Mindey, 
跨學科的語言文字
Introduce a tiny set of exhaustive transdisciplinary concepts that most people are intimately familiar with, that can represent all processes that people go through to build their worlds, then use them to organize information, and get people understand the modern world.
One of the hardest things in ensuring access know-how is knowledge representation. It is not easy to find concepts that most people would be intimately familiar with, and yet that those concepts would map well across disciplines. So, here is one set of such concepts: to ask Questions, then proceeding to search for Ideas to answer them, then to start and run Projects based on those ideas, and to do Tasks (or "Challenges") with respect to relevant Places to obtain Results. The below is an illustration of how transdisciplinary those concepts are, comparing them with the equivalent concepts in computer science and how they are related to the physics.
In Computer Sciences, these concepts correspond to what programmer does within their programs, that rely on TYPES, FUNCTIONS, VARIABLES, VALUES, OPERATIONS and PROCESSES. However, as Alan Turing had shown, these processes decompose into simple INPUT/OUTPUT of bits.
In Physics, scientists study INPUT/OUTPUT processes of MATTER and ENERGY instead, and use mathematics to model the changes they see, formalizing and approximating their behaviors with field equations describing phase spaces. (Note: as we discussed on the idea "MRSGREN", humans do that I/O of M/E as they live as well.)
Think of "FIELD FLUX" in the image as computation -- in that sense, our industries are "physics apps", and if projects are examples of groups of humans "self-programming" to execute on their dreams, then having such examples of contextualized execution (or running of projects) in public would work like open source software, and this is an idea of one set of hypothetical of concepts to achieve this. This idea is currently is being tested here on 0oo.
The sole purpose is to introduce, evolve and maintain data alignment protocol.
This protocol (NRV v3) is based on this idea. Correspondance:
- 'VALUE' is '100 RESOURCE',
- 'TYPE' is '200 CATEGORY',
- 'FUNCTION' is '300 METHOD',
- 'PROCESS' is '400 SYSTEM',
- 'OPERATION' is '500 OPERATION',
- 'VARIABLE' is '600 LOCATION'
I / O如何變爲：<類型，函數，過程，操作，變量和值？>
10100111011010101101101101101100101011 這一切都使我感到奇怪，一個簡單的圖靈機如何變成這6種事物，我們如此共通。 明顯地：
-值：這是 Turing Tape 數據或狀態。 -類型：僅是圖靈磁帶上的常見圖案。 -變量：放在 Turing Tape 上。 -操作：當時鐘帶有讀寫位置時，會發生這種情況。 -過程：是物理的時鐘使操作得以實現-圖靈機的電機和讀/寫頭。 -函數：它是“變量”的“函數”類型，它存儲儘可能多的值來執行操作。
不難看出計算機的所有功能如何清晰地分解爲這些東西。我很想學習如何完全由NAND門構建計算機，或者如何完全由異步神經元構建大腦，以及從這些低級事物如何變成這6個概念的理解中我們可以獲得什麼見解，因爲我確實看到了這些相同的concepts跑步協會。也許瞭解信息流程如何變成這六件事，將有助於提出一些根本上是新的東西（例如組織形式）。還有更多抽象的東西，例如等式模型，甚至還有更多抽象的東西，例如polycontext metasymbol。是否存在普遍可加性之類的東西，它如何收斂到普遍性物品？
有趣的是，“類別”和“類型”是“問題”，“任務”和“查詢” ...與“目標”，“意圖”，“問題”，“領域”，“興趣”相吻合。這意味着查詢是類別：每個搜索查詢都是一個類別。
How does I/O become:
10100111011010101010101101101100101011 That all makes me wonder, how does a simple Turing Machine become these 6 things, that we are so common with. Obviously:
It's not hard to see how everything what computer does, clearly breaks down to these things. I would be interested in learning how computer is built entirely out of NAND gates, or how brain is built entirely out of asynchronous neurons, and what insights can we get from the understanding of how these low-level things becomes these 6 concepts, cause I do see these same concepts running societies. Perhaps understanding how information processes become these 6 things, would be help to come up with something (like a form of organization) that's fundamentally new. There are more abstract things, like the equation model, and even more abstract thing like polycontext metasymbol. Is there something like universal additivity, and how can it converge towards the universal good?
It's interesting to observe, that
CategoriesandTypesareQuestions,QuestsandQueries... coincide withGoals,Intents,Problems,Domains,Interests. It means that Queries are Categories: every search query is a category.我認爲重要的是要注意類型、變量、類、方法、類型類只是編譯器使用的語法糖。因此，它們實際上並不存在於計算機代碼中。
最終，編譯器生成的是一系列指令。沒有針對變量、方法或類型的說明。
編譯器抽象導致沒有計算！我認爲計算機如此不兼容的部分原因是這是一個不幸的限制。重要的是實現之間共享的數據結構和字段。這就是使代碼兼容的原因。
我將爲不兼容的計算機代碼創建一個類別。
I think it is important to note that types, variables, classes, methods, typeclasses are just syntactic sugar used by the compiler. As such they dont actually exist in computer code.
Ultimately what gets generated by the compiler is a sequence of instructions. There is no instruction for a variable or a method or a type.
Compiler abstractions result in no computation! I think part of why computers are so incompatible is that this is an unfortunate limitation. What matters is the data structure and fields that are shared between implementations. This is what makes code compatible.
I will make a category for incompatible computer code.