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The limitations of the current von-Neumann and network architectures-based
uni-type node-processing Computing Paradigm (nCP)

 
The Current  2 uni-type node-processing architectures, i.e., von Neumann node-core and nodes-connecting network
with 2 node-processing Hardware/Software theoretic foundations,
creating planar open-end client-server nodes-processing infrastructures for the central-server/datacenter,
 
to generate syntax-model solutions without human control, 
which is dangerous,
due to unpredictable recursive self-improvement, and
 
to provide client-users with weak-AI token-model processing services,
which are laden with unsolvable security and privacy issues that may lead to dire consequences,
due to no real-time direct user-controllability.

The advantages of Ten(10) workgroup aggregated/integrated control architectures based
multi-type-node workgroup-control Computing Paradigm (wCP)

 
The must-have 10 multi-type-node aggregated/integrated workgroup control architectures
with 10 workgroup control Hardware/Software theoretic foundations
that encapsulate 2 uni-type node-processing architectures,
 
creating 7-generation hierarchical semantic domain-control self-evolving systems and
enabling all the Businesses, Consumers and Individuals (B/C/I entities), to semantically control and build their own
 
strong-Business-AI (small, medium, large enterprises)/Consumer-AI (home, car, robot apparatuses)/individual-AI (standalone, portable, wearable PDAs)
real-time dynamic entity-oriented PS services for internal users/controllers and external entity-to-entity contractual users,
which are equipped with domain security and privacy protections.

The Right AI Rationale:
Building bottom-up hierarchical Strong-AI systems for all Businesses, Consumers and individuals to provide top-down domain-control services,

instead of building centralized planar weak-AI infrastructures for service providers to provide client-server model-processing services.

 
Now, the bottom-up human-control hierarchical AI-systems that deliver real-time dynamic strong-AI top-down domain semantic agent-control
over encapsulated agentic-workflow models-processing services can be established.
 
Then, build your own AI-semantic-domain control systems on top of encapsulated nodes-processing infrastructures and
deliver strong-AI domain-control services for all system users using natural languages
to real-time dynamically and concurrently solve users’ service-domain-oriented problems, 
 
instead of license and pay for the weak-AI syntax-token models-processing services per month
offered by AI-infrastructural service providers for you client-user only
to prompt up the feasible agentic-workflow models to solve your personal productivity problems.

"The Strong-AI Roadmap from the real-world analogy"

I.   building 3-level-hierarchical  semantic-terms domain-control systems  (1) 

VS building one-level planar uni-type workstations syntax-tokens model-processing client server infrastructures

  • Descriptions:
    •  building 3-level-hierarchical semantic-terms domain-control systems with menu-services (1) 
    • VS building one-level planar uni-type workstations syntax-tokens model-processing client server infrastructures
  • (1)  3-level hierarchical Conveyer domain control systems via multi-type (attribute/traffic/control)-workstations aggregated/integrated architectures.  
    • 1) encapsulate all the hierarchical business service-oriented problem domains,
      • from bottom-up production/assembly/fabrication/transaction/business-locale/eco-business organization/virtual business organization.
    • 2) build build their own strong-AI Business-Enterprise services
      • for small, medium, large organizations,
      • based on business internal hierarchical service-oriented problem domains.
    • 3) enable real-time dynamic  hierarchical domain-Problem solving capabilities,
      • for employees from business contract/portfolio/project processors, managers to business policy/strategy management to CEOs,
      • to achieve the business objectives in overall Efficiency and cost effectiveness. 
    •  
    • 4) use the bottom-up hierarchical Business-control terminology language (i.e., Natural Language).  
    • 5) better domain secuirty, due to closed-loop core,  
    • 6) better concurrent control 
    • 7) better MIMO-services with more controllers.  
  • (2)  one-level planar workstations
    • 1) encapsulate all the hierarchical business service-oriented problem domains,
      • from bottom-up production/assembly/fabrication/transaction/business-locale/eco-business organization/virtual business organization.
    • 2) build build their own strong-AI Business-Enterprise services
      • for small, medium, large organizations,
      • based on business internal hierarchical service-oriented problem domains.
    • 3) enable real-time dynamic  hierarchical domain-Problem solving capabilities,
      • for employees from business contract/portfolio/project processors, managers to business policy/strategy management to CEOs,
      • to achieve the business objectives in overall Efficiency and cost effectiveness. 
    • 4) use the bottom-up hierarchical Business-control terminology language (i.e., Natural Language).  

II.     3-level-semantic-domain control hierarchical Restaurant systems (3)

VS one-level token-model-processing planar vending-stations food-court  (4)

