作者 | JaySoon 编辑 | 自动驾驶之心 原文链接： https://zhuanlan.zhihu.com/p/1989354576726487157 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 在算法工程这个行业里， 决定成长速度的从来不只是技术水平，而是你在关键时刻是否敢做判断、敢为判断承担后果。 这些年，我见过太多技术能力并不差的算法工程师，在模型、代码、实验层面都足够扎实，却始终卡在一个看不见的天花板下。回头看，他们往往有一个共同特征： 面对关键决策时，永远选择"最稳妥"的那条路。如之前文章《算法工程师的成长：反直觉决策能力》提到，只做符合直觉的决策。 他们谨小慎微、害怕失败，把"不出错"当成最高原则，却没意识到—— 这种安全感，正在成为成长的最大阻力。 一、算法工程师的工作，本质上是一连串"决策博弈" 很多人低估了"决策"在算法工作中的比重。 事实上，从你入行第一天开始，就不断在做选择： 每一个选择背后，都是 风险、收益与责任的权衡 。问题在于， ...

记忆已成为并将继续成为基于基础模型的智能体的核心能力。它支撑着长程推理、持续适应以及与复杂环境的有效交互。随着智能体记忆研究的快速扩张 并吸引空前关注，该领域也日益呈现碎片化。当前统称为"智能体记忆"的研究工作，在动机、实现、假设和评估方案上往往存在巨大差异，而定义松散的 记忆术语的激增进一步模糊了概念上的清晰度。诸如长/短期记忆之类的传统分类法已被证明不足以捕捉当代智能体记忆系统的多样性和动态性。 在这些智能体的核心能力中，记忆 尤为关键，它明确地促成了从静态大语言模型（其参数无法快速更新）到自适应智能体的转变，使其能够通过环境交 互持续适应（Zhang et al., 2025r; Wu et al., 2025g）。从应用角度看，许多领域都要求智能体具备主动的记忆管理能力，而非短暂、易忘的行为：个性化聊 天机器人（Chhikara et al., 2025; Li et al., 2025b）、推荐系统（Liu et al., 2025b）、社会模拟（Park et al., 2023; Yang et al., 2025）以及金融调查（Zhang et al., 2024）都依赖于智能体处理、存储和管 ...

Core Insights - The AI wave is reshaping every industry, leading to a surge in AI-related startups that present both opportunities and challenges for entrepreneurs [1][2]. Opportunities in AI - Key opportunities lie in addressing inefficiencies in various sectors, particularly in areas that remain "low efficiency" despite AI applications [4][5]. - Entrepreneurs should focus on practical implementations of AI to drive meaningful revenue growth rather than chasing the elusive "trillion-dollar company" dream [5][19]. - The concept of "results-oriented AI" is emphasized, highlighting the need for effective application of AI tools to achieve tangible outcomes [6][17]. - Vertical market efficiency is identified as a significant opportunity, where startups can solve niche problems that larger companies may overlook [6][18]. Traps in AI Entrepreneurship - A major trap is the failure to apply AI in a way that delivers useful results, with a high failure rate of current AI applications indicating many remain in the "toy" phase [6][9]. - The competitive landscape is increasingly dominated by tech giants, raising concerns about the viability of new startups becoming the next major players [6][18]. - The rapid pace of AI development means that traditional competitive advantages, or "moats," may not be sustainable, necessitating continuous innovation and adaptation [7][24]. Industry Transformation - AI is fundamentally transforming industries, with media moving towards AI-generated content and personalized content aggregation [9][26]. - In advertising, AI is enhancing recommendation systems and creative intelligence, leading to more effective ad placements and faster iterations [10][29]. - The gaming industry is also experiencing significant efficiency gains through AI, allowing smaller teams to compete with larger companies by leveraging AI tools [10][35]. Commercialization of AI - The commercialization of AI requires bridging the gap between technological vision and practical business models, as many startups struggle to monetize their innovations effectively [11][28]. - Entrepreneurs are encouraged to focus on solving real problems and improving efficiency rather than solely pursuing grand technological ambitions [11][27].

Core Insights - The article discusses the challenges faced by companies during digital transformation, highlighting the disconnect between IT, business, and management perspectives, which leads to ineffective technology investments and unsatisfactory outcomes [1][5]. IT Thinking - IT thinking is characterized by a focus on advanced technology and system architecture, often leading to over-engineered solutions that do not align with actual business needs [3][5]. - An example is given of a pancake shop that invested in a fully automated pancake-making robot, which resulted in long wait times for customers and underutilized technology [3][4]. Business Thinking - Business thinking prioritizes immediate results and user experience, often at the expense of proper system implementation and data management [4][5]. - The pancake shop's manager demanded quick solutions, leading to manual processes that were error-prone and inefficient [4][5]. Management Thinking - Management thinking focuses on cost control and short-term returns, often neglecting the need for long-term investment in technology [4][5]. - The shop owner opted for the cheapest cash register, which led to operational issues and ultimately hindered the digital transformation efforts [4][5]. Babel Tower Dilemma - The article introduces the "Babel Tower Dilemma," where miscommunication between departments leads to wasted resources and stalled projects [6][8]. - Each department blames the others for failures, resulting in a lack of accountability and progress in digital initiatives [8]. Solutions to the Dilemma - To resolve the Babel Tower Dilemma, companies should align goals, mechanisms, and culture among IT, business, and management [9][12]. - Establishing a common language and shared vision can help bridge the gap between technical capabilities and business needs [10]. - Creating cross-departmental teams can ensure effective communication and execution of digital transformation projects [11]. Conclusion - The article emphasizes the need for a unified approach where IT, business, and management work together to create a cohesive digital strategy, transforming the "three kingdoms" into a collaborative entity [15].

Strategic Insights - The key to a successful AI strategy is not technological superiority but deep integration with business processes [2] - Not all problems are suitable for AI solutions; traditional methods can often provide more efficient and cost-effective results [3] - Pursuing long-term value in AI strategies often leads to greater success, as seen in the example of Amazon's investment in recommendation systems [4] - The ultimate measure of AI project success is the enhancement of business value, not the advancement of technology [5] Technical Considerations - The biggest barrier to AI implementation is not talent or funding, but "data silos" that hinder effective training and deployment of AI models [6] - Purchasing existing AI solutions is often more suitable for most companies than developing everything in-house [7] - Simpler, interpretable models are often more practical than complex models with large parameters [8] - The safety, ethics, and accountability of AI models are critical concerns that must be prioritized [9] Talent and Organization - Companies need talent that understands both business and AI, acting as a bridge between the two [10] - AI empowerment requires a culture where all employees understand AI's capabilities and limitations, rather than relying solely on a few experts [11] - Failures in AI projects are often due to organizational, cultural, and communication issues rather than technical shortcomings [12] - Cross-disciplinary talent is essential in the AI era to address the complexities of business [13] Implementation and Operations - AI deployment is not a one-time investment but requires ongoing optimization and monitoring [14] - Focusing on clearly defined small problems is often more successful than attempting to disrupt entire industries [15] - The user experience of AI tools is more important than the intelligence of the models themselves [17]