Tacit Knowledge

The distinction theory between Explicit Knowledge and Tacit Knowledge was proposed by philosopher Michael Polanyi in the 1950s.

The concept of tacit knowledge was first proposed by philosopher Michael Polanyi in 1958

The concept of tacit knowledge was proposed by cognitive scientist Michael Polanyi, whose core insight is 'we can know more than we can tell'.

Tacit Knowledge is a concept proposed by philosopher Michael Polanyi, referring to knowledge that is difficult to express in words and is acquired through accumulated experience.

The concept of tacit knowledge was first proposed by Michael Polanyi in 1958

The concept of "Tacit Knowledge" was systematically elaborated by philosopher Michael Polanyi in 1966, with the core characteristic that "we can know more than we can tell."

Polanyi's Paradox states that humans know far more than they can express, with explicit knowledge being easily acquired by AI while tacit knowledge remains a uniquely human advantage

硬件工程中的仪器使用（示波器、万用表等）涉及大量隐性知识（Tacit Knowledge），无法通过文字描述传递给AI

In large-scale game development, the loss of tacit knowledge caused by staff turnover is often the primary cause of project delays and quality fluctuations.

Human managers accumulate tacit knowledge through long-term observation, informal communication, and empathy, whereas current AI systems can only judge based on explicit data and preset rules, and cannot capture soft information such as office politics, emotional atmosphere, and historical grudges.

"Contextual intuition" is called "tacit knowledge" in cognitive science and organizational management, namely experiential judgment that is difficult to express explicitly through words or rules yet is crucial to decision-making.

A company's tacit knowledge is scattered across emails, meeting minutes, business rule engines, and employees' minds, and is rarely fully written into databases.

System integration knowledge is largely embedded in specific system documentation, community-documented pitfalls, and tacit knowledge passed down informally within teams.

认知科学家迈克尔·波兰尼将隐性知识总结为"我们所知道的，永远多于我们能言说的"

判断力和方向感因高度依赖隐性知识与价值判断而难以被自动化，成为AI时代新的稀缺品

Implicit conventions in software engineering correspond to "Tacit Knowledge," while historical baggage is commonly referred to as "Technical Debt"

Controlled wet-lab studies have found that tacit knowledge and physical operational execution remain barriers that AI has difficulty overcoming.

Organizations should retain full control over their own unique knowledge and tacit knowledge, avoiding lock-in from using any particular model.

Vector databases excel at storing and retrieving explicit knowledge, but have no way to handle tacit knowledge.

Much tacit knowledge that affects results (such as the need for a special learning rate warm-up strategy in the first few hundred steps of an optimizer) is difficult to fully capture in standardized tables.