  • Descriptions: 
    • building 3-level semantic domain control hierarchical Restaurant systems with internal self-improving/self-learning knowledge domains-PS services and external self-evolving banquet-menu services 
    • VS
    • Building one-level token-model processing planar vending stations food-court with lead-time solution-model improvement via external agentic-model service providers) and external client dish-manual services on the client-server infrastructures.
  • (3)  3-level-control Restaurants  
    • 1) with solution, knowledge domain and PS-capabilities, with reciprocal-control and feedback-control capabilities with fail-over and fail-safe control.  )
    •  
    • encapsulate all the hierarchical consumer service-oriented problem domains,
      • from bottom-up business-service domain, to  additional eco-consumer and virtual-consumer service domains.
    • 2) build their own strong-AI Consumer-Apparatus services
      • for home, car and robots Enterprise-AI for small, medium, large organizations,
      • based on business internal 11-level hierarchical service-oriented problem domains.
    • 3) enable real-time dynamic hierarchical-Problem solving capabilities
      • for consumers to achieve the apparatus benefits in overall Efficiency and cost effectiveness.
    • 4) use the bottom-up hierarchical Consumer-control terminology language  (i.e., Natural Language).
  • (4)  one-level-processing vending stations
    • 1) encapsulate all the hierarchical consumer service-oriented problem domains,
      • from bottom-up business-service domain, to  additional eco-consumer and virtual-consumer service domains.
    • 2) build their own strong-AI Consumer-Apparatus services
      • for home, car and robots Enterprise-AI for small, medium, large organizations,
      • based on business internal 11-level hierarchical service-oriented problem domains.

III.    Multi-(8)-level-semantic domain control hierarchical Theme-Park Enterprise Systems  (5)

VS one-level token-model-processing planar Amusement-Park Association (6). 

  • Descriptions:

    • Multi-(8)-level-semantic domain control hierarchical Theme-Park Enterprise Systems with real-time self-improving/self-learning collaborative and coordinative PS services and external self-evolving Business-menu contractual services

    • VS one-level token-model-processing planar Amusement-Park Association with lead-time internal agentic-model PS-improvement (via third-party agentic-model-PS services) and external multi-model-agentic-orkestrated client-manual services (6)

  • (5) Theme Park Enterprise, (with 8 hierarchical business service domains with experts/agents and eco-agent-domains)  
    • 1) encapsulate all the individual hierarchical service-oriented problem domains,
      • from bottom up consumer service domains to additional eco-individual and virtual individual service domain. 
    • 2) build their own Strong-AI individual-PDA services 
      • for standalones, portables and wearables,
      • based on individual internal hierarchical service domains.
    • 3) enable real-time dynamic hierarchical Problem -Solving capabilities,
      • for individuals to achieve the smart-PDA benefits in overall Efficiency and cost-effectiveness.
    • 4) Use the bottom-up hierarchical individual-control terminology languages (i.e., Natural Language).
  • (6) Theme Park Enterprise
    • 1) encapsulate all the individual hierarchical service-oriented problem domains,
      • from bottom up consumer service domains to additional eco-individual and virtual individual service domain. 
    • 2) build their own Strong-AI individual-PDA services 
      • for standalones, portables and wearables,
      • based on individual internal hierarchical service domains.
    • 3) enable real-time dynamic hierarchical Problem -Solving capabilities,
      • for individuals to achieve the smart-PDA benefits in overall Efficiency and cost-effectiveness.
    • 4) Use the bottom-up hierarchical individual-control terminology languages (i.e., Natural Language).

IV.    Multi-(9)-level-web-domain control virtual-hierarchical Theme Park Enterprise Systems on the semantic/secure Internet (7)

VS one-level web-model-processing planar-Amusement Park Association with datacenter on the syntax Internet (8). 

  • Descriptions:

    • Multi-(9)-level-web-domain control virtual-hierarchical Theme Park Enterprise Systems for real-time internal self-improving/self-learning PS-services and external menu-contractual services on the semantic/secure Internet (7) 

    • VS one-level web-model-processing planar-Amusement Park Association with datacenter for internal agentic-model-PS via Cloud service providers and external pre-set client-app manual services on the syntax Internet (8)

  • (7)  virtual Theme-Park Enterprises: (restaurants, hotels and Theme-park as one Enterprise)   
    • 1) encapsulate
      • all the bottom up business virtual service domains by adding virtual individual service domain. 
    • 2) build their own Strong-AI individual-PDA services 
      • for standalones, portables and wearables,
      • based on individual internal hierarchical service domains.
    • 3) enable real-time dynamic hierarchical Problem -Solving capabilities,
      • for individuals to achieve the smart-PDA benefits in overall Efficiency and cost-effectiveness.
    • 4) Use the bottom-up hierarchical individual-control terminology languages (i.e., Natural Language).
  • (8)  web Amusement Park Association (food/recreation vending stations)  
    • 1) encapsulate
      • all the bottom up business virtual service domains by adding virtual individual service domain. 
    • 2) build their own Strong-AI individual-PDA services 
      • for standalones, portables and wearables,
      • based on individual internal hierarchical service domains.
    • 3) enable real-time dynamic hierarchical Problem -Solving capabilities,
      • for individuals to achieve the smart-PDA benefits in overall Efficiency and cost-effectiveness.
    • 4) Use the bottom-up hierarchical individual-control terminology languages (i.e., Natural Language).
 

"The Right Strong-AI Strategy"

I.    The right AI Strategy:

Install HTR’s strong-AI hierarchical workgroup control systems (WCS)

for building bottom-up strong-AI with real-time dynamic top-down control PS services
for all system’s internal users/controllers and external contractual users.

