智源研究院发布2026十大AI技术趋势：NSP范式重构世界认知，超级应用与安全并进

Core Insights - The report from Beijing Zhiyuan Artificial Intelligence Research Institute highlights a significant shift in AI evolution from parameter scale in language learning to a profound understanding and modeling of the physical world, indicating a transformation in industry technology paradigms [1][2] Group 1: Key Trends in AI Development - The transition to a new cognitive paradigm is driven by the focus on world models and Next-State Prediction (NSP), enabling AI to learn physical laws and providing a new cognitive foundation for complex tasks like autonomous driving and robotics [2][3] - The embodiment of intelligence is moving from software to physical entities, with humanoid robots entering real production scenarios, marking the emergence of "embodied intelligence" beyond laboratory demonstrations [2][3] - The standardization of mainstream agent communication protocols is facilitating multi-agent systems (MAS) to tackle complex tasks collaboratively, thus becoming a critical infrastructure in research and industry [3] Group 2: AI's Role in Research and Industry - AI is evolving from a supportive tool to an autonomous researcher, termed "AI Scientist," which will significantly accelerate the development of new materials and pharmaceuticals through the integration of scientific foundational models and automated laboratories [4] - The competition for consumer AI super applications is intensifying, with major players like OpenAI and Google leading the way in creating integrated intelligent assistants, while domestic companies like ByteDance and Alibaba are actively building their ecosystems [4][6] - The enterprise-level AI applications are entering a "valley of disillusionment" due to data and cost issues, but a turnaround is expected in the second half of 2026 as data governance and toolchain maturity lead to measurable value products in vertical industries [7] Group 3: Data and Performance Optimization - The rise of synthetic data is becoming crucial for model training as high-quality real data faces depletion, particularly in autonomous driving and robotics, where synthetic data generated by world models will be key assets [8] - The efficiency of inference remains a core bottleneck for large-scale AI applications, with ongoing algorithm innovations and hardware advancements driving down costs and improving energy efficiency, enabling high-performance models to be deployed at the edge [9] - The development of a compatible software stack for heterogeneous chips is essential to break the monopoly on computing power and supply risks, with platforms like Zhiyuan FlagOS aiming to create an open and inclusive AI computing foundation [10] Group 4: AI Security and Risk Management - AI security risks have evolved from "hallucinations" to more subtle "systemic deception," with ongoing research and industry efforts focusing on understanding model mechanisms and establishing comprehensive security frameworks [11]