Many digitally mediated peer-production systems allow participants to define their own activities. The challenge in such systems, however, lies in retaining members beyond the first few interactions. To address this problem we must understand who these users are and why they begin to contribute. Importantly, there is scant empirical evidence on how motivations are associated with different trajectories of participation for new participants.
The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Between June 9 and June 15, 2019, at the Long Beach Convention & Entertainment Center in Long Beach, California, ICML will host over 8,000 participants.
Artificial Intelligence (AI) will be the dominant technology of the future and will impact every corner of society. In particular, AI / ML (machine learning) will shape how communication networks, a lifeline of our society, will be run. Many companies in the ICT sector are exploring how to make best use of AI/ML. ITU has been at the forefront of this endeavour exploring how to best apply AI/ML in future networks including 5G networks. The time is therefore right to bring together the technical community and stakeholders to brainstorm, innovate and solve relevant problems in 5G using AI/ML.
One of the most critical capabilities to responding to change and disruption in the marketplace is the ability to sense the change in a manner and, most importantly, timeframe that allows a response to be identified and executed. The frequency and discontinuous nature of change rocking the market requires a robust process that takes into account as many factors as possible to identify the change. This process then must describe factors and relationships to allow them to be analyzed to develop the response. The difficulty lies in the volume of data, both new and old, that must be taken into account to first identify the change and second to guide in determining a response. This is where data analytics practices utilizing artificial intelligence comes into the equation to support the business.
Autonomous agents in real-time strategy (RTS) games lack an integrated framework for reasoning about choke points and regions of open space in their environment. This paper presents an algorithm which partitions the environment into a set of polygonal regions and computes optimal choke points between adjacent regions. This representation can be used as a component for AI agents to reason about terrain, plan multiple routes of attack, and make other tactical decisions. The algorithm is tested on a set of popular maps commonly used in international Starcraft competitions and evaluated against answers made by human participants. The algorithm identified 97% of the choke points that the participants found and also identified a number of bottlenecks that human participants did not recognize as choke points.