Core Viewpoint - The rapid integration of generative AI into the lives of minors presents both opportunities and significant risks, particularly in educational contexts where reliance on AI for homework and learning is growing [1][2][10]. Group 1: AI in Education - Generative AI tools are increasingly being used for homework assistance, personalized tutoring, and essay writing, leading to concerns about over-reliance on these technologies by students [2][10]. - A recent evaluation of 10 mainstream AI applications revealed that while accuracy is generally high for elementary and middle school questions, errors become more frequent with high school-level problems [3][4]. - Instances of AI providing multiple conflicting answers to the same question highlight the unreliability of these tools, with some applications changing their responses based on user prompts [4][9]. Group 2: Risks of Over-Reliance - The phenomenon of students depending on AI for homework has led to concerns about diminished critical thinking and problem-solving skills, with some students being labeled as "search party" for their reliance on AI [10][15]. - Teachers have observed that AI-generated content often lacks depth and originality, raising alarms about the impact on students' learning processes [10][15]. - The absence of a youth mode in many AI applications raises questions about the appropriateness of direct answers being provided to minors, with only a few applications implementing parental verification systems [10][11]. Group 3: Regulatory and Developmental Considerations - The Chinese Ministry of Education has issued guidelines prohibiting students from directly copying AI-generated content for assignments and exams, emphasizing the need for differentiated application based on educational stages [14][15]. - Experts suggest that fostering students' exploration, innovation, and critical thinking skills is essential in the age of AI, advocating for parental guidance and control over AI usage among minors [15]. - Companies are encouraged to develop content suitable for minors and to establish clear boundaries regarding what AI can present to young users [15].

Core Insights - The report highlights the significant short-term market disturbance effects caused by Trump's public statements, particularly regarding trade and the Federal Reserve, necessitating risk aversion strategies [1][2] - It emphasizes that while Trump's rhetoric increases policy uncertainty, long-term market trends remain driven by economic fundamentals, with the market gradually adapting to his communication style [1][2] Group 1: Trump's Influence on Market Sentiment - The report discusses the construction of a "Trump Trading Sentiment Index" using AI tools to analyze Trump's public statements and their impact on market sentiment [2] - It notes that since August 2024, Trump's sentiment index showed a trend of rising optimism followed by a decline, correlating with the election cycle and policy adjustment expectations [2][38] Group 2: Policy Focus Areas - Trump's second term policy statements focus on federal government operations and economic issues, with a strong emphasis on "America First" [1][20] - The report outlines key areas of Trump's policy focus, including economic protectionism, immigration, national security, and climate energy policies, reflecting a more aggressive stance compared to his first term [26][27] Group 3: Market Reactions to Trump's Statements - The report details the Volfefe Index, which quantifies the impact of Trump's tweets on market sentiment, showing a strong negative correlation with stock market performance during periods of heightened policy uncertainty [15][16] - It provides historical examples of significant market reactions to Trump's statements, illustrating the volatility induced by his rhetoric [15][16] Group 4: AI Tools in Analyzing Market Sentiment - The report describes the use of AI tools like DeepSeek, KIMI, and Manus to quantify Trump's statements and their emotional impact on market sentiment [24][25] - It highlights the methodology for analyzing Trump's tweets, including sentiment scoring and the correlation with market movements, demonstrating the effectiveness of AI in financial analysis [27][28][31]

（二）与其他推理模型的对比 与其他推理模型（如RE模型）相比，DeepSeek的R1模型在深度思考和多角度分析方面表现更为出色。RE模型虽然也能够理解 句子背后的含义，进行多角度分析和深度推演， 但在处理复杂问题时，R1模型的推理过程更加清晰，能够更好地启发用户。例 如，在生成旅行攻略时，R1模型会考虑更多的因素，如天气、景点、小吃、住宿等，而不仅仅是概率上的组合。 在AI技术飞速发展的今天，DeepSeek作为一款强大的AI工具，正以其独特的功能和优势，为我们的工作和生活带来前 所未有的便利。今天，就让我们一起深入探索DeepSeek的使用技巧，解锁AI深度思考的奥秘，开启一场AI深度探索之 旅。 二、使用DeepSeek的小技巧 一 、 DeepSeek与其他AI工具的比较 （一）提示词的运用 在使用DeepSeek时，提示词的编写至关重要。 但与传统语言模型不同，DeepSeek的推理模型对提示词的依赖程度较低。 因 此，我们不需要编写特别长的提示词，而是要简洁明了地表达我们的需求。 同时，要忘掉过去囤积的专业提示词，因为它们可 能会限制DeepSeek的推理过程。 例如，如果你想让DeepSeek为你 ...