Let the machine imitate the deep learning of human brain thinking behavior is a major contributor to the accelerated development of artificial intelligence (AI) in various industries in recent years. This year, AI has shown a new look in the future of technology generation in many fields. The research institute IDC also estimates that the global AI layout, including manufacturing, medical, retail, banking, etc., will be the industry that will invest the most in AI in the next five years. With the widespread use of AI applications, artificial intelligence has become the key to improving the competitiveness or productivity of governments and enterprises. Japan is planning to fully adopt AI in 2021 to strengthen its ability to respond to cyber attacks. By learning and analyzing the common points and signs of past cyber attacks, the relevant units can more effectively resist new virus attacks. The mainland is also through Skynet. The surveillance system took only 7 minutes to catch the suspects across the country, and behind it was the face recognition technology to accomplish this impossible task. Artificial intelligence is almost ubiquitous, and it is also true on Wall Street. Investment experts admit that the learning machine that has acquired insights from big data is preparing to handle 99% of the investment transactions. The AI ​​is in charge of the investment world, and the artificial intelligence is over the chess queen. Then, is it necessary to fight the war? For the manufacturing industry, the product yield determines the quality of the manufacturing quality, and even the corporate brand image. The traditional method of quality inspection is checked by naked eyes through a large amount of manpower. However, in recent years, the manufacturing industry is facing the dilemma of increasing labor costs, and the human eye is prone to fatigue, resulting in different detection values, which reduces the detection efficiency. When quality control can't catch up with manufacturing, such traditional inspection methods are even less able to respond to the manufacturing trend of rapid production in the future. Therefore, in the manufacturing field, it has been found that the use of deep learning technology not only allows machine vision to greatly improve the detection efficiency, but also details that are easily overlooked or undetectable by the naked eye can be carefully selected. When the process detection link is introduced into the AI, the machine will identify the characteristics of various good and bad products in advance through self-learning, and then quickly screen out the online products based on the analysis results. The same method can be applied to agricultural products. A large amount of labor is required to screen out damaged potatoes. Now, as long as the damaged potato is abnormally detected, once the machine detects the defective product, the production line will remember the warning sound and only want to provoke After the defective product, the production line can be resumed, and the final production line only needs one person who is responsible for picking the potato. Former Baidu chief scientist Wu Enda said that AI technology is very suitable to solve the challenges faced by the manufacturing industry, such as unstable quality and yield, lack of flexibility in production line design, difficulty in capacity management and rising production costs. For example, Wu Enda, even a board that looks no problem, can still detect subtle scratches on the surface by deep learning algorithms. The detection algorithm corrected by AI can make the detection time of each part only need 0.5 seconds. In the process of human beings facing illness and death, only the disease can be resisted by the help of external forces. Medical technology has always been the goal of human beings to study for the rest of their lives. Therefore, deep learning has great potential in the medical field, which can help medical diagnosis more accurate and more efficient. However, many research experts still agree that even if deep learning can assist the doctor, the final diagnosis must still be decided by the doctor himself. Doctors need an industry where experience is accumulated, and the final diagnosis is deeply influenced by the judgment of experience. The application of deep learning in the medical field has widely used image recognition technology to assist doctors in medical imaging, including the Google research team to diagnose diabetic retinopathy and breast cancer metastasis. Diabetic retinopathy is the main cause of the increase in the number of blind people in the world. In order to prevent blindness, diabetic patients are required to undergo fundus image screening every year. The severity of the disease depends on the morphology of the retinopathy, such as bleeding, hard exudates, and the like. Image acquisition must be graded by a professional ophthalmologist, but this is difficult for India, where doctors are short. Therefore, the Google team applied deep learning technology, and with the help of professional doctors, from the 128,000 retinal fundus image datasets, it created a model that the professional doctor judged the lesion ability. In the future, the model can be obtained on the system. Diagnostic results. In the past, doctors usually spend time looking at X-rays, computed tomography (CT) or nuclear magnetic resonance (MRI) image reports. For experienced doctors, you may know the problem at a glance, the future. In the face of the replacement of medical talents, fortunately, valuable medical experience can be passed down through scientific and technological methods. Even if it is further combined with medical equipment in the future, it can develop front-end detection and back-end remittance reports, speed up the detection process, and allow doctors to use more time for medical diagnosis. Autopilot is undoubtedly the most popular topic when humans discuss how AI can improve the quality of life. Safety is an important key to the popularity of autonomous driving. In addition to ensuring the safety of the driving process through external in-vehicle devices such as cameras or various sensors, the vehicle itself is smart, and it is possible to make an independent analysis of the road conditions for analysis. The most effective boost is developed. Once the machine has the ability to learn autonomously, it can gradually improve its driving skills and improve the driving safety of the vehicle in the process of continuous learning. Li Xingyu, director of automotive business of Continental Horizon Robotics, pointed out that the development of autonomous driving is built on a very complicated system. Deep learning can not only be as good as people, but even better than people, and such a complicated driving environment is It is the place where deep learning can best be used. On the other hand, it is a subtle relationship between the driver and the car. Today's automatic driving is defined as the decision of the car to judge the road conditions, and the deeper level in the future is the cooperation between the car and the driver. Simply put, let the car understand you. Therefore, in the future, the field of autonomous driving must also face the learning of driving habits, even the driving style, which feels like the car becomes the driver's driving butler. Fiber Optic Cabinet,Fiber Cabinet,Fiber Distribution Cabinet,Outdoor Fiber Cabinet Cixi Dani Plastic Products Co.,Ltd , https://www.danifiberoptic.com