Artificial intelligence (AI) is becoming increasingly embedded in scientific research - analysing data, proposing hypotheses, and in some cases, even writing papers. We are relying on systems that may lead us to answers faster and more efficiently, but what happens when we don't understand precisely how those answers were reached? Can science, which has long valued explainability and transparency, embrace a collaborator that often produces results without revealing how they were reached?

By considering how today’s most advanced generative AI systems — such as large language models — work, Claire Malone will explore how tools like ChatGPT generate predictions not by understanding concepts but by calculating what is statistically most likely to come next in any given context, examining how this is accelerating scientific discovery.

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