Survey on Data-driven Intelligent Computing and Its Applications

In the era of big data with the rapid development of information technology, data presents the characteristics of massive, diverse and high-speed. Traditional computing paradigms face challenges such as low efficiency and poor adaptability when dealing with large-scale, high-dimensional and complex data, and data-driven intelligent computing emerges. With the help of machine learning, deep learning and other technologies, this computing mode can automatically learn knowledge and build models from data to realize intelligent analysis and decision-making of complex problems. Intelligent computing models include supervised learning, unsupervised learning and reinforcement learning. In practical applications, image recognition and computer vision, natural language processing, intelligent recommendation systems and other fields have achieved remarkable results, but they also face challenges such as data quality, model interpretability, computing resource requirements, and privacy security. Data-driven intelligent computing will develop in the direction of integration with knowledge graph, edge computing, cloud computing, and cross-domain application expansion, and continue to promote the intelligent process in various fields.