AAAI Publications, Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence

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Biplav Srivastava, Aurelie Lozano, Janusz Marecki, Irina Rish, Ruslan Salakhudtinov, Gerald Tesauro, Manuela Veloso

Last modified: 2014-06-18


This workshop seeks to augment human decision making by exploiting synergies across two areas of AI research where exciting research progress has been made in recent years, but which so far have not had an explicit common venue. The first area has to do with powerful new learning techniques that may have the potential to automatically learn complex tasks by directly training on massive amounts of raw data, much of which may be unlabeled, unstructured, and multi-modal in form (natural language text/speech, audio, video, and others). These techniques include deep learning, manifold learning, sparsity-based techniques, and transfer/cross-modal learning and inference methods. Researchers employing such techniques have recently achieved quantum performance leaps in speech and image recognition tasks, and have also demonstrated the ability to learn complex feature representations entirely from unlabeled data. The second area has to do with enabling computers to understand and work with naturalistic input from humans, in the form of natural language speech or text, visual input such as gestures or facial expressions, and haptic (touch-based) inputs. The most exciting demonstrations of these capabilities in the last few years include Question-Answering systems such as Watson and Wolfram Alpha, and commercially deployed personal assistant technology such as Siri, Google Now, Dragon Mobile Assistant, Nina, and TellMe. Synergistic advances in these two trends could vastly improve human decision making in many scenarios, including information overload (such as driving), cognition impairment (for example Alzheimer's) or collective (multi-objective) decision-making (such as conference program scheduling, disaster response). Cognitive computing is an emerging research topic inspired by

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