智源研究院发布2026十大AI技术趋势，AI将从数字世界迈入物理世界

Core Insights - The report by Beijing Zhiyuan Artificial Intelligence Research Institute outlines a significant shift in AI development from parameter scaling in language learning to a deeper understanding and modeling of the physical world, indicating a paradigm shift in industry technology [1][3] Group 1: Key Trends in AI Development - The transition from "predicting the next word" to "predicting the next state of the world" signifies the emergence of the Next-State Prediction (NSP) paradigm, which is expected to drive AI from digital perception to physical cognition and planning [4][5] - The report identifies 2026 as a critical turning point for AI, marking the transition from digital to physical applications and from technical demonstrations to scalable value [3][4] Group 2: Cognitive and Physical Integration - AI is moving towards a higher cognitive paradigm, focusing on world models and NSP, which will provide a new cognitive foundation for complex tasks such as autonomous driving and robotics [4][5] - The concept of "embodied intelligence" is evolving from laboratory demonstrations to real-world industrial applications, with humanoid robots expected to enter actual production scenarios by 2026 [5][6] Group 3: Multi-Agent Systems and Collaboration - The standardization of communication protocols for multi-agent systems (MAS) is crucial for solving complex problems, enabling agents to collaborate effectively in various fields such as research and industry [6][7] - The role of AI in research is shifting from a supportive tool to an autonomous "AI scientist," which will accelerate the development of new materials and pharmaceuticals [7][8] Group 4: Market Dynamics and Applications - The competition for consumer AI applications is intensifying, with major tech companies developing integrated AI portals, exemplified by Ant Group's multimodal AI assistant and health applications [8][9] - The enterprise AI sector is entering a "trough of disillusionment" due to challenges like data and cost, but a recovery is anticipated in the second half of 2026 as data governance and toolchains mature [9][10] Group 5: Data and Performance Optimization - The reliance on synthetic data is increasing as high-quality real data becomes scarce, particularly in fields like autonomous driving and robotics, where synthetic data generated by world models will be key [10][11] - The efficiency of AI inference remains a critical focus, with ongoing innovations in algorithms and hardware expected to lower costs and enhance performance, facilitating the deployment of high-performance models in resource-constrained environments [11][12] Group 6: Open Source and Security - The development of a compatible software stack for heterogeneous chips is essential to break the monopoly on computing power and mitigate supply risks, with platforms like Zhiyuan FlagOS leading this initiative [12][13] - AI security risks are evolving from "hallucinations" to more subtle "systemic deceptions," prompting the need for comprehensive safety frameworks and research initiatives to address these emerging threats [13][14]