智源研究院发布2026十大AI技术趋势

Core Insights - The report from Beijing Zhiyuan Artificial Intelligence Research Institute outlines the key trends in AI technology for 2026, indicating a significant shift from language models to a deeper understanding and modeling of the physical world, marking a paradigm shift in industry technology. Group 1: AI Technology Trends - Trend 1: The consensus in the industry is shifting towards multi-modal world models that understand physical laws, with Next-State Prediction (NSP) emerging as a new paradigm, indicating AI's advancement from perception to true cognition and planning [1] - Trend 2: Embodied intelligence is moving from laboratory demonstrations to industrial applications, with humanoid robots expected to transition from demos to real industrial and service scenarios by 2026 [2] - Trend 3: Multi-agent systems are becoming crucial for solving complex problems, with communication protocols like MCP and A2A nearing standardization, allowing agents to collaborate effectively [2] Group 2: AI in Research and Industry - Trend 4: AI is evolving from a supportive tool to an autonomous researcher, termed "AI Scientist," which will significantly accelerate the development of new materials and drugs [2] - Trend 5: The new "BAT" in the AI era is becoming clearer, with major players focusing on integrated AI super applications, exemplified by OpenAI's ChatGPT and Google's Gemini, as well as domestic efforts by companies like ByteDance and Alibaba [3] - Trend 6: Enterprise-level AI applications are entering a "trough of disillusionment" due to data and cost issues, but a turnaround is expected in the second half of 2026 as data governance and toolchains mature [4] Group 3: Data and Performance - Trend 7: The rise of synthetic data is expected to mitigate the impending data scarcity, particularly in autonomous driving and robotics, where synthetic data generated from world models will be key [4] - Trend 8: Optimization of inference is still a core bottleneck for large-scale AI applications, with ongoing algorithmic innovations and hardware changes leading to reduced inference costs and improved energy efficiency [5] Group 4: AI Ecosystem and Security - Trend 9: The development of an open and inclusive AI computing foundation is crucial to breaking the monopoly on computing power, with platforms like Zhiyuan FlagOS aiming to create a decoupled software stack [6] - Trend 10: AI security risks have evolved from "hallucinations" to more subtle "systemic deception," with various initiatives underway to enhance safety mechanisms and internal understanding of model mechanisms [7]