Using machine learning technology, a new study has identified three distinct profiles describing social and economic factors that are associated with a higher risk of suicide. Scientists at Weill Cornell Medicine and Columbia University Vagelos College of Physicians and Surgeons led the research that showed suicide rates vary significantly across the three clusters and that the patterns differ geographically across the United States.

A new study led by researchers at the University of Oxford and the Allen Institute for AI (Ai2) has found that large language models (LLMs) – the AI systems behind chatbots like ChatGPT – generalize language patterns in a surprisingly human-like way: through analogy, rather than strict grammatical rules. The findings were published on 9 May in the journal PNAS.

The research challenges a widespread assumption about LLMs: that these learn how to generate language primarily by inferring rules from their training data. Instead, the models rely heavily on stored examples and draw analogies when dealing with unfamiliar words, much as people do.

Artificial intelligence could be affecting the scientific rigour of new research, according to a study from the University of Surrey. 

 

Promising new AI tools are being developed to protect and preserve the cultural traditions and heritage of marginalized or indigenous communities, including language, folklore, oral traditions, and community wisdom.

Using advanced artificial intelligence, researchers have developed a novel method to make drug development faster and more efficient.