User:VjiaoBlack/sandbox
The following is a draft of my edits to the History of AI page for my CIV class.
Deep learning, big data and artificial general intelligence: 2000–present
[edit]In the first decades of the 21st century, access to large amounts of data (known as "big data"), faster computers and advanced machine learning techniques were successfully applied to many problems throughout the economy. By 2016, the market for AI related products, hardware and software reached more than 8 billion dollars and the New York Times reported that interest in AI had reached a "frenzy".[1] The applications of big data began to reach into other fields as well, such as training models in ecology[2] and for various applications in economics[3]. Advances in deep learning (particularly deep convolutional neural networks and recurrent neural networks) drove progress and research in image and video processing, text analysis, and even speech recognition.[4]
Deep learning
[edit]![[icon]](/wiki/File:Wiki_letter_w_cropped.svg){kind=small} | This section needs expansion. You can help by adding to it. (September 2016) |
Deep learning is a branch of machine learning that models high level abstractions in data by using a deep graph with many processing layers.[4] According to the Universal approximation theorem, deep-ness isn't necessary for a neural network to be able to approximate arbitrary continuous functions. Even so, there are many problems that are common to shallow networks (such as overfitting) that deep networks help avoid.[5] As such, deep neural networks are able to realistically generate much more complex models as compared to their shallow counterparts.
However, deep learning has problems of its own. A common problem for recurrent neural networks is the vanishing gradient problem, which is where gradients passed between layers gradually shrink and literally disappear as they are rounded off to zero. There have been many methods developed to approach this problem.
State-of-the-art deep neural network architectures can sometimes even rival human accuracy in fields like computer vision, specifically on things like the MNIST database, and traffic sign recognition.[6]
Artificial general intelligence
[edit] | This section needs expansion. You can help by adding to it. (October 2016) |
Artificial general intelligence (AGI) describes research that aims to create machines capable of general intelligent action. Although there are still no human-constructed AGI systems, one can certainly argue that we are making progress on that path.
Language processing engines powered by smart search engines can easily beat humans at answering general trivia questions (such as IBM Watson), and recent developments in deep learning have produced astounding results in competing with humans, in things like Go, Doom (which, being a FPS, has sparked some controversy), and even running data centers.[7][8][9][10][11]
See also
[edit]- ^ Steve Lohr (October 17, 2016), ""IBM Is Counting on Its Bet on Watson, and Paying Big Money for It"", New York Times
- ^ Hampton, Stephanie E; Strasser, Carly A; Tewksbury, Joshua J; Gram, Wendy K; Budden, Amber E; Batcheller, Archer L; Duke, Clifford S; Porter, John H (2013-04-01). "Big data and the future of ecology". Frontiers in Ecology and the Environment. 11 (3): 156–162. doi:10.1890/120103. ISSN 1540-9309.
- ^ "How Big Data is Changing Economies | Becker Friedman Institute". bfi.uchicago.edu. Retrieved 2017-06-09.
- ^ a b LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "Deep learning". Nature. 521 (7553): 436–444. doi:10.1038/nature14539. PMID 26017442. S2CID 3074096.
- ^ Baral, Chitta; Fuentes, Olac; Kreinovich, Vladik (June 2015). "Why Deep Neural Networks: A Possible Theoretical Explanation". utep.edu. Retrieved June 9, 2017.
- ^ Ciregan, D.; Meier, U.; Schmidhuber, J. (June 2012). "Multi-column deep neural networks for image classification". 2012 IEEE Conference on Computer Vision and Pattern Recognition: 3642–3649. doi:10.1109/cvpr.2012.6248110. ISBN 978-1-4673-1228-8. S2CID 2161592.
- ^ Markoff, John (2011-02-16). "On 'Jeopardy!' Watson Win Is All but Trivial". The New York Times. ISSN 0362-4331. Retrieved 2017-06-10.
- ^ "AlphaGo: Mastering the ancient game of Go with Machine Learning". Research Blog. Retrieved 2017-06-10.
- ^ "Innovations of AlphaGo | DeepMind". DeepMind. Retrieved 2017-06-10.
- ^ University, Carnegie Mellon. "Computer Out-Plays Humans in "Doom"-CMU News - Carnegie Mellon University". www.cmu.edu. Retrieved 2017-06-10.
- ^ "DeepMind AI Reduces Google Data Centre Cooling Bill by 40% | DeepMind". DeepMind. Retrieved 2017-06-10.