Why is It so Difficult for Traditional Industries to Get AI Blessings? — Part2
Wu Enda believes that in terms of AI applications, industries other than the Internet industry face three major challenges: small data sets, high customization costs, and the long process from validating ideas to deploying AI production.
In this regard, Zhu Pengfei, a data expert feels the same. He analyzed the traditional manufacturing industry as an example. Data is a very prominent issue for traditional manufacturing enterprises in the process of transforming to intelligent manufacturing. First, there is a certain degree of difficulty in obtaining data. They do not have sensors to collect real-time data, and there is no data center. Therefore, the data is scattered, and the lack is severe.
Secondly, many of the data of various factories in the industry have commercial value, so the factories are strictly confidential, which leads to the lack of data circulation and no way to share, which in turn forms the data barrier and affects the optimization of the AI algorithm model.
“When we are developing an AI algorithm model, because of the confidentiality of the data, the data we get is often ‘desensitized’, which also seriously affects our judgment. However, companies in traditional industries lack technical personnel to develop AI algorithm models. So both sides have high barriers in the process of cooperative research and development.” Zhu Pengfei said.
In addition, data sources in traditional industries do not come from a single scenario like the Internet field. Complex business scenarios result in data that is often “dirty.” They must be “cleaned” to remove a large amount of invalid information so that AI algorithm models can be fed efficiently. “It’s like we teach children knowledge. Children learn faster when they are taught clean and understandable knowledge points. If there is a lot of useless information, the children won’t be able to distinguish. And the learning efficiency will decrease.” Zhu Pengfei said. While the work of labeling “knowledge points” for data is enormous and cumbersome. It requires people with expertise to do it, and it takes a lot of time and energy.
“For traditional manufacturing to obtain high-quality data, it must carry out informatization and the intelligent transformation of production equipment.” Zhu Pengfei said. The transformation requires enterprises to invest a lot of time and energy. And it will also increase production costs and become a barrier to the application of AI in traditional manufacturing.
High demand of labeled data
At present, the demand for the highest quality AI training data in various industries is urgent. AI is implemented in various fields, such as education, law, intelligent driving, banking, and finance, etc. Each field has requirements for subdivision and specialization.
Among them, in particular, traditional enterprises with intelligent transformation and technology enterprises need the assistance of training data service providers with rich project experience to help sort out the data labeling instruction and to obtain more suitable data. The use of high-quality data in special scenarios reduces the research and development cycle, accelerates the implementation process, and helps enterprises to make faster and better intelligent transformations.
In the process of in-depth industrial landing, there is still a gap between artificial intelligence technology and enterprise needs. The core goal of enterprise users is to use artificial intelligence technology to achieve business growth. Actually, artificial intelligence technology itself cannot directly solve all the business needs. It needs to create products and services that can be implemented on a large scale based on specific business scenarios and goals.
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