Back in high school (around 1978) I got the inspiration for these ads from my buddy, Phil Waite. We went to Highlands Christian Academy (HCA) in More

Back in high school (around 1978) I got the inspiration for these ads from my buddy, Phil Waite. We went to Highlands Christian Academy (HCA) in Pompano Beach, Florida. Phil would talk about seeing the famous Reverend Ernest Ansley on TV saying "Put your hands on the TV set" and slapping people silly on their heads to "heal" them. So, in the late 1980's when I was thinking about opening a used car lot, I thought "wouldn't it be funny to play a fake reverend on TV, dress up in a ridiculous white suit with gold sequins, wear an outrageous pompadour wig, and slap people upside the head and say "I'm gonna Heal your Credit!"" And the rest is history... And, if you want straight talk from a buy here pay here dealer that will tell you exactly what you need to do to get the car you want with the bad credit car loan you need, then come to Apple Auto Sales at 2800 Freedom Drive in Charlotte NC or call us at 704-399-7283. We are a convenient drive time from Uptown Charlotte NC. Special thanks to the "Bruise Brothers" - Allen Johnson and Duke Seegars for their roles in this ad.
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Google Tech Talks November, 29 2007 In the 1980's, new learning algorithms for neural networks promised to solve difficult classification tasks, More

Google Tech Talks November, 29 2007 In the 1980's, new learning algorithms for neural networks promised to solve difficult classification tasks, like speech or object recognition, by learning many layers of non-linear features. The results were disappointing for two reasons: There was never enough labeled data to learn millions of complicated features and the learning was much too slow in deep neural networks with many layers of features. These problems can now be overcome by learning one layer of features at a time and by changing the goal of learning. Instead of trying to predict the labels, the learning algorithm tries to create a generative model that produces data which looks just like the unlabeled training data. These new neural networks outperform other machine learning methods when labeled data is scarce but unlabeled data is plentiful. An application to very fast document retrieval will be described. Speaker: Geoffrey Hinton Geoffrey Hinton received his BA in experimental psychology from Cambridge in 1970 and his PhD in Artificial Intelligence from Edinburgh in 1978. He did postdoctoral work at Sussex University and the University of California San Diego and spent five years as a faculty member in the Computer Science department at Carnegie-Mellon University. He then became a fellow of the Canadian Institute for Advanced Research and moved to the Department of Computer Science at the University of Toronto. He spent three years from 1998 until 2001 setting up the Gatsby Computational Neuroscience Unit at University College London and then returned to the University of Toronto where he is a University Professor. He holds a Canada Research Chair in Machine Learning. He is the director of the program on "Neural Computation and Adaptive Perception" which is funded by the Canadian Institute for Advanced Research. Geoffrey Hinton is a fellow of the Royal Society, the Royal Society of Canada, and the Association for the Advancement of Artificial Intelligence. He is an honorary foreign member of the American Academy of Arts and Sciences, and a former president of the Cognitive Science Society. He received an honorary doctorate from the University of Edinburgh in 2001. He was awarded the first David E. Rumelhart prize (2001), the IJCAI award for research excellence (2005), the IEEE Neural Network Pioneer award (1998) and the ITAC/NSERC award for contributions to information technology (1992). A simple introduction to Geoffrey Hinton's research can be found in his articles in Scientific American in September 1992 and October 1993. He investigates ways of using neural networks for learning, memory, perception and symbol processing and has over 200 publications in these areas. He was one of the researchers who introduced the back-propagation algorithm that has been widely used for practical applications. His other contributions to neural network research include Boltzmann machines, distributed representations, time-delay neural nets, mixtures of experts, Helmholtz machines and products of experts. His current main interest is in unsupervised learning procedures for neural networks with rich sensory input.
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