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Creating the Engine for Scientific Discovery www.nature.com/npjsba PERSPECTIVE OPEN Nobel Turing Challenge: creating the engine for scientific discovery ✉ Hiroaki Kitano 1 Scientific discovery has long been one of the central driving forces in our civilization. It uncovered the principles of the world we live in, and enabled us to invent new technologies reshaping our society, cure diseases, explore unknown new frontiers, and hopefully lead us to build a sustainable society. Accelerating the speed of scientific discovery is therefore one of the most important endeavors. This requires an in-depth understanding of not only the subject areas but also the nature of scientific discoveries themselves. In other words, the “science of science” needs to be established, and has to be implemented using artificial intelligence (AI) systems to be practically executable. At the same time, what may be implemented by “AI Scientists” may not resemble the scientific process conducted by human scientist. It may be an alternative form of science that will break the limitation of current scientific practice largely hampered by human cognitive limitation and sociological constraints. It could give rise to a human-AI hybrid form of science that shall bring systems biology and other sciences into the next stage. The Nobel Turing Challenge aims to develop a highly autonomous AI system that can perform top-level science, indistinguishable from the quality of that performed by the best human scientists, where some of the discoveries may be worthy of Nobel Prize level recognition and beyond. npj Systems Biology and Applications (2021) 7:29 ; https://doi.org/10.1038/s41540-021-00189-3 1234567890():,; NOBEL TURING CHALLENGE AS AN ULTIMATE GRAND EURISKO6,8. It continues to be one of the main topics of AI9,10. CHALLENGE Recently, an automated experimental system that closed-the-loop Understanding, reformulating, and accelerating the process of of hypothesis generation, experimental planning, and execution scientific discovery is critical in solving problems we are facing and has developed for budding yeast genetics that clearly marks the 11–13 exploring the future. Building the machine to make it happen next step towards an AI Scientist . While these pioneering could be one of the most important contribution to society, and it works have focused on a single data set or a specific task using will transform many areas of science and technology including limited resources, it signifies the state-of-the-art of technology systems biology. Since scientific research has been one of the today that can be the basis of more ambitious challenges. most important activities that drove our civilization forward, the The obvious next step is to develop a system that makes implications of such development will be profound. scientific discoveries that shall truly impact the way we do science Attempts to understand the process of scientific discoveries and aim for major discoveries. Therefore, I propose the launch of a have a long tradition in the philosophy of science as well as grand challenge to develop AI systems that can make significant artificial intelligence. Karl Popper introduced a concept of scientific discoveries that can outperform the best human falsifiability as a criterion and process of solid scientific process scientist, with the ultimate purpose of creating the alternative but the process of hypothesis and concept generation do not form of scientific discovery14. Such a system, or systems, may be have particular logic behind it1. Thomas Kuhn proposed a concept called “AI Scientist” that is most likely a constellation of software of a Paradigm shift where two competing paradigms are and hardware modules dynamically interacting to accomplish incommensurable and a set of knowledge, rather than a single tasks. Since the critical feature that distinguishes it from knowledge, has to be switched with the transition of paradigm2. conventional laboratory automation is its capability to generate Imre Lakatos reconciles them by proposing actual science makes hypotheses, learn from data and interactions with humans and progress based on a “research program” composed of a hardcore other parts of the system, reasoning, and a high level of that is immune to revision and flexible peripheral theories3. autonomous decision-making, the term “AI Scientist” best Contrary to these positions, Paul Feyerabend argued that there are represents the characteristics of the system to be developed. no methodological rules in the scientific process4. Although these The best way to accelerate the grand challenge of this nature is to arguments are important thoughts in the philosophy of science, define a clear mission statement with an audacious yet ideas are philosophical and not concrete to be implemented provocative goal such as winning the Nobel Prize14. Therefore, I computationally. In addition, these studies are focused on how propose the Nobel Turing Challenge as a grand challenge for science is carried out as a part of human social activities. A rare artificial intelligence that aims at “developing AI Scientists capable example of implementing such concepts can be seen in the model of autonomously carrying out research to make major scientific inference system implemented by Ehud Shapiro that reflects discoveries and win a Nobel Prize by 2050”. While the previous Popper’s falsifiability5. article14 focused on rationales for such a challenge with emphasis Not surprisingly, scientific discovery has been a major topic in on human cognitive limitations and needs for exhaustive search of artificial intelligence research that dates back to DENDRAL6 and hypothesis space, this article formulates the vision as the Nobel META-DENDRAL, followed by MYCIN, BEACON7, AM, and Turing Challenge and implications of massive and unbiased search 1The Systems Biology Institute, Tokyo, Japan; Okinawa Institute of Science and Technology Graduate School, Okinawa, Japan; Sony Computer Science Laboratories, Inc., Tokyo, ✉ Japan; Sony AI, Inc., Tokyo, Japan; and The Alan Turing Institute, London, UK. email: [email protected] Published in partnership with the Systems Biology Institute H. Kitano 2 of hypothesis space and verification, architectural issues, and The core of the research program shall be about “Science of interaction with human scientists are discussed as a transforma- Science” rather than “Science of the process of science by human tive paradigm in science. The distinct characteristic of this scientists”. As in the case of past AI grand challenges, the best and challenge is to field the system into an open-ended domain to perhaps the only way to demonstrate that scientific discovery can explore significant discoveries rather than rediscovering what we be reformulated computationally is to develop an AI system that already know or trying to mimic speculated human thought outperforms the best human scientists. Furthermore, it is not processes. The vision is to reformulate scientific discovery itself sufficient to have AI Scientist make one discovery, it shall generate and to create an alternative form of scientific discovery. a continuous flow of discoveries at scale. The fundamental The accomplishment of this challenge requires two goals to be purpose behind this challenge is to uncover and reformulate the achieved that are: (1) to develop an AI Scientist that performs process of scientific discovery and develop a scalable system to scientific research highly autonomously enabling scientific dis- perform it that may result in an alternative form of scientific coveries at scale, and (2) to develop an AI Scientist capable of discovery that we have not seen before. making strategic choices on the topic of research, that can communicate in the form of publications and other means to Case studies: scientific discovery as a problem-solving explain the value, methods, reasoning behind the discovery, and Herbert Simon argued that science is problem-solving in his article their applications and social implications. When both goals are “The Scientist as Problem Solver”18. Scientists set themselves tasks met, the machine will be (almost) comparable to the top-level of solving significant scientific problems. If this postulation holds, human scientist as well as being scientific collaborators. The defining the problem and strategy and tactics to solve these question and challenge is whether the machine will be problems is the essence of scientific discoveries. indistinguishable from the top-level human scientists and most An example of the discovery of cellular reprogramming leading likely pass the Feigenbaum Test which is a variation of the Turing to iPS cell and regenerative medicine by Shinya Yamanaka is Test15, or whether it will exhibit patterns of scientific discovery consistent with this framework19,20. It has a well-defined goal with that are different from human scientists. obvious scientific and medical implications, and search and It should be noted “winning the Nobel Prize” is used as a optimization has been performed beautifully to discover cellular symbolic target illustrating the level of discoveries the challenge is reprogramming capability using four transcription factors, now aiming at. The value lies in the development of machines that can known to be Yamanaka factors21. make discoveries continuously and autonomously, rather than Another example is the discovery of conducting polymer by 1234567890():,; winning any award including the Nobel Prize. It is used as a Hideki Shirakawa, Alan MacDiarmid, and Alan Heeger. It started symbolism that triggers inspiration and controversy. with
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