Artificial intelligence data labeling method and device

Abstract

The invention provides an artificial intelligence data labeling method and device. The method comprises: acquiring a to-be-labeled data set; obtaining an AI label with the highest probability score of each piece of to-be-labeled data and a probability score based on the established AI model; for any to-be-labeled data, determining whether the probability score is greater than a first preset threshold; when it is determined that the probability score is larger than a first preset threshold value and sampling inspection is carried out on the to-be-labeled data, or when it is determined that the probability score is not larger than the first preset threshold value, labeling an artificial label on the to-be-labeled data; and when it is determined that the probability score is greater than a first preset threshold and it is determined that the to-be-labeled data is not sampled, labeling the to-be-labeled data by using the acquired AI label with the highest probability score. According to the method, the manual marking cost and the implementation time cost are saved, and marking errors caused by human subjective factors and marking personnel technical backgrounds are reduced.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to an artificial intelligence data labeling method and device. Background technique [0002] With the rapid development of technologies such as the Internet, machine learning, big data, and cloud computing, all kinds of information data continue to grow at an exponential rate. In the context of the big data era, artificial intelligence has already empowered multiple industries relying on massive data , Breeding a variety of industry applications. [0003] At present, most of the machine learning and deep learning algorithms that artificial intelligence relies on are data-dependent, requiring a large amount of data to train algorithms in a supervised or semi-supervised manner for customized deployment. Due to the huge volume of big data in my country, the complex data types and high data dimensions of various industries pose a huge challenge to the data labeling task. In ge...

AI Technical Summary

Problems solved by technology

[0004] High labor costs for data labeling: AI algorithm training requires a large number of labeled samples, and the current massive data labeling tasks rely on manual methods. "As much as there is labor, there is as much intelligence", resulting in high costs for making data sets;

[0005] The quality of data labeling is difficult to guarantee: labeling tasks are subject to the subjective influence of labelers and reviewers, which will introduce certain labeling errors, and data consistency is difficult to guarantee;

[0006] The threshold for labeling professional datasets is high: professional datasets such as medical care, education, and telecommunications networks need to be labeled by professionals in the professional field. It is also difficult to maintain a consistent scale

[0007] It can be seen that the labeling cost of artificial intelligence data is high and the accuracy is not high

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