In recent years, my country's data element market has shown a trend of rapid growth. According to statistics from the National Industry and Information Security Center, as of 2022, the scale of my country's data element market has reached 81.5 billion yuan, a year-on-year increase of 49.51%.
As a key element in the digital economy era, data elements are an important support for building a new development pattern, and their importance has become increasingly prominent. The Party Central Committee and the State Council attach great importance to cultivating the data element market. The Fourth Plenary Session of the 19th Central Committee of the Communist Party of China made data a new factor of production for the first time, and the Fifth Plenary Session of the 19th Central Committee once again established the market position of data elements. The State Council has clearly compared data with production factors such as land and technology.
In the field of artificial intelligence, data is regarded as the "fuel" of artificial intelligence. For deep learning algorithms, data is a key factor necessary for training and optimizing models. By using large amounts of labeled data, algorithms can learn various types of patterns and regularities, improving accuracy and performance. In addition, having rich, complete and high-quality training data can enhance the credibility of the conclusions of the algorithm model to a certain extent.
"The quality and quantity of data will be the key to leading the technical capabilities of large models in the next stage." Wu Chao, director of the expert committee of CITIC Think Tank and director of China Securities Research Institute, said at the 2023 World Artificial Intelligence Conference (WAIC) It is proposed that "20% of the quality of a model in the future will be determined by the algorithm, and 80% will be determined by the quality of the data. Next, high-quality data will be the key to improving the performance of the model."
For artificial intelligence to truly realize its potential and achieve better results, it must have high-quality, diverse and adequately representative data sets. This is an indispensable and important factor in the development of AI.
However, where does high-quality data come from? At present, the data industry still faces many problems that need to be solved urgently. More and more business managers are beginning to think and explore how to get the maximum value from data.
Jinglianwen Technology is a high-tech enterprise and AI basic data service enterprise with scientific research background and technology development-oriented. In 2016, based on the original fingerprint collection business, Jinglianwen Technology fully expanded into AI basic data services, providing high-quality, scene-based data to technology companies, AI companies, and artificial intelligence algorithm models.
The self-developed data labeling platform covers most of the mainstream labeling tools. After years of polishing, the interaction is smooth and efficient. Support computer vision: multiple types of data labeling such as frame labeling, semantic segmentation, key point labeling, line labeling, object tracking, and image classification.
The data labeling platform is equipped with SAM-related algorithms to improve labeling efficiency. Supports automatic recognition of the object type of the current picture, automatically adds category labels to the recognition results, and performs feature classification or classification; supports intelligent AI semantic segmentation model and manual supplementary points; can quickly complete the classification and labeling of object areas of pixel-level image categories ;Support automatic dot marking on the content of picture objects; mature video memory allocation mechanism, support processing larger and more complex images; support output of multiple segmentation results; support one-key panorama segmentation; Tracking and localization of the same object in images frame after frame.
Supports natural language processing: OCR transcription, text information extraction, NLU sentence generalization, part-of-speech tagging, machine translation, sentiment judgment, intention judgment, reference resolution, slot filling and other types of data annotation. According to the difficulty of the project, the project manager and labeling team with many years of experience in NLP labeling project management are equipped; the project structure is analyzed according to the project requirements, and the project is decomposed into a tree diagram layer by layer according to the internal structure and the order of the implementation process based on the WBS principle , to form a relatively independent, easy-to-manage and inspect project responsibility and progress of each unit of the project, and implement it specifically to each participant of the project to ensure the quality of labeling.
The Jinglianwen technology data labeling platform opens up the data closed loop, and conducts data distribution, cleaning, labeling, quality inspection, delivery and other links in an orderly manner, strictly monitors the project progress, ensures the data quality is qualified, and greatly accelerates the iterative cycle of artificial intelligence related applications. Improve the efficiency of enterprise AI data training, promote the rapid development of the artificial intelligence industry, and achieve a significant improvement in the effect of large-scale implementation of AI applications.
JLW Technology｜Data Collection｜Data Labeling
Helping artificial intelligence technology, empowering the intelligent transformation and upgrading of traditional industries
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