In fact, Professor Hayton of the AI ​​community and the University of Toronto in Canada proposed the concept of deep learning as early as in 2006. The shallow learning algorithm was widely recognized by the academic community as early as the 1980s. The reason why the application of this field has gradually increased in recent years is because the development of AI can not be separated from the support of two aspects, big data and computing resources.
First, from "dark blue" to "AlphaGO", artificial intelligence has gone through 20 years
Twenty years after the “Deep Blue†War Kasparov in 1996, “AlphaGO†once again added a history of artificial intelligence development through the form of man-machine competition. Standing today, we can ridicule that the once infamous "dark blue" is actually just a great chess program running on a supercomputer. In order to support this process, the IBM team built a 1.2-ton heavy-duty chess program. , a monster with 480 chess special chips.
Unlike the traversal search strategy of “deep blue†relying on super-computing capabilities, the “AlphaGO†design incorporates deep learning algorithms that have made significant progress in recent years. Deep learning is called "depth" and is related to shallow learning algorithms such as forward error feedback neural networks and support vector machines. The limitation of the latter is that in the case of finite samples and computational units, the ability to express complex functions is limited, and it is necessary to rely on human experience to draw sample features. The deep learning algorithm constructs a deep nonlinear network structure to achieve complex function approximation and automatic feature extraction. It has a powerful ability to mine statistical data from a small number of sample sets.
In the field of face recognition based on deep learning methods, in 2014, Facebook's DeepFace project and the Hong Kong Chinese University's DeepID project identified 97.45% and 97.35% of the accuracy in the outdoor face recognition database, which is comparable to human 97.5. % recognition rate. In addition, in areas such as image classification and natural speech recognition, deep learning has also proved its incomparable advantages, especially the success on Go in one of the most complex complete information games, indicating that the algorithm has great potential. Can be digging.
There is also an unknown episode about AlphaGO. Prior to the match against Li Shishi, AlphaGO had won the European Go champion Fan Yi in a score of 5:0 in January 2016. After watching the game, Li Shishi expressed his confidence in defending the final honor of human beings in chess. However, within a short period of one month, Google replaced AlphaGO's core computing unit with a dedicated deep learning chip from the CPUGPU. So we saw the smile of the "Stone Buddha" and trembling fingers.
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