Investment Rating - The report does not explicitly provide an investment rating for the industry Core Insights - Artificial Intelligence (AI) is revolutionizing communication services, enhancing user interaction and creating new business models within the industry [3][7] - The integration of AI into terminal devices is transforming smartphones, wearables, and robots, leading to smarter and more interactive user experiences [28][39] - The communication industry is expected to undergo significant changes as AI technologies advance, fostering collaboration among various stakeholders [47][50] Summary by Sections 1. Introduction - AI is rapidly transforming industries and society, driven by advancements in deep learning, algorithms, and data accumulation [3] - AI is reshaping application design and user interaction, moving beyond traditional keyword-based models to more sophisticated, context-aware systems [3] 2. AI Opens New Frontiers for Communication Services - AI is creating a more intuitive and user-centered interaction experience by integrating multiple modes of communication [10][11] - Enhanced call experiences are being developed through real-time translation and AI-driven features that personalize user interactions [13][14] - AI is revolutionizing information delivery by enabling smart Q&A, filtering, and cross-language communication [19][21] - The introduction of immersive communication technologies is transforming various sectors, including healthcare and education [24][25] 3. AI Paves New Paths for Terminal Development - AI is driving the evolution of smartphones, wearables, and robots, enhancing their capabilities and user interactions [28][39] - The market for AI smartphones is projected to grow significantly, with a compound annual growth rate of 63% from 2023 to 2028 [30] - Wearable devices are becoming more autonomous and integrated, providing personalized health monitoring and smart interactions [31][33] 4. AI Revitalizes the Communication Service Ecosystem - New entrants, including AI solution providers and vertical industry players, are injecting vitality into the communication ecosystem [48][49] - AI is expected to deepen collaboration among ecosystem participants, leading to innovative applications and service models [50][51] - The traditional role of operators is shifting from "pipeline providers" to "new information service providers," creating diverse service ecosystems [52][53] 5. Challenges in Service Innovation - The implementation costs of AI services are high, and the effectiveness often falls short of user expectations [56] - Technical challenges arise from the increased data transmission demands of AI applications, impacting network performance [58][59] - Security challenges include the risk of technology misuse and data safety concerns, necessitating robust protective measures [61][62] 6. Promoting Industry Cooperation for AI-Driven Communication Services - The integration of AI and communication technologies is expected to drive the intelligent transformation of various industries [67] - Operators and partners are encouraged to explore innovative AI-driven communication applications to enhance service efficiency and user experience [68] - There is a need for advanced network capabilities and enhanced data security measures to support the growing demands of AI applications [70]

Investment Rating - The report does not explicitly state an investment rating for the industry Core Insights - The importance of data assets is increasingly recognized as a core driver of modern economic activities, enabling businesses to gain market insights and optimize operations [7][9] - Tokenization of data assets transforms static information into dynamic, tradable assets, enhancing their value and facilitating market transactions [13][15] - The rise of the data asset circulation economy signifies a new economic era centered around data, necessitating efficient and secure data trading markets [17][18] Summary by Sections Introduction - Data assets are crucial for competitive advantage across various sectors, including finance, retail, healthcare, and urban management [7][9] - Tokenization broadens the trading boundaries of assets, allowing for lower costs and increased efficiency in transactions [10][11] Background of Data Asset Tokenization - The emergence of data asset circulation economy marks a significant leap in productivity, driven by advancements in big data, cloud computing, and AI [17] - Challenges in data circulation include unclear ownership, privacy protection, security risks, and the need for a robust market mechanism [19][20] Theoretical Foundation and Key Technologies - The theoretical basis for data asset tokenization combines modern economics and information theory, emphasizing the value realization mechanism of data [26] - Key technologies include blockchain for secure transactions, distributed digital identity for identity verification, and encryption for data protection [27][28][29] Implementation Pathways - The implementation of data asset tokenization involves three key stages: data rights confirmation and authorization, token design and issuance, and market construction [34][41] - Data rights confirmation ensures clear ownership and control over data, while token design focuses on creating a viable economic model for data assets [39] Application Cases - In the telecommunications industry, data asset tokenization allows for the trading of aggregated and anonymized statistical data, enhancing decision-making for various stakeholders while ensuring user privacy [46]

Group 1: Background and Development - The rapid development of artificial intelligence has made multimodal large models a focal point since the introduction of the Transformer model in 2017, with significant advancements seen in models like GPT-4[2] - Multimodal large models can process diverse data types, including text, images, and audio, showcasing their potential in various applications such as video analysis and multi-target recognition[2] - The need for an objective and scientific evaluation system for these models is critical for their development and application in real-world scenarios[2] Group 2: Evaluation Challenges - Evaluating multimodal large models faces challenges such as diverse evaluation data, complex tasks, and high costs, necessitating a comprehensive evaluation framework[3] - The high complexity of these models requires careful selection of evaluation tasks to accurately reflect their capabilities without exceeding their limitations[11] - The subjective nature of some evaluation tasks, particularly in creative outputs, demands a standardized assessment framework to ensure fairness and consistency[14] Group 3: Evaluation Framework - The "Yiheng" evaluation system is proposed, featuring a "2-4-6" structure that includes 2 evaluation scenarios, 4 evaluation elements, and 6 evaluation dimensions[33] - Key evaluation dimensions include functionality, accuracy, reliability, safety, and interactivity, ensuring a comprehensive assessment of the models' capabilities[33] - The evaluation framework emphasizes user perspectives, aiming to align model performance with real-world application needs[32]