Abstract: Over the past few decades, artificial intelligence (AI) technology has rapidly developed from systems capable of performing simple automated tasks to complex intelligent robots that are reshaping industry and daily life. However, while AI has demonstrated remarkable capabilities in processing vast amounts of data and executing predefined tasks, the essence of true intelligence lies in its ability to interact deeply with dynamic environments and make adaptive decisions in real time. AI systems must transcend their current limitations and evolve into entities that can autonomously perceive, learn, and adapt to their surroundings without relying on human intervention. A notable challenge in achieving this goal is the fundamental contradiction between the discrete symbolic processing methods employed by traditional AI systems and the continuous nature of the physical world. Real-world environments are inherently continuous, with phenomena such as motion, aging, and light variations that unfold seamlessly over time. In contrast, AI systems often rely on discrete representations, such as pixelated images or labeled data points, which inherently fail to capture the subtle, cumulative effects of continuous changes. The disconnect between discrete processing and continuous reality underscores the need for AI systems to develop new paradigms that enable continuous environmental modeling and real-time adaptation. Another critical limitation is in the constrained feature spaces of traditional AI systems, which makes it difficult to handle the infinite possibilities of open environments. Dynamic environments are characterized by an ever-expanding state space in which parameters such as object shapes, lighting conditions, and interactions between entities combine in countless unforeseen ways. Current AI systems, constrained by their predefined feature dimensions, struggle to adapt to novel scenarios outside of their training data. To address this limitation, AI systems must develop elastic cognitive frameworks that can dynamically expand their feature spaces, thereby enabling them to adaptively process unforeseen information. Furthermore, the static deployment model of traditional AI systems poses a barrier to achieving true intelligence. Most AI systems are trained offline and deployed with fixed algorithms, which limit their ability to learn and adapt in real time as they interact with their environments. Intelligent entities must be capable of continuous learning and evolution, updating their knowledge, and decision-making processes based on new experiences. This requires overturning existing static learning paradigms, enabling AI systems to continuously refine their models and adapt to changing circumstances. The future of AI lies in its ability to deeply interact with and adapt to complex and dynamic environments. By addressing the limitations of discrete processing, fixed feature spaces, and static deployment models, and by leveraging advancements in electronic materials, we can pave the way for truly intelligent systems that operate autonomously and evolve alongside their environments. In this paper, we provide a brief overview of the interaction technologies between AI and the environment, discussing current advancements and exploring future innovations and applications of AI in environmental interactions, as well as the challenges from the perspective of electronic materials. It aims to inspire further research and innovation in this transformative field, ultimately unlocking the full potential of AI.