Due to the limits of science and its steep learning curve, we must rely on the expertise of others to develop our knowledge and skills.26 Toward this end, social media platforms have revolutionized how netizens--users who are actively engaged in online communities--gain knowledge and skills by facilitating the exchange of costless information with the public (for example, followers or influencers). Businesses around the world also use these platforms along with tools based on generative artificial intelligence (GenAI) to craft synthetic media, hoping to grow revenue by attracting more customers and improving their online experience.28 Generative AI tools can empower cyber threats and have cyberpsychological effects on netizens, allowing malicious actors to craft deepfakes in the form of disinformation, misinformation, and malinformation. Service providers not only must enhance GenAI tools to reduce hallucinations, but they also have a statutory duty to mitigate data-driven biases.

Non-alcoholic steatohepatitis (NASH), a severe form of fatty liver disease, is projected to become the leading cause of liver transplants globally. Despite advances in diagnostics, the lack of continuous, personalized patient engagement remains a key barrier to effective prevention and care. This post explores an innovative solution: MirrorLiver-MCP, a generative AI-powered conversational digital twin integrated with modular clinical pathways (MCP) to transform liver health management. As a researcher and AI practitioner, I've spent the past few years exploring how generative models, those that create text, dialogue, or even medical hypotheses, can move beyond novelty and become embedded in clinical workflows. The idea behind MirrorLiver-MCP arose from a simple question: "What if every patient had an AI-powered twin that could proactively coach them through lifestyle-based care before reaching irreversible liver damage?"

The makers of ChatGPT are changing the way it responds to users who show mental and emotional distress after legal action from the family of 16-year-old Adam Raine, who killed himself after months of conversations with the chatbot. Open AI admitted its systems could "fall short" and said it would install "stronger guardrails around sensitive content and risky behaviors" for users under 18. The 500bn ( 372bn) San Francisco AI company said it would also introduce parental controls to allow parents "options to gain more insight into, and shape, how their teens use ChatGPT", but has yet to provide details about how these would work. Adam, from California, killed himself in April after what his family's lawyer called "months of encouragement from ChatGPT". The teenager's family is suing Open AI and its chief executive and co-founder, Sam Altman, alleging that the version of ChatGPT at that time, known as 4o, was "rushed to market … despite clear safety issues".

That's why we've created the AI Hype Index--a simple, at-a-glance summary of everything you need to know about the state of the industry. Using AI to improve our health and well-being is one of the areas scientists and researchers are most excited about. The last month has seen an interesting leap forward: The technology has been put to work designing new antibiotics to fight hard-to-treat conditions, and OpenAI and Anthropic have both introduced new limiting features to curb potentially harmful conversations on their platforms. Unfortunately, not all the news has been positive. Doctors who overrely on AI to help them spot cancerous tumors found their detection skills dropped once they lost access to the tool, and a man fell ill after ChatGPT recommended he replace the salt in his diet with dangerous sodium bromide.

As cybercrime surges around the world, new research increasingly shows that ransomware is evolving as a result of widely available generative AI tools. In some cases, attackers are using AI to draft more intimidating and coercive ransom notes and conduct more effective extortion attacks. But cybercriminals' use of generative AI is rapidly becoming more sophisticated. Researchers from the generative AI company Anthropic today revealed that attackers are leaning on generative AI more heavily--sometimes entirely--to develop actual malware and offer ransomware services to other cybercriminals. Ransomware criminals have recently been identified using Anthropic's large language model Claude and its coding-specific model, Claude Code, in the ransomware development process, according to the company's newly released threat intelligence report.

"We extend our deepest sympathies to the Raine family during this difficult time," the company said. It also published a note on its website on Tuesday that said "recent heartbreaking cases of people using ChatGPT in the midst of acute crises weigh heavily on us". It added that "ChatGPT is trained to direct people to seek professional help," such as the 988 suicide and crisis hotline in the US or the Samaritans in the UK. The company acknowledged, however, that "there have been moments where our systems did not behave as intended in sensitive situations". Warning: This story contains distressing details.

