The Technological Evolution, Applications and Future Prospects of Representative Artificial Intelligence Models

This article aims to systematically sort out the development trajectory of artificial intelligence technology from theory to application, and clarify the evolution of its core model, the current challenges and the future development trend, in order to provide a clear reference for in-depth research in this field. For this reason, this article first traces the development process and milestone events of artificial intelligence since the mid-20th century. Then, the core concepts and architectures of four representative model types (Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Transformer) and the successful application cases in key fields such as health care and finance are analyzed. In addition, through structured analysis, this article deeply explores the three core challenges faced by the current artificial intelligence system in the process of moving towards reliable and reliable deployment: model interpretability ("black box" problem), insufficient generalization ability and security vulnerabilities. In response to these problems, this article explores how key technical paths such as artificial intelligence, migration learning and federal learning can provide a blueprint for building the next generation of reliable artificial intelligence systems. Analysis shows that the development of artificial intelligence is shifting from the simple pursuit of performance breakthroughs to the construction of robust, safe and understandable intelligent systems. This transformation requires the coordinated development of algorithm innovation and ethical, legal and social frameworks.