Abstract: In this talk, I will explain how current approaches of machine, and deep learning based on traditional statistics and information theory fail to capture fundamental properties of our world and are ill-equipped to deal with high-level functions such as inference, abstraction, and understanding, they are fragile and can easily be deceived. In contrast, we will explore recent attempts to combine symbolic and differentiable computation in a form of unconventional hybrid computation that is more powerful and may eventually display and grasp these higher-level elements of human intelligence. In particular, I will introduce the field of Algorithmic Information Dynamics and that of Algorithmic Machine Intelligence based on the theories of computability and algorithmic probability, and how these approaches promise to shed light on the weaknesses of current AI and how to attempt to circumvent some of their limitations.

Biography: Dr. Zenil has a BSc in mathematics (UNAM), a master's in logic (ENS/Paris), a PhD in computer science (Lille) and a PhD in epistemology (Sorbonne). He has more than 20 years of experience in academia and industry in six countries. This experience includes designing/writing tracking software for NASA as a member of the Payload team at MIT for the Mars Gravity Biosatellite; helping develop computational linguistics for Wolfram|Alpha; and publishing more than 100 papers in journals such as Nature Machine Intelligence, Nucleic Research Acids and Philosophical Transactions of the Royal Society A. He has been a Senior Researcher (faculty member) in the Department of Computer Science at the University of Oxford and Lab Leader at the Unit of Computational Medicine, the Center for Molecular Medicine, SciLifeLab and the Karolinska Institute (the institution that awards the Nobel Prize in Medicine or Physiology). Before that, he was a research associate at the Behavioural and Evolutionary Theory Lab at the Department of Computer Science of the University of Sheffield. He is also the editor of Complex Systems, the first journal in the field, founded in 1987 by Stephen Wolfram. He has been a visiting scholar and a visiting professor at Carnegie Mellon University and the National University of Singapore. He is the director of Oxford Immune Algorithmics, an Oxford University spinout that translates cutting-edge science into medical technological innovation, and the President of the Automacoin Foundation. His main interest is the study of causation through computation and algorithmic complexity with a particular interest in applications to living systems. He was recently appointed by the Alan Turing Institute at the British Library in London to advise the UK and allies on AI for scientific discovery in the next 30 years.