Editor's view: What makes science successful?

This editorial examines the factors contributing to the success of science, tracing its evolution from fundamental human curiosity to contemporary advancements propelled by technology, data, and artificial intelligence (AI). Beginning with the hypothesis-testing process, it highlights how imaginative individuals throughout history have offered explanations for the natural world, designed experiments, and amassed evidence to confirm or reject their ideas and theories, thus generating new knowledge and understanding of nature. Early humans formulated simple myths and legends as the first scientific hypotheses, partly to lessen their fear of the unknown. A more scientific turn appeared when rare explorer-scientists ventured beyond their ancestral homes, gathered empirical information using their limited senses, made choices based on observations, and sometimes relocated entire communities. Their efforts reflected the timeless elements of the scientific method: from generating a hypothesis to its experimental proof, broad validation and application of new knowledge. The paper then examines the characteristics of successful scientific disciplines. They attract many researchers who generate novel ideas and hypotheses, building an accelerating momentum of discovery. Further hallmarks of such fields are swift and fair peer validation and robust mechanisms for applying new knowledge to improve human well-being. By contrast, less successful fields will struggle with attracting talent, leading to slower progress, which could also be coupled with resistance to new ideas and obstacles to real-world translation of new knowledge. A central theme of the paper is the contribution of measurement and tools to science's success. Modern instruments, from microscopes and telescopes to satellites and statistical tools, have extended our perception of nature, revealing realms far smaller and far larger than human senses can access. The paper also addresses the revolution of 'hypothesis-free science', driven by computers and big data. Rather than framing a single hypothesis, modern researchers gather enormous datasets and use algorithms to test large numbers of possible hypotheses simultaneously and systematically, free of human bias introduced through existing knowledge. Finally, the paper explores how AI could advance science to unprecedented successes: not just by improving human senses like a microscope does, providing additional ones like the Large Hadron Collider does, or extending human memory and computational capacity like computers do, but also by expanding human reasoning itself. Unlike previous tools, AI can synthesise human knowledge and generate hypotheses, design studies, explore patterns and write papers, thus becoming both a 'philosopher 2.0' and a 'scientist 2.0'. Therefore, AI may transform science from a human-centred endeavour into collaborative effort that relies on hybrid intelligence. This unprecedented new frontier will require attention to questions of its explainability, bias, authorship, ethics, and accountability. In the future, science will remain successful by staying aligned with its fundamental mission: to improve the human condition through the expansion of knowledge and understanding of our world.