The expansion of the AI data application industry
With increasing data demand from companies that must develop and introduce AI, The AI data industry is expanding from the IT industry to various fields such as healthcare, autonomous driving, finance, and retail. In the early days of the AI industry, IT companies developed. Since then, more and more attempts have been made to introduce AI to improve business performance in various fields. So the IT industry, which accounts for the highest portion of the AI data pre-processing market, is expanding horizontally.
If you take a look at the prospects for 2028, the non-IT industry is expected to account for
about 70% of the AI data market, which is about 500% growth compared to 2020!
In addition, according to a survey of domestic agencies conducted by the Korea Institute for Information and Communication Policy (KISDI), 90.7% of the agencies surveyed said they were willing to continue AI technology. And 53.7% said they were willing to introduce other AI technologies. Isn't that a huge increase considering that only 14.7% of AI agencies were introduced in 21 years? Accordingly, the importance of collecting and processing AI data is also expected to increase.
Today, we will focus on the rapidly evolving autonomous driving industry.
Self-driving cars such as Google's Waymo, Tesla, and Hyundai Motor's Momentum are no longer imaginary materials that appeared in science fiction movies. Self-driving cars have already been implemented as a reality in our lives with the development of AI and ICT. In addition, many companies in the mobility industry, such as BMW and Ford, are studying autonomous driving technology. What's the most significant factor in creating these technology gaps? The power of the software!
Data plays a very important role in software. This is because the software requires large-scale data to build an autonomous driving infrastructure such as precision maps and vision recognition. According to the KDB Future Strategy Research Institute, AI collects and analyzes external information and controls vehicles based on collected data through various sensors and V2X (technology for exchanging information with other vehicles, mobile devices, roads, etc.). In particular, full-driving cars require complex algorithms and deep learning-based AI to cope with various sensor information collection and convergence, situational awareness, driving route selection, and unexpected situations, requiring high-quality AI data.
If you are looking for data collection and processing, and you have a self-driving industry client, please focus on the following! We’re going to tell you about the characteristics of the AI data market in the autonomous driving industry.
First, you need a skilled workforce. Autonomous driving is a part that is directly related to human life, so precise and thorough semantic segmentation and processing should be carried out. These tasks require considerable proficiency. Therefore, you must continue to operate training and management programs that can improve workforce proficiency in the crowd-worker pool. And you should operate a number of projects on the platform to obtain a sufficient number of highly skilled workforces.
Second, the processing takes a considerable amount of time and requires many workforces. The data required for autonomous driving is 10 to 30 frames per second, and it takes much more time to process data than normal data because the meaning of all objects on the screen must be segmented. Unlike normal data processing, which typically involves processing only the target objects in the data, Semantic Segmentation, which is used for autonomous data processing, requires all objects ranging from 20 to 30 objects to one data. Therefore, to quickly develop high-performance self-driving models, large-scale personnel must be operated and managed.
Finally, sophisticated data processing tools are needed. After winning the self-driving data build project, Crowdworks have added various tools and features to our internal platforms, including polygon semantic segmentation tools to distinguish all objects in images. And We also added transparency controls for comparison to original images, and magnetic tools for pixel-wise tasks!
Self-driving data processing tool and function development case 01: Tagging property value verification function
Self-driving data processing tools and functional development case 02: Magnetic tools
If you have any questions about AI data, please feel free to visit Crowdworks!
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