Jing-Zong Zhou

Extensive Reading in the Age of Artificial Intelligence Key Constructs in Language Teaching

Physical AI Governance: From Theory to Practice Across Life Cycle

With the emergence of Physical AI, artificial intelligence is extending beyond screen-based applications to embodied systems that perceive, interact with, and act in the physical world. Unlike traditional AI, Physical AI operates under real-time safety constraints, continuously interacts with dynamic environments, and coexists with humans, introducing governance challenges that existing AI governance frameworks do not explicitly address. This paper presents a comprehensive survey of Physical AI governance from both scientific and operational perspectives. We synthesize existing governance principles and organize them into a unified governance framework tailored to physical AI systems. Building on this foundation, we propose a five-stage Physical AI lifecycle comprising research, design, data, model development, and deployment, and demonstrate how governance can be operationalized across each stage through concrete implementation practices. By connecting governance principles with engineering workflows, this survey provides a structured reference for researchers, developers, and policymakers to build Physical AI systems that are safe, trustworthy, and aligned with societal values.

Commercial applications and optimization of deep learning image recognition based on artificial intelligence

In the current era of rapid development of artificial intelligence technology, the application of deep learning algorithms in the field of image recognition has become a key driving force for business innovation. Traditional image recognition methods, due to their insufficient efficiency and accuracy, are unable to meet the complex and variable requirements of business scenarios. This article focuses on the deep learning algorithms based on artificial intelligence, and deeply explores their specific applications in commercial fields such as precise diagnosis of medical images, efficient quality inspection of industrial products, and real-time monitoring of intelligent security. It demonstrates the value of enhancing business operation efficiency and quality. At the same time, in response to practical application challenges such as high data annotation costs and poor model robustness, research proposes optimization paths, covering technical means such as data augmentation, lightweighting of models, and transfer learning. Experiments show that the optimized algorithm significantly improves image recognition performance, providing practical guidance for the promotion of deep learning algorithms based on artificial intelligence in the commercial image recognition field.