Videos that are tagged with: machinelearning

• AGIRI 2006 Workshop Keynote Speaker: Dr. Stan Franklin (Dir. Institute for Intelligent Systems, University of Memphis) - A Cognitive Theory of Everything: The LIDA Technology as an Artificial General Intelligence.
  "Implementing and fleshing out a number of psychological and neuroscience theories of cognition, the LIDA conceptual model aims at being a cognitive 'theory of everything.' With modules or processes for perception, working memory, episodic memories, 'consciousness,' procedural memory, action selection, perceptual learning, episodic learning, deliberation, volition, and non-routine problem solving, the LIDA model is ideally suited to provide a working ontology that would allow for the discussion, design, and comparison of AGI systems. The LIDA technology is based on the LIDA cognitive cycle, a sort of 'cognitive atom.' The more elementary cognitive modules play a role in each cognitive cycle. Higher-level processes are performed over multiple cycles. This talk will give a quick overview of the LIDA conceptual model, and its underlying computational technology." May 20, 2006. (more)
• Alex (Sandy) Pentland, director of the Human Dynamics Group at MIT, describes Reality Mining.
  "Alex (Sandy) Pentland, director of the Human Dynamics Group at MIT, describes a future in which cell phones log data about their owners' behavior. He reasons that this data can be used to strengthen social networks, generate recommendations, help track diseases, and monitor personal health." 2008?. (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 - 2005: Natural Language Processing.
  Natural language processing offers a rich problem domain for machine learning approaches. Many NLP problems require the induction of a mapping that involves complex, discrete structures such as strings, labeled sequences, or trees. In this distinguished lecture, Michael Collins [Massachusetts Institute of Technology] describes how 'large margin' methods in machine learning can be generalized to 'structured' problems found in NLP. December 7, 2005. (more)
• Computer Chronicles: Neural Networks.
  "Neural networks are artificial intelligence systems modeled after the human brain. This program looks at several examples and applications. Included are Braincel 1.1 from Promised Land Technologies [demonstrated by Murray Ruggiero], BrainMaker Professional 2.0 from California Scientific Software [demonstrated by Mark Lawrence], MacBrain 3.0 from Neurix [demonstrated by Matt Jensen], NeuroSMARTS from Cognition Technology [demonstrated by Richard Mansfield], and ExploreNet from HNC. Also includes visits to NASA [Max Reid describes HONN: Higher Order Neural Network] and Intel [Mark Holler describes ETANN: Electronically Trainable Analog Neural Network] to see the work they're doing on neural networks." Also appearing on the show is Tom J. Schwartz (The Schwartz Assoc.). Hosted by Stewart Cheifet and Jan Lewis. May 15, 1991. (more)
• DARPA LAGR Program: Learning Applied to Long-Range Vision using a Collision-Free Navigation Platform.
  New York University uses machine learning to extend the vision of the DARPA LAGR robots. July 14, 2008. (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)
• Eric Horvitz with Microsoft Research on “Surprise Modeling”.
  Eric Horvitz, head of the Adaptive Systems and Interaction group at Microsoft Research, talks about surprise modeling. 2008?. (more)
• ICML 2007 - The 24th Annual International Conference on Machine Learning.
  Collection of over 40 lectures on machine learning given at the Intl. Conf. on Machine Learning, Corvallis, OR, 2007. Invited talks by Bernhard Schölkopf, Josh Tenenbaum, and David Heckerman. June 20-24, 2007. (more)
• Interview with Tom Mitchell.
  8 min. interview with Tom Mitchell about machine learning, from CMU's 2006 ML Autumn School. 2006. (more)
• Lighthill Controversy Debate at the Royal Institution with Professor Sir James Lighthill, Professor Donald Michie, Professor Richard Gregory and Professor John McCarthy.
  Professors Donald Michie [Edinburgh], Richard Gregory [Bristol] and John McCarthy [Stanford] challenge the pessimistic findings & views of Professor Sir James Lighthill [Cambridge], author of "The Lighthill Report" [Artificial Intelligence: A General Survey, in Artificial Intelligence: a paper symposium, Science Research Council (1973)]. June 1973. (more)
• Mind Reading.
  “As pollsters have so well demonstrated this presidential primary season, reading minds, whether of voters or the person next to you, is close to impossible. However as this ScienCentral News video explains, scientists are actually one step closer to reading our thoughts. … [T]he new research is aimed at the biology underlying thoughts-- or, as scientists call them, ‘cognitive processes.’ Carnegie Mellon cognitive psychologist Marcel Just teamed up with machine learning expert Tom Mitchell to conduct the research.” February 2, 2008. (more)
• NATO Advanced Study Institute Workshop on Mining Massive Data Sets for Security (MMDSS 2007) presentation by Ekrem Duman (Dogus University, Turkey) - Detecting Money Laundering Actions Using Data Mining and Expert Systems.
  "Nowadays terrorism is one of the biggest troubles that almost every country faces. It mainly influences the economy and the well being of the citizens and this effect is relatively larger in the developed countries. Since the financial sources of terrorist groups can be regarded as black money, the solutions against the money laundering actions can be expected to identify the transactions of the terrorists. Then, blocking their accounts could slow down their actions if cannot stop. In many countries, the financial institutions are expected to inform compliance regulation bodies about any persons or transactions that they think suspicious. To cope with this necessity, various software packages for anti money laundering (AML) have been developed and are commercially available." In this talk, Ekrem Duman explores the factors that must be addressed in building these programs. Q&A follows the talk.. September 17, 2007. (more)
• Overview Talk on Informatics by Edward "Ted" H. Shortliffe, MD, PhD., presented at the Biomedical Informatics @ Arizona State University Symposium 2006.
  An overview of the field, from inception to current trends, and suggestions for how to establish a new Biomedical Informatics academic program. January 19, 2006. (more)
• Scientific American Frontiers with Alan Alda: "Almost Human" segment from the "Robots Alive!" broadcast.
  Rodney Brooks is beginning to build the first robot with human-like senses, allowing it to learn about the world for itself, like a human baby. April 9, 1997. (more)
• Scientific American Frontiers with Alan Alda: "Robot Independence" segment from the "Life's Really Big Questions" broadcast.
  Natural selection is at work in the artificial world, as robots learn to reproduce without us. December 19, 2000. (more)
• Self-Improving Artificial Intelligence.
  Lecture at Stanford by Stephen Omohundro, Self-Aware Systems. "We are on the verge of a radical new paradigm for both computer software and hardware. "Self-improving systems" will have detailed models ... all » ... all » of their own designs and will improve themselves by learning from their own operation. They will continuously adapt themselves to the tasks they need to perform. Eventually they will be able to improve every aspect of themselves: their programs, programming languages, specification logics, instruction sets, and hardware architectures. In this talk we present fundamental principles that underlie the operation of this kind of system. ... We conclude with a discussion of some of the broader social implications of this kind of system.". October 31, 2007. (more)
• Technology Review Documentary: Evolutionary Design.
  Computers can provide design variations that no human would have imagined. September 2006. (more)
• The Age of Intelligent Machines: The Film. By Raymond Kurzweil.
  From the original video notes: A survey of Artificial Intelligence showing AI at work and under development. The paradoxes, promise and challenges of advanced computer science, with authorities Marvin Minsky, Roger Schank, Raj Reddy and other leaders in the field. 1987. (more)
• The Discipline and Future of Machine Learning.
  Seminar talk by Tom Mitchell at the Carnegie Mellon University School of Computer Science Machine Learning Department. March 1, 2007. (more)