plus experimental evaluation on live customer traffic from AT&T's national deployment for customer ... Mathematics from the CUNY Graduate Center in 1980.
IUI 03 Invited Speakers
2003 International Conference on Intelligent User Interfaces Miami, Florida, USA / January 1212-15, 2003
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Semantic Information Processing of Spoken Language How May I Help You? Allen Gorin AT&T Laboratories - Speech Research Florham Park, New Jersey The next generation of voice-based user interface technology will enable easy-to-use automation of new and existing communication services, achieving a more natural human-machine interaction. By natural, we mean that the machine understands what people actually say, in contrast to what a system designer expects them to say. This approach is in contrast with menu-driven or strongly-prompted systems, where many users are unable or unwilling to navigate such highly structured interactions. AT&T’s ‘How May I Help You?’ (HMIHY)(sm) technology shifts the burden from human to machine wherein the system adapts to peoples’ language, as contrasted with forcing users to learn the machin’s jargon. We have developed algorithms which learn to extract meaning from fluent speech via automatic acquisition and exploitation of salient words, phrases and grammar fragments from a corpus. In this talk I will describe the speech, language and dialog technology underlying HMIHY, plus experimental evaluation on live customer traffic from AT&T's national deployment for customer care. Allen Gorin is the Head of the Speech Interface Research Department at AT&T Laboratories, with long-term research interests focusing on machine learning methods for spoken language understanding. In recent years, he has led a research team in applying speech, language and dialog technology to AT&T’s “How May I Help You?” (HMIHY) (sm) service, which has been deployed nationally for long distance customer care. He was awarded the 2002 AT&T Science and Technology Medal for his research contributions to spoken language understanding for HMIHY. He received the B.S. and M.A. degrees in Mathematics from SUNY at Stony Brook, and the Ph.D. in Mathematics from the CUNY Graduate Center in 1980. From 1980-83 he worked at Lockheed investigating algorithms for target recognition from time-varying imagery. In 1983 he joined AT&T Bell Labs where he was the Principal Investigator for AT&T's ASPEN project within the DARPA Strategic Computing Program, investigating parallel architectures and algorithms for pattern recognition. In 1987, he was appointed a Distinguished Member of the Technical Staff. In 1988, he joined the Speech Research Department at Bell Labs. He has served as a guest editor for the IEEE Transactions on Speech and Audio, and was a visiting researcher at the ATR Interpreting Telecommunications Research Laboratory in Japan. He is a member of the Acoustical Society of America, Association for Computational Linguistics and an IEEE Senior Member. Home page for Allen Gorin: http://www.research.att.com/info/algor.
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What Users Want Daniel Weld WRF / TJ Cable Professor Department Computer Science & Engineering University of Washington, Seattle, WA Today’s computer interfaces are one size fits all. Users with little programming experience have only limited opportunities to customize their interface to their task and work habits (e.g., adding buttons to a toolbar). Furthermore, the overhead induced by generic interfaces will be proportionately greater on small form-factor PDAs, embedded applications and wearable devices. Searching for a solution, researchers argue that productivity can be greatly enhanced if interfaces anticipated their users, adapted to their preferences, and reacted to high-level customization requests. But realizing these benefits is tricky, because there is an inherent tension between the dynamism implied by automatic interface adaptation and the stability required in order for the user to maintain an accurate mental model, predict the computer’s behavior, and feel in control. In this talk, I discuss several principles governing effective adaptation, describe algorithms for data mining user action traces, and suggest mechanisms for dynamically transforming interfaces. After formative education at Phillips Academy, Daniel Weld received bachelor's degrees in both Computer Science and Biochemistry at Yale University in 1982. He landed a Ph.D. from the MIT Artificial Intelligence Lab in 1988, received a Presidential Young Investigator's award in 1989, an Office of Naval Research Young Investigator's award in 1990, and was named AAAI Fellow in 1999. Weld is on the editorial board of Artificial Intelligence, was a founding editor and member of the advisory board for the Journal of AI Research, was guest editor for Computational Intelligence and Artificial Intelligence, edited the AAAI report on the Role of Intelligent Systems in the National Information Infrastructure, and was Program Chair for AAAI-96. Weld has published two books and scads of technical papers. Weld is an active entrepreneur with several patents and technology licenses. In May 1996, he cofounded Netbot Incorporated, creator of Jango Shopping Search and now part of Excite@Home. In October 1998, Weld co-founded AdRelevance, a revolutionary monitoring service for internet advertising which is now a Division of Jupiter Media Metrix. In June 1999, Weld co-founded Nimble Technology which is developing advanced data integration products. In June 2000, Weld co-founded Asta Networks, which develops products enabling Internet Service Providers to increase their quality of service. In January 2001, Weld joined the Madrona Venture Group as a Venture Partner and member of the Technical Advisory Board.
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