AI Magazine

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ISSN / EISSN : 0738-4602 / 0738-4602
Total articles ≅ 689
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Stefano Bistarelli, Lars Kotthoff, Francesco Santini, Carlo Taticchi
AI Magazine, Volume 42, pp 70-73;

The Third International Competition on Computational Models of Argumentation (ICCMA’19) focused on reasoning tasks in abstract argumentation frameworks. Submitted solvers were tested on a selected collection of benchmark instances, including artificially generated argumentation frameworks and some frameworks formalizing real-world problems. This competition introduced two main novelties over the two previous editions: the first one is the use of the Docker platform for packaging the participating solvers into virtual “light” containers; the second novelty consists of a new track for dynamic frameworks.
Michael Wollowski
AI Magazine, Volume 42, pp 77-78;

Three panelists, Ashok Goel, Ansaf Salleb-Aouissi and Mehran Sahami explain some of the tools and techniques they used to keep their students engaged during virtual instruction. The techniques include the desire to take one’s passion for the learning materials to the virtual classroom, to ensure teacher presence, provide for cognitive engagement with the subject and facilitate social interactions. Finally, we learn about tools used to manage a large online course so as to move the many active learning exercises to the virtual classroom.
Thorsten Joachims, Ben London, Yi Su, Adith Swaminathan, Lequn Wang
AI Magazine, Volume 42, pp 19-30;

In recent years, a new line of research has taken an interventional view of recommender systems, where recommendations are viewed as actions that the system takes to have a desired effect. This interventional view has led to the development of counterfactual inference techniques for evaluating and optimizing recommendation policies. This article explains how these techniques enable unbiased offline evaluation and learning despite biased data, and how they can inform considerations of fairness and equity in recommender systems.
Kristen Venable, Odd Erik Gundersen
AI Magazine, Volume 42, pp 79-80;

Artificial Intelligence has witnessed an exponential growth in the last decade and, thanks to its many successful and pervasive applications, it has now become a research field with profound societal impacts. The interest in AI has reached an all-time high from all sectors of our modern society, including industry, health, education and government. AI Magazine, founded in 1980, has documented the rise of AI from an elite and almost esoteric field to its current status of key player in modern society. Under the leadership of exceptional scientists with a global vision of the field, David Leake first and then Ashok Goel, it has provided a venue for vibrant discussion on technological transformations, research trends and fundamental breakthroughs.
Jon Gulla, Rolf Svendsen, Lemei Zhang, Agnes Stenbom, Jørgen Frøland
AI Magazine, Volume 42, pp 55-69;

The adoption of recommender systems in online news personalization has made it possible to tailor the news stream to the individual interests of each reader. Previous research on commercial recommender systems has emphasized their use in large-scale media houses and technology companies, and real-world experiments indicate substantial improvements of click rates and user satisfaction. It is less understood how smaller media houses are coping with this new technology, how the technology affects their business models, their editorial processes, and their news production in general. Here we report on the experiences from numerous Scandinavian media houses that have experimented with various recommender strategies and streamlined their news production to provide personalized news experiences. In addition to influencing the content and style of news stories and the working environment of journalists, the news recommender systems have been part of a profound digital transformation of the whole media industry. Interestingly, many media houses have found it undesirable to automate the entire recommendation process and look for approaches that combine automatic recommendations with editorial choices.
Yongqing Zheng, Han Yu, Yuliang Shi, Kun Zhang, Shuai Zhen, Lizhen Cui, Cyril Leung, Chunyan Miao
AI Magazine, Volume 42, pp 28-37;

As demand for electricity grows in China, the existing power grid is coming under increasing pressure. Expansion of power generation and delivery capacities across the country requires years of planning and construction. In the meantime, to ensure safe operation of the power grid, it is important to coordinate and optimize the demand side usage. In this paper, we report on our experience deploying an artificial intelligence (AI)–empowered demand-side management platform – the Power Intelligent Decision Support (PIDS) platform – in Shandong Province, China. It consists of three main components: 1) short-term power consumption gap prediction, 2) fine-grained Demand Response (DR) with optimal power adjustment planning, and 3) Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. PIDS has been deployed since August 2018. It is helping over 400 companies optimize their power usage through DR, while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS due to forced shutdown has been reduced from 16% to 0.56%.
Ruchir Puri, Neil Yorke-Smith
AI Magazine, Volume 42, pp 3-4;

This special issue presents nine articles that are comprehensive case studies of deployed applications, carefully selected out of the breadth of papers from the 32nd Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-20). IAAI is the premier conference for applied AI research, providing a forum for disseminating work on novel uses of AI technology — ranging from the insightful application of existing techniques to previously unexplored domains through to new or enhanced AI methods that show significant impact for applications considered previously.
Ashok Goel
AI Magazine, Volume 42, pp 87-88;

I have been affiliated with AI Magazine for a long time. In 2010, David Leake, the then Editor-in-Chief of AIM, invited me to join the magazine’s editorial board. About five years later, David and Mike Hamilton, AIM’s Managing Editor, invited me to become the magazine’s next Editor-in-Chief. After serving as an Associate Editor for about a year and receiving approval from AAAI’s Executive Council, I became the Editor-in-Chief of AI Magazine on August 1, 2016. About that time, I wrote an editorial titled RethinkingAI Magazine (Winter 2016, pp. 3-4). Now, almost five years later, as I step down as the AIM’s Editor-in-Chief, I want to revisit the vision articulated in that editorial.
Nisha Dalal, Martin Mølna, Mette Herrem, Magne Røen, Odd Erik Gundersen
AI Magazine, Volume 42, pp 38-49;

Utility companies in the Nordics have to nominate how much electricity is expected to be lost in their power grid the next day. We present a commercially deployed machine learning system that automates this day-ahead nomination of the expected grid loss. It meets several practical constraints and issues related to, among other things, delayed, missing and incorrect data and a small data set. The system incorporates a total of 24 different models that performs forecasts for three sub-grids. Each day one model is selected for making the hourly day-ahead forecasts for each sub-grid. The deployed system reduced the mean average percentage error (MAPE) with 40% from 12.17 to 7.26 per hour from mid-July to mid-October, 2019. It is robust, flexible and reduces manual work. Recently, the system was deployed to forecast and nominate grid losses for two new grids belonging to a new customer. As the presented system is modular and adaptive, the integration was quick and needed minimal work. We have shared the grid loss data-set on Kaggle.
Anxiang Zeng, Han Yu, Qing Da, Yusen Zhan, Yang Yu, Jingren Zhou, Chunyan Miao
AI Magazine, Volume 42, pp 50-58;

Learning to rank (LTR) is an important artificial intelligence (AI) approach supporting the operation of many search engines. In large-scale search systems, the ranking results are continually improved with the introduction of more factors to be considered by LTR. However, the more factors being considered, the more computation resources required, which in turn, results in increased system response latency. Therefore, removing redundant factors can significantly improve search engine efficiency. In this paper, we report on our experience incorporating our Contextual Factor Selection (CFS) deep reinforcement learning approach into the Taobao e-commerce platform to optimize the selection of factors based on the context of each search query to simultaneously maintaining search result quality while significantly reducing latency. Online deployment on demonstrated that CFS is able to reduce average search latency under everyday use scenarios by more than 40% compared to the previous approach with comparable search result quality. Under peak usage during the Single’s Day Shopping Festival (November 11th) in 2017, CFS reduced the average search latency by 20% compared to the previous approach.
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