The 2018 AAAI Conference on Artificial Intelligence, the 2018 Innovative Applications of Artificial Intelligence, and the 2018 Symposium on Educational Advances in Artificial Intelligence were held February 2–7, 2018 at the Hilton New Orleans Riverside, New Orleans, Louisiana, USA.  This report, based on the prefaces contained in the AAAI-18 proceedings and program, summarizes the events of the conference.

This year's conference broke a number of traditions set in America, HCOMP 2018 returned to Europe, where the very first HCOMP workshop had taken place in 2009. Besmira Nushi, Ece Kamar, and Eric interdisciplinary communities, we fostered new connections Horvitz were also singled out with an honorable among collective intelligence, crowdsourcing, mention for their paper "Towards Accountable AI: and human computation scholars and practitioners, Hybrid Human-Machine Analyses for Characterizing across diverse fields including humancomputer System Failure." Finally, Vikram Mohanty, David interaction (HCI), artificial intelligence, Thames, and Kurt Luther's presentation, "Are 1,000 economics, business, and design. Features Worth A Picture? Combining Crowdsourcing HCOMP was started by researchers from diverse and Face Recognition to Identify Civil War Soldiers," fields who wanted a high-quality scholarly venue for was given the Best Poster / Demo Presentation the review and presentation of the highest quality award. For this, we invited previous AAAI HCOMP conferences (and four submissions to a Works-in-Progress (WIP) and HCOMP workshops before that) to promote the most Demonstrations track, co-organized by Alessandro rigorous and exciting scholarship in this fast-emerging, Bozzon (Delft University of Technology) and Matteo multidisciplinary area.

ICWSM-18 and HCOMP-if any spots remain open.The organizers Munich, Germany. For information 18 were both successful, with increases of the AAAI/ACM SIGAI Job Fair are about paper submissions, as well as the in attendance. Smith noted that John Dickerson (University of Maryland, planned program, please refer to although we budgeted for a deficit of USA) and Chris Amato (Northeastern icwsm.org/2019.

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s Sixth AAAI Conference on Human Computation and Crowdsourcing was held on the campus of the University of Zurich in Zurich, Switzerland on 5 July 2018. There were three full-day workshops in the program: CrowdBias: Disentangling the Relation between Crowdsourcing and Bias Management; Subjectivity, Ambiguity, and Disagreement in Crowdsourcing; Work in the Age of Intelligent Machines; a three-quarter day workshop, Advancing Human Computation with Complexity Science; and Project Networking; and a quarter day Project Networking workshop. This report contains summaries of three of the events.  

The Workshop Program of the Association for the Advancement of Artificial Intelligence’s 12th International Conference on Web and Social Media (AAAI-18) was held at Stanford University, Stanford, California USA, on Monday, June 25, 2018. There were fourteen workshops in the program: Algorithmic Personalization and News: Risks and Opportunities; Beyond Online Data: Tackling Challenging Social Science Questions; Bridging the Gaps: Social Media, Use and Well-Being; Chatbot; Data-Driven Personas and Human-Driven Analytics: Automating Customer Insights in the Era of Social Media;  Designed Data for Bridging the Lab and the Field: Tools, Methods, and Challenges in Social Media Experiments; Emoji Understanding and Applications in Social Media; Event Analytics Using Social Media Data; Exploring Ethical Trade-Offs in Social Media Research; Making Sense of Online Data for Population Research; News and Public Opinion; Social Media and Health: A Focus on Methods for Linking Online and Offline Data; Social Web for Environmental and Ecological Monitoring and The ICWSM Science Slam. Workshops were held on the first day of the conference. Workshop participants met and discussed issues with a selected focus — providing an informal setting for active exchange among researchers, developers, and users on topics of current interest. Organizers from nine of the  workshops submitted reports, which are reproduced in this report. Brief summaries of the other five workshops have been reproduced from their website descriptions.

This paper describes the discipline of distance metric learning, a branch of machine learning that aims to learn distances from the data. Distance metric learning can be useful to improve similarity learning algorithms, and also has applications in dimensionality reduction. We describe the distance metric learning problem and analyze its main mathematical foundations. We discuss some of the most popular distance metric learning techniques used in classification, showing their goals and the required information to understand and use them. Furthermore, we present a Python package that collects a set of 17 distance metric learning techniques explained in this paper, with some experiments to evaluate the performance of the different algorithms. Finally, we discuss several possibilities of future work in this topic.

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We develop algorithms for contextual bandits with linear payoffs that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias. We provide the first regret bound analyses for linear contextual bandits with balancing and show that our algorithms match the state of the art theoretical guarantees. We demonstrate the strong practical advantage of balanced contextual bandits on a large number of supervised learning datasets and on a synthetic example that simulates model misspecification and prejudice in the initial training data.

The Azure Machine Learning service speeds up the process of identifying useful algorithms and machine learning pipelines, which automates model selection and tuning. This can cut development time from days to hours, said Bharat Sandhu, director of product marketing, big data and analytics at Microsoft. It also provides DevOps capabilities, via integrated CI/CD tooling, to enable experiment tracking and manage machine learning models deployed in the cloud and on the edge, said Venky Veeraraghavan, group program manager for Microsoft Azure, in a blog post. The Azure Machine Learning service supports popular open source frameworks, including PyTorch, TensorFlow and scikit-learn, so developers and data scientists can use familiar tools. Developers can use Visual Studio Code, Visual Studio, PyCharm, Azure Databricks notebooks or Jupyter notebooks to build apps that use the service.

When you think of "data science" and "machine learning", do the two terms blur together, like Currier and Ives or Sturm and Drang? If so, you've come to the right place. This article will clarify some important and often-overlooked distinctions between the two to help you better focus your learning and hiring. Machine learning has seen much hype from journalists who are not always careful with their terminology. In popular discourse, it has taken on a wide swath of meanings and implications well beyond its scope to practitioners.

Often survey analysis collects data to try to identify response patterns leading to groupings of respondents with different characteristics as revealed by answers provided to survey questions. Without additional background information on respondents, it is often very difficult (and many times impossible) to verify the accuracy of groupings resulting from the analysis. This paper examines one such situation in which high school students in low-income neighbourhood schools in Bolivia responded to a standard periodic institutional survey and responses were analysed to better understand respondents' socioeconomic contexts. In this case study, the question to be answered was "can we identify the most impoverished students based on a 22 questions standard survey alone?". With no known dependent variable and an inability to objectively capture the socioeconomic condition of the students being surveyed, the task of coming to a conclusive answer becomes unfeasible as there is no way to validate at least some portion of the students identified as most impoverished.