{"id":1546,"date":"2021-04-27T10:00:00","date_gmt":"2021-04-27T01:00:00","guid":{"rendered":"https:\/\/blog.testworks.co.kr\/en\/?p=1546"},"modified":"2022-12-28T15:41:40","modified_gmt":"2022-12-28T06:41:40","slug":"the-importance-of-gan-in-creating-ai-training-datasets","status":"publish","type":"post","link":"https:\/\/blog.aiworkx.ai\/en\/the-importance-of-gan-in-creating-ai-training-datasets\/","title":{"rendered":"The Importance of GAN in Creating AI Training Datasets"},"content":{"rendered":"\n
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At the International Space Conference (IAC)[i]<\/strong><\/span> held in Guadalajara, Mexico on September 27, 2016, SpaceX and Tesla CEO Alan Musk said, \u201cWe will send our first rover to Mars by 2018 and build our first space colony in 2024.\u201d In the movie, Iron Man’s AI assistant Jarvis performs complex tasks ranging from designing, manufacturing, assembling, and driving a robot suit as well as everyday life according to the gestures and voices of ‘Robert Downey Jr.’ With the efforts and imagination of scientists around the world, it seems that we will be able to enjoy the fruits of technology in the not-too-distant future.<\/p>\n\n\n\n

In June 2015, immediately after the launch of Google Photos, the news of a picture of a black man living in the United States with a black friend was classified as ‘gorillas’ was revealed, leading to a controversy over ‘racism’. Google promised to correct it immediately, but in the end, the American information technology magazine Wired exposed in 2018 that Google simply resolved the problem by simply deleting the keywords and classifications from the system. In April 2021 (current time), I had a short conversation with an AI speaker that did not work properly except for \u201cTell me the weather today,\u201d boarded a human-driven ‘non-autonomous vehicle’, and went to work at the Jamsil Testworks headquarters. Still, there is a greater gap between people’s expectations for artificial intelligence and the actual reality [ii]<\/span><\/strong>. As a person whose job is to develop an AI model, I feel embarrassed.<\/p>\n\n\n\n

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[Figure 1] Gartner Hype Cycle shows the progression of technology from expectations to reality<\/strong><\/figcaption><\/figure><\/div>\n\n\n\n
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Then, despite the innovative announcements made by world-class scholars and CEOs of global IT companies every year, why hasn’t our life changed so much? Data that is the foundation of AI training is important for the success of the ‘4th Industrial Revolution’ led by AI. In industries that have not yet benefited from the AI innovation, developers tend to say, ‘Give us AI too’, but the Machine Learning engineers respond with ‘Give us data first’. I would like to show that Generative Adversarial Network (GAN) [iii]<\/span><\/strong> technology is one way to solve the lack of data problem.<\/p>\n\n\n\n

Ian goodfellow, who first proposed the GAN, likened it to a game between the police and a counterfeiter. GAN repeats learning with the goal of generating counterfeit bills that can deceive the police in such a way that it synthesizes the fake data at a level that the police cannot distinguish between real and fake. This technology plays two important roles for data for artificial intelligence training.<\/p>\n\n\n\n

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First, it solves the problem of sparse data through quantitative and qualitative advancement of data.<\/h1>\n\n\n\n

In order to build a data set for AI learning, 1) data collection and 2) data annotation are required. If even one of these processes is not complete, it is difficult to learn the expected artificial intelligence model.<\/p>\n\n\n\n

1) Examples of collection difficulties<\/strong><\/p>\n\n\n\n