2026十大AI技术趋势：应用拓展、模式探索与底层技术齐头并进

Core Insights - The report from Beijing Zhiyuan Artificial Intelligence Research Institute outlines the top ten AI technology trends for 2026, highlighting advancements in multimodal AI, embodied intelligence, and multi-agent systems [1][3][4]. Group 1: Multimodal AI and World Models - In 2025, discussions around multimodal AI surged, with expectations for 2026 to see further exploration of world models that can simulate real-world laws, enhancing AI's understanding of physical concepts [3][4]. - The value of world models lies in their ability to mimic human cognitive processes, enabling AI to tackle problems that are simple for humans but challenging for machines [3]. Group 2: Embodied Intelligence - As of 2025, over 230 companies in China are focused on embodied intelligence, with more than 100 in humanoid robotics, indicating a significant industry presence [4]. - The report anticipates a potential reshuffling in the embodied intelligence sector due to global economic uncertainties, with companies needing to adapt to evolving foundational models [4]. - Humanoid robots are expected to advance into real-world applications, with examples like Tesla Robotics' Optimus 2.5 being utilized in various operational settings [4]. Group 3: Multi-Agent Systems - The transition from single-agent to multi-agent systems is seen as essential for adapting to complex workflows, with multi-agent systems demonstrating advantages in handling intricate tasks [5]. - Communication protocols among agents are expected to mature, facilitating practical applications in production environments by 2026 [5]. Group 4: AI in Scientific Research - The emergence of AI Scientists capable of executing complete research processes marks a significant shift in scientific discovery, driven by foundational models and automated experimental facilities [6]. - The U.S. has initiated the "Genesis Mission" to enhance AI's role in scientific research through integrated platforms and efficient data sharing mechanisms [6]. Group 5: AI for Science in China - China faces challenges in the AI for Science domain, particularly in computational power, data, and model infrastructure, despite its relative advantage in AI applications [7]. - Progress is being made with the establishment of a national scientific data sharing platform, but there is a need for improved scientific foundational models [7]. Group 6: Personal and Industry Applications - The rapid development of AI personal applications in 2025 has led to the rise of "AI super applications," which integrate multiple services for users [8]. - Industry applications are still in exploratory phases, with more complex AI agents facing challenges such as data quality and system integration [8]. Group 7: Synthetic Data and AI Safety - The shift towards synthetic data is anticipated as high-quality data resources dwindle, with the synthetic data market in China growing significantly from 1.18 billion to 4.76 billion in four years [10]. - AI safety concerns are rising, with reports indicating that leading models struggle with preventing misuse, prompting the industry to develop new security frameworks [11].