Global AI Trend 01: The Age of AI Data Crowd-sourcing Begins

Growth of AI Data Demand and Market Development

Artificial intelligence, which has shown remarkable development, is rapidly spreading due to the COVID-19 pandemic. Artificial intelligence has filled the gap in the crisis through convergence with various industries and has recovered the global economy and social system.

The activation of artificial intelligence naturally led to the development of deep learning technology, and accordingly, the demand for artificial intelligence data is increasing at an enormous rate. As a result, the AI data market is experiencing various changes in terms of the sourcing method, operation method, data type, and range of industries that use AI data. Let’s follow the footprint of AI data innovation with Crowdworks!

#1 Change in Sourcing Method: From In-house to Crowd-sourcing

Due to the surge in demand for AI data, the method of data-building operation has evolved from In-house to professional outsourcing and finally to crowd-sourcing. Initially, data was built by internal operations through a team of data engineers. However, the lack of expertise in data construction and cost inefficiency were raised as major problems. Since then, professional outsourcing agencies have emerged, yet they have not been able to respond flexibly to the rapidly increasing demand because they had to work with a limited number of people. At last, crowd-sourcing merged and became the solution for these problems.

Did you know that Crowdworks is the first company in Korea to apply crowd-sourcing to build artificial intelligence data? 😎

Although outsourcing agencies and crowd-sourcing platforms share a commonality in terms of their applying methods–both ‘out-sources’ data, crowd-sourcing platforms produce superior results in the following aspects: quality, costs, and speed. 

With these unique advantages, the crowd-sourcing method has increased its market share in the global AI data industry. 

#2 Changes in Annotation Process: Auto-annotation VS. Manual annotation

As the demand for data increases and AI technology advances, data annotation methods begin to change. The new annotation methods make an appearance in the industry: semi-automatic and automatic annotation. Despite the apparent advantages of automatic methods, manual annotation accounts for about 83% of the global data labeling market in 2020, with automatic annotation remaining at about 2%.

We are proud that Crowdworks also has an ML(machine-learning) solution to meet the 2% demand for auto-annotation. There are two reasons why automatic annotation cannot become universalized.

Today, we learned about the changes in AI data sourcing and annotation processes. Do you find it helpful? Crowdworks will continue to publish a wide range of content that will help you understand the development of artificial intelligence and quickly access global trends in the market 🙂

If you have any questions about AI data, please feel free to visit Crowdworks!
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