Back issues are available on-line at www.aimagazine.org The purpose of AI Magazine is to disseminate timely and informative articles that represent the current state of the art in AI and to keep its readers posted on AAAI-related matters. Regular features in AI Magazine include feature articles, workshop, symposium, and conference summaries, book reviews, editorials, news about the Association for the Advancement of Artificial Intelligence, letters to the editor, forum discussions, calendar of events, recruitment and product advertising, and columns on various topics including AI in the news. AI Magazine publishes original articles that are reasonably self-contained and aimed at a broad spectrum of the AI community. The magazine welcomes the contribution of articles on the theory and practice of AI as well as general survey articles, tutorial articles on timely topics, conference or symposia or workshop reports, and reviews of books.

Over the years, I've been asked what I like best about my job. Since I spent the majority of my career in the public sector, one top answer is that I love the challenge of helping organizations with security solutions and enabling new technologies to help the business of government. I also enjoy learning and sharing what works and doesn't work in different contexts. This sharing works out in press interviews or speeches on cyberthreats, I really enjoy moderating panels and leading executive roundtables with public- and private-sector leaders at security and technology events. I often get asked to be a moderator for a few sessions at SecureWorld Expo events, InfraGard Conferences and regional technology forums, such as the upcoming MidWest Technology Leaders event. During these panel sessions, the participants typically talk about a range of (hopefully intriguing) topics that include top cybercrime trends, cyberthreat intelligence, attracting and retaining cybertalent, big industry security breaches, internal security incidents or the always interesting (but overused question) "what's keeping you up at night?" Inevitably, security and technology topics include well known themes that I have written about such as ransomware, IoT botnets, cloud computing, smart cities, smartphone security, government CISO plans, securing the smart grid, end-user training, etc. Hopefully, we get beyond the problems and spend a few minutes on solutions.

I'm often asked what I like best about my job. One of my top answers is public speaking, learning and networking at security and technology events around the world. Besides giving press interviews or speeches on cyberthreats, I really enjoy moderating panels and leading executive roundtables with public- and private-sector leaders at security and technology events. I often get asked to be a moderator for a few sessions at SecureWorld Expo events, InfraGard Conferences and regional technology forums, such as the upcoming MidWest Technology Leaders event. During these panel sessions, the participants typically talk about a range of (hopefully intriguing) topics that include top cybercrime trends, cyberthreat intelligence, attracting and retaining cybertalent, big industry security breaches, internal security incidents or the always interesting (but overused question) "what's keeping you up at night?" Inevitably, security and technology topics include well known themes that I have written about such as ransomware, IoT botnets, cloud computing, smart cities, smartphone security, government CISO plans, securing the smart grid, end-user training, etc. Hopefully, we get beyond the problems and spend a few minutes on solutions.

This close relationship between AI Magazine and IAAI is no coincidence. Much of our society knows about and interacts with AI mainly through its applications. Applications are the source of many problems that AI research seeks to address, and we often measure our progress through successful applications. Applications are how AI makes an impact on the world. Hence, as "the journal of record for the AI community," it is only logical for AI Magazine to cover AI applications extensively.

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.