Institute of Advanced Research in Artificial Intelligence (IARAI) Information

The Institute advances basic and applied research in Artificial Intelligence. We are building a unique environment of world class researchers and industrial scale real-world data openly shared with the scientific community. With critical analyses and fundamental studies we seek novel insights that shape the future of our society. Focus areas include climate change, smart cities, sustainable mobility, fleet management, and helping discover new drugs. World Simulation The spectacular success of modern control theory realized by reinforcement learning suggests that many real-world problems can be solved via a new paradigm of simulation or gamification. Averting climate change, making mass-mobility sustainable through smart city control and large-scale fleet management, building safe self-driving cars, or revolutionizing any real-world logistics problem. At IARAI, we build powerful simulators from industrial-scale real-world data advancing learning strategies or test one-shot/few-shot learning to help solve some of humanity most pressing problems. Prediction trough description Recent advances in modern machine learning allow exploiting Big Data to study complex challenges that were unsolvable before. At IARAI we focus on pressing problems with great impact on society and the planet. We can now, for instance, predict patterns in urban traffic or large-scale rainfall, assess climate change, classify biomedical images, and help discover new drugs. A theory of AI The field of machine learning has undergone a revolution: well-known black box algorithms have shown their potential and now dominate methods delivering the state-of-the-art performance on many benchmark problems. What makes these algorithms black box is that we have no rigorous human intuitive understanding of their success. As a result, questions with no clear answer arise: What architecture should a neural network have to handle a certain new data type? How can one combine different networks effectively?