With the increasing use of generative Artificial Intelligence (AI) methods to support science workflows, we are interested in the use of discourse-level information to find supporting evidence for AI generated scientific claims. A first step towards this objective is to examine the task of inferring discourse structure in scientific writing. In this work, we present a preliminary investigation of pretrained language model (PLM) and Large Language Model (LLM) approaches for Discourse Relation Classification (DRC), focusing on scientific publications, an under-studied genre for this task. We examine how context can help with the DRC task, with our experiments showing that context, as defined by discourse structure, is generally helpful. We also present an analysis of which scientific discourse relation types might benefit most from context.

In this episode of, we run through the top stories of the week and look closely at people's complaints to the FTC alleging that ChatGPT led them or loved ones into AI psychosis. In today's episode, Zoë Schiffer is joined by senior editor Louise Matsakis to run through five stories that you need to know about this week--from how SEO is changing in the era of AI to how frogs became a protest symbol. Then, Zoë and Louise dive into why some people have been filing complaints to the FTC about ChatGPT, arguing it has led them to AI psychosis. People Who Say They're Experiencing AI Psychosis Beg the FTC for Help The FTC Is Disappearing Blog Posts About AI Published During Lina Khan's Tenure Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Today on the show, we're bringing you five stories that you need to know about this week. And later, we'll dive into our main story about how several people have filed complaints to the FTC claiming OpenAI's ChatGPT led them or people they love into supposed AI psychosis. I'm joined today by WIRED's senior business editor, Louise Matsakis. It's great to be here. So Louise, our first story this week is actually one that we worked on together, part of our ongoing collaboration with Model Behavior, and it's all about how this holiday season, more shoppers are expected to use chatbots to figure out what to buy.

A float would support Sam Altman's ambitions to splash trillions of dollars on building datacentres. A float would support Sam Altman's ambitions to splash trillions of dollars on building datacentres. OpenAI is reportedly gearing up for a stock market listing valuing the company at $1tn (£760bn) as soon as next year, in what would be one of the biggest ever initial public offerings. The developer behind the hit AI chatbot ChatGPT is considering whether to file for an IPO as soon as the second half of 2026, according to Reuters, which cited people familiar with the matter. The company is thought to be looking to raise at least $60bn.

Everything is a conspiracy theory now. Conspiracists are all over the White House, turning fringe ideas into dangerous policy. America's institutions are crumbling under the weight of deep suspicion and the lasting effects of covid isolation. Online echo chambers are getting harder to escape, and generative AI is altering the fabric of truth. A mix of technology and politics has given an unprecedented boost to once-fringe ideas--but they are pretty much the same fantasies that have been spreading for hundreds of years. MIT Technology Review helps break down how this moment is changing science and technology--and how we can make it through.

Turns out many believers do respond positively when presented with the right evidence and arguments. It's become a truism that facts alone don't change people's minds. Perhaps nowhere is this more clear than when it comes to conspiracy theories: Many people believe that you can't talk conspiracists out of their beliefs. It turns out that many conspiracy believers respond to evidence and arguments--information that is now easy to deliver in the form of a tailored conversation with an AI chatbot. In research we published in the journal this year, we had over 2,000 conspiracy believers engage in a roughly eight-minute conversation with DebunkBot, a model we built on top of OpenAI's GPT-4 Turbo (the most up-to-date GPT model at that time). Participants began by writing out, in their own words, a conspiracy theory that they believed and the evidence that made the theory compelling to them.

Sumitomo President Shingo Ueno says the evolution of generative artificial intelligence is transforming business operations across various fields. Trading house Sumitomo has announced its plan to make Tokyo-based major information technology service provider SCSK a wholly owned subsidiary through a takeover bid valued at some ¥882 billion ($5.77 billion). Sumitomo already holds a 50.6% equity stake in SCSK. The purchase price is set at ¥5,700 per share, according to the announcement on Wednesday, about 30% higher than SCSK's closing price of ¥4,334 on the same day. By fully acquiring SCSK, Sumitomo aims to improve management efficiency and strengthen its artificial intelligence business.

