by Sander Bohte (CWI) and Hung Son Nguyen (University of Warsaw)
While the discipline of machine learning is often conflated with the general field of AI, machine learning specifically is concerned with the question of how to program computers to automatically recognise complex patterns and make intelligent decisions based on data. This includes such diverse approaches as probability theory, logic, combinatorial optimisation, search, statistics, reinforcement learning and control theory. In this day and age with an abundance of sensors and computers, applications are ubiquitous, ranging from vision to language processing, forecasting, pattern recognition, games, data mining, expert systems and robotics.
by Laurent Romary (Inria)
There is currently a tug-of-war going on within the arena of scientific communication: scientists are exploring new, more efficient and affordable ways to disseminate research results, but at the same time, a web of private publishing companies (and even learned societies) are endeavouring to preserve their financial turnover on the basis of models from a previous era. This tension is echoed in the recent news relating to scholarly communication within Europe as a whole, and within individual countries:
by Jos Baeten (CWI) and Claude Kirchner (Inria)
At its October 2014 meeting, the EEIG ERCIM board installed a task group Boost Open Access Mastering (BOM), chaired by us, with the goal of facilitating the sharing of information and the strategies of ERCIM participants in regard to open access. The ensuing report [L1], a plea for author control, which was adopted by the board in October 2015, recommends an open-access strategy and identified tools shared or to be shared by several ERCIM members.
by Peter Murray-Rust (University of Cambridge)
Scholarly publications, especially science and medicine, have huge amounts of untapped knowledge, but it’s a technical challenge to extract it and there’s a political fight in Europe as to whether we can legally do it.