Supercategories for Public Intelligence Standardization


Create standard super-categories for classification of data sources, based on the equation ontology, and make data more actionable.

YAML Idea Base Administration

So the idea is that, using the equation model ontology (F(X)=Y), we could create specific supercategories to classify classification systems and data sources. Check the collaborative document. For example, version-zero of it may look something like this:


1. Goals

  • NPL: National Policy Goal,
  • NLG: National Legislature Goal,
  • RLG: Regional Legislature Goal,
  • OP: Organization Policy Goal,
  • RDG: Regional Development Goal,
  • NMG: NGO/NPO Mission Goal,
  • ECI: Ethnic-Cultural Intent,
  • INTT: Inter-National Treaty,
  • TSV: Technological-Scientific Vision.

2. Ideas

  • ICAT: Industry Category Code,
  • PCAT: Product Category Code (e.g., HS),
  • ACAT: Economic Activity Category Code (e.g., NACE, SIC, NAICS),
  • PTN: Patent Number,
  • SPN: Scientific Publication Number (e.g., DOI),
  • TRP: Technical Report,
  • PROT: Laboratory or Medical Protocol (e.g., Protocol-Online),
  • INBS: Innovative Brainstoriming Idea (e.g., Halfbakery),
  • CD-REPO: Code Repository (ideas for runtime processes),
  • ISTD: Industrial Standard Code.

3. Plans

  • CPN: Company Project Name,
  • CSPN: Consortium Programme,
  • PPN: Personal Project Name,
  • PCN: Project Codename,
  • MID: Mission ID.


4. Operations

  • CPU-OPS: Floating Point Operation,
  • NET-RQST: Network Request Operation,
  • UI-MOVE: User Interface Movement,
  • ORG-TASK: Organization Task,
  • ORG-PROD: Organization Product (manufacturing operation),
  • TRD-ORD: Market Trade Order,
  • MTF: Money Transfer,
  • ATF: Asset Transfer (e.g., Shipment),
  • ITF: Information Transfer (e.g., Message, File Upload, etc., overlaps with NET-RQST),
  • MED-OP: Medical Operation,
  • LAB-OP: Laboratory Operation,
  • WEB-DEPLOYMENTS: CI/CD-based online systems deployment operation.


5. Assets

↳ 1) Agents

  • CRED: Company Registry ID (e.g., D-U-N-S),
  • CNID: Company National ID,
  • INID: Individual National ID,
  • SNET: Social Net ID.

↳ 2) Things

  • NREIDs: National Real Estate IDs (e.g., Cadastre),
  • NTEID: National Tangible Asset IDs (e.g., National car registry, National Phone registry),
  • INSTRID: Instrumentation/Industrial Machinery ID,
  • COMIDS: Commodity Product Unit ID,
  • FINIDS: Financial Product Unit ID,
  • WASID (Web Asset IDs, e.g., MAC address).

↳ 3) Topics

  • BPST: Blogposts,
  • NPST: Newspost.

6. Places

  • RLOC: Real Location (e.g., Address), WGS, WCS/FITS,
  • VLOC: Virtual Location (e.g., IP, IPv6 address, neural net region), DNS (Computer/phone address registries ( 4.3+ bn. IP addresses )), locations of thoughts in neural networks, etc.

7. Events

  • TS: Timestamp,
  • DT: Date.



May be useful even for making order in our categories for imported data even here, on 0 -> oo :)

Currently, while importing datasets, started using it, auto-generating categories for sources, like so:

Y:IDEA:TRP:NTRS, to refer to NASA Technical Reports Server.

//while importing datasets How do I import a dataset?

There is something more to consider. Today, we have companies deep-learning specific models to answer specific questions. For example, identity and face recognition models, weather models, etc., and these specific models are being used as a resource by integrative decision systems to make decisions.

So, just like we had layers of abstraction while building network protocols one upon another (e.g., layers in OSI model), we could actually have standards for deep-learned models, build social AI from ground up, combining multiple standardized AI models.

Having versioned and standardized machine-learned models would allow us to work on specifying the qualities and blind-spots of these models, and take actions to confidently version, incrementally improve, and use them in derived applications.

For example, imagine that definition of a concept "Manga" is defined not by a dictionary, but by an ANN, like Manga GAN, and becomes something like an ISO standard model of what "Manga" looks like. Many AI systems are already versioned, like, for example Google Translate, and the properties of them are known. So, think of many concepts and complex phenomena that we build AI models of, and standardize.

Perhaps this comment merits a separate post, of an idea of ISO standardization for AI models.

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