Ever since its origin in post-war research, AI has been subject to profound hyperbole, rapturous prognostications, and projected nightmares. In 2019, things have once again reached fever pitch in what Science Board co-chair and External Professor Melanie Mitchell wryly notes is a hype cycle that routinely ripples through her fellow computer scientists and those who fund them. Her illuminating new book, Artificial Intelligence: A Guide for Thinking Humans, lays bare the inner workings of these potent tools, exposing their realistic limits and patiently detailing our deployment errors. It is a solid history of how we got from pocket calculators to facial recognition and self-driving cars, a lucid tour of how these systems operate, and a tempered read on just how far we have to go before we’re obsolete.
Many real systems can be modeled as networks, where the elements of the system are nodes and interactions between elements are edges. An even larger set of systems can be modeled using dynamical processes on networks, which are in turn affected by the dynamics. Networks thus represent the backbone of many complex systems, and their theoretical and computational analysis makes it possible to gain insights into numerous applications. Networks permeate almost every conceivable discipline—including sociology, transportation, economics and finance, biology, and myriad others—and the study of "network science" has thus become a crucial component of modern scientific education.
The school "Complex Networks: Theory, Methods, and Applications" offers a succinct education in network science. It is open to all aspiring scholars in any area of science or engineering who wish to study networks of any kind (whether theoretical or applied), and it is especially addressed to doctoral students and young postdoctoral scholars. The aim of the school is to deepen into both theoretical developments and applications in targeted fields.
— REKA ALBERT, Pennsylvania State University
— GUIDO CALDARELLI, IMT Lucca
— MARTON KARSAI, Central European University
— JOSE FERNANDO MENDES, University of Aveiro
— NATASA PRZULJ, Barcelona Supercomputing Center
COMPLEX NETWORKS: THEORY, METHODS, AND APPLICATIONS
Lake Como School of Advanced Studies
Villa del Grumello, Como, Italy, 18-21 May 2020
The International Conference on Complex Networks (CompleNet) brings together researchers and practitioners from diverse disciplines working on areas related to complex networks. In its 11th year, we are delighted to have the next CompleNet in Exeter UK hosted by the University of Exeter.
Over the past two decades we have witnessed an exponential increase in the number of publications and research centers dedicated to this field. From biological systems to computer science, from technical to informational networks, from economic to social systems, complex networks are becoming pervasive for dozens of applications. It is the interdisciplinary nature of complex networks that CompleNet aims to capture and celebrate.
11th International Conference on Complex Networks
31 March-3 April 2020
The research conducted by this year’s Laureates has considerably improved our ability to fight global poverty. In just two decades, their new experiment-based approach has transformed development economics, which is now a flourishing field of research.
Despite recent dramatic improvements, one of humanity’s most urgent issues is the reduction of global poverty, in all its forms. More than 700 million people still subsist on extremely low incomes. Every year, around five million children under the age of five still die of diseases that could often have been prevented or cured with inexpensive treatments. Half of the world’s children still leave school without basic literacy and numeracy skills.
This year’s Laureates have introduced a new approach to obtaining reliable answers about the best ways to fight global poverty. In brief, it involves dividing this issue into smaller, more manageable, questions – for example, the most effective interventions for improving educational outcomes or child health. They have shown that these smaller, more precise, questions are often best answered via carefully designed experiments among the people who are most affected.