The Role of Data Labeling In The Development of AI
At present, the development field of Artificial Intelligence (AI) is expanding, and the application of intelligence is gradually changing our lives. As we know, in the entire AI system, computing power, algorithms, and data are the three decisive factors affecting the evolution of AI, respectively bearing the role of AI in infrastructure capabilities, work guidance methods, and evolution basis. It is under the interaction of these three elements that AI can complete a highly intelligent evolutionary level.
Behind the rapid development of the AI industry, data plays a vital role, and the field of data labeling has also played an important role in promoting the process of AI industrialization. The data that the AI algorithm can learn must be labeled one by one. Now, let us learn about the role of data labeling in the development of artificial intelligence.
Data labeling provides more high-quality data for AI
AI is going to be industrialized eventually, and the products that landed have strict requirements on the reduction of data scenes and the quality of labeling. The domestic AI data service representative brand cloud measurement data uses self-built data labeling bases and science laboratories to create a professional customized collection and high-quality labeling teams to help AI companies obtain more high-quality specific scene data, and continue to provide safe and reliable, accurate, and high-quality data services for AI training and optimization.
At present, we provide data labeling services for smart driving, smart home, face recognition, product recognition, and other fields, and fully support the processing of various types of data such as text, voice, image, and video.
Data labeling is a necessary process of AI
Data is the blood of AI. The current is the era of AI-based on big data and deep learning of data intelligence. It can be said that whoever masters the data is likely to do well. AI is also called data intelligence, at this stage of development, the more layers of the neural network, the deeper the neural network, and the larger the amount of data that needs to be used for training. For example, the face recognition system of young people, they go to work by subway or go shopping in a supermarket, a large amount of facial data is collected by the public system. But just collecting the data is useless. For deep learning, data is meaningful only if it is labeled, and then can be used for machine learning and evolution.
Data labeling is the main force behind AI
With the development of AI, the amount of data training is very large, and data labeling companies have emerged. At present, my country has a huge data processing team. There are more than 100 companies specializing in data labeling in Beijing alone. There are probably more than 10 million people engaged in this work all over the world. Many Internet technology companies in the head have their own data labeling company. These companies operate on the Internet. A platform has product managers and project managers. When they receive a task, they will find someone to do it. After everyone signs up through the network group, the product manager will train them, and then they will receive their own tasks and login credentials to the account to the label. Data labeling for AI is like gasoline for engines. The more and more accurate the labeled data, the smarter and easier to use the model obtained after algorithm training, and the greater the value for the commercial end, which helps AI quickly land to occupy the market, drive industry applications, and promote industry upgrades.
Currently, AI has entered the technology landing stage, and its application scenarios involve major industries such as security, finance, home furnishing, and transportation. In the future, the data labeling industry will also enter the segmented market chasing stage along with the AI industry.
By utilizing AI, big data, and cloud technologies, CCJK can provide training data labeling services for artificial intelligence and machine learning applications, including text labeling, image labeling, audio labeling, video labeling, etc.
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