Applications of Data Visualization Technology in Artificial Intelligence

Abstract

This paper systematically explores the applications of data visualization technology in artificial intelligence (AI). Data visualization plays a crucial role in various stages of AI, from data preprocessing, feature engineering, model training, and evaluation, to result interpretation and presentation. By offering intuitive visual representations, data visualization aids in understanding and exploring complex high-dimensional data, thereby enhancing data processing efficiency and model reliability. In the data preprocessing stage, visualization tools effectively identify outliers and noise, assisting in data cleaning and quality control. During feature engineering, techniques like Principal Component Analysis (PCA) and t-SNE help in feature selection and dimensionality reduction, improving model performance. Model training and evaluation benefit from visualizing performance metrics, facilitating model adjustment and optimization. Explainable AI (XAI) methods, such as LIME and SHAP, use visualization to enhance model transparency and credibility by illustrating decision-making processes. Despite the advantages, challenges remain in visualizing large-scale, high-dimensional, and real-time data, requiring advanced computational power and sophisticated algorithms. User cognition and interpretation pose additional challenges, necessitating intuitive and interpretable visualization interfaces. Data privacy and security also need to be ensured in the visualization process. Future developments in data visualization will focus on enhancing user interaction through technologies like VR and AR, integrating automation and intelligent features, and leveraging efficient computing resources. Open data and open-source tools will promote cross-disciplinary collaboration and technological innovation. Overall, data visualization technology will continue to play a vital role in AI, enhancing the efficiency, transparency, and interpretability of AI systems while addressing the ongoing challenges through continuous technological advancements.