AntResearchNLP团队 投稿 量子位 | 公众号 QbitAI 相信大家都有这样一个体验。 跟AI无论什么对话，感觉都是说空话套话。 有时候为了让AI懂自己，许多用户甚至不得不学习复杂的"提示词技巧"，手动编写长长的指令，像是在给AI做"岗前培训"。 那么如何实现高情商AI？蚂蚁通用人工智能研究中心自然语言处理实验室提出了一个叫AlignXplore的方法—— 通过强化学习，AlignXplore能够通过深度思考从用户行为中归纳出他/她的偏好，并且这种对人类偏好的洞察可以随着用户行为的变化而动态更新。 更有趣的是，当把归纳好的偏好描述迁移到一个下游对齐模型时，能够让这个模型的个性化对齐能力得到显著提升。 △"千人一面"的对齐方式无法满足用户多样的个性化需求，红字蓝字是对应用户的偏好描述 事实上，AI早已对演绎推理（Deductive Reasoning）驾轻就熟，具备令人惊叹的数学解题和代码编写能力。 你给它一个确定的前提（如"求解二次方程 ax²+bx+c=0"）和一套不变的规则（求根公式），它就能通过一步步严密的逻辑推演，给出一个唯一、可验证 的正确答案。这是一个典型的"自上而下"（Top-Do ...

AntResearchNLP 团队 投稿 量子位 | 公众号 QbitAI 相信大家都有这样一个体验。 跟AI无论什么对话，感觉都是说空话套话。 那么如何实现高情商AI？ 蚂蚁通用人工智能研究中心自然语言处理实验室 提出了一个叫AlignXplore的方法—— 有时候为了让AI懂自己，许多用户甚至不得不学习复杂的"提示词技巧"，手动编写长长的指令，像是在给AI做"岗前培训"。 | 策略一:精准定义任务,减少模糊性 | 如何实现精准定义:明确的核心问题、具体化的 生成指令、去除多余信息 | | --- | --- | | 策略二:适当分解复杂任务,降低Al认知负荷 | 分解任务的技巧:分段生成、逐层深入、设置逻 | | | 强结构 | | 策略三:引入引导性问题,提升生成内容的深度 | 引导性问题的设计要点:设置多个层次的问题 | | | 促使Al对比或论证、引导思维的多样性 | | | 控制提示语长度的技巧:避免嵌套复杂的指令、 | | 策略四:控制提示语长度,确保生成的准确性 | 保持简洁性、使用分步提示 | | 策略五:灵活运用开放式提示与封闭式提示 | 开放式提示:提出开放性问题,允许Al根据多个 角度 ...

Core Viewpoint - The article discusses the evolution of education from ancient times to the AGI era, emphasizing the need for a balanced approach that incorporates broad knowledge, practical skills, and interdisciplinary thinking in modern liberal education. Group 1: Historical Context of Education - Ancient Greek education focused on cultivating political and cultural abilities for the elite, while practical skills were reserved for common citizens and slaves [1] - The Roman era saw a shift towards practical subjects like law and rhetoric, enhancing the utility of education [1] - The 19th-century Prussian compulsory education system laid the groundwork for modern education, aiming to equip all social classes with necessary work skills [1] Group 2: Five Key Abilities for Modern Education - The five essential abilities for modern liberal education in the AGI era are: 1. Research: The ability to ask the right questions [7] 2. Statistics: Understanding relationships among various phenomena [10] 3. Logic: Inferring the unknown from known information [14] 4. Psychology: Understanding oneself and others [19] 5. Rhetoric: Persuading others and leading innovation [24] Group 3: Research Ability - The essence of research lies in formulating high-quality questions, which is crucial for scientific inquiry [9] - The ability to conduct literature reviews and apply critical thinking helps identify gaps in existing knowledge and challenge traditional assumptions [9] Group 4: Statistical Analysis - Quantitative funds utilize AI and big data to analyze social media sentiment and market data for trading strategies, reshaping financial markets [10] - The characteristics of big data (volume, velocity, variety, veracity) necessitate a data-driven mindset rather than just technical skills [11] Group 5: Logic and Reasoning - Deductive reasoning, based on first principles, allows for innovative thinking and problem-solving beyond existing knowledge frameworks [18] - The U-shaped thinking process encourages deep exploration of problems to reconstruct answers rather than seeking immediate solutions [18] Group 6: Psychological Insights - The concept of "Jonah complex" highlights the internal barriers individuals face in achieving success, emphasizing the importance of self-understanding [19] - Happiness is structured in three layers: material, psychological, and social, reflecting different stages of human pursuit [20] Group 7: Rhetoric and Communication - Rhetoric is essential for influencing others and shaping collective values, particularly in a rapidly globalizing society [24] - AI can enhance the effectiveness of rhetoric by analyzing social sentiments and facilitating cross-cultural communication [25]