* 00. 【前言】
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舊時代人類的思維框架習慣線性邏輯,但在新時代的 AI邏輯是呈點狀的關鍵字擴散,因此,我們若要讓 AI 成為我們的夥伴,該學習的不是邏輯本身,而是應該改變對邏輯的使用方式。
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* 01. 【舊時代的線型思維路徑】
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* 路徑:A (觀念) → B(腦袋形成新概念)→ C(行為) → D (結果)
|
線性思維是一種「單向」的過程,它強調因果連結在推導上的嚴密性,但人類大腦的運算力有限,在 A 到 B 的過程中會消耗大量能量進行「理解與記憶」,導致路徑緩慢且容易被過時的知識禁錮。
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因此舊時代的「知識產權」在保護的是 D(結果),因為達到結果的過程(A → C)太辛苦,所以我們習慣保護產出的結果。
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* 02. 【新時代的點狀擴散邏輯】
|
* 路徑:{ A × N } → B (AI 集成轉換爲全新概念 )→ C (行為)→ D (結果)
|
新時代的思維不再是單線推導,而是「點狀擴散」。人類負責搜集多個互不相關的觀念原點 { A × N },透過 AI 作為集成引擎,在無數個節點間進行同步碰撞與交叉感染,最終由人類直覺定錨出全新概念 B。
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「思維產權」保護的是這段由點成網的「意識軌跡」。在 AI 時代這段路徑會由於它的私密性產生需要保護的個體價值。
|
* 03. 【思維核心能力的轉向:搜集能力 > 理解能力】
|
未來的核心會在於觀念的搜集能力而不是理解能力,在知識半衰期極短的今天,深度理解單一領域的邊際效益遞減;相反地,具備廣泛的「觀念搜集力」,能讓你在 AI 的點狀擴散中提供更多高品質的思維能力。
|
* 04. 【思維溯源 (前置原文)】
|
人類的思維框架仍然習慣線性思維,但 AI 的邏輯是點狀的關鍵字擴散,因此我們要讓 AI 成為我們的夥伴,該學習的不是邏輯本身,而是應該改變對邏輯的使用方式。舊時代的線型邏輯思維路徑:A (觀念)- B(腦袋形成新概念)- C(行為) - D (結果),新時代的點狀擴散邏輯 路徑:A x N (觀念群,只需了解概論)- B(AI 集成觀念群轉換爲全新概念) - C (行為)- D(結果) ,思維的跨域能力 觀念的搜集能力,不是理解能力。
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*【The Core Shift of Thought in the Post-AI Era: From Linear Logic to Point-Distributed Diffusion】
|
* 00. 【Preface】
|
The traditional human cognitive framework is accustomed to linear logic, whereas AI operates through point-distributed keyword diffusion. To truly partner with AI, our focus should not be on mastering logic itself, but on fundamentally changing our utilization of logic.
|
* 01. 【The Legacy Path: Linear Thinking】
|
* Path: A (Concept) → B (Formation of New Concept in Mind) → C (Action) → D (Result)
|
Linear thinking is a "unidirectional" process emphasizing rigorous causal deduction. However, due to the brain's limited computational power, the transition from A to B consumes vast energy on "understanding and memorization," making the path sluggish and easily constrained by obsolete knowledge.
|
Consequently, legacy "Intellectual Property" (IP) focuses on protecting D (Result), as the arduous journey from A to C necessitates the safeguarding of the final output.
|
* 02. 【The New Era: Point-Distributed Diffusion Logic】
|
* Path: { A × N } → B (AI Integrated Transformation into New Concept) → C (Action) → D (Result)
|
In the new era, thought is no longer a unidirectional linear deduction, but a "Point-Distributed Diffusion." Humans are responsible for aggregating multiple, seemingly unrelated conceptual origins { A × N }. AI then serves as an integration engine, facilitating simultaneous collisions and cross-pollination across countless nodes, until human intuition finally anchors a brand-new concept, B.
|
"Cognitive Property" (CP) protects this "Trajectory of Consciousness"—the process of transforming scattered points into a cohesive network. In the AI era, this specific path gains unique individual value precisely due to its inherent privacy and subjective origin, thus necessitating systematic protection.
|
* 03. 【The Shift in Core Capacity: Collection > Comprehension】
|
The core competency of the future lies in the capacity to collect concepts rather than the depth of comprehension. In an age of rapidly shrinking knowledge half-lives, the marginal utility of deep-diving into a single field is diminishing. Conversely, a broad "Concept Collection Capacity" provides high-quality fuel for AI’s point-distributed diffusion, enhancing one's overall cognitive power.
|
* 04. 【Thought Provenance (Original Premise)】
|
Human cognitive frameworks remain tethered to linear thinking, while AI thrives on keyword-based diffusion. To co-evolve with AI, we must move beyond learning logic as a skill and instead evolve our strategic application of logic. Legacy Linear Logic Path: A (Concept) - B (New Concept Formation) - C (Action) - D (Result) ,New Era Diffusion Logic Path: A x N (Conceptual Cluster/Overview) - B (AI Integration & Transformation) - C (Action) - D (Result) ,Cross-Domain Capacity The ability to aggregate concepts over the ability to fully internalize them.
