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.
His message is clear: our world is built on abundant energy, around 80% of which has come from fossil fuels over the past 50 years. Because supplies are limited, energy consumption will peak in decades - sooner if humans attempt to limit climate change. To keep global warming below 1.5 °C by 2100, the use of fossil fuels must fall by 5-8% each year - a pace that is too fast for low-carbon energy to keep up with.
Biodiversity loss is continuing at an unprecedented rate, with species becoming extinct at between 100 and 1,000 times the average pre-human, or 'background', rate. Human activities are the main cause. Although there are hundreds of local, regional and international initiatives to conserve and sustainably use species and ecosystems, many conservation scientists worry that measures such as interventions to conserve individual species or incentives to create protected areas are not supported by strong evidence from research.
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.
Consistent with the general trend of incorporating artificial intelligence into nearly every field, researchers and politicians are increasingly using AI models trained on scientific data to infer answers to scientific questions. But can AI ultimately replace scientists? The Trump administration signed an executive order on Nov. 24, 2025, that announced the Genesis Mission, an initiative to build and train a series of AI agents on federal scientific datasets "to test new hypotheses, automate research workflows, and accelerate scientific breakthroughs."
The exponential growth of scientific literature presents an increasingly acute challenge across disciplines. Hundreds of thousands of new chemical reactions are reported annually, yet translating them into actionable experiments becomes an obstacle1,2. Recent applications of large language models (LLMs) have shown promise3,4,5,6, but systems that reliably work for diverse transformations across de novo compounds have remained elusive. Here we introduce MOSAIC (Multiple Optimized Specialists for AI-assisted Chemical Prediction), a computational framework that enables chemists to harness the collective knowledge of millions of reaction protocols.
Some clinicians have an uncanny quality. A colleague describes herself and others with this instinct as "witchy"-a capacity to know things about patients they haven't said yet, to follow a stray association to a song lyric or a half-remembered cultural reference and arrive, reliably, at something the patient urgently needed to say but couldn't reach on their own. We see with artificial intelligence these intriguing possibilities for discovery, especially as connections that human beings never would see pop out of apparently unrelated data.
I'm less interested in topics than in questions, and I'm less interested in publishing than I am in curation. When I've testified before Congress or dealt with an appropriations bill or a budget negotiation, this question, of what is the return on investments when you're doing R&D, comes up quite often. It's been asked by economists in very formal ways since at least the 1950s, but the data and the methods that were available were really not very strong.
In fact, Stawicki was on a mission to save the lives of around 1,000 zebrafish ( Danio rerio) in her laboratory. Similarities between lines of hair cells on the fish's flanks and those in the mammalian inner ear enable her to use them as a model to study hearing problems in humans caused by some antibiotics and chemotherapy drugs. A sensor had picked up that the lab's heating system had been knocked out by a power fault.
Last November, the UK government announced a bold plan to phase out animal testing in some areas of research. Animal tests for skin irritation are scheduled for elimination this year, and some studies on dogs should be slashed by 2030. The long-term vision is 'a world where the use of animals in science is eliminated in all but exceptional circumstances'.