Czerwinski, Mary, see Nguyen, Trung 1992 AAAI Robot Exhibition and Competition see Dean, Thomas 1992 Workshop on Design Rationale Capture and Use, The, see Lee, Jintae Advances in Real-Time Expert System Technologies, see Barachini, Franz AI and Creativity: 1993 Spring Symposium Report, see Kim, Steven AI and N&Hard Problems: 1993 Spring Symposium Report, see Crawford, James AI Research and Application Development at Boeing's Huntsville Laboratories see Tanner, Steve Anick, Peter; and Simoudis, Evange-10s. Agent Architectures, see Hanks, Steve Berman, Jay I. see Wright, Jon R. Bonasso, R. Peter see Dean, Thomas Bookman, Lawrence, see Sun, Ron Brown, Karen E. see Wright, Jon R. Building Lexicons Two Winner see Congdon, Clare Carnes, Ray, see Tanner, Steve Case-Based Reasoning and Information Retrieval: 1993 Spring Symposium Report, see Anick, Peter Chandrasekaran, B.; Narayanan, N. Hari; and Iwasaki, Yumi. Charniak, Eugene, see Goldman, Robert l? Chien, Steve, see Gat, Erann. Cohen, Paul R., see Hanks, Steve Compaq Quicksource: Providing the Consumer with the Power Drummond, Mark, see Lansky, Amy Engineering Design through Constraint-Based Reasoning, see Murtagh, Niall Etzioni, Oren. Goal-Driven Learning: Fundamental Issues: A Symposium Report, see Leake, David Goldman, Robert l?; Charniak, Eugene; Gale, William; and Norvig, Peter.

Bylaws of the American Association for Artificial Intelligence, 13(1): Spring 1992, A2-A9 Adler, Mark see Rewari, Anil. Anick, Peter see Rewari, Anil. Architecture for Real-Time Distributed Scheduling, An, 13(3): Fall 1992, 46-56. Billmers, Meyer see Rewari, Anil. Bylaws of the American Association for Artificial Intelligence, 13(1): Spring 1992, A2-A9 Cambridge Center for Behavioral Studies see Weintraub, Joseph.

See Artificial Intelligence zn Canada: A Review. See Steamer: an Interactive Inspectable Samulation-Based Trazning System Wilensky, Robert.

See Toward the Principled Enganeering of Knowledge. Expert Systems: Where are we? And where do we go from here? Feigenbaum, Edward A, See Signal-to-symbol transformation: HASP/SIAP case study. Research in Progress Vol IV, No. 4, p. 58, Winter, 1983 THE AI MAGAZINE Spring 1984 83 K Minsky, Marvin Why People Think Computers Can't.

This editorial introduction provides an overview of artificial intelligence for computational sustainability, and introduces the next two special issue articles that will appear in AI Magazine. The emerging interdisciplinary field of computational sustainability (Gomes 2009) draws techniques from computer science, information science, mathematics, statistics, operations research, and related disciplines to help balance environmental and socioeconomic needs for sustainable development. Artificial intelligence (AI) techniques play a key role in computational sustainability research, enabling the solution of sustainability problems that involve modeling or decision making in dynamic and uncertain environments. Since 2011, the main AAAI conference has included a special track on computational sustainability, encouraging AI research in this area and broader participation of sustainability researchers in the AAAI community. Sustainable solutions must balance between environmental, societal, and economic demands (United Nations General Assembly 2005).

There is a critically important dialogue going on across the extended global automotive industry about the future evolution of transportation and mobility. This debate is driven by the convergence of a series of industry-changing forces and mega-trends (see figure 1). Innovative technologies are changing how companies develop and build vehicles. Electric and fuel-cell powertrains tend to offer greater propulsion for lower energy investment at lower emission levels.1 New, lightweight materials enable automakers to reduce vehicle weight without sacrificing passenger safety.2 Further breakthroughs are advancing the introduction of autonomous vehicles; increasingly, daily news reports suggest that driverless cars will soon become a commercial reality.3 We have already seen rapid advances in the "connected car--?--innovations that integrate communications technologies and the Internet of Things to provide valuable services to drivers.4

What should higher education leaders be doing now to prepare for a future where Artificial Intelligence (AI) plays a growing role? This webcast provides a survey of the current state of AI in education, its existing and potential applications, and questions raised for practice, policy, and advocacy of AI in teaching and learning.

GANs are one of the very few machine learning techniques which has given good performance for generative tasks, or more broadly unsupervised learning. In particular, they have given splendid performance for a variety of image generation related tasks. Yann LeCun, one of the forefathers of deep learning, has called them "the best idea in machine learning in the last 10 years". Most importantly, the core conceptual ideas associated with a GAN are quite simple to understand (and in-fact, you should have a good idea about them by the time you finish reading this article). In this article, we'll explain GANs by applying them to the task of generating images.

At a high level, Artificial Intelligence (AI) is a branch of computer science that makes machines imitate intelligent human behavior, simulating (and often exceeding) human performance. AI has finally emerged as the future, after unfulfilled hype that goes back to the 1950s, due to developments such as the availability of an immense amount of data, the open-sourcing of ML algorithm development, and advances in high-density parallel processing infrastructure. In fact, IBM now believes the technology solutions market for AI amounts to a staggering $2 trillion over the next decade. Data is the new oil, and 90% of data in the world right now has been created in the last 2 years alone. The power of data has actually lagged the technical capability to monetize it efficiently and effectively, in a world where the use of data is moving from a competitive advantage to a requirement to compete.

With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting that deep learning models have a huge potential for solving the problem. However, the convolutional recurrence structure in ConvLSTM-based models is location-invariant while natural motion and transformation (e.g., rotation) are location-variant in general. Furthermore, since deep-learning-based precipitation nowcasting is a newly emerging area, clear evaluation protocols have not yet been established. To address these problems, we propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections. Besides, we provide a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory, a new training loss, and a comprehensive evaluation protocol to facilitate future research and gauge the state of the art.