  • 1) encapsulate all the hierarchical business service-oriented problem domains,
    • from bottom-up production/assembly/fabrication/transaction/business-locale/eco-business organization/virtual business organization.
  • 2) build build their own strong-AI Business-Enterprise services
    • for small, medium, large organizations,
    • based on business internal hierarchical service-oriented problem domains.
  • 3) enable real-time dynamic  hierarchical domain-Problem solving capabilities,
    • for employees from business contract/portfolio/project processors, managers to business policy/strategy management to CEOs,
    • to achieve the business objectives in overall Efficiency and cost effectiveness. 
  • 4) use the bottom-up hierarchical Business-control terminology language (i.e., Natural Language).  
    •  

II.     The wrong AI strategy: 

Install weak-AI cloud-service providers’ fee-based agentic-processing models
to prompt up the feasible agentic-workflow PS-services
for client-users only.

  • 1) encapsulate all the hierarchical consumer service-oriented problem domains,
    • from bottom-up business-service domain, to  additional eco-consumer and virtual-consumer service domains.
  • 2) build their own strong-AI Consumer-Apparatus services
    • for home, car and robots Enterprise-AI for small, medium, large organizations,
    • based on business internal 11-level hierarchical service-oriented problem domains.
  • 3) enable real-time dynamic hierarchical-Problem solving capabilities
    • for consumers to achieve the apparatus benefits in overall Efficiency and cost effectiveness.
  • 4) use the bottom-up hierarchical Consumer-control terminology language  (i.e., Natural Language).

III.    Conclusions:

  • 1) planar infrastructural is not AI,
    • from bottom up consumer service domains to additional eco-individual and virtual individual service domain. 
  • 2) Bottom-up AI systems with real-time dynamic top-down domain-control problem solving 
    • for standalones, portables and wearables,
    • based on individual internal hierarchical service domains.
  • 3) enable real-time dynamic hierarchical Problem -Solving capabilities,
    •  
  • 4) Use the bottom-up hierarchical individual-control terminology languages (i.e., Natural Language).
 

"3 must-have Strong-AI Strategic Actions"

I.    Strategic Action-1:

Install HTR’s 1-5 generation strong-AI hierarchical workgroup control systems (WCS)

for building strong Business-AI.

  • 1) encapsulate all the hierarchical business service-oriented problem domains,
    • from bottom-up production/assembly/fabrication/transaction/business-locale/eco-business organization/virtual business organization.
  • 2) build build their own strong-AI Business-Enterprise services
    • for small, medium, large organizations,
    • based on business internal hierarchical service-oriented problem domains.
  • 3) enable real-time dynamic  hierarchical domain-Problem solving capabilities,
    • for employees from business contract/portfolio/project processors, managers to business policy/strategy management to CEOs,
    • to achieve the business objectives in overall Efficiency and cost effectiveness. 
  • 4) use the bottom-up hierarchical Business-control terminology language (i.e., Natural Language).  
    •  

II.     Strategic Action-2:

Install HTR’s 6th generation strong-AI workgroup control systems (WCS)

for building strong Consumer-AI.

Once the Enterprise-AI can be established,
then the consumer Apparatus-AI (fault-free smart-home, level-5 autonomous smart-car, team-worked smart-robots)
can be achieved, by installing our 6th-generation strong-AI hierarchical workgroup control systems.

  • 1) encapsulate all the hierarchical consumer service-oriented problem domains,
    • from bottom-up business-service domain, to  additional eco-consumer and virtual-consumer service domains.
  • 2) build their own strong-AI Consumer-Apparatus services
    • for home, car and robots Enterprise-AI for small, medium, large organizations,
    • based on business internal 11-level hierarchical service-oriented problem domains.
  • 3) enable real-time dynamic hierarchical-Problem solving capabilities
    • for consumers to achieve the apparatus benefits in overall Efficiency and cost effectiveness.
  • 4) use the bottom-up hierarchical Consumer-control terminology language  (i.e., Natural Language).

III.    Strategic Action-3:

Install HRT’s 7th generation strong-AI workgroup control systems (WCS)

for building strong individual-AI. 

Once the consumer-AI can be established,
then the individual PDA-AI (stationary, portable, wearable) can be achieved
by installing our 7th-generation strong-AI hierarchical workgroup control systems.

  • 1) encapsulate all the individual hierarchical service-oriented problem domains,
    • from bottom up consumer service domains to additional eco-individual and virtual individual service domain. 
  • 2) build their own Strong-AI individual-PDA services 
    • for standalones, portables and wearables,
    • based on individual internal hierarchical service domains.
  • 3) enable real-time dynamic hierarchical Problem -Solving capabilities,
    • for individuals to achieve the smart-PDA benefits in overall Efficiency and cost-effectiveness.
  • 4) Use the bottom-up hierarchical individual-control terminology languages (i.e., Natural Language).
 

"HTR's 7-generation bottom-up hierarchical workgroup-control systems (WCS)"

I.   Generation-1:

Workgroup production-control systems for enabling production domain task-menu services.

  • 1) Descriptions: 
    • (multi-productivity aggregated/integrated)-production-control expert/agent systems to encapsulate production service domains with real-time dynamic top-down control task-service menu.

II.     Generation-2:

Workgroup assembly-control systems for enabling assembly domain job-menu services.

  • 1) Descriptions:
    • (multi-production aggregated/integrated) assembly-control expert/agent AI-PS systems to encapsulate assembly service domains with real-time dynamic top-down-control job-service menu.

III.  Generation-3:

Workgroup fabrication-control systems for enabling fabrication domain case-menu services.

  • 1) Descriptions:
    • multi-assembly aggregated/integrated) fabrication-control expert/agent systems to encapsulate fabrication-service domains with case-service menu.

