From imagination to reality, from the impossible to the possible, in recent years, people's exploration of AI has never stopped, and artificial intelligence-generated content (AIGC), as an important part of the new generation of artificial intelligence technology, is also evolving, and quietly leading a change, and its application will have a profound impact on the development of the digital economy and society.
2023 is the first year of AIGC's development, and as one of the large-scale model application scenarios, AIGC has completed the "first jump" from simple "chat" to "work" to service business scenarios and promote industrial transformation in a short period of time. So, what will be the development of AIGC in 2024?
Top 10 trends in the AIGC application layer
Recently, IDC, an internationally renowned consulting organization, jointly released the "Top Ten Trends of AIGC Application Layer in 2024"**, which provides in-depth insights into the future development of AIGC, including technological progress, application innovation, business model change, and security and ethical challenges, and emphasizes the important role of AIGC in promoting industrial transformation and promoting economic development, and puts forward ** and suggestions for future development trends.
IDC**, there will be ten major development trends in the AIGC application layer in 2024:
Top 10 Development Trends of AIGC Application Layer.
First, application layer innovation has become the definite direction of the development of the AIGC industry; Second, the large model has changed from "fashionable" to "really useful", and has become a means of improving efficiency; Third, exclusive and self-built models will emerge in medium and large enterprises; Fourth, the multi-modal large model expands the service boundary and brings a richer user experience. Fifth, AI agent (artificial intelligence**) is the mainstream form of large model landing business scenarios; Sixth, AIGC accelerates the formation of super entrances; Seventh, the business process has shifted to "non-sensory intelligence"; Eighth, applications are moving from cloud-native to AI-native; ninth, the gradual universalization of AIGC; Tenth, AIGC applications need to match the security measures.
**》It is believed that AIGC applications will be the first to be implemented in enterprise office and productivity scenarios, among which knowledge management is now the most popular application scenario for enterprises, which will provide B-end enterprise customers with more production optimization path options to reduce costs and increase efficiency.
According to ***, AI agent enables AIGC technology to have the ability to perceive, remember, plan and act, and can perform complex tasks across applications, making "human-machine collaboration" the new normal. In the future, AI agent will change the form of organizations, and more and more innovations will come from super individuals and small organizations.
Anmei's exploration of AIGC
As a technology company, Longyi has also been paying attention to and exploring the application of AIGC, and actively deploying the deep integration of AI, digital twins, big data and other fields with informatization, accelerating the output of AIGC, empowering the industry exploration and use of AI agent, and striving to explore more accurate AI in the vertical field of market segments.
Among them, the AMTT AI artificial intelligence platform under the Longyi AMTT system is a comprehensive management platform focusing on digital twin intelligent operation and maintenance developed based on AI technology and AIGC exploration.
The AMTT AI platform includes the following five core functions:
Data analysis**
Use machine learning and statistical methods to extract important features from massive data, establish first-class models, and continuously optimize and adjust.
Natural language processing
Based on neural network and deep learning technology, it realizes the understanding and interaction of human-machine natural language.
Computer vision
Based on neural network and deep learning technology, it realizes the understanding and interaction of human-machine natural language.
Data analysis**
Hyperparameter search, schema search and automatic machine learning methods are used to continuously optimize machine learning models from the perspectives of model selection, adjustment and integration.
Adaptive learning
Incremental learning, federated learning, reinforcement learning and transfer learning methods are used to realize the dynamic adjustment and knowledge transfer of machine learning models, so that their generalization ability on new tasks or data can be improved.
Through AI algorithm models and big data analysis capabilities, the platform conducts machine learning through massive data to achieve intelligent identification, analysis, human-computer interaction and other capabilities, helping enterprises reduce labor costs, improve efficiency, promote the development of enterprise intelligence and automation, facilitate user decision-making, and improve user experience.
Reference**: China Science News, etc.
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