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【“数”话江苏新实践④】瞧,AI大模型让多场景应用提速
Yang Zi Wan Bao Wang· 2025-12-04 12:45
Core Insights - The articles highlight the integration of advanced AI technologies into various sectors, emphasizing their practical applications in enhancing productivity and learning experiences [1][4][7]. Group 1: AI in Office and Communication - AISPEECH DFM language model enables office tools to understand dialects and automatically summarize spoken content, enhancing workplace efficiency [1]. - The AI office tool, Turbo, can transcribe meetings in real-time, generate mind maps, and filter out redundant information, achieving a significant reduction in time spent on meeting summaries [3]. - The technology supports 17 dialects and 9 foreign languages, maintaining high accuracy even in challenging environments [3]. Group 2: AI in Education - The AI classroom experience at Qingrui Intelligent Technology showcases features like pronunciation correction and intelligent essay grading, significantly improving teaching and learning efficiency [5][6]. - The Ms. Aryn AI tutor has evolved to cover multiple subjects, employing an inquiry-based approach to enhance students' core competencies [6]. - AI tools are being implemented in over 30,000 public schools across hundreds of cities, demonstrating widespread adoption and impact on educational outcomes [5]. Group 3: AI in Industrial Applications - The "Wolong Mingli" multimodal model developed by Xianwei Information Technology integrates language, vision, and predictive capabilities, supporting various industrial applications [7][8]. - The model has been successfully applied in sectors such as transportation, manufacturing, and energy, facilitating data-driven decision-making processes [8]. - A case study involving a subsidiary of China Aluminum Group illustrates the model's ability to manage extensive data types and indicators for operational efficiency [8]. Group 4: Industry Development and Future Directions - The development of large models in Jiangsu is driven by advancements in computational infrastructure, data resource aggregation, and algorithm upgrades [9]. - Recommendations for enhancing the large model industry include strengthening technological innovation, deepening industry applications, and optimizing the ecosystem for better collaboration [9].