|
* 0221.2026.2:15pm
|
#CognitiveProperty #思維產權 #ThoughtProvenance #思維溯源
|
*『On-Chain』
* 00. 【前言】
|
舊時代人類的思維框架習慣線性邏輯,但在新時代的 AI邏輯是呈點狀的關鍵字擴散,因此,我們若要讓 AI 成為我們的夥伴,該學習的不是邏輯本身,而是應該改變對邏輯的使用方式。
|
* 01. 【舊時代的線型思維路徑】
|
* 路徑:A (觀念) → B(腦袋形成新概念)→ C(行為) → D (結果)
|
線性思維是一種「單向」的過程,它強調因果連結在推導上的嚴密性,但人類大腦的運算力有限,在 A 到 B 的過程中會消耗大量能量進行「理解與記憶」,導致路徑緩慢且容易被過時的知識禁錮。
|
因此舊時代的「知識產權」在保護的是 D(結果),因為達到結果的過程(A → C)太辛苦,所以我們習慣保護產出的結果。
|
* 02. 【新時代的點狀擴散邏輯】
|
* 路徑:{ A × N } → B (AI 集成轉換爲全新概念 )→ C (行為)→ D (結果)
|
新時代的思維不再是單線推導,而是「點狀擴散」。人類負責搜集多個互不相關的觀念原點 { A × N },透過 AI 作為集成引擎,在無數個節點間進行同步碰撞與交叉感染,最終由人類直覺定錨出全新概念 B。
|
「思維產權」保護的是這段由點成網的「意識軌跡」。在 AI 時代這段路徑會由於它的私密性產生需要保護的個體價值。
|
* 03. 【思維核心能力的轉向:搜集能力 > 理解能力】
|
未來的核心會在於觀念的搜集能力而不是理解能力,在知識半衰期極短的今天,深度理解單一領域的邊際效益遞減;相反地,具備廣泛的「觀念搜集力」,能讓你在 AI 的點狀擴散中提供更多高品質的思維能力。
|
* 04. 【思維溯源 (前置原文)】
|
人類的思維框架仍然習慣線性思維,但 AI 的邏輯是點狀的關鍵字擴散,因此我們要讓 AI 成為我們的夥伴,該學習的不是邏輯本身,而是應該改變對邏輯的使用方式。舊時代的線型邏輯思維路徑:A (觀念)- B(腦袋形成新概念)- C(行為) - D (結果),新時代的點狀擴散邏輯 路徑:A x N (觀念群,只需了解概論)- B(AI 集成觀念群轉換爲全新概念) - C (行為)- D(結果) ,思維的跨域能力 觀念的搜集能力,不是理解能力。
-
-
*【The Core Shift of Thought in the Post-AI Era: From Linear Logic to Point-Distributed Diffusion】
|
* 00. 【Preface】
|
The traditional human cognitive framework is accustomed to linear logic, whereas AI operates through point-distributed keyword diffusion. To truly partner with AI, our focus should not be on mastering logic itself, but on fundamentally changing our utilization of logic.
|
* 01. 【The Legacy Path: Linear Thinking】
|
* Path: A (Concept) → B (Formation of New Concept in Mind) → C (Action) → D (Result)
|
Linear thinking is a "unidirectional" process emphasizing rigorous causal deduction. However, due to the brain's limited computational power, the transition from A to B consumes vast energy on "understanding and memorization," making the path sluggish and easily constrained by obsolete knowledge.
|
Consequently, legacy "Intellectual Property" (IP) focuses on protecting D (Result), as the arduous journey from A to C necessitates the safeguarding of the final output.
|
* 02. 【The New Era: Point-Distributed Diffusion Logic】
|
* Path: { A × N } → B (AI Integrated Transformation into New Concept) → C (Action) → D (Result)
|
In the new era, thought is no longer a unidirectional linear deduction, but a "Point-Distributed Diffusion." Humans are responsible for aggregating multiple, seemingly unrelated conceptual origins { A × N }. AI then serves as an integration engine, facilitating simultaneous collisions and cross-pollination across countless nodes, until human intuition finally anchors a brand-new concept, B.
|
"Cognitive Property" (CP) protects this "Trajectory of Consciousness"—the process of transforming scattered points into a cohesive network. In the AI era, this specific path gains unique individual value precisely due to its inherent privacy and subjective origin, thus necessitating systematic protection.
|
* 03. 【The Shift in Core Capacity: Collection > Comprehension】
|
The core competency of the future lies in the capacity to collect concepts rather than the depth of comprehension. In an age of rapidly shrinking knowledge half-lives, the marginal utility of deep-diving into a single field is diminishing. Conversely, a broad "Concept Collection Capacity" provides high-quality fuel for AI’s point-distributed diffusion, enhancing one's overall cognitive power.
|
* 04. 【Thought Provenance (Original Premise)】
|
Human cognitive frameworks remain tethered to linear thinking, while AI thrives on keyword-based diffusion. To co-evolve with AI, we must move beyond learning logic as a skill and instead evolve our strategic application of logic. Legacy Linear Logic Path: A (Concept) - B (New Concept Formation) - C (Action) - D (Result) ,New Era Diffusion Logic Path: A x N (Conceptual Cluster/Overview) - B (AI Integration & Transformation) - C (Action) - D (Result) ,Cross-Domain Capacity The ability to aggregate concepts over the ability to fully internalize them.
|
* 0221.2026.2:15pm
|
#CognitiveProperty #思維產權 #ThoughtProvenance #思維溯源
|
*『On-Chain』
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