Deep Learning Technologies and Their Applications in the Advancement of Artificial Intelligence

Abstract

The study briefly summarizes and analyzes the use of deep learning technology in the development of artificial intelligence. In response to the actual needs of AI’s evolution from perceptual intelligence to cognitive and decision-making intelligence, the algorithm of multi-layer representation learning model for deep learning has changed from the initial convolutional recurrent networks to residual structures and generative adversarial networks. In order to carry out large-scale training in computing, it is particularly noteworthy that real-time object detection, semantic segmentation, semantic understanding of natural language and machine translation in computer vision are being analyzed, and new usage methods in important fields such as judging complex feedback learning and generative model are being analyzed. The research shows that deep learning uses end-to-end feature abstraction and data-driven mechanism, which greatly improves the scope and practical application advantages of AI system, effectively promoting the implementation of strategic applications such as autonomous driving, precision medicine, intelligent interaction and autonomous decision-making. However, there are still some unsolved problems such as model interpretability, energy consumption optimization, data bias, etc. Future research needs to be more deeply integrated with symbolic reasoning, causal learning, edge computing and other technologies to establish a safer and more efficient general-purpose AI system. This study provides an organized reference framework for the theoretical discovery and industrial practice of deep learning-enabled AI.