Generating a roadmap to guide the future of AI development
Across the world, public, private and third sector organisations are working to realise first-order gains from AI application. We are making substantial and meaningful advances that enable us to go even further to consider how AI can accelerate the art of producing new knowledge itself; that is, to accelerate Science. Not just to ‘speed up’ knowledge production, but to find new ways to undertake science that enhances our ability to solve complex problems.
Three technological trends have driven the application of AI in the Sciences: improved computer hardware and processors; improved data availability across the sciences; and improved AI software and new machine learning. The convergence of these accelerating trends provides a once-in-a-generation opportunity to advance scientific discovery for the public good.
The U.S. and Japanese governments are leading promising, centrally-coordinated, mission-based research, whilst private sector companies make important discrete advances in domain-specific areas. Meanwhile, the UK has an active community of scientists and a history of contributing to this emerging field. This project aims to draw on a global community of scientists at the vanguard of AI for science, in order to generate a multi-year roadmap with technical quests and intermediate milestones for scientific discovery, across scientific domains such as biomedicine, environmental sciences, and materials science.
Explaining the science
The application of AI in scientific discovery presents us with very different challenges compared to games such as chess, shogi, and Go. Scientific discoveries require an order of magnitude larger hypothesis space, a more elaborate verification process and the integration of scientific devices and modules (King et al., 2008, Kitano 2002). We also need to ensure that AI-driven discoveries are relevant to human civilisation and aligned with our values. This capability requires a broader knowledge about the world, a deep understanding of the human scientific discovery process and good reasoning about potential applications and social implications.
We envision future AI systems that do not have these limitations and will have the potential to transform scientific discovery and enable an unprecedented expansion of knowledge. Building on the legacy of the Turing Test and the RoboCup model, we invite the community to join us in the Turing AI Scientist Grand Challenge: developing AI systems capable of making Nobel quality scientific discoveries highly autonomously at a level comparable, and possibly superior, to the best human scientists by 2050.
The explicit goal of the challenge is to develop an AI system that has highly autonomous capabilities to perform research. While human scientists already use a wide range of AI tools for specific tasks that contribute to discoveries, these cannot be considered AI Scientists because they only address narrow aspects of the scientific process and they lack autonomy in setting their goals, interpreting results, and communicating findings.
Achieving the Turing AI Scientist Grand Challenge will take several decades of research, but we expect that it will drive progress in both science and AI research. Along the way, intermediate grand challenges will need to be defined to structure specific problems and demonstrate tangible progress. A key criterion for these challenges will be the synergistic aspects of discovery in scientific domains, and the development of AI Scientist technology. The challenge requires an integrated AI system with very diverse capabilities. The AI system must be able to make a strategic choice about research goals, design protocols and experiments to collect the necessary data, notice and characterise a significant discovery, communicate in the form of publications and other scientific means to explain the innovation and methods behind the discovery and articulate the significance of the discovery its impact.
We envision intelligent systems that can support scientists in the areas of (Gil et al. 2018):
- Knowledge Representation and Capture: Developing expressive and efficient frameworks to represent and capture a wide range of scientific knowledge about processes, models, and hypotheses. Capturing scientific knowledge will push the limits of state of art. This work is expected to have a significant impact on the reproducibility of science, as automating science requires semantic precision.
- Sensing and Robotics: Prioritizing data collection based on the available scientific knowledge. The trend in science is for more and more of the physical execution of experiments to be done using robotics. This will increase the productivity of science as robots work cheaper, faster, more accurately, and longer than humans.
- Information Integration: Representing data and models as a “system of systems” where all knowledge is interconnected.
- Machine Learning: Enriching algorithms with knowledge and models of the relevant underlying processes. Methods will need to be enriched with models of the relevant science, and their results explainable to scientists. How best to include background knowledge in machine learning is one of the most important open questions in the field.
- Interfaces and Interactive Systems: Exploring and understanding user context using interconnected knowledge. The AI systems’ interactions with scientists must be guided by a knowledge-rich user model that enables the AI systems to act as colleagues. Information from human scientists must be able to be understood by the AI systems, and the AI systems must be able to explain their conclusions to human scientists.
Starting in January 2021, we will:
Create a landscaping study for AI systems that have highly autonomous capabilities to perform scientific research. This includes defining challenges for AI research that will push discoveries beyond the state of the art and scope collaborative research work for AI and experts from other disciplines.
Generate a multi-year roadmap with technical quests and intermediate milestones for scientific discovery in biomedicine, environmental sciences, and materials science.
Facilitate coordination, resource development and sharing.
Create distributed collaborative AI hubs around the world (US-UK-Japan).