      IV.  Generation-4:

      Workgroup transaction-control systems for enabling transaction domain contract-menu services.

  • 1) Descriptions:
    • (multi-fabrication aggregated/integrated) transaction-control expert/agent (i.e., task-job-case contract-expert/ task-job-case contract-agent) systems to encapsulate transaction service domains with contract-service menu.

V.  Generation-5:

Workgroup business-locale/eco/virtual agent-control systems

for enabling business-AI smart-Enterprise agent-menu services.

  • 1) Locale-agent-control systems:
    • workgroup (multi-transaction aggregated/integrated) 4-tier hierarchical business-locale-service-control experts/agents systems, to encapsulate locale business service domains with business contract service menu.
  • 2) eco-agent-control systems:
    • workgroup (4-tier eco-agents aggregated/integrated) eco-business-organization service-control eco-agentic Enterprise-AI systems, to encapsulate eco-organization-service domains with business multi-contract-portfolio/project/policy/strategy service menu.
  • 3) virtual-agent control systems:
    • workgroup (3-platform virtual-agent aggregated/integrated) virtual business organization service control AI-PS virtual-agentic1,2,3 Enterprise-AI systems, to encapsulate virtual-business service domains with business virtual PS-chore and NF-contract service menu.

VI.  Generation-6:

Workgroup consumer-locale/eco/virtual agent-control systems

for enabling consumer-AI smart-Apparatus (home, car, robot) agent-menu services.

  • 1) Locale-agent-control systems:
    • workgroup (multi-business agentic-device aggregated/integrated) 4-tier hierarchical business-locale-service-control experts/agents AI-PS systems, to encapsulate locale consumer service domains with consumer contract service menu.
  • 2) eco-agent-control systems:
    • workgroup (4-tier eco-agents aggregated/integrated) eco-consumer organization service-control AI-PS eco-agentic Apparatus-AI systems, to encapsulate eco-consumer service domains with consumer multi-contract-portfolio/project/policy/strategy service menu.
  • 3) virtual-agent control systems:
    • workgroup (1-platform virtual-agent aggregated/integrated) virtual consumer service control AI-PS virtual-agentic-4 Apparatus-AI systems, to encapsulate virtual consumer service domains with consumer virtual PS-chore and NF-contract service menus.

III.  Generation-7:

Workgroup individual-locale/eco/virtual agent-control systems

for enabling individual-AI smart-PDA agent-menu services.

  • 1) Locale-agent-control systems:
    • workgroup (multi-business/consumer agentic-device aggregated/integrated) 4-tier hierarchical individual locale-service-control AI-PS experts/agents systems, to encapsulate locale individual-service domains with individual contract service menu.
  • 2) eco-agent-control systems:
    • workgroup (4-tier eco-agents aggregated/integrated) eco-individual organization service-control AI-PS eco-agentic PDA-AI systems, to encapsulate eco-organization-service domains with individual multi-contract-portfolio/project/policy/strategy service menu.
  • 3) virtual-agent control systems:
    • workgroup (1-platform virtual-agent aggregated/integrated) virtual consumer organization service control AI-PS virtual-agentic-5 PDA-AI-systems, to encapsulate virtual-individual service domains with individual virtual PS-chore and NF-contract service menu.
 

"HTR workgroup control computing patents"

I. US-patent# 6,715,100, titled “Method and Apparatus for implementing Workgroup Server Array”. (awarded 2004) 

  • 1) Descriptions: 
    • Our WSAs are the fundamental BBBs (Basic-Building-Block) for generating all the workgroup computing hardware structural circuitry, just like gate arrays as the fundamental BBBs for generating node-computing hardware structural circuitry via Programmable-Gate-Array (PGA),
    • Since 2000, after 5-year WSA-infrastructure endeavor, we knew that node-computing paradigm has limitations that cannot achieve AI-PS capabilities. We started out by thinking out of the box and we knew that it’s inevitable that unicellular-like node-computing will have to evolve into multi-cellular-like workgroup-computing..

II. US patent# 11, 132,236 (awarded 9/28/2021 , patent# 11,609,795, (awarded 3/21/2023) and patent# 12,379,969 (awarded 8/5/2025),

titled “Workgroup Hierarchical Core Structures (wHCS) for building Real-Time Workgroup Systems. 

  • 1) Description
    • Together, they are focused on 10 workgroup architectures, creating 7-generation wHCSs based on 3-level WSAs.
    • we discovered the way for building up wHCSs and evolve them over 7-generations from 2010 to 2019, developing 1) multi-productivity production service systems, 2) multi-production assembly workgroup control systems, 3) multi-assembly fabrication workgroup control systems, 4) multi-fabrication transaction service control systems, 5) multi-transaction business locale-service control systems for departments, divisions, management offices and central office service control systems, 6) multi-business locale consumer-locale service control systems and 7) multi-consumer locale individual-locale service control systems. All of which are disclosed in US patent# 11,132,236. This US-patented wHCSs can evolve and generate all these 7 hierarchical workgroup-control systems for all real-world controllers.  

III. US-Patent# 11-797,299 (awarded 10/24/2023), titled “ 3-level Real-time Concurrent production operation workgroup systems

for fine-grained proactive closed-loop problem solving operations”.

