Why is It so Difficult for Traditional Industries to Get AI Blessings? — Part3
High-quality Data is a Prerequisite for the Application
In the past ten years, most AI research, development, and application have been “software-centric” driven. With massive data support, the software and algorithms are continuously optimizing to obtain higher accuracy. In the case that traditional industries cannot improve the quality and quantity of data， Wu Enda, an AI expert believes that traditional industries should adopt a “data-centric” model. Under this kind of thinking, some good application cases have already emerged in traditional industries. For example, the image recognition AI system in the medical field can help doctors examine CT images, identify tumors and other lesions, and assist doctors in making judgments.
Zhu Pengfei, the vice-professor of Tianjin University introduced that the data is relatively accurate for some AI products, and the AI algorithm model has made rapid progress in the learning process. At present, the accuracy of many image recognition systems can reach more than 90%. As their job is like an assistant, doctors are required to make medical decisions in the end, but this level of accuracy has greatly reduced the work intensity.
“Although there are some successful cases of AI technology in traditional industries, if you want to better integrate with AI, you have to work hard to improve data quality.” Zhu Pengfei suggested that first of all, traditional industries that have accumulated massive amounts of data should actively release data, under the premise of ensuring data security. There will be a lot of space for development when mining the value hidden in the data and linking it with the demand. Secondly, for emerging industries, such as new energy vehicles, when building smart factories, factors such as data collection should be taken into consideration.
However, Zhu Pengfei emphasized that while using AI technology in traditional industries, we should avoid AI abuse. It should be assessed carefully before applying in the real scenario. If production efficiency cannot be improved, blindly using AI technology is a waste of resources.
For example, some application scenarios require AI algorithms to achieve an accuracy of more than 99% before they can be used. Through evaluation, the existing model algorithms can only achieve an accuracy of 90%, so there is no need to force AI technology in this scenario. “All in all, for the application of AI technology, data must come first. It is difficult to have good applications without good data.” Zhu Pengfei said.
With the acceleration of the commercialization of AI and the application of AI technologies such as assisted driving and customer service chatbot in all walks of life, the expectation of data quality in the special scenarios is getting higher and higher. High-quality labeled data would be one of the core competitiveness of AI companies.
If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.
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