This position paper describes an AI application for game spectators, e.g., those watching Twitch. The aim of this application is to automatically generate game plays by nonplayer characters -- not human players -- and recommend those plays to spectators. The generation part leads to development of a new field: procedural play generation (PPG). The recommendation part requires new techniques in recommender systems (RS) for incorporation of play content into RS to obtain promising recommendation results. Rather than proposing solutions to all relevant topics, this paper aims at drawing attention to this new field and serves as a seed for discussion and collaboration among the readers, workshop participants, and authors.

A major challenge for using data to make predictions is distinguishing what is meaningful from noise. The image represents one approach that visually indicates the complexity of the problem by highlighting some links in a network and deleting other possible links, with the hole indicating the more meaningful information. We have tried to predict the future since ancient times when shamans looked for patterns in smoking entrails. As this special section explores, prediction is now a developing science. Essays probe such questions as how to allocate limited resources, whether a country will descend into conflict, and who will likely win an election or publish a high-impact paper, as well as looking at how standards should develop in this emerging field.

According to a recent press release out of the company, "Deloitte predicts that over 300 million smartphones, or more than one-fifth of units sold in 2017, will have machine learning capabilities within the device in the next 12 months. The 16th edition of the'Technology, Media & Telecommunications (TMT) Predictions' showcases how mobile devices will be able to perform machine learning tasks even without connectivity, which will significantly alter how humans interact with technology across every industry, market and society. However, over time machine learning on-the-go will not just be limited to smartphones. These capabilities are likely to be found in tens of millions (or more) of drones, tablets, cars, virtual or augmented reality devices, medical tools, Internet of Things (IoT) devices and unforeseen new technologies."

MIT researchers have developed an algorithm that they say will enable robots to learn and adapt to humans so they can soon work side-by-side on factory floors. Traditionally, robots working in factories are large, imposing and sectioned off in metal cages as they move heavy loads and perform menial, repetitive tasks. However, Julie Shah, the Boeing Career Development Assistant Professor of Aeronautics and Astronautics at MIT, said robots can be more than they've been in a manufacturing setting. It's time for robots to begin working more closely with humans, making workers jobs' safer and easier. Shah, in a statement, said this is especially true in the airplane manufacturing industry.

Collective Intelligence: It's All in the Numbers (Abstract)