  • 1) the Descriptions:
    • It is focused on workgroup architected HCSs implemented 3-level OS-control mechanisms that enable real-time dynamic semantic-control Operation and Management programming.

IV. US-application # 63/672,166 (filed 7/16/2024), titled “computing systems with AI Problem solving Competencies”

  • 1) Descriptions:
    • It is focused on how to aggregate Solution domain with real-time dynamic Operation& Management-control programming and knowledge domain with real-time dynamic O&M control programming into Problem solving domains with real-time automatic OM-MO reciprocal-control programming methods that can enable Solution domain with real-time self-improving capabilities and Knowledge domain with real-time self-learning capabilities.

V. Conclusions.

  • 1) Descriptions:
    • Based on the above 4 major patents, iterative hierarchical control entity-systems of lower-level entity systems, can be created over 7 evolutionary generations, generating hierarchical workgroup-expert/agent-entity and B/C/I organization entity systems, such as smart-Enterprise, smart-apparatus (home, car, robot) and smart-PDA systems to encapsulate all the real-world 7 hierarchical service-oriented problem domains, allowing everyone as real-world business-service/consumer-service/individual-service controllers to real-time dynamically control daily working and living “problem-solving-chore” and Maslow-5 “need-fulfilling-contract” services.

"10 must-have workgroup control architectural
theoretic foundations for enabling Strong-AI computing"

I. Workgroup 3-level aggregate concurrent executing and monitoring control architecture,

  • 1) Descriptions:
    • creating long-running heterogeneous processes for real-time adding and prolonging top-level control processes, mid-level traffic-control processes and base-level attribute-aggregate processes.

II. Workgroup 3-level integrate closed-loop semantic Solution and Knowledge (S&K) domains control architecture,

  • 1) Description:
    • creating semantic control solution domain and knowledge domain for syntax to/fro semantic meta/meso compiling, training S/K domain experts with real-time dynamic semantic-control programming. 

III. Workgroup 3-level aggregate reciprocal S&K and Problem-Solving (PS)-domains control architecture,

  • 1) the Descriptions:
    • creating self-improving/self-learning PS-domains for real-time dynamic PS capabilities via Solution-R&R (Request&Reply) and Knowledge Q&A chained-automation.

IV. Workgroup 3-level integrate feedback PS-service system control architecture, 

  • 1) Descriptions:
    • creating self-developing PS-service systems for real-time interactive PS services by generating PS-service-menu.

V. Workgroup 3-level aggregate fail-over PS-service system control architecture,

  • 1) Descriptions:
    • creating fail-over self-growing PS-service systems for real-time dynamic hot-swap/growing PS-services.

VI. Workgroup 3-level integrate fail-safe PS service entity system 7-generation evolutionary control architecture,

  • 1) Descriptions: 

    • creating fail-safe self-evolving PS-service systems over 7-generation (i.e., production/assembly/fabrication/transaction/Business-locale/Consumer-locale/Individual-locale) agent-control PS-service systems for real-time dynamic (solution)-self-improve/knowledge self-learning/PS self-developing/PS-service-menu self-increasing) internal PS-services.

VII. Workgroup 4-tier aggregate diverse eco-agent control architecture,

  • 1) Description:
    • creating eco-tier-agentic organization systems for real-time dynamic eco-4-tier-zone PS services with diversity. 

VIII. Workgroup 4-tier integrate complex eco-agent control architecture,

  • 1) the Descriptions: 
    • creating eco-tier agentic organization systems for real-time interactive eco-organization PS services with complexity.

IX. Workgroup 5-platform aggregate diverse virtual-agent control architecture, 

  • 1) Descriptions: 
    • creating virtual platform-agentic systems for real-time dynamic virtual-5-platform PS services with diversity.

X. Workgroup 5-platform integrate complex virtual-agent control architecture,

  • 1) Descriptions:  
    • creating virtual platform-agentic organization systems for real-time interactive dynamic virtual organization PS services with complexity.

"Technological Advantages of HTR's 7-generation AI-PS systems"

I. Better semantic-interactive domain-control security VS token-interface model-processing security:

  • 1) Descriptions:
    • Better Secure semantic-(terms) 3-level-hierarchical S/K/PS control Domains VS insecure 1-level-planar syntax-(data)-S/K/PS-processing Models.  (3-level conveyer domain, top-controller, mid-traffic-controller and base-level workstation-processors
    • VS one-level workstation-processors).

II. Better subject-oriented semantic-domain control-terminology language VS object-oriented syntax-model processing-ontology language:

  • 1) Description:
    • Better Subject-oriented semantic-term-control domain-terminology-(top-level menu/base-level manuals)-language (i.e., menu-ordering-Natural Languages)
    • VS object-oriented syntax-data-processing model-ontology-(one-level manual)-language (i.e., token-prompting Natural Languages). 

III. Better real-time dynamic semantic-terms domain-control Operation & Management programming VS lead-time developed syntax-tokens model-processing Application programming:

  • 1) the Descriptions:
    • Better real-time dynamic hierarchical semantic-term S/K system domain control programming (with real-time situation operational subject-oriented conditions and requirements and real-time operation-managerial metrics/KPIs  = deep domain control programming for hierarchical PS-services
    • VS lead-time versioned syntax-token S/K/PS infrastructural-model processing programming (with pre-conditions and fixated results and without real-time managerial metrics to real-time adjust the consequential results.

