如何在只有下行路的情况下上升：人工智能的教训来克服你的局部最大值（有声书）

2024 | English | B0DK2B14V4 | MP3 192 kbps | 6 hours and 30 minutes | 537 MB

A well-paid executive feels trapped in her very respected but unsatisfying job. A startup founder has paying customers, but knows that unless he "fires" them and pivots the business, his startup won't make it. A senior government planner is tasked with undoing the nation's reliance on outdated infrastructure.

These are all examples of individuals stuck in a Local Maximum; we've reached a peak, but not the one that fulfills the highest potential. In order to move up in our pursuits, we must first move back down—a realization which can lead to frustration, decision-making paralysis, and lost opportunity.

In How to Move Up When the Only Way Is Down, Judah Taub draws from his perspective guiding early stage AI startups, his years serving in military intelligence, and experiences leading innovation throughout his career. With his off-the-beaten path perspective, Judah shares insights into how humans can achieve better decision-making by learning how AI overcomes local maximums. Explore

- How to anticipate and identify Local Maximums

- How to overcome psychological Local Maximum blocks and biases

- How to build skills and apply strategies to succeed in complex decision-making

- And more

一个待遇优厚的高管被困在她非常受尊敬但不令人满意的工作中。一位初创公司的创始人拥有付费客户，但他知道除非他“解雇”他们并转向新的业务方向，否则他的初创公司无法生存。一位高级政府规划者被要求去修复国家对过时基础设施的依赖。 这些都是个体陷入局部最大值的例子；我们已经到达了一个峰值，但并不是实现最高潜力的那个。为了在追求上取得进步，我们必须首先向下移动——这个想法可以导致沮丧、决策瘫痪以及失去机会的机会。 在《如何通过下降来晋升》中，朱达·塔夫（Judah Taub）借鉴了他指导早期阶段AI初创公司时的观点，他的多年军事情报服务的经历，以及他在职业生涯中领导创新的经验。凭借其非传统视角，朱达分享了有关人类如何通过学习AI如何克服局部最大值来实现更好决策的见解。探索以下内容： - 如何预测和识别局部最大值 - 如何克服心理上的局部最大值障碍和偏差 - 如何构建技能并应用策略以成功应对复杂决策 - 更多内容

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