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Jürgen Schmidhuber at AGI-2011: Fast Deep/Recurrent Nets for AGI Vision

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 Jürgen Schmidhuber's short talk on fast deep neural networks at AGI 2011 at Google Headquarters, CA. Co-authors: Dan Ciresan, Ueli Meier, Jonathan Masci, Alex Graves. The deep / recurrent neural networks of Schmidhuber's team keep winning important visual pattern recognition competitions, and are starting to achieve human-competitive results: 9. August 2011: IJCNN 2011 on-site Traffic Sign Recognition Competition (0.56% error rate, nearly three times better than 2nd best algorithm - the only method outperforming humans) 8. June 2011: ICDAR 2011 offline Chinese Handwriting Recognition Competition (1st & 2nd rank) 7. MNIST Handwritten Digit Recognition Benchmark (perhaps the most famous machine learning benchmark). New record (0.35% error rate) in 2010, improved to 0.31% in March 2011, then 0.27% for ICDAR 2011 6. NORB Object Recognition Benchmark. New record (2.53% error rate) in 2011 5. CIFAR-10 Object Recognition Benchmark. New records in 2011, now down to 12% error rate 4. January 2011: Online German Traffic Sign Recognition Contest (1st & 2nd rank; 1.02% error rate) 3. ICDAR 2009 Arabic Connected Handwriting Competition, like the others below won by LSTM recurrent nets (deep by nature). 2. ICDAR 2009 Handwritten Farsi/Arabic Character Recognition Competition 1. ICDAR 2009 French Connected Handwriting Competition based on data from the RIMES campaign Overview sites <b>...</b>
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AGI 2011: Thursday Evening Keynote - Ernst Dickmanns

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 Thursday Evening Keynote Dynamic Vision as a Key Element for AGI Presented by Ernst D. Dickmanns
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AGI 2011: Saturday Keynote - Zhongzhi Shi

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 Saturday Keynote #4 Presented by Zhongzhi Shi AGI Research Progress in Intelligent Sciences Lab at Chinese Academy of Sciences
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AGI 2011: Friday Morning Keynote Session

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 Friday Morning Keynote Sessions Aaron Sloman: The biological bases of mathematical competences: a challenge for AGI www.cs.bham.ac.uk Ed Boyden: Technologies for Understanding How Brain Circuits Perform Computations
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AGI 2011: Architectures Part I

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 Architectures Session, Part I Javier Snaider, Ryan Mccall and Stan Franklin: The LIDA Framework as a General Tool for AGI ccrg.cs.memphis.edu Paul Rosenbloom. From Memory to Problem Solving: Mechanism Reuse in a Graphical Cognitive Architecture agi-conf.org Joscha Bach. A Motivational System for Cognitive AI micropsi.com Zhenhua Cai, Ben Goertzel and Nil Geisweiller. OpenPsi: Realizing Dorner's "Psi" Cognitive Model in the OpenCog Integrative AGI Architecture goertzel.org Matthew Ikle and Ben Goertzel. Nonlinear-Dynamical Attention Allocation via Information Geometry faculty.adams.edu
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AGI 2011: The Future of AGI Workshop Part 1 - Ethics of Advanced AGI

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 The Future of AGI Workshop Part 1 - Ethics of Advanced AGI Workshop intro: Ben Goertzel Steve Omohundro, Design Principles for a Safe and Beneficial AGI Infrastructure agi-conf.org Anna Salamon, Can Whole Brain Emulation help us build safe AGI? agi-conf.org Anna Salamon, Risk-averse preferences as AGI safety technique agi-conf.org Mark Waser, Rational Universal Benevolence: Simpler, Safer, and Wiser than "Friendly AI" becominggaia.wordpress.com Itamar Arel, Reward Driven Learning and the Risk of an Adversarial Artificial General Intelligence agi-conf.org Ahmed Abdel-Fattah & Kai-Uwe Kuehnberger, Remarks on the Feasibility and the Ethical Challenges of a Next Milestone in AGI agi-conf.org Matt Chapman, Maximizing The Power of Open-Source for AGI agi-conf.org Ben Goertzel and Joel Pitt, Nine Ways to Bias Open-Source AGI Toward Friendliness agi-conf.org
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AGI 2011: Self-Programming Workshop

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 Self-Programming Workshop: Self-Programming = Learning about Intelligence-Critical System Features. Presented by Ben Goertzel. www.iiim.is An Implemented Architecture for Feature Creation and General Reinforcement Learning. Presented by Brandon Rohrer. www.iiim.is Behavioral Self-Programming by Reasoning. Presented by Pei Wang. www.iiim.is Heuristic Search in Program Space for the AGINAO Cognitive Architecture. Presented by Wojciech Skaba. www.iiim.is Emergent inference, or how can a program become a self-programming AGI system? Presented by Sergio Pissanetzky. www.iiim.is Self-Programming through Imitation. Presented by J. Storrs Hall. www.iiim.is
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AGI 2011 - Probabilistic Programs: A New Language for AI

The Fourth Conference on Artificial General Intelligence Mountain View, California, USA August 3-6, 2011 Probabilistic Programs: A New Language for AI Presented by Noah Goodman, Stanford University ABSTRACT How can logical and probabilistic approaches to understanding intelligence be reconciled? I will argue that probabilistic programming is the best way to merge logic and probability, providing a new set of tools for thinking about representation and inference in systems with human-like intelligence. I will illustrate these ideas by introducing the probabilistic programming language Church (a stochastic LISP), describing two universal inference algorithms (ie algorithms that can perform probabilistic inference for any Church program), and giving a series of examples. These examples, drawn from cognitive science and AI, will include multi-agent reasoning and concept learning. About Noah Goodman: stanford.edu
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