Semantic change

Tackling Climate Change: Unfinished Business from the Last "Winter"

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• published: 11 Dec 2012
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VideoLectures.Net View the talk in context: http://videolectures.net/iswc2012_musen_climate_change/ View the complete 11th International Semantic Web Conference (ISWC), Boston 2012: http://videolectures.net/iswc2012_boston/ Speaker: Mark A. Musen, Stanford Center for Biomedical Informatics Research License: Creative Commons CC BY-NC-ND 3.0 More information at http://videolectures.net/site/about/ More talks at http://videolectures.net/ In the 1990s, as the World Wide Web became not only world wide but also dense and ubiquitous, workers in the artificial intelligence community were drawn to the possibility that the Web could provide the foundation for a new kind of AI. Having survived the AI Winter of the 1980s, the opportunities that they saw in the largest, most interconnected computing platform imaginable were obviously compelling. With the subsequent success of the Semantic Web, however, our community seems to have stopped talking about many of the issues that researchers believe led to the AI Winter in the first place: the cognitive challenges in debugging and maintaining complex systems, the drift in the meanings ascribed to symbols, the situated nature of knowledge, the fundamental difficulty of creating robust models. These challenges are still with us; we cannot wish them away with appeals to the open-world assumption or to the law of large numbers. Embracing these challenges will allow us to expand the scope of our science and our practice, and will help to bring us closer to the ultimate vision of the Semantic Web. 0:00 Tackling Climate Change: Unfinished Business from the Last "Winter" 1:43 My background 2:17 International Semantic Web Conference 2002 2:33 2012: Some numerology 3:59 The lure of the Semantic Web 4:54 Were we serious? 5:45 Gartner Research Hype Cycle 6:30 Back in the early 80s 7:17 IJCAI 85 7:37 Trade show 8:20 AAAI Total Assets (in USD) 8:52 What happened next? 9:21 The AI Winter was Good for AI 10:13 Banff Knowledge Acquisition for Knowledge-Based Systems Workshop 11:08 We learned during the last AI Winter - 1 12:23 Components of a knowledge-based system - 1 13:20 R1: The first commercially successful rule-based system 14:41 The Undoing of R1 15:39 Backward chaining in MYCIN: Determining the value for REGIMEN 16:18 Our approach on the Web is not getting any better ... 16:55 Semantic Web layer cake 17:23 We learned during the last AI Winter - 2 17:49 Belief Network 18:26 And we still have all those rules ... 18:43 We continue to search for ways to make rule bases comprehensible 19:20 Data cloud 20:26 Open Biomedical Ontologies community 21:32 We learned during the last AI Winter - 3 21:57 Components of a knowledge-based system - 2 23:17 How people interact ... 24:22 Communication in print media 25:22 Communication via knowledge media - 1 26:26 MYCIN expert system diagnosed infectious diseases ... 29:27 The Undoing of R1 29:56 Two Rules in MYCIN