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随着政策的推动以及资本的关注，人工智能产业仍将保持迅猛发展态势，2020年中国人工智能核心产业规模将超1500亿元。当前人工智能的商业化主要是基于计算机视觉、智能语音、自然语言处理等技术，技术应用面广泛，涉及智能医疗、智能驾驶、智能家居等多场景。2018年中国人工智能领域共融资1311亿元，增长率超过100%，投资者看好人工智能行业的发展前景，资本将助力行业更好地发展。在人工智能与出行结合领域，路径规划、网络约车、交通管理、自动驾驶等技术的研发解决了传统出行不便的痛点，其中深兰科技深耕智能交通、智能环境、智能城市等细分领域，已实现人工智能产品落地。其深兰科技熊猫智能公交车已实现在广州、天津等国内多个城市试运行。而人工智能与安防、医疗、零售等产业的结合，均解决了一定行业痛点，利用机器学习算法、深度学习和NLP促进行业发展。随着5G商用时代的逐渐来临，人工智能技术连接效率也将进一步提升，深度学习、数据挖掘、自动程序设计等领域也将在更多的应用领域得到实现。
With the promotion of policy and the attention of capital, the AI industry will continue to develop rapidly, and the scale of China's AI core industry will exceed 150 billion yuan by 2020. At present, the commercialization of AI is mainly based on computer vision, intelligent voice, natural language processing and other technologies. It has a wide range of applications, including intelligent medical, intelligent driving, intelligent home and other scenarios. In 2018, China's AI sector raised 131.1 billion yuan, with a growth rate of more than 100%. Investors are optimistic about the prospects of AI industry, and capital will help the industry develop better. In the field of combination of artificial intelligence and travel, the research and development of technologies such as path planning, network car appointment, traffic management and automatic driving have solved the pain points of traditional travel inconvenience. Among them, Shenlan Science and Technology has realized the landing of artificial intelligence products in the sub-fields of intelligent transportation, intelligent environment, intelligent city and so on. Its Shenlan Panda Intelligent Bus has been put into trial operation in Guangzhou, Tianjin and other domestic cities. The combination of AI and security, medical, retail and other industries has solved some industry pain points, using machine learning algorithm, in-depth learning and NLP to promote the development of the industry. With the gradual advent of 5G commercial era, the efficiency of artificial intelligence technology connection will be further improved. In-depth learning, data mining, automatic programming and other fields will also be realized in more application areas.
