Core Insights - The 2025 Artificial Intelligence Industry Conference highlighted ten key trends in AI, emphasizing the convergence of technology, applications, and ecosystems, leading to a clearer vision of a smart-native world [1]. Group 1: Foundation Super Models - In 2025, foundational models and reasoning models are advancing simultaneously, with a comprehensive capability increase of over 30% from late 2024 to August 2025 [3][4]. - Key features of leading large models include the integration of thinking and non-thinking modes, enhanced understanding and reasoning abilities, and built-in agent capabilities for real-world applications [4][6]. - The emergence of foundational super models simplifies user interaction, enhances workflow precision, and raises new data supply requirements [6]. Group 2: Autonomous Intelligent Agents - Highly encapsulated intelligent agent products are unlocking the potential of large models, showing better performance in complex tasks compared to single models [9][10]. - Current intelligent agents still have significant room for improvement, particularly in long-duration task execution and interconnectivity [12]. Group 3: Embodied Intelligence - Embodied intelligence is transitioning from laboratory settings to real-world applications, with models being deployed in practical scenarios [15][16]. - Challenges remain in data quality, model generalization, and soft-hard coordination for effective task execution [18]. Group 4: World Models - World models are emerging as a core pathway to general artificial intelligence (AGI), focusing on capabilities like data generation, action interpretation, environment interaction, and scene reconstruction [21][22]. - The development of world models faces challenges such as unclear definitions, diverse technical routes, and limited application scope [22]. Group 5: AI Reshaping Software - AI is transforming the software development lifecycle, with significant increases in token usage for programming tasks and the introduction of advanced AI tools [25][28]. - The role of software developers is evolving into more complex roles, leading to the emergence of "super individuals" [28]. Group 6: Open Intelligent Computing Ecosystem - The intelligent computing landscape is shifting towards an open-source model, fostering collaboration and innovation across various sectors [30][32]. - The synergy between software and hardware is improving, with domestic hardware achieving performance parity with leading systems [30]. Group 7: High-Quality Industry Data Sets - The focus of AI data set construction is shifting from general-purpose to high-quality industry-specific data sets, addressing critical quality issues [35][38]. - New data supply chains are needed to support advanced technologies like reinforcement learning and world models [38]. Group 8: Open Source as Standard - Open-source initiatives are reshaping the AI landscape, with significant adoption of domestic open-source models and a growing number of active developers [40][42]. - The business model is evolving towards "open-source free + high-level service charges," promoting cloud services and chip demand [42]. Group 9: Mitigating Model Hallucinations - The issue of hallucinations in large models is becoming a significant barrier to application, with ongoing research into mitigation strategies [44][46]. - Various approaches are being explored to enhance data quality, model training, and user-side testing to reduce hallucination rates [46]. Group 10: AI as an International Public Good - Global AI development is uneven, necessitating international cooperation to promote equitable access to AI technologies [49][51]. - Strategies are being implemented to address challenges in cross-border compliance and data flow, aiming to make AI a truly shared international public good [51].

Group 1 - The conference highlighted five major trends in artificial intelligence, including accelerated iteration of foundational large models, a shift in focus towards post-training and inference stages, deep collaboration between hardware and software, the rise of intelligent agents and the intelligent agent economy, the promotion of open-source ecosystems, and increasing demands for AI safety governance [1] - Beijing Economic-Technological Development Area is committed to building a comprehensive AI city, with plans to establish a national AI data training base, the largest public computing power platform in the city, and to implement special policies and funding exceeding 1 billion yuan to support major projects in various AI-related fields [1] - By the end of 2025, the development goals include opening 100 landmark application scenarios, gathering 600 core enterprises, and achieving an industry scale target of 80 billion yuan [1] Group 2 - The AI hardware and software testing and verification center was officially launched, aiming to provide key testing and verification capabilities for AI hardware and software, with four core capabilities established [2] - The center has partnered with major companies to create innovation labs and testing facilities to accelerate the innovation and prosperity of intelligent computing technologies [2] - Five major achievements in AI hardware and software collaborative innovation were announced, showcasing significant breakthroughs across the entire technology chain from foundational computing power to framework software [2] Group 3 - The center completed the first batch of testing and evaluation for the adaptation of large models and domestic hardware and software, with several companies successfully passing the evaluation [3] - The conference awarded certificates to institutions that passed the unified benchmark testing, marking a new stage in the standardized and quantifiable development of AI collaborative innovation ecosystems [3] - The AI safety governance initiative was highlighted, with 18 companies disclosing their safety practices, contributing to the establishment of a solid foundation for responsible AI development [3] Group 4 - The vice president of the China Academy of Information and Communications Technology emphasized the urgent need to address challenges in hardware and software collaboration for building an open intelligent computing ecosystem [4] - The AISHPerf 2.0 benchmark system was officially released, featuring upgrades to support multiple inference engines and domestic open-source model loads, addressing various evaluation needs [4] - The academy has initiated a series of collaborative testing and verification efforts based on AISHPerf, focusing on large model adaptation and key collaborative technologies [4]