IV. Better semantic agent-control domain-PS-menu over all the potential syntax-agentic-workflow model-PS manuals VS lead-time developed syntax-agentic workflow model-PS-manuals.  

  • 1) Descriptions:
    • Better semantic-agent domain-PS control over potential syntax-agentic-workflow models for real-time dynamic situational-conditioned PS-control services VS one tokens-prompted syntax-agentic workflow models-PS for lead-time pre-conditioned PS-processing services
    • Better real-time hierarchical semantic-term PS-system domain-control over situational PS-agentic-workflows generation for real-time dynamic hierarchical domain-PS-services
    • VS lead-time planar syntax-token planar PS model pre-defined one agentic-workflow generation for lead-time fixated planar model-PS-services.

V. Better real-time 7-generation hierarchical workgroup-control systems with 5 self-evolving strong AI-control services VS lead-time version-developed nodes-processing infrastructures with 5 version-developed weak-AI processing services: (security, solution, knowledge, PS and NF services) 

  • 1) Descriptions:
    • Workgroup control systems with internal self-improving solution domains, self-learning knowledge domains and self-growing PS-domains
    • VS PS-infrastructures with lead-time version-developed solution/knowledge/PS-agentic models.

"The advantages of wCP over nCP
from real-world analogical reasoning"

#1: Hardware Architecture Comparison:

Real-world analogy:


#A: 2-environmental Street-vending 2D-Structures:


Street-vending 2-environmental Planar-aggregated 1D/2D-Hub Structures,

The current primitive 2-environmental node-computing structures are similar to Street-vending 1D/2D planar structures in 1) internal one chef/helpers node-processing and 2) external chef-to-customer node-communicating environments.

#B: 3-environmental Restaurant 3D-Structures:


Restaurant 3-environmental hierarchical 3-level integrated Core Structures,

New and better 3-environmental workgroup-hierarchical 3D-core structures are similar to Restaurant 3-level hierarchical-core structures in 1) internal chefs/helpers workgroup processing, 2) internal chefs/busboys/waiters workgroup collaborating and 3) external waiters-to-customers workgroup communicating environments.

Comparisons:

The Primitive
von Neumann-1D/2D architected open-end sequential IO-Flow 2-environmental uni-Node Structures
VS New and Better
workgroup-3D architected closed-loop
concurrent IO-flow multi-node-integrated
3-environmental workgroup hierarchical core-structures

#A: 2-environmental 2D-planar architectures:

Setting up 1D/2D node-structures with one CPU/GPUs(NPUs/TPUs), one-main memory/caches and multiple IO-devices is like setting up 2-environmental “street vending structures” for one chef(CPU)/helpers (GPUs), with one main workbench(memory) multiple-level sub-tables (caches)” to process multiple IO-machineries(IO-devices).

#B: 3-environmental 3D-hierarchical architectures:

Setting up workgroup hierarchical core structures is like setting up 3-environmental 3-level-hierarchical restaurant facilities for base-level multiple chefs/helpers, mid-level multiple busboys/(messages-conveyor dispatchers) and top-level multiple waiters, where multiple chefs/helpers can execute “dish-processing data-manuals” to produce “dishes-terms information-menu” for waiters to communicate with external customers concurrently, using interactive semantic natural-languages.

HTR vital patents in 3-environmental 3D-hierarchical workgroup architectures:


Patented Ten (10) workgroup multi-node hierarchical 3D-integration architectures to encapsulate the current one (1) von Neumann uni-node planar-1D/2D-aggregation architecture.

Patented “TeamServers” can be used to set up hierarchical structures and patented “WSA (workgroup server array)” as the basic building blocks can be used to hierarchically evolve into bigger hierarchical workgroup structures for productions, assemblies, fabrications, transactions and locale/zone/cloud services.

#2: Software OS Comparison:

Real-world analogy:


#A: 2-environmental Street-vending Processing Manuals:


Street-Vending Chef-helpers open-end Cascaded Process Manuals.


#B: 3-environmental Restaurant Operation (semantic)-Menu of processing (data)-Manuals:


Restaurant Chefs-helpers/Busboys/Waiters workgroup collaborative closed-loop-Feedback operation menu of processing manuals.


Comparisons:

The Primitive
2-environmental “Open-End-IO” Traffic-Control node-processor Operating Systems (nOS)
VS New and Better
3-environmental “Closed-loop-IO” Feedback-Control 3-level hierarchical workgroup-processor’s Operating Systems (wOS)

#A: 2-environmental open-end Operating systems (OS):


Street-Vending Chef-helpers open-end Cascaded Process Manuals (= 2D-planar open-end node-OSs)

The current primitive 2-environmental open-end nOSs, such as WIN7-11, Linux, iOS and Android, are similar to the process manuals for Street-vender chef/helpers.

All the current open-end node-processor-OSs are like the processing manual for one vender/chef, who has to handle internal processes and external customers in two processing environments.

All these node-computing OSs are only to control Time-sharing Multiple-in Multiple-out (MIMO) processes. They cannot have the closed-loop feedback-control-supervise of each processing program’s overall IO activities, due to they are just for completing one-node cooking processes, it needs another paired supervising-node to real-time monitoring the cooking processes, so that mistakes can be detected and new and better suggestive cooking processes can be implemented in the real-time manner. Therefore, they are only equipped with process-manager and memory manager and without having the program managers.

