#Emergingtrends

A massive new study has uncovered detectable “AI fingerprints” in millions of scientific papers, revealing that artificial intelligence has quietly become a pervasive force in academic publishing. Researchers found that at least 13.5% of biomedical research abstracts published in 2024 showed evidence of being written with some assistance from large language models (LLMs) such as ChatGPT and Google Gemini—raising fresh questions about research integrity and the future of scholarly communication (phys.org).

A global study shows AI-assisted language appears in about 13.5% of biomedical abstracts since 2024, as large language models like ChatGPT and Google Gemini quietly influence scientific writing. The finding reflects a shift in authorship dynamics and raises questions about originality, transparency, and how science is communicated. For Thai researchers, the trend comes at a moment when the country expands its research capacity and aims to publish more in international journals.

A new artificial intelligence (AI) system, developed by international researchers, is turning heads worldwide for its uncanny ability to predict human decisions with unprecedented accuracy—raising both hopes of revolutionary applications and urgent questions about privacy and ethics. This breakthrough, recently published in the journal Nature, introduces the AI model “Centaur”, which has outperformed decades-old cognitive models in forecasting how people think, learn, and act across diverse scenarios (studyfinds.org).

Centaur’s creators set out with an ambitious goal: develop a single AI system capable of predicting human behaviour in any psychological experiment, regardless of context or complexity. To achieve this, they compiled a massive “Psych-101” dataset spanning 160 types of psychological tests—ranging from memory exercises and risk-taking games to moral and logical dilemmas—amassing data from over 60,000 people and more than 10 million separate decisions. Unlike traditional models tuned for specific tasks, Centaur was trained to generalise, understanding the plain-language descriptions of each experiment.

A groundbreaking AI system is drawing attention for its ability to forecast human choices with impressive accuracy. Published in Nature, the Centaur model seeks to predict how people think, learn, and decide across diverse tasks. The research team says Centaur generalizes beyond single experiments, offering new ways to study decision-making in real time.

Centaur was trained on a vast “Psych-101” dataset containing 160 types of psychological tests. The data come from more than 60,000 participants and over 10 million decisions. The system learns language-driven task descriptions rather than task-specific rules. Unlike older models designed for narrow tasks, Centaur aims to apply broad reasoning to novel experiments.

A recent collaboration between Chinese and French neuroscientists has produced groundbreaking findings on the neural mechanisms underlying consciousness, signaling potential advances in how the human mind is understood and how altered states of awareness might be approached in medical settings. This research, as reported by the South China Morning Post, has captured the global scientific community’s attention by illuminating the intricate neural networks believed to govern conscious experience, and it raises important implications for the future of neuroscience, medicine, and even philosophy.