AI and the future of scientific leadership
Today AI-enabled advances are delivering generational progress in biology, transforming science and making our roads safer.
But this is only the beginning.
If we fully capitalize on this opportunity, we can usher in a new era of discovery — empowering scientists across disciplines to solve challenges once thought unsolvable at a speed once deemed impossible.
That’s why as global decisionmakers and technology leaders head to the Artificial Intelligence Action Summit in Paris next week, our message to policymakers is clear: While AI has the potential to revolutionize science and deliver significant benefit to people and society, continued progress is not guaranteed — it will be possible only through immediate and sustained action on the part of the private and public sectors.
The opportunity to advance science in the AI era
AI has already begun to enable landmark advances in science — with much more to come. It is changing how we conduct scientific research, dramatically accelerating the scientific process (sometimes condensing hundreds or even thousands of years of traditional experimentation and research into a few months or days), and allowing scientists to look at many things in new ways simultaneously. AI is also making it possible for many more people to participate in research.
For example, AlphaFold alone has been accessed by 2.5 million researchers across 190 different countries. We’ve also made many of our landmark, AI-powered advances in connectomics, pangenome, weather, materials science and climate models widely available to scientists. All of this creates a huge moment of opportunity — offering tangible benefits for people on real world problems and powering economic growth.
But realizing this immense potential of AI in science requires more than just technological breakthroughs; it demands a concerted effort to build the foundation for continued progress.
Which is why countries that want to lead here need to work together to put in place the infrastructure, investments, and legal frameworks that support scientists, engineers and a culture of ongoing innovation.
To give policymakers immediate, actionable steps, today we’re releasing our Policy Framework for Building the Future of Science with AI.
The Three I’s of science in the AI era:
- Infrastructure — Increase access to AI infrastructure. Most scientists won’t need to train their own large AI model, but they will need access to resources to fine-tune large models, run simulations to generate high-quality data, or train smaller AI models on their specialized data. And without an established infrastructure for AI-powered scientific research and development, they have to devote energy to coordinating compute resources, data and model access and become proficient with AI tools, all of which detracts from their core research activities. That’s why it’s imperative for governments to build the infrastructure necessary to make AI-enabled research tools and resources more accessible to more scientists in more places. They can achieve this by setting up National AI for Science Resource Centers, similar in concept to the U.S. National AI Research Resource (NAIRR), which makes high-quality data, AI models, compute capacity, software and educational resources accessible for AI research.
- Investment — Invest in the science of AI. Groundbreaking scientific discoveries often require long-term commitment and sustained investment. Over the years, government funding has played a crucial role in supporting ambitious basic research endeavors, encouraging collaboration among academia, industry, and the public sector, and attracting additional private (foreign or domestic) investments. Governments should create a list of priority areas to direct their funding and incentivize research collaboration through public challenges aimed at solving the most pressing issues. Novel public-private partnerships and funding models can play an important role in fostering a thriving ecosystem and building a strong pool of scientific and engineering talent.
- Innovation — Implement pro-science and pro-innovation legal frameworks. With global AI competition accelerating, we need to support innovation while establishing frameworks for high-risk applications. Regulatory uncertainty slows innovation and creates barriers for scientists and private investors. To address this issue, governments should establish pro-innovation regulatory regimes that support responsible and reasonable use of data, flexible copyright frameworks, and harmonized data privacy laws. Trade policies should support cross-border data flows, enhancing the diversity of data needed for AI discoveries.
There are many more challenges out there for AI to solve — and many ways for countries to work together to promote major AI-led breakthroughs.
With the right policy and investment frameworks, governments can help accelerate scientific progress by clearing the way for scientists to continue to deliver the kinds of breakthroughs that will power a brighter future for people everywhere.
