Core Insights - Smart materials have emerged as a cutting-edge direction in the new materials field, with the potential to profoundly change human society through deep integration with artificial intelligence [1][6] Summary by Sections Definition and Characteristics of Smart Materials - Smart materials are defined as materials that can sense environmental changes, make judgments, and execute commands, distinguishing them from traditional materials [2] - Key characteristics include the ability to actively change shape, hardness, color, and function in response to external stimuli, integrating sensing, driving, and information processing [2][3] Types and Applications of Smart Materials - At least twenty types of smart materials have been identified, including: - Piezoelectric materials for energy conversion and vibration control - Shape memory alloys used in medical devices like stents - Magnetostrictive materials for ultrasound transducers - Electrorheological and magnetorheological materials for adaptive dampers in vehicles - Self-healing materials for automotive applications [3][4][5] Integration with Artificial Intelligence - The concept of "wise materials" has been introduced, where smart materials are enhanced with AI capabilities, allowing them to make autonomous decisions and adapt to their environment [6] - Examples of applications include AI-controlled prosthetics and smart lenses that adjust focus based on eye movement [6] Market Potential and Future Outlook - The global smart materials market is projected to reach up to $250 billion by 2030, driven by advancements in AI, brain science, and biotechnology [7] - In various sectors, smart materials are expected to lead to innovations such as personalized medical treatments, green energy solutions, and enhanced safety systems [7] Current Status and Challenges - While China leads in basic research and certain advanced fields, there remains a gap in the transformation of consumer products and the establishment of an industrial ecosystem compared to developed countries [8] - Emphasis is placed on the need for interdisciplinary collaboration to accelerate product development and application [8]

Core Viewpoint - The article discusses the latest advancements and trends in artificial intelligence and robotics, highlighting various innovative fields and technologies that are shaping the future of these industries. Group 1: Artificial Intelligence and Robotics - Artificial intelligence aims to replicate human-like intelligence in machines, encompassing areas such as robotics, language recognition, and image recognition [12][19][22] - Key technologies in AI include machine learning, neural networks, and natural language processing, which are essential for developing intelligent systems [19][22] - The rise of swarm intelligence is noted, where collective behavior of multiple agents can lead to enhanced problem-solving capabilities in various applications [15][16] Group 2: Innovative Fields - Nine major innovative fields are identified, including human-machine interaction, biohybrids, and radical social innovation breakthroughs [8][89] - The article emphasizes the importance of interdisciplinary research in driving advancements in these fields, particularly in integrating AI with other technologies [8][89] Group 3: Emerging Technologies - Technologies such as hyperspectral imaging, speech recognition, and touchless gesture recognition are highlighted for their potential applications in various sectors [10][13][29] - The development of flying cars and autonomous vehicles is discussed, emphasizing the need for advancements in materials and battery technology to make these innovations feasible [32][33] Group 4: Material Innovations - Liquid metal technology is presented as a frontier material with applications in electronics and flexible devices, showcasing its unique properties [34][37] - The article also covers the advancements in high-temperature alloys and carbon fiber, which are crucial for aerospace and automotive industries [39][56] Group 5: Future Directions - The article suggests that the integration of AI with neuroscience could lead to breakthroughs in understanding human cognition and developing smarter systems [24][90] - It calls for continued investment in research and development to maintain competitiveness in the global market for AI and robotics technologies [86][88]