The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in Data Science is apparent from their sharply divergent nature at an elementary level—in computer science, the growth of the number of data points is a source of “complexity” that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of “simplicity” in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as “runtime” in core statistical theory and the lack of a role for statistical concepts such as “risk” in core computational theory. I present several research vignettes aimed at bridging computation and statistics, focusing on methods for trading off the speed and accuracy of inference, and highlighting the role played by optimization theory.

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive and biological sciences. Prof. Jordan is a member of the National Academy of Sciences, a member of the National Academy of Engineering and a member of the American Academy of Arts and Sciences. He is a Fellow of the American Association for the Advancement of Science. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the IJCAI Research Excellence Award in 2016, the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009.

Nobel Turing Challenge is one of the ultimate challenges the scientific community can tackle. It aims at (1) developing AI system including substantial robotics components that can make major scientific discoveries some of which worth Nobel Prize (called as “Scientific Discovery Challenge”), and (2) actually win the prize without the selection committee noticed that it is actually an AI system, not a human researcher (Cybernetic Personality Challenge). Primary focus on this challenge will be biomedical science area for Physiology and Medicine Award (Kitano, H., AI Magazine, 37(1) 2016).

This grand challenge project shall take a form of globally distributed “Virtual Big Science” project (Kitano, H., et al., Nature Chemical Biology, 7, 323-326, 2011). A part of the project shall resemble RoboCup (Kitano, H., et al., AI Magazine, 18(1) 73-85, 1997), but it will have substantially different aspects reflecting the difference of domains and objectives.

In the mid 90s, I advocated “Systems Biology” with the aim of promoting systems-oriented view in biology and to introduce more systematic measurements, proper applications of engineering, mathematical, and information science principles into life science (Kitano, H., Science, 295, 1662-1664, 2002; Kitano, H., Nature, 420, 206-210, 2002). This endeavor has been successful and systems biology is one of normal approach in biomedical and pharmaceutical sciences. The progress in systems biology revealed new limitations in life science that stems from our cognitive limitations to understand complex, non-linear, high dimensional, and dynamical systems, with overwhelming data and publications each of which unveils only a fragment of systems.

With recent breakthroughs in AI, exponentially increasing data production capabilities, and massive computing power, disruptive innovations in biomedical sciences are on the horizon. Time is ripe to embark on a new aggressive challenge. The fundamental breakthrough will come at the stage AI to generate hypotheses and quickly verify them using their knowledge bases, simulation, and robotics experimental systems. It means that AI systems can keep discovering new knowledge with minimal or zero human interventions. Even a mid-term achievement of Scientific Discovery Challenge alone will be a game changer. It will trigger fundamental transformations of industry and more largely on the shape of our civilization.

Hiroaki Kitano is President and CEO at Sony Computer Science Laboratories, Inc., Corporate Executive at Sony Corporation, President at The Systems Biology Institute, Tokyo, Professor at Okinawa Institute of Science and Technology Graduate University, Okinawa, and Director at Laboratory for Disease Systems Modeling, RIKEN Center for Integrative Medical Sciences, Kanagawa, and a member of AI and Robotics Council of the World Economic Forum.

He received a B.A. in physics from the International Christian University, Tokyo, and a Ph.D. in computer science from Kyoto University. Since 1988, he has been a visiting researcher at the Center for Machine Translation at Carnegie Mellon University. His research career includes a Project Director at Kitano Symbiotic Systems Project, ERATO, Japan Science and Technology Corporation followed by a Project Director at Kitano Symbiotic Systems Project, ERATO-SORST, Japan Science and Technology Agency where numbers of spin-offs were created including ZMP Inc., iXs Research, RT Coporation, Flower Robotics Inc., Xiborg Inc., etc.

Kitano is a Founding President of The RoboCup Federation, a founder and president of International Society for Systems Biology (ISSB), and an Editor-in-Chief of Nature Partner Journal (npj) Systems Biology and Applications. He served as a president of International Joint Conference on Artificial Intelligence (IJCAI) during 2011-2013. Kitano received The Computers and Thought Award from the International Joint Conferences on Artificial Intelligence in 1993, Prix Ars Electronica 2000, and Nature Award for Creative Mentoring in Science 2009, as well as being an invited artist for Biennale di Venezia 2000 and Museum of Modern Art (MoMA) New York in 2001.

Data Science has become increasingly popular since more and more sciences follow a research strategy that is based on measurements and simulations. Combining domain expertise with mathematical methods and those of computer science constitutes the field. The amount of measurements is increasing due to enhanced cyber physical systems of many kinds. These large amounts of data require computational analytics. A special challenge is in the first analysis of the raw data, which should be close to the data gathering instrument or be even integrated into it and process the data in real-time. Since scientists can hardly verify data summaries or learned models manually, their theoretically well based properties need to be shown.
In this talk, interdisciplinary work from the research center on data analysis under resource constraints will illustrate this reasoning.

Katharina Morik is full professor for computer science at the TU Dortmund University, Germany. She earned her Ph.D. (1981) at the University of Hamburg and her habilitation (1988) at the TU Berlin. Starting with natural language processing, her interest moved to machine learning ranging from inductive logic programming to statistical learning, then to the analysis of very large data collections, high-dimensional data, and resource awareness. She is a member of the National Academy of Science and Engineering and the North-Rhine- Westphalia Academy of Science and Arts. She leads the research center SFB876 on Data Analysis under Resource Constraints comprising 14 projects, 20 professors and about 50 PhD students.