Across disciplines as varied as biology, physics, mathematics and social science, artificial intelligence (AI) is transforming the scientific enterprise. From machine-learning techniques that hunt for patterns in data, to the latest general-purpose algorithms that can generate realistic synthetic outputs from vast corpuses of text and code, AI tools are accelerating the pace of research and providing fresh directions for scientific exploration.
This special website looks at how these changes are affecting different areas of science — and how it should respond to the challenges the tools present. It includes selected articles from journalists as well as editorials and comment from Nature, including subscriber-only content. The site will be updated with more content as it is published.
Background to the AI revolution
Whereas the 2010s saw the creation of machine-learning algorithms that can help to discern patterns in giant, complex sets of scientific data, the 2020s are bringing in a new age with the widespread adoption of generative AI tools. These algorithms are based on neural networks and produce convincing synthetic outputs, sampling from the statistical distribution of the data they have been trained on.
The sheer pace of innovation is breathtaking and, for many, bewildering — requiring a level-headed assessment of what the tools have already achieved, and of what they can reasonably be expected to do in the future.
AI in scientific life
From designing proteins and formulating mathematical theories, to enabling quick literature syntheses or helping to write research papers, AI tools are revolutionizing how scientists conduct their research and what they are able to achieve.
But these developments are playing out differently across the scientific enterprise. Diving into the trends in different disciplines provides a guide to the potential of AI-fuelled research and its possible pitfalls.
Challenges of AI – and how to deal with them
Although there is little doubt about the potential of AI to supercharge certain aspects of scientific discovery, there is also widespread disquiet. Many of the concerns surrounding the use of AI tools in science mirror those in wider society — transparency, accountability, reproducibility, and the reliability and biases of the data used to train them.