A thought-provoking study led by UCL researchers has demonstrated that large language models (LLMs) can predict neuroscience study results more accurately than human experts. Using a novel benchmark called BrainBench, the study found that LLMs achieved 81% accuracy compared to 63% for human experts in identifying real study abstracts. The research highlights LLMs’ ability to synthesize vast amounts of scientific literature, potentially accelerating research across fields. A specialized model, BrainGPT, further improved performance to 86% accuracy. These findings suggest a future where AI tools could assist in experiment design and outcome prediction, while also raising questions about scientific innovation and the role of human expertise in research.

A study conducted in a materials science R&D lab reveals significant impacts of AI on scientific research and innovation. Key findings show substantial productivity gains, with AI-assisted researchers discovering 44% more materials, increasing patent filings by 39%, and boosting product innovation by 17%. However, these benefits were unevenly distributed, with top performers seeing the greatest gains. Despite increased productivity, 82% of scientists reported reduced job satisfaction due to decreased creativity and skill underutilization. The study highlights the need for balancing AI integration with maintaining scientific curiosity and job satisfaction. It also emphasizes the importance of human judgment and expertise in leveraging AI effectively, suggesting potential long-term impacts on workforce composition and scientific careers.