Videos that are tagged with: representation

• ArsDigita University Curriculum - Artificial Intelligence course taught by Patrick Winston. Lecture #1 (of 4): AI Overview, Rule-Based Expert Systems and Knowledge Engineering.
  ArsDigita University Curriculum: "The curriculum was modeled on the undergraduate CS program at MIT. Several of the courses were straightforward adoptions of MIT courses. A few were specifically designed for the program, which was roughly in line with the ACM's 2001 Model Curricula for Computing." June 4, 2001. (more)
• ArsDigita University Curriculum - The Structure and Interpretation of Computer Programs course: Holly Yanco's lecture about Data Structures (Trees, Trees, Trees). This is lecture video #8 (of 19) for the course.
  The Structure and Interpretation of Computer Programs course: "An introduction to programming and the power of abstraction, using Abelson and Sussman's classic textbook of the same name. Key concepts include: building abstractions, computational processes, higher-order procedures, compound data, data abstractions, controlling interactions, generic operations, self-describing data, message passing, streams and infinite data structures, meta-linguistic abstraction, interpretation of programming languages, machine model, compilation, and embedded languages." October 12, 2000. (more)
• CSE Colloquia - 2005: Learning, Logic, and Probability - A Unified View.
  "Artificial intelligence systems must be able to learn, reason logically, and handle uncertainty. Research has focused on each of these goals individually, and only recently have attempts been made to achieve all three at once. In this colloquium, Pedro Domingos, UW Computer Science & Engineering, describes Markov logic: a representation that combines the full power of first-order logic and probabilistic graphical models, and algorithms for learning and inference in it. Experiments in a real-world university domain." November 2, 2004. (more)
• CSE Colloquia - 2006: Turing’s Dream and the Knowledge Challenge.
  In this Turing Center distinguished lecture, Lenhart Schubert [University of Rochester] explains that there is a set of clear-cut challenges for artificial intelligence, all centering around knowledge. The solution to those challenges could realize Alan M. Turing's dream - the dream of a machine capable of intelligent human-like response and interaction. Schubert presents preliminary results of recent efforts to extract 'shallow' general knowledge about the world from large text corpora. November 10, 2005. (more)
• Discussion of and Demonstrations of Learning Programs for Robots.
  The first half of the film is a lecture by Marvin Minsky describing the basic ideas of Patrick Winston's learning program, using examples and "near misses" to refine the program's model of what an "arch" is. The second half of the film is a narration by Dave Waltz describing other robotics research at MIT. He discusses Tim Finin's program that uses Winston-like models to recognize objects that match the model even when parts of the object are obscured. It uses hypotheses about dimensions of the objects that it can not directly observe. 1975??. (more)
• IWSC / ASWC 2007 Invited Speaker: Chris Welty (IBM T J Watson Research Center) - How I was right even when I was wrong.
  "For the past several years I have warned people not to ask me to predict the future, because my predictions are usually wrong. Undaunted by failure, in this talk I will try to predict the future of the semantic web based on a very personal view of its history, the history of the internet, web, semantic web, and AI, and the mistakes I've made predicting where and how they would be valuable." November 15, 2007. (more)
• Linking Brains, Computers.
  Because it has been around for such a long time, and has either misled or annoyed so many people over the years, it ought to have a name. Let's call it the Synapse Equivalency Fallacy. Synapses are the interconnections between the neurons that make up the brain and nervous system. The fallacy occurs when a writer likens the transistors in a computer to the synapses in a brain, usually as part of an effort to make computers seem like brains. July 09, 2008. (more)
• Symbol System: excerpt from AI: What Can it Do? Where is it Going?.
  Herbert A. Simon explains the hypothesis that intelligent behavior (be it humans or computers) requires the ability to deal with symbols/patterns. March 21, 1990. (more)
• Technology Review Documentary: Tim Berners-Lee on the Semantic Web.
  "The inventor of the World Wide Web explains how the Semantic Web works and how it will transform how we use and understand data." March, 2007. (more)
• UK Future TV: Future Technology episode with Austin Tate.
  "Austin Tate of the University of Edinburgh talks about artificial intelligence techniques and their use in emergency response centres." March 10, 2007. (more)
• Visual Elements in Robotics: excerpt from "AI: What Can it Do? Where is it Going?".
  Excerpt from lecture by Herbert A. Simon. March 21, 1990. (more)