Modern scientific societies are increasingly vulnerable due to their dependence on membership fees and journal subscriptions, which are being challenged by the rise of virtual networking and open-access publishing.
Computer programs that check mathematical arguments have existed for decades, but translating a human-written proof into the strict programming language of a computer is extremely time-consuming, often taking months or even years.
CIRC posts come with excellent resources and generous salaries. But the current round is being filled on an extraordinarily tight timeline. We assume that this is to take advantage of some US scholars' urgency to leave, and to keep pace with other countries hoping to achieve similar results (such as France, which is running a high-profile campaign to lure US scholars).
Within a couple of years of ChatGPT coming out, I had come to rely on the artificial-intelligence tool, for my work as a professor of plant sciences at the University of Cologne in Germany. Having signed up for OpenAI's subscription plan, ChatGPT Plus, I used it as an assistant every day - to write e-mails, draft course descriptions, structure grant applications, revise publications, prepare lectures, create exams and analyse student responses, and even as an interactive tool as part of my teaching.
A few years ago, I put together what I felt was a truly innovative concept, which I presented in a conference poster at an international meeting in my field. After the presentation, I spoke to another early-career scientist about my work and how it might apply to their findings. Two years later, they scooped me by publishing a preprint paper that presented my idea, with many of the same verbal formulations and an identical flow of ideas, without any acknowledgement or attribution to my work.
Scientists are increasingly turning to artificial-intelligence systems for help drafting the grant proposals that fund their careers, but preliminary data indicate that these tools might be pulling the focus of research towards safe, less-innovative ideas. These data provide evidence that AI-assisted proposals submitted to the US National Institutes of Health (NIH) are consistently less distinct from previous research than ones written without the use of AI - and are also slightly more likely to be funded.
In 2023, Australia abandoned its expensive and bureaucratic scholar-led research-assessment programme. New Zealand followed suit soon after. The hope, according to a transition plan unveiled by the Australian federal government's Department of Education and the research sector, was to find a "more modern, data-driven approach". In the United Kingdom, where financial pressures on universities are especially acute, there are similar calls to reform the Research Excellence Framework (REF), the country's performance-based research-funding system.