#B: 3-environmental closed-loop Operating Systems:


Restaurant Chefs-helpers/Busboys/Waiters workgroup collaborative closed-loop-Feedback operation menu of processing manuals. (= 3D-hierarchical closed-loop workgroup OSs)

New and better 3-environmental 3-level-hierarchical closed-loop wOSs are similar to the Restaurant’s operation manuals for base-level chefs-helpers, mid-level busboys-messengers and top-level waiters.

The closed-loop-IO Feedback-(flow-program)-control top-level TeamProcessor’s workgroup-OSs are similar to the operation manual for the waiters, The open-end-IO traffic-control TeamProcessors’ workgroup-OSs are similar to the process-manual for bus-boys/dispatchers. And the base-level attribute-control TeamProcessor’s workgroup-OSs are similar to the process-manual for chefs/helpers.

HTR vital patents in 3-environmental workgroup-control OSs:


Patented “3-level workgroup-OSs” to encapsulate and enhance the current node-OSs.

Patented “3-level workgroup-OS closed-loop architecture enable the top-level workgroup-control-OSs (wcOS) with bottom-up meta-program manager and top-down meso-program manager, so that real-time feedback-control can be established. Furthermore, the overall closed-loop architecture also enhance the current node-OSs into mid-level workgroup-traffic-OSs (wtOS) and base-level workgroup-attribute-OSs (waOS).

#3: Software Programming Comparison:

Real-world analogy:


#A: 2-environmental Street-vending food services:


Street-vending customer’s fixated Coarse-Grained-reactive (CGR)-solution food-services and post-CGR-solution accounting services for the vender/chef.


#B: 3-environmental Restaurant food services:


Restaurant customers’ real-time Fine-Grained Proactive (FGP)-solution menu-ordering and dishes-delivering food-services and real-time post-FGP-solution details-reporting and performances-ranking accounting-services for onsite managers and management/owner.


Comparison:

The Primitive
Syntax-data interface-language-based sequential open-end-IO/non-feedback-control
lead-time fixated syntax-data Application programming
VS More Advanced
Semantic sentential interactive-language-based concurrent closed-loop-IO/feedback-control
real-time dynamic semantic-sentential Operation and Management programming

#A: lead-time developed syntax-data processing programming:

The current data-solution application programs are similar to the Chef/helpers CGR solution food-service programs, completing the captive-user self-chore CGR-food-vending services, and the current data-manipulation application (database) programs are similar to the Bookkeeper’s accounting-service programs. Lead-time fixated CGR-solution and CGR-data-manipulation node application programs are two (2) lead-time developed node service-oriented programs.

#B: real-time dynamic semantic-sentential control programming:

New and better real-time workgroup FGP-solution operation and management programs are similar to the menu-ordering and dishes-delivering closed-loop food-services completed by the waiters/waiter supervisors, busboys/messengers and chefs/helpers, and the real-time workgroup FGP-knowledge operation and management programs are similar to the onsite details-reporting operation-accounting programs for managers and performance-ranking management-accounting programs for management/owner. Real-time interactive FGP-solution and FGP-knowledge workgroup operation and management programs are four (4) real-time dynamic workgroup service-oriented programs.

HTR vital patents in real-time dynamic semantic sentential workgroup programming:

“Real-time dynamic 3-environmental workgroup closed-loop Feedback-information-exchange operation and management programming” with bottom-up (syntax data-processing manual to semantic term-information-exchange menu) and top-down (order-menu to processing manuals) methods, to encapsulate all the current 2-environmental open-end data-processing application programming.

Closed-loop-IO Feedback-ccontrol solution operation programs can be real-time controlled by the workgroup OS-(meta, bottom-up-half-loop) program manager (conveyer-controller) that can generate information-(menu-solution) for external requesters and when the request comes in, the workgroup OS-(meso, top-down-half-loop) program manager can real-time dynamically decipher the order-semantics and real-time select the current best experienced dishes-solution based on customers’ preferences.

#4: Weak-AI syntax-token predicate-processing Model-PS
(Problem-Solving) Methods:

Real-world analogy:


#A: 2-environmental Fixated Coarse-Grained-Reactive (CGR) Street-vending food services and weak-AI accounting services:

The street-vending Chef with (data-management by accountant), can only find out from CGR-model food-service-oriented data-based reports on current Inventory, costs and revenues, spend time for deep-learning and decide the best cooking solution-processes for tomorrow’s customers. This is dubbed “a weak-AI PS method”.

Definition of 2-environmental “weak-AI syntax-data Model-PS:


Syntax-data solution and post-data-manipulation application-based 2 types of data-programs’ lead-time interface activities, enabling lead-time data-solution improving and lead-time data-knowledge deep learning, dubbed “weak-AI” Problem-Solving methods.

Alternatively speaking, “Weak-AI” fixated data-Model-Problem-Solving (PS) with lead-time data-solution version-improving and lead-time data-knowledge deep-learning based on open-end data-solution application and data-manipulation (database) application 2-programs’ lead-time cross-interface activities using centralized syntax big-data repositories.

The current Weak-AI-PS patents:


All the current Node-computing data-problem solving patents are weak, due to they all are based on pre-set/generative PS-Models.

All the current node-computing theories, from computational complexity and computability are weak, due to they are all defined from the node-computing weak-AI open-end model-PS point of view.

