未可知人工智能研究院·2025-07-16 10:34Core Viewpoint - The event highlighted the importance of integrating AI into daily workflows, emphasizing that the future workplace will differentiate between those who can leverage AI to enhance their capabilities and those who cannot [7][11]. Group 1: Event Overview - Dr. Du Yu, director of the Unknown AI Research Institute, conducted a training session at Shanghai Electric Group, focusing on AI [1]. - The session revolved around two new books: "DeepSeek User Guide: Practical Applications in All Professional Scenarios" and "DeepSeek: Understanding the Underlying Logic of the AI Era," which were well-received by participants [2][15]. Group 2: AI Integration and Methodology - DeepSeek is described as a set of pluggable workplace capability modules rather than just a chat tool, capable of generating industry reports in 3 minutes, summarizing 50-page meeting notes into a single PPT in 10 seconds, and automatically creating travel documents in one sentence [5]. - Participants experienced hands-on demonstrations, leading to comments about the ease of writing weekly reports [6]. Group 3: AI Value Model - Dr. Du presented the "AI Three-Layer Value Model," addressing concerns about AI replacing jobs by stating that future professionals will fall into two categories: those who use AI to amplify their work and those who do not [7]. - The three layers of value include: 1. Efficiency Layer: Automating repetitive tasks to free up time for high-value decisions [8]. 2. Insight Layer: Quickly identifying market opportunities or risks based on proprietary company data [8]. 3. Co-Creation Layer: Using AI as a "external brain" for brainstorming and iterating solutions [8]. Group 4: AI Thinking and Mindset - The core message of the books is that competitive advantage in the AI era lies not in the proficiency with tools but in internalizing "AI thinking" as a new operational system [11]. - The transition involves: 1. Moving from "doing it oneself" to "question-based management," where precise questions lead to valuable AI-generated results [12]. 2. Shifting from "experience-driven" to "data empathy," where AI enhances traditional methods by expanding sample sizes and revealing blind spots [13]. 3. Evolving from "closed-loop processes" to "iterative flywheels," where AI-generated drafts can be rapidly improved through human feedback [14].