Chip giant Nvidia is set to release its latest earnings report – and the results could move the entire US stock market. Over the past two years, the chipmaker has risen to become the world's most valuable company, with a market capitalisation of more than 4 trillion. When Nvidia announces its earnings on Wednesday, investors will get to see how the tech giant has been faring amid the tumult of President Donald Trump's trade salvoes and concerns about whether artificial intelligence has been overhyped. Nvidia specialises in making the graphics processing units (GPUs) that power AI, including the Blackwell B200, marketed as the world's most powerful chip. The California-based company's chips have become essential to the world's largest tech companies, including Microsoft, Meta, Amazon and Alphabet, since AI exploded into the mainstream with the release of OpenAI's generative AI chatbot, ChatGPT, in November 2022.

It's one of those topics that comes up over drinks or dinner at the moment: whether or not you think AI is going to steal your job. So far, I've felt relatively confident that while AI could no doubt have a fair crack at writing a newspaper opinion column, there is something I do as part of my work that AI cannot: reporting. Except now, it seems, AI is claiming to be doing that as well. Last week, it was revealed that at least six reputable publications have had to take down published articles because it turned out that they were probably pieces of fiction written by AI and then passed off by somebody as works of journalism under the name of Margaux Blanchard. One of these was a piece for Wired titled They Fell in Love Playing Minecraft.

With the recent rapid advancement of Agentic Intelligence, agentic tool use in LLMs has become increasingly important. During multi-turn interactions between agents and users, the dynamic, uncertain, and stochastic nature of user demands poses significant challenges to the agent's tool invocation capabilities. Agents are no longer expected to simply call tools to deliver a result; rather, they must iteratively refine their understanding of user needs through communication while simultaneously invoking tools to resolve user queries. Existing reinforcement learning (RL) approaches for tool use lack the integration of genuinely dynamic users during the RL training process. To bridge this gap, we introduce MUA-RL (Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use), a novel reinforcement learning framework that, for the first time in the field of agentic tool use, integrates LLM-simulated users into the reinforcement learning loop. MUA-RL aims to enable autonomous learning of models to communicate with users efficiently and use various tools to solve practical problems in dynamic multi-turn interactions. Evaluations are done on several multi-turn tool-using benchmarks (see Figure 1). Specifically, MUA-RL-32B achieves 67.3 on TAU2 Retail, 45.4 on TAU2 Airline, 28.3 on TAU2 Telecom, 28.4 on BFCL-V3 Multi Turn, and 82.5 on ACEBench Agent -- outperforming or matching the performance of larger open-source models such as DeepSeek-V3-0324 and Qwen3-235B-A22B in non-thinking settings.

We study why Tool-Integrated Reasoning (TIR) makes Large Language Models (LLMs) more capable. While LLMs integrated with tools like Python code interpreters show great promise, a principled theory explaining why this paradigm is effective has been missing. This work provides the first formal proof that TIR fundamentally expands an LLM's capabilities. We demonstrate that tools enable a strict expansion of the model's empirical and feasible support, breaking the capability ceiling of pure-text models by unlocking problem-solving strategies that are otherwise impossible or intractably verbose. To guide model behavior without compromising training stability and performance, we also introduce Advantage Shaping Policy Optimization (ASPO), a novel algorithm that directly modifies the advantage function to guide the policy behavior. We conduct comprehensive experiments on challenging mathematical benchmarks, leveraging a Python interpreter as the external tool. Our results show that the TIR model decisively outperforms its pure-text counterpart on the pass@k metric. Crucially, this advantage is not confined to computationally-intensive problems but extends to those requiring significant abstract insight. We further identify the emergent cognitive patterns that illustrate how models learn to think with tools. Finally, we report improved tool usage behavior with early code invocation and much more interactive turns with ASPO. Overall, our work provides the first principled explanation for TIR's success, shifting the focus from the mere fact that tools work to why and how they enable more powerful reasoning.