OpenAI is considering filing with securities regulators as soon as the second half of 2026, some people familiar with the matter said. SAN FRANCISCO - OpenAI is laying the groundwork for an initial public offering that could value the company at up to $1 trillion, three people familiar with the matter said, in what could be one of the biggest IPOs of all time. OpenAI is considering filing with securities regulators as soon as the second half of 2026, some of the people said. In preliminary discussions, the company has looked at raising $60 billion at the low end and likely more, the people said. They cautioned that talks are early and plans -- including the figures and timing -- could change depending on business growth and market conditions.

This study provides an in_depth analysis of the ethical and trustworthiness challenges emerging alongside the rapid advancement of generative artificial intelligence (AI) technologies and proposes a comprehensive framework for their systematic evaluation. While generative AI, such as ChatGPT, demonstrates remarkable innovative potential, it simultaneously raises ethical and social concerns, including bias, harmfulness, copyright infringement, privacy violations, and hallucination. Current AI evaluation methodologies, which mainly focus on performance and accuracy, are insufficient to address these multifaceted issues. Thus, this study emphasizes the need for new human_centered criteria that also reflect social impact. To this end, it identifies key dimensions for evaluating the ethics and trustworthiness of generative AI_fairness, transparency, accountability, safety, privacy, accuracy, consistency, robustness, explainability, copyright and intellectual property protection, and source traceability and develops detailed indicators and assessment methodologies for each. Moreover, it provides a comparative analysis of AI ethics policies and guidelines in South Korea, the United States, the European Union, and China, deriving key approaches and implications from each. The proposed framework applies across the AI lifecycle and integrates technical assessments with multidisciplinary perspectives, thereby offering practical means to identify and manage ethical risks in real_world contexts. Ultimately, the study establishes an academic foundation for the responsible advancement of generative AI and delivers actionable insights for policymakers, developers, users, and other stakeholders, supporting the positive societal contributions of AI technologies.

The diagnosis of most mental disorders, including psychiatric evaluations, primarily depends on dialogues between psychiatrists and patients. This subjective process can lead to variability in diagnoses across clinicians and patients, resulting in inconsistencies and challenges in achieving reliable outcomes. To address these issues and standardize psychiatric diagnoses, we propose a Fine-Tuned Large Language Model (LLM) Consortium and OpenAI-gpt-oss Reasoning LLM-enabled Decision Support System for the clinical diagnosis of mental disorders. Our approach leverages fine-tuned LLMs trained on conversational datasets involving psychiatrist-patient interactions focused on mental health conditions (e.g., depression). The diagnostic predictions from individual models are aggregated through a consensus-based decision-making process, refined by the OpenAI-gpt-oss reasoning LLM. We propose a novel method for deploying LLM agents that orchestrate communication between the LLM consortium and the reasoning LLM, ensuring transparency, reliability, and responsible AI across the entire diagnostic workflow. Experimental results demonstrate the transformative potential of combining fine-tuned LLMs with a reasoning model to create a robust and highly accurate diagnostic system for mental health assessment. A prototype of the proposed platform, integrating three fine-tuned LLMs with the OpenAI-gpt-oss reasoning LLM, was developed in collaboration with the U.S. Army Medical Research Team in Norfolk, Virginia, USA. To the best of our knowledge, this work represents the first application of a fine-tuned LLM consortium integrated with a reasoning LLM for clinical mental health diagnosis paving the way for next-generation AI-powered eHealth systems aimed at standardizing psychiatric diagnoses.

Legal interpretation frequently involves assessing how a legal text, as understood by an 'ordinary' speaker of the language, applies to the set of facts characterizing a legal dispute in the U.S. judicial system. Recent scholarship has proposed that legal practitioners add large language models (LLMs) to their interpretive toolkit. This work offers an empirical argument against LLM interpretation as recently practiced by legal scholars and federal judges. Our investigation in English shows that models do not provide stable interpretive judgments: varying the question format can lead the model to wildly different conclusions. Moreover, the models show weak to moderate correlation with human judgment, with large variance across model and question variant, suggesting that it is dangerous to give much credence to the conclusions produced by generative AI.