PAIR Distinguished Lecture Series: Artificial Intelligence and Autonomous Systems

Biography

Joseph Sifakis is Emeritus Senior CNRS Researcher at Verimag. His current research interests cover fundamental and applied aspects of embedded systems design. The main focus of his work is on the formalization of system design as a process leading from given requirements to trustworthy, optimized and correct-by-construction implementations.

Joseph Sifakis has been a full professor at Ecole Polytechnique Fédérale de Lausanne (EPFL) for the period 2011-2016. He is the founder of the Verimag laboratory in Grenoble, which he directed for 13 years. Verimag is a leading research laboratory in the area of embedded systems, internationally known for the development of the Lustre synchronous language used by the SCADE tool for the design of safety-critical avionics and space applications.

In 2007, Joseph Sifakis has received the Turing Award for his contribution to the theory and application of model checking, the most widely used system verification technique today.

Joseph Sifakis has had numerous administrative and managerial responsibilities both at French and European level. He has actively worked to reinvigorate European research in embedded systems as the scientific coordinator of the « ARTIST » European Networks of Excellence, for ten years. He has participated in many major industrial projects led by companies such as Airbus, EADS, France Telecom, Astrium, and STMicroelectronics.

Joseph Sifakis is a member of the French Academy of Sciences, a member of the French National Academy of Engineering and a member of Academia Europea and a member of the American Academy of Arts and Sciences, and a member of the National Academy of Engineering. He is a Grand Officer of the French National Order of Merit, a Commander of the French Legion of Honor. He has received the Leonardo da Vinci Medal in 2012.

Joseph Sifakis has received in 2009 the Award of the Hellenic Parliament Foundation for Parliamentarism and Democracy. He is a commander of the Greek Order of the Phoenix.

He has been the President of the Greek Council for Research and Technology for the period February 2014 – April 2016.

Abstract

We argue that, despite the spectacular achievements of machine learning techniques, we are still far from Artificial General Intelligence (AGI). A big step toward this goal would be to develop autonomous systems capable of replacing human agents working in complex organizations, as envisioned by the IoT. This requires in particular, the convergence between Computing and AI integrating data-based machine learning and model-based systems engineering.

We discuss the relevance of existing criteria for comparing human and machine intelligence and show some notable analogies and differences between scientific knowledge and that produced by neural networks.

Emphasizing that autonomy is an important step towards AGI, we present a characterization of autonomous systems, and show key differences with mental systems equipped with common sense knowledge and reasoning.

We conclude by advocating challenging work directions, including the development of a new foundation for systems engineering and scientific knowledge, and the joint exploration of physical and mental phenomena that embody human intelligence.