Three talks in the Salam 2020 lecture series are scheduled:

Lecture 1: TODAY, Monday 27 January at 16.30 hrs: "Artificial Intelligence: success, limits, myths and threats"
Abstract: Artificial Intelligence is about to have a dramatic impact on many sectors of human activity. In the last ten years, thanks to the development of machine learning in “deep networks”, we have experienced spectacular breakthroughs in diverse applications such as automatic interpretation of images, speech recognition, consumer profiling, or go and chess playing. Algorithms are now competing with the best professionals at analyzing skin cancer symptoms or detecting specific anomalies in radiology; and much more is to come. Worrisome perspectives are frequently raised, from massive job destruction to autonomous decision-making “warrior” robots.
The talk will  open the black box of deep networks and explore how they are programmed to learn from data by themselves. This will allow to understand their limits, to question whether their achievements have anything to do with “intelligence”, and to reflect on the foundations of scientific intelligence.
For more information, see also: http://indico.ictp.it/event/9269/

Lecture 2: Tuesday 28 January at 16.30 hrs: "The spin glass cornucopia"
Abstract: Motivated originally by some anomalies in the magnetic response of some magnetic alloys, spin glass theory has developed in the last 40 years into a vast research field. Apart from providing a theoretical framework for understanding glassy phases in various physical systems, it has also provided a corpus of concepts and methods, like the replica and the cavity method, that have found applications in various other branches of science, where one is interested in emerging properties in systems built from many interacting elementary components: computer science, information theory, economy, biology, etc.
The talk will review some of the basic concepts and methods originating from spin glass theory, and present some aspects of their relevance to other fields.
For more information, see also: http://indico.ictp.it/event/9275/

Lecture 3: Wednesday 29 January at 11.00 hrs: "Statistical physics of inference and machine learning"
Abstract: A major challenge of contemporary statistical inference is the large-scale limit, where one wants to discover the values of many hidden parameters, using large amount of data. In recent years, ideas from statistical physics of disordered systems, notably the cavity method, have helped to develop new algorithms for important inference problems, ranging from error correcting codes in information theory to compressed sensing, machine learning and generalized linear regression. The talk will review these developments and explain how they can be used, together with the replica method, to identify phase transitions in benchmark statistical ensembles of inference problems.
For more information, see also: http://indico.ictp.it/event/9276/