Many Artificial Intelligence tasks need large amounts of commonsense knowledge. Because obtaining this knowledge through machine learning would require a huge amount of data, a better alternative is to elicit it from people through human computation. We consider the sentiment classification task, where knowledge about the contexts that impact word polarities is crucial, but hard to acquire from data. We describe a novel task design that allows us to crowdsource this knowledge through Amazon Mechanical Turk with high quality. We show that the commonsense knowledge acquired in this way dramatically improves the performance of established sentiment classification methods.

The aim of the Disco: Human and Machine Learning in Games workshop was to extend upon the focus of two past workshops and explore the intersection of entertainment, learning and human computation. The goal of the workshop was to examine both human learning and machine learning in games and human computation. Human computation methods let machines learn from humans where games can provide humans the opportunity to learn. The workshop was thus devoted to I learn, in Latin disco, for machines and humans alike. The First AAAI Conference on Human Computation and Crowdsourcing Was Held in the Southern California Desert Community of Palm Springs.

The preliminary Call for Papers, including an The IAAI-14 technical program will feature overview of the special tracks and a preview of talks on seven award-winning deployed AI planned innovations is available at www.aaai.

The in AI program will be held in 36th Annual Conference of the Cognitive Conference Fete will be held at the conjunction with AAAI-14. The main Science Society, July 23-26, 2014; beautiful Le Theatre and Cabaret du goal of this program is to increase participation the Conference on Uncertainty in Artificial Capitole de Québec and will be open to of women and members of Intelligence, July 23-27, 2014; all attendees! Other special events are underrepresented groups in Artificial the Computational Neuroscience planned, including an update to the Intelligence by providing community Meeting, July 26-31, 2014; and Artificial 2013 Puzzle Hunt, so stay tuned for building and networking sessions as General Intelligence 2014, August more!

This article focuses on contributions that AI can make to address long-term educational goals. It describes five challenges that would support: (1) mentors for every learner; (2) learning twenty-first century skills; (3) interaction data to support learning; (4) universal access to global classrooms; and (5) lifelong and life-wide learning. A vision and brief research agenda are described for each challenge along with goals that lead to access to global educational resources and the reuse and sharing of digital educational resources. Instructional systems with AI technology are described that currently support richer experiences for learners and supply researchers with new opportunities to analyze vast data sets of instructional behavior from big databases, containing elements of learning, affect, motivation, and social interaction. Personalized learning is described using computational tools that enhance student and group experience, reflection, and analysis, and supply data for development of novel theory development.

Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with non-stationarity, low-complexity solutions, non-Gaussian noise models and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.

The deadline for the discounted AAAI rate is June AAAI-13 invites short papers on latebreaking for volunteer applications is May 1, 19, 2013. A special student rate has al-developments in the field of 2013.

We will like to call cities that enable such capabilities as, "semantic cities." In a semantic city, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Enabling city information as a utility, through a robust (expressive, dynamic, scalable) and (critically) a sustainable technology and socially synergistic ecosystem could drive significant benefits and opportunities. Data (and then information and knowledge) from people, systems, and things is the single most scalable resource available to city stakeholders to reach the objective of semantic cities. Two major trends are supporting semantic cities -- open data and semantic web.

Online labor marketplaces offer the potential to automate a variety of tasks too difficult for computers, but present requesters with significant difficulties in obtaining accurate results. We share experiences from building MobileWorks, a crowd platform that departs from the marketplace model to provide robust, high-quality results. Three architectural contributions yield measurably improved accuracy on input tasks.  A dynamic work routing system identifies expertise in the crowd and ensures that all work posted into the system is completed with bounded completion times and at fair worker prices. A peer management system ensures that incorrect answers are prevented by experienced members of the crowd. Last, social interaction techniques give the best workers the ability and incentives to manage, teach & supervise other members of the crowd, as well as to clarify tasks. This process filters worker error and allows the crowd to collectively learn how to solve unfamiliar tasks. (extended abstract)

This paper introduces SIFU, a system that recruits in real time native speakers as online volunteer tutors to help answer questions from Chinese language learners in reading news articles. SIFU integrates the strengths of two effective online language learning methods: reading online news and communicating with online native speakers. SIFU recruits volunteers from an online social network rather than recruits workers from Amazon Mechanical Turk.Initial experiments showed that the proposed approach is able to effectively recruit online volunteer tutors, adequately answer the learners' questions, and efficiently obtain an answer for the learner. Our field deployment illustrates that SIFU is very useful in assisting Chinese learners in reading Chinese news articles and online volunteer tutors are willing to help Chinese learners when they are on social network service.