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Artificial intelligence, as the hottest technology in the current field of science and technology, has attracted the attention of many people both inside and outside the industry. However, the information we focus on daily is mostly about the investment and financing trends in the field of artificial intelligence, the dynamics of AI unicorn companies, the layouts of tech giants in the AI field, and the technical research and development status in the AI sector. We rarely take the time to sort out the artificial intelligence industry chain. However, to have a deeper and more long-term concern for artificial intelligence, we must first have a clear understanding of the artificial intelligence industry chain.
The artificial intelligence industry chain includes three major parts: AI technology, underlying hardware (AI chips, visual sensors), and application fields (smart homes, smart hardware, robotics, autonomous driving, industry applications), providing everyone with a comprehensive and clear understanding of the artificial intelligence industry.
Currently, AI technology includes five main components: computer vision, speech recognition, natural language processing, machine learning, and big data.
Computer vision, as the name suggests, is about enabling computers to observe and recognize like the human eye. More specifically, it refers to using cameras and computers to replace human eyes in recognizing, tracking, and measuring targets, and further processing images to make them more suitable for human observation or transmission to detection instruments.
So what is the relationship between computer vision and artificial intelligence?
As a scientific discipline, computer vision studies the relevant theories and technologies, attempting to establish artificial intelligence systems that can obtain “information” from images or multi-dimensional data. Currently, computer vision mainly stays in the stage of image information expression and object recognition, while artificial intelligence emphasizes reasoning and decision-making.
Currently, computer vision is mainly applied in security cameras, traffic cameras, autonomous driving, drones, finance, healthcare, and more. Representative companies in China include traditional giants like Hikvision and Dahua Technology, as well as unicorn companies like SenseTime, Yitu Technology, CloudWalk Technology, and Megvii Technology, along with startups like SiLan Technology, Sogou Technology, Yuyun Technology, Yuntian Lifa, Yi+, TuYang Information, Malong Technology, and Insta360.
Speech recognition technology enables machines to convert speech signals into corresponding text or commands through recognition and understanding processes. It mainly includes feature extraction technology, pattern matching criteria, and model training technology. Speech recognition is the foundation of human-computer interaction, primarily solving the problem of making machines understand what humans say. Currently, the most successful application of artificial intelligence is in speech recognition technology.
Speech recognition is mainly applied in areas such as the Internet of Vehicles, intelligent translation, smart homes, and autonomous driving. The most representative company in China is iFlytek, along with startups like Yunzhisheng, Peking University Information, VoiceSmart Technology, and GMEMS.
Natural Language Processing
Natural language processing broadly includes two parts: natural language understanding and natural language generation. Achieving natural language communication between humans and machines means enabling computers to understand the meaning of natural language text and express given intentions and thoughts in natural language text. The former is called natural language understanding, while the latter is called natural language generation. Natural language processing is an important direction in the field of computer science and artificial intelligence. The ultimate goal of natural language processing is to communicate with computers using natural language, allowing people to use computers in their most familiar language without spending a lot of time and effort learning various unnatural and unfamiliar programming languages.
For specific applications, practical systems with considerable natural language processing capabilities have emerged. Typical examples include natural language interfaces for multilingual databases and expert systems, various machine translation systems, full-text information retrieval systems, and automatic summarization systems. The domestic companies BAT, JD.com, and iFlytek are involved in natural language processing, and new companies like Aitman, Qunar, Sibilich, Muran Cognition, Triangle Beast Technology, Senyi Intelligent, Yixue Education, and Zhichu Customer Service have also emerged.
Machine learning enables machines to possess the ability to learn like humans, specifically studying how computers can simulate or implement human learning behaviors to acquire new knowledge or skills, reorganize existing knowledge structures, and continuously improve their performance. It is the core of artificial intelligence.
Machine learning has already been widely applied in areas such as data mining, computer vision, natural language processing, biometric recognition, search engines, medical diagnosis, credit card fraud detection, securities market analysis, DNA sequencing, speech and handwriting recognition, strategic games, and robotics. Domestic companies focusing on machine learning include Ubtech, Turing Robot, Cluster Automation, and Jizhijia Technology.
Big data, also known as massive data, refers to information assets that require new processing models to have stronger decision-making, insight, and process optimization capabilities. In other words, the ability to quickly obtain valuable information from various types of data is what big data technology is about. Big data is the foundation for upgrading and evolving the intelligence level of AI. With big data, AI can continuously simulate and practice, moving closer to true artificial intelligence.
The five major technologies of computer vision, speech recognition, natural language processing, machine learning, and big data are interrelated and complementary, while different application levels have their own focuses. From these five technologies, it is not difficult to see the complexity of artificial intelligence technology and the numerous difficulties that technological advancement must overcome.
