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AI news, read the way machines read it. Sydney.

Curated daily by AISearch Global. Every story links to its original source - we don't republish, we round up.

AI News

Nvidia just showed that the harness, not the AI model, is now the real hero

Nvidia research shows that AI agents can perform well, and not go off the deep end, through fine-tuning, even if the AI model isn't that great at the task.

AI News · Human-AI Research

The Download: threats from space mirrors and credit for AI drugs

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. This company’s plans to deploy space mirrors could jeopardize the night sky for many A company that plans to beam sunlight from space to Earth on demand might unintentionally brighten the…

AI News · Human-AI Research

When AI designs a drug, who gets the credit?

When the biotech company Insilico Medicine used its computer models to propose a promising drug for pulmonary fibrosis, it enthusiastically claimed in a press release that the molecule had been “discovered by” its generative AI platform. Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think…

Human-AI Research

Large Language Models in Mental Health: A Systematic Review of Applications, Innovations, and Ethical Challenges

arXiv:2608.18080v1 Announce Type: new Abstract: We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy support tools, prompt engineering, multimodal learning, and ethical considerations. We integrate findings from interdisciplinary studies utilizing diverse data sources such as social media posts, electronic medical records, and multimodal inputs to enable early detection of depression, suicide risk assessment, personalized therapy support, and psychoeducational content generation. Our review highlights advancements in LLM models and annotation strategies that enhance interpretability and clinical relevance, while we also emphasize the critical role of prompt engineering for domain adaptation. We also discuss emerging multimodal fusion techniques integrating text, speech, and sensor data for improved mental health diagnosis and monitoring. Finally, we address ongoing ethical, sociotechnical, and regulatory challenges, and advocate frameworks to ensure safe, equitable, and accountable deployment of LLMs in real-world mental health care.

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