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The unprecedented speed and scale of progress with artificial intelligence (AI) in recent years suggests society may be living through an inflection point. The virality of platforms such as ChatGPT and Midjourney, which can generate human-like text and image content, has accelerated public interest in the field
and raised flags for policymakers who have concerns about how AI-based technologies may be integrated into wider society. Beyond this, comments made by prominent computer scientists and public figures regarding the risks AI poses to humanity have transformed the subject into a mainstream political issue. For scientific researchers, AI is not a novel topic and has been adopted in some form for decades. However, the increased investment, interest, and adoption within academic and industry-led research has led to a ‘deep learning revolution’1 that is transforming the landscape of scientific discovery.

Enabled by the advent of big data (for instance, large and heterogenous forms of data gathered from telescopes, satellites, and other advanced sensors), AI-based techniques are helping to identify new patterns and relationships in large datasets which would otherwise be too difficult to recognise. This offers substantial potential for scientific research and is encouraging scientists to adopt more complex techniques that outperform existing methods in their fields. The capability of AI tools to identify patterns from existing content and generate predictions of new content, also allows scientists to run more accurate simulations and create synthetic data. These simulations, which draw data

from lots of different sources (potentially in real time), can help decision-makers assess more accurately the efficacy of potential interventions and address pressing societal or environmental challenges.

The opportunities of AI for scientific research are highlighted throughout this report and explored in depth through three case studies on its application for climate science, material science, and rare disease diagnosis.

Alongside these opportunities, there are various challenges arising from the increased adoption of AI. These include reproducibility
(in which other researchers cannot replicate experiments conducted using AI tools); interdisciplinarity (where limited collaboration between AI and non-AI disciplines can lead to
a less rigorous uptake of AI across domains); and environmental costs (due to high energy consumption being required to operate
large compute infrastructure). There are also growing barriers to the effective adoption
of open science principles due to the black- box nature of AI systems and the limited transparency of commercial models that power AI-based research. Furthermore, the changing incentives across the scientific ecosystem
may be increasing pressure on researchers
to incorporate advanced AI techniques at the neglect of more conventional methodologies, or to be ‘good at AI’ rather than ‘good at science’2.

These challenges, and potential solutions, are detailed throughout this report in the chapters on research integrity; skills and interdisciplinarity; innovation and the private sector; and research ethics.