If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Lally, Adam (Information Technology and Services) | Bagchi, Sugato (IBM Research) | Barborak, Michael A. (IBM T. J. Watson Research Center) | Buchanan, David W. (IBM T. J. Watson Research Center) | Chu-Carroll, Jennifer (IBM Research) | Ferrucci, David A. (Bridgewater) | Glass, Michael R. (IBM Research) | Kalyanpur, Aditya (IBM T. J. Watson Research Center) | Mueller, Erik T. (Capital One) | Murdock, J. William (IBM T. J. Watson Research Center) | Patwardhan, Siddharth (IBM T. J. Watson Research Center) | Prager, John M. (IBM T. J. Watson Research Center)
Most existing Question Answering (QA) systems adopt a type-and-generate approach to candidate generation that relies on a pre-defined domain ontology. This paper describes a type independent search and candidate generation paradigm for QA that leverages Wikipedia characteristics. This approach is particularly useful for adapting QA systems to domains where reliable answer type identification and type-based answer extraction are not available. We present a three-pronged search approach motivated by relations an answer-justifying title-oriented document may have with the question/answer pair. We further show how Wikipedia metadata such as anchor texts and redirects can be utilized to effectively extract candidate answers from search results without a type ontology. Our experimental results show that our strategies obtained high binary recall in both search and candidate generation on TREC questions, a domain that has mature answer type extraction technology, as well as on Jeopardy! questions, a domain without such technology. Our high-recall search and candidate generation approach has also led to high overall QA performance in Watson, our end-to-end system.
Ferrucci, David (IBM T. J. Watson Research Center) | Brown, Eric (IBM T. J. Watson Research Center) | Chu-Carroll, Jennifer (IBM T. J. Watson Research Center) | Fan, James (IBM T. J. Watson Research Center) | Gondek, David (IBM T. J. Watson Research Center) | Kalyanpur, Aditya A. (IBM T. J. Watson Research Center) | Lally, Adam (IBM T. J. Watson Research Center) | Murdock, J. William (IBM T. J. Watson Research Center) | Nyberg, Eric (Carnegie Mellon University) | Prager, John (IBM T. J. Watson Research Center) | Schlaefer, Nico (Carnegie Mellon University) | Welty, Chris (IBM T. J. Watson Research Center)
IBM Research undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV Quiz show, Jeopardy! The extent of the challenge includes fielding a real-time automatic contestant on the show, not merely a laboratory exercise. The Jeopardy! Challenge helped us address requirements that led to the design of the DeepQA architecture and the implementation of Watson. After 3 years of intense research and development by a core team of about 20 researches, Watson is performing at human expert-levels in terms of precision, confidence and speed at the Jeopardy! Quiz show. Our results strongly suggest that DeepQA is an effective and extensible architecture that may be used as a foundation for combining, deploying, evaluating and advancing a wide range of algorithmic techniques to rapidly advance the field of QA.
The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, held the 1998 Spring Symposium Series on 23 to 25 March at Stanford University. The topics of the eight symposia were (1) Applying Machine Learning to Discourse Processing, (2) Integrating Robotic Research: Taking the Next Leap, (3) Intelligent Environments, (4) Intelligent Text Summarization, (5) Interactive and Mixed-Initiative Decision-Theoretic Systems, (6) Multimodal Reasoning, (7) Prospects for a Common-Sense Theory of Causation, and (8) Satisficing Models.