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What use does the development of AI technology have for the digital economy?

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2024-02-28 21:50:42832browse

The artificial intelligence (AI) industry is an important part of the digital economy. In the early stages of digital transformation, enterprises pay more attention to basic data applications, such as management support or process support based on data query and data analysis.

What use does the development of AI technology have for the digital economy?

Many digital practitioners believe that data itself is a product. Once you have completed data governance, being able to clearly identify the data and understand its true business significance is quite an achievement.

The future trend of digital transformation will mainly benefit from the application of artificial intelligence technology. In recent years, the rise of large-scale models has furthered this trend.

More and more companies are beginning to realize that artificial intelligence technology is the core of digitalization 2.0.

Digitalization has evolved into digital intelligence, which means that software companies will face new challenges. Traditional SaaS and ERP logic often revolves around process design and IT implementation, and these logic may need to be rethought.

Under the trend of intelligence, both Party A and software manufacturers should pay attention to the actual value of data and transform from process-oriented to data element-oriented.

The value of data is divided into explicit value and implicit value. Explicit value is already reflected when data is integrated and used smoothly, while implicit value needs to be processed and discovered with the help of advanced algorithm technology.

It is reasonable to compare data to ingredients. The quality of data is crucial, and the technology and means of processing data are equally crucial, equivalent to being an excellent chef. With the popularization of cloud computing capabilities and the development of "low-code" MaaS platforms, the threshold for artificial intelligence technology has gradually been lowered, allowing more people to access and apply AI technology.

After enterprises can easily obtain AI technology, the next key step is to establish a data governance process specifically for AI applications. This will be a new direction for the development of data governance.

In AI data governance activities, in addition to continuously improving basic data quality improvement work, it is also necessary to build high-quality AI data sets.

For example, based on a specific strategy, select representative data samples that are of great value to model improvement, or use manual or semi-automatic methods to build a regularized data set that conforms to the training process paradigm.

Then the question is, what are the specific implementation directions for digital applications based on AI?

In fact, it is very simple. The essence of AI is automation, and artificial intelligence itself is also automation. important branch of technology.

The first is perception applications. Automatically extract valuable business information from multi-modal data (pictures, text, video, audio, etc.) to answer what now questions. what happened.

For example, intelligent text analysis, speech feature recognition, real-time image monitoring, etc.

The second is cognitive applications. Use the above information to predict unknown scenarios (currently unknown scenarios or future situations) and answer questions related to what future.

For example, financial indicator prediction, natural disaster warning, equipment risk assessment, etc.

The third is decision-making (generation) applications. Based on the answers to what now and what future, tell people or machines what to do and answer how questions.

For example, automatic content recommendation, intelligent document generation, dynamic resource scheduling, maintenance plan formulation, etc.

The intelligent attributes of AI technology come from the business knowledge and expert experience contained in the data resources themselves.

Constructing and deploying data elements in the form of AI models can quickly replicate business capacity and create a highly efficient knowledge-based and intelligent organization!

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