particularly in areas requiring specialized knowledge. In this work we propose a retrieval-augmented generation (RAG)-based multi-agent LLM system to support analysis and decision-making in the context of natural hazards and extreme weather events. As a proof of concept, we present WildfireGPT, a specialized system focused on wildfire hazards. The architecture employs a user-centered, multi-agent design to deliver tailored risk insights across diverse stakeholder groups. By integrating natural hazard and extreme weather projection data, observational datasets, and scientific literature through an RAG framework, the system ensures both the accuracy and contextual relevance of the information it provides. Evaluation across ten expert-led case studies demonstrates that WildfireGPT significantly outperforms existing LLM-based solutions for decision support.

Unresolved questions about how autonomous vehicles (AVs) should meet the informational needs of riders hinder real-world adoption. Complicating our ability to satisfy rider needs is that different people, goals, and driving contexts have different criteria for what constitutes interaction success. Unfortunately, most human-AV research and design today treats all people and situations uniformly. It is crucial to understand how an AV should communicate to meet rider needs, and how communications should change when the human-AV complex system changes. I argue that understanding the relationships between different aspects of the human-AV system can help us build improved and adaptable AV communications. I support this argument using three empirical studies. First, I identify optimal communication strategies that enhance driving performance, confidence, and trust for learning in extreme driving environments. Findings highlight the need for task-sensitive, modality-appropriate communications tuned to learner cognitive limits and goals. Next, I highlight the consequences of deploying faulty communication systems and demonstrate the need for context-sensitive communications. Third, I use machine learning (ML) to illuminate personal factors predicting trust in AVs, emphasizing the importance of tailoring designs to individual traits and concerns. Together, this dissertation supports the necessity of transparent, adaptable, and personalized AV systems that cater to individual needs, goals, and contextual demands. By considering the complex system within which human-AV interactions occur, we can deliver valuable insights for designers, researchers, and policymakers. This dissertation also provides a concrete domain to study theories of human-machine joint action and situational awareness, and can be used to guide future human-AI interaction research. [shortened for arxiv]

In recent years, the detection of AI-generated text has become a critical area of research due to concerns about academic integrity, misinformation, and ethical AI deployment. This paper presents COT Fine-tuned, a novel framework for detecting AI-generated text and identifying the specific language model. responsible for generating the text. We propose a dual-task approach, where Task A involves classifying text as AI-generated or human-written, and Task B identifies the specific LLM behind the text. The key innovation of our method lies in the use of Chain-of-Thought reasoning, which enables the model to generate explanations for its predictions, enhancing transparency and interpretability. Our experiments demonstrate that COT Fine-tuned achieves high accuracy in both tasks, with strong performance in LLM identification and human-AI classification. We also show that the CoT reasoning process contributes significantly to the models effectiveness and interpretability.

Rep. Jasmine Crockett said she would "absolutely" take a head-to-head IQ test against President Donald Trump during an interview with late-night host Jimmy Kimmel. Rep. Jasmine Crockett, D-Texas, told late-night host Jimmy Kimmel on Tuesday that she would "absolutely" take a head-to-head IQ test against President Donald Trump. "He also called you low IQ, I'm sure you're aware of that. Would you be willing to take an IQ test publicly head-to-head against the President of the United States?" Kimmel played a clip of Trump talking about the Democratic lawmaker, during which he called Crockett the Democrats' "new star," and suggested the party was in trouble if that was the case.

The examples are nothing if not relatable: preparing breakfast, or playing a game of chess or tic-tac-toe. Yet the idea of learning from the environment and taking steps that progress toward a goal apparently was under-studied when ACM A.M. Turing Award recipients Andrew G. Barto and Richard S. Sutton took on the topic in the late 1970s. Eventually, their research led to the creation of reinforcement learning algorithms that sought not to recognize patterns but maximize rewards. Barto and Sutton spoke about how it all unfolded, and what's next for the techniques that are so celebrated for their success in AlphaGo and AlphaZero. Let's start with the earliest days of your collaboration.

Today's text-entry tools offer a plethora of interface technologies to support users in a variety of situations and with a range of different input methods and devices.16 Recent hardware developments have enabled remarkable innovations, such as virtual keyboards that allow users to type in thin air, or to use their body as a surface for text entry. Similarly, advances in machine learning and natural language processing have enabled high-quality text generation for various purposes, such as summarizing, expanding, and co-authoring. As these technologies rapidly develop, there has been a rush to incorporate them into existing systems, often with little thought for the interactivity problems this may cause. The use of large language models (LLMs) to speed up text generation and improve prediction or completion models is becoming increasingly commonplace, with enormous theoretical efficiency savings;29 however, the implementation of these LLMs into text-entry interfaces is crucial to realizing their potential.

Dear Prudence is Slate's advice column. I was browsing a men-seeking-men dating app when I came across the profile of my neighbor, "Gary." He described himself as "single and looking for fun." I happen to know that Gary is married with two kids under 3 years old. The thing is, I don't know his wife "Bethany" that well; we've only ever waved to one another in the neighborhood and briefly engaged in small talk when we run into each other.