Peter Norvig
Peter Norvig is a Director of Research at Google Inc; previously he directed Google's core search algorithms group. He is co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field, and co-teacher of an Artificial Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.
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Google Publications
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Machine learning now powers a huge range of applications, from speech recognition systems to search engines, self-driving cars, and prison sentencing systems. Many applications that were once designed and programmed by humans now combine human-written components with behaviors learned from data. This shift presents new challenges to computer science (CS) practitioners and educators. In this article, we consider how the rising importance of machine learning might change what we consider to be core computer science knowledge and skills, and how this should impact the design of both machine learning courses and the broader CS university curriculum.
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Google's Hybrid Approach to Research
Alfred Spector
Communications of the ACM, vol. 55 Issue 7 (2012), pp. 34-37
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In this viewpoint, we describe how we organize computer science research at Google. We focus on how we integrate research and development and discuss the benefits and risks of our approach.
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Quantitative Analysis of Culture Using Millions of Digitized Books
Jean-Baptiste Michel
Yuan Kui Shen
Aviva Presser Aiden
Adrian Veres
Matthew K. Gray
The Google Books Team
Joseph P. Pickett
Dale Holberg
Dan Clancy
Steven Pinker
Martin A. Nowak
Erez Lieberman Aiden
Science (2010)
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We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of ‘culturomics,’ focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.
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Mark Dredze
IJCAI'09: Proceedings of the 21st International Joint Conference on Artifical intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2009), pp. 1414-1419
Inference in Text Understanding
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AAAI Spring Symposium on Machine Reading (2007)
Internet Searching
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Computer Science: Reflections on the Field, Reflections from the Field, Computer Science and Telecommunications Board of the National Academies (2004)
A Retrospective on "Paradigms of AI Programming"
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Vivek (A Quarterly in Artificial Intelligence), vol. 15 (2003)
PowerPoint: Shot with its own bullets
The Lancet, vol. 362(9381) (2003), pp. 343-344
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Imagine a world with almost no pronouns or punctuation. A world where any complex thought must be broken into seven-word chunks, with colourful blobs between them. It sounds like the futuristic dystopia of Kurt Vonnegut's short story Harrison Bergeron, in which intelligent citizens receive ear-splitting broadcasts over headsets so that they cannot gain an unfair advantage over their less intelligent peers. But this world is no fiction—it is the present-day reality of a PowerPoint presentation, a reality that is repeated an estimated 30 million times a day.
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Computers, Use Of
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Encyclopedia of Space Sciences: Macmillan Science Library (2002)
Virtual database technology: Transforming the Internet into a Database
A Modern, Agent-Oriented Approach to Introductory Artificial Intelligence
Verbmobil: A Translation System for Face-to-Face Dialog
Review of Text-Based Intelligent Systems
Correcting a Widespread Error in Unification Algorithms
Softw., Pract. Exper., vol. 21 (1991), pp. 231-233
A Critical Evaluation of Commensurable Abduction Models for Semantic Interpretation
Marker Passing as a Weak Method for Text Inferencing
Cognitive Science, vol. 13 (1989), pp. 569-620
Inference in Text Understanding
AAAI (1987), pp. 561-565
G. Lakoff, M. Johnson, Metaphors We Live By
Artif. Intell., vol. 27 (1985), pp. 357-361
Six Problems for Story Understanders
AAAI (1983), pp. 284-287
Frame Activated Inferences in a Story Understanding Program
IJCAI (1983), pp. 624-626
