Recently, iResearch published the "2021 China Artificial Intelligence Industry Research Report" (hereinafter referred to as the "report"). The report mainly analyzed the development progress of artificial intelligence industrialization at the present stage, and proposed that "AI has become the core core productivity in the era of digital economy". the opinion of.
The report pointed out that the scale of the artificial intelligence core industry is expected to reach 199.8 billion yuan in 2021, and the corresponding scale will exceed 600 billion yuan in 2026. Among them, finance, medical care, and industry have become the top three fields. Machine learning is the core of artificial intelligence and the root of various algorithms. Under the strong background of the digital economy, industries with basic digital advantages such as finance, manufacturing, and medicine are gradually deepening their understanding of data applications. The digitalization of government affairs, education, agriculture, etc. Low-level industries have also gradually realized the necessity of digital development. It can be said that all walks of life are planning, launching or carrying out digital engineering, and digital engineering is taking the value of machine learning and promoting machine learning products and services as its key business. point.
Artificial intelligence needs data to build intelligence, especially machine learning. The more data artificial intelligence uses, the more accurate the results will be. Data is a key element of continuous innovation in artificial intelligence, and it is also a pain point for the implementation of AI applications. Data governance spends more than 90% of the energy in the implementation of Al applications, and in the face of Al applications' high-quality requirements for data quality, magnitude, field richness, and real-time performance, it is necessary to collect all aspects of data , cleaning, information extraction, labeling, quality inspection, management and other links for more detailed control.
As a professional data collection and labeling service provider, Jinglianwen Technology provides full-chain AI data services, from data collection, cleaning, labeling, to the whole process of on-site, one-stop AI data services, assisting artificial intelligence enterprises to solve the entire artificial Corresponding issues in the data labeling link in the smart chain.
Jinglianwen Technology has a strong ability to collect scene-based data, which can provide more vertical and rich data for the implementation of artificial intelligence subdivision scenes, and can better meet the needs of enterprises in long-tail scenarios.
In order to accelerate the development of the industry, Jinglianwen Technology has established an advanced data labeling platform and a mature labeling, review, and quality inspection mechanism for data custom labeling services, and supports various types of data labeling. The quality training of labeling personnel has been comprehensively improved, which can better ensure the accuracy of labeling data.
Jinglianwen Technology has given full play to the power of "underlying technology + service capabilities", possesses profound industry-related knowledge, and has built an artificial intelligence-oriented data governance system, which can streamline, standardize and systemize the data governance process for AI applications It effectively reduces the cost of repeated data preparation, feature screening, model tuning, and iteration for enterprises, thereby shortening the development and construction cycle of AI models for enterprises, and ultimately significantly improving the efficiency of large-scale implementation of AI applications.
Jinglianwen Technology has always focused on various needs in artificial intelligence scenarios such as smart driving, smart home, public safety, smart city, smart medical care, smart finance, smart education, and smart justice, providing underlying technical support for AI technology. In the future, Jinglianwen Technology will continue to focus on high-quality, scene-based AI data services, and create a service layout for the entire life cycle of AI data with general data sets, data labeling platforms & data management tools, and customized data collection/data labeling.
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