The new algorithm is expected to produce a "smart brain"
The new algorithm is expected to produce a "smart brain" November 12, 2018 Source: Science and Technology Daily The reporter learned from Tianjin University that the team of Professor Hu Qinghua has made important progress in the field of artificial intelligence deep learning. He first proposed the "generalized multi-view learning framework" theory, which is expected to improve the limitations of "machine deep learning" and create a true realization of "early integration." "Analytical thinking" "smart brain." Related research was published in the new authoritative journal "IEEE Pattern Analysis and Machine Intelligence Journal" in the field of global artificial intelligence. Deep learning is a computational method that makes machines more intelligent. The principle is to perform representational learning on images, sounds, and texts, mimic human brain mechanisms to interpret these data, and obtain data from a large number of instances, learning tasks, and analyzing conclusions. At present, the mainstream deep learning algorithm is “not smartâ€, and there are defects such as one-sided analysis and difficulty in obtaining regularity. How to combine complex multi-source information for data analysis? How to make the machine realize "seeing the six ways, listening to all directions, thinking together"? This is a formidable challenge in the study of deep learning algorithms. The “generalized multi-view learning framework†algorithm proposed by the Hu Qinghua team pioneered the research idea of ​​“multi-source information early integration, joint learning with specific tasks, and expanding information fusion directionâ€. Compared with the previous artificial intelligence deep learning algorithm, the innovation of the "generalized multi-view learning framework" mainly has two aspects: First, the cross-platform and cross-dimensional information "early integration" is realized, and the big data in different fields are summarized into three-dimensional. The second is to build a mathematical model that allows the machine to "consciously learn", instead of "stacking analysis" of large amounts of data, but through a reasonable analysis of the comprehensive network data, a streamlined regularity is obtained. It is even possible to predict and judge complex tasks, and it is expected to realize a leap from "deep learning" to "integrated thinking". Disposable Laparoscopic Trocar Disposable laparoscopic trocars are typically made of plastic or metal and are designed for single-use only. They are available in a range of sizes and shapes to accommodate different patient needs and surgical procedures. The trocar is typically inserted into the patient's abdomen using a technique called "blind insertion," which involves inserting the trocar through the skin without direct visualization of the underlying tissue.
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