Ajit Jaokar is a leading expert working at the intersection of Data Science, IoT, AI, Machine Learning, Big Data, Mobile, and Smart Cities. He teaches IoT and Data Science at Oxford and also is a director of Smart Cities Lab in Madrid. Ajit's work involves applying machine learning techniques to complex problems in the IoT and Telecoms domains. You can follow him on twitter @AjitJaokar and his blogs at Future Text. We are beyond thrilled to announce that Ajit will not only be speaking at our Big Data, Berlin meetup February 17, but he will also be at the head of the second workshop of our'Dataconomy Presents' series.
Intelligent machines won't be ruling the world anytime soon – but what happens when they turn you down for a loan, crash your car or discriminate against you because of your race or gender? On one level, the answer is simple: "It depends," says Bryant Walker Smith, a law professor at the University of South Carolina who specializes in the issues raised by autonomous vehicles. But that opens the door to a far more complex legal debate. "It seems to me that'My Robot Did It' is not an excuse," says Oren Etzioni, CEO of the Seattle-based Allen Institute for Artificial Intelligence, or AI2. The rapidly rising challenges that face America's legal system and policymakers were the focus of today's first-ever White House public workshop on artificial intelligence, presented at the University of Washington School of Law.
Machine intelligence capable of learning complex procedural behavior, inducing (latent) programs, and reasoning with these programs is a key to solving artificial intelligence. The problems of learning procedural behavior and program induction have been studied from different perspectives in many computer science fields such as program synthesis , probabilistic programming , inductive logic programming , reinforcement learning , and recently in deep learning. However, despite the common goal, there seems to be little communication and collaboration between the different fields focused on this problem. Recently, there have been many success stories in the deep learning community related to learning neural networks capable of using trainable memory abstractions. This has led to the development of neural networks with differentiable data structures such as Neural Turing Machines , Memory Networks , Neural Stacks [7, 8], and Hierarchical Attentive Memory , among others. Simultaneously, neural program induction models like Neural Program-Interpreters  and the Neural Programmer  have created much excitement in the field, promising induction of algorithmic behavior, and enabling inclusion of programming languages in the processes of execution and induction, while remaining trainable end-to-end. Trainable program induction models have the potential to make a substantial impact on many problems involving long-term memory, reasoning, and procedural execution, such as question answering, dialog, and robotics. The aim of the NAMPI workshop is to bring together researchers and practitioners from both academia and industry, in the areas of deep learning, program synthesis, probabilistic programming, inductive programming and reinforcement learning, to exchange ideas on the future of program induction with a special focus on neural network models and abstract machines. Through this workshop we look to identify common challenges, exchange ideas and lessons learned from the different fields, as well as establish a (set of) standard evaluation benchmark(s) for approaches that learn with abstraction and/or reason with induced programs.
Achtner, Wolfgang, Aimeur, Esma, Anand, Sarabjot Singh, Appelt, Doug, Ashish, Naveen, Barnes, Tiffany, Beck, Joseph E., Dias, M. Bernardine, Doshi, Prashant, Drummond, Chris, Elazmeh, William, Felner, Ariel, Freitag, Dayne, Geffner, Hector, Geib, Christopher W., Goodwin, Richard, Holte, Robert C., Hutter, Frank, Isaac, Fair, Japkowicz, Nathalie, Kaminka, Gal A., Koenig, Sven, Lagoudakis, Michail G., Leake, David B., Lewis, Lundy, Liu, Hugo, Metzler, Ted, Mihalcea, Rada, Mobasher, Bamshad, Poupart, Pascal, Pynadath, David V., Roth-Berghofer, Thomas, Ruml, Wheeler, Schulz, Stefan, Schwarz, Sven, Seneff, Stephanie, Sheth, Amit, Sun, Ron, Thielscher, Michael, Upal, Afzal, Williams, Jason, Young, Steve, Zelenko, Dmitry
The Workshop program of the Twenty-First Conference on Artificial Intelligence was held July 16-17, 2006 in Boston, Massachusetts. The program was chaired by Joyce Chai and Keith Decker. The titles of the 17 workshops were AIDriven Technologies for Service-Oriented Computing; Auction Mechanisms for Robot Coordination; Cognitive Modeling and Agent-Based Social Simulations, Cognitive Robotics; Computational Aesthetics: Artificial Intelligence Approaches to Beauty and Happiness; Educational Data Mining; Evaluation Methods for Machine Learning; Event Extraction and Synthesis; Heuristic Search, Memory- Based Heuristics, and Their Applications; Human Implications of Human-Robot Interaction; Intelligent Techniques in Web Personalization; Learning for Search; Modeling and Retrieval of Context; Modeling Others from Observations; and Statistical and Empirical Approaches for Spoken Dialogue Systems.
Albrecht, Stefano V. (University of Edinburgh) | Beck, J. Christopher (University of Toronto) | Buckeridge, David L. (McGill University) | Botea, Adi (IBM Research, Dublin) | Caragea, Cornelia (University of North Texas) | Chi, Chi-hung (Commonwealth Scientific and Industrial Research Organisation) | Damoulas, Theodoros (New York University) | Dilkina, Bistra (Georgia Institute of Technology) | Eaton, Eric (University of Pennsylvania) | Fazli, Pooyan (Carnegie Mellon University) | Ganzfried, Sam (Carnegie Mellon University) | Giles, C. Lee (Pennsylvania State University) | Guillet, Sébastian (Université du Québec) | Holte, Robert (University of Alberta) | Hutter, Frank (University of Freiburg) | Koch, Thorsten (TU Berlin) | Leonetti, Matteo (University of Texas at Austin) | Lindauer, Marius (University of Freiburg) | Machado, Marlos C. (University of Alberta) | Malitsky, Yui (IBM Research) | Marcus, Gary (New York University) | Meijer, Sebastiaan (KTH Royal Institute of Technology) | Rossi, Francesca (University of Padova, Italy) | Shaban-Nejad, Arash (University of California, Berkeley) | Thiebaux, Sylvie (Australian National University) | Veloso, Manuela (Carnegie Mellon University) | Walsh, Toby (NICTA) | Wang, Can (Commonwealth Scientific and Industrial Research Organisation) | Zhang, Jie (Nanyang Technological University) | Zheng, Yu (Microsoft Research)
AAAI's 2015 Workshop Program was held Sunday and Monday, January 25–26, 2015 at the Hyatt Regency Austin Hotel in Austion, Texas, USA. The AAAI-15 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included AI and Ethics, AI for Cities, AI for Transportation: Advice, Interactivity and Actor Modeling, Algorithm Configuration, Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Beyond the Turing Test, Computational Sustainability, Computer Poker and Imperfect Information, Incentive and Trust in E-Communities, Multiagent Interaction without Prior Coordination, Planning, Search, and Optimization, Scholarly Big Data: AI Perspectives, Challenges, and Ideas, Trajectory-Based Behaviour Analytics, World Wide Web and Public Health Intelligence, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, and Learning for General Competency in Video Games.