2025人工智能产业十大关键词

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].