Therefore, the current computational complexity theory can be rephrased as “Memory-expansion-growth limited-evolvable PS-Entity with fixated model-PS capabilities, it has nothing to do with real-time dynamic and intelligent problem solving based on evolvable bigger Solution System’s with better domain-PS capabilities.

3D Artificial Intelligence

#4: Strong-AI semantic-term sentential-control Domain-PS
(Problem-Solving) Methods:

Real-world analogy:


#B: 3-environmental User-centric Fine-Grained-Proactive (FGP)-Restaurant food services and strong-AI real-time operation and management services:

Restaurant (menu)-solution-operators (waiters/chefs) with real-time solution-managers (waiter managers), together with onsite post-(menu)-solution-experience-based knowledge operators (accountants/book-keepers) and managers (management) comprise four restaurant-workgroups and these four workgroups’ real-time interaction among them can provide solution operator (waiters/chefs) with real-time improved FGP-solutions, and enhance knowledge manager (management) with real-time self-learned knowledge. This is dubbed “a strong-AI PS method”.

Definitions of Strong-AI/AGI Domain-PS:


Semantic-sentential solution operation and management-based and post-solution knowledge operation and management-based 4 types of sentential-programs’ real-time interactivities, enabling real-time sentential solution-improving and real-time sentential-knowledge learning, dubbed “Strong-AI” Problem-Solving Methods.

Alternatively speaking, “Strong-AI” dynamic sentential-Domain-PS with real-time semantic-solution self-improving and real-time semantic-knowledge self-learning based on closed-loop Feedback-control solution and knowledge workgroup operation and management 4-programs’ real-time cross interactivities using localized semantic information libraries.

HTR vital patents in Strong-AI Domain-PS Methods:


Strong-AI via local information/semantic libraries with real-time interactive self-improving solutions and self-learning knowledge to replace the centralized pre-set syntax-modelled databases.

The closed-loop Feedback solution and knowledge workgroup operation and management 4-programs’ real-time interaction among them will real-time enable self-improving solution and knowledge operation programming and self-learning solution and knowledge management programming based on solution and knowledge libraries.

#5: Hardware/Software-Self Evolvable PS-Entity Comparison:

Real-world Analogy:


#A: 2-environmental Street-vending Facilities with limited uni-node evolutions, providing short-running services:


2-envirnmental “Street-vending facilities cannot evolve well and cannot integrate with other facilities, only aggregate with other open-end-street-vending facilities.

Street vending/chef facilities can only expand with more helpers (GPUs) with tables (caches), more cooking machineries (IO-devices), but they cannot grow out of the 2-environmental processing structures due to the confinement of one main workbench (main-memory). Multiple street-vending facilities cannot be integrated into bigger facilities with better food-services, they can only be aggregated into 2-environmental open-end food-fairs as well as amusement parks (= LAN-ANN Infrastructures). However, they are inherited with unsolvable perimeter security issues.

#B: 3-environmental Restaurant Facilities with hierarchical evolutions, providing better long-running services:


3-environmental restaurant facilities can grow into bigger/fail-over/fail-safe restaurants with better/more variety food services and they become an integrable unit (workgroup subsystem) to evolve into bigger facilities (workgroup system) with better FGP-service capabilities.

The restaurant facility can grow internally as well as become an integrable unit to build bigger food-court with assembly services, which can become an integrable unit for building Hotels, theme Parks under one 3-environmental Strong-AI service-oriented organization that can provide FGP services.

The Comparison:


#A: 2-environmental uni-node (hardware-only) prokaryotes-evolved node-entities with lead-time data-model-PS capabilities:

2-environmental weak-AI node-Computing PS-Entities that can only have one (1) node-evolutionary generation,
due to limitations of von Neumann node/1D/2D hardware architectures.

#B: 3-environmental multi-node (both hardware and software) eukaryotes-evolved workgroup-Entities with real-time Domain-PS capabilities:

3-environmental Strong-AI/AGI workgroup-Computing PS-Entities that can evolve over seven (7) workgroup-evolutionary generations, due to 3D-hierarchical hardware architectures.

HTR vital patents in 3-environmental multi-node-evolved workgroup-entities with Domain-PS capabilities:


workgroup closed-loop-(hardware and software) duality-evolved PS-entities with domain-PS theories to “replace” node-(open-end hardware)-cascaded PS-entity with model-PS theories. Furthermore, these duality-evolved PS Entities can evolve into various bigger workgroup PS (species)-entities with better (specialty)-domain-PS capabilities.

3-environmental workgroup evolutionary and PS theories can create strong-AI workgroup service-oriented production, assembly, fabrication, transaction-Expert/Agent workgroup systems and strong-AGI locale-service, zone-service and cloud service smart-Enterprises (small, mid, large), smart-Apparatus (homes, cars, robots) and smart-PDAs (stationary-PCs, portables and wearables) along the 7-genertion workgroup evolutionary timeline.

The inevitable weak-AI nCP shift to strong-AI wCP

2-environmental "weak-AI" node-Computing Paradigm (nCP)
Shift to
3-environmental "Strong-AI/AGI" workgroup-Computing Paradigm (wCP)

The current primitive “2-environmental Street-Vending-scenario uni-node-evolved” “weak-AI” node-computing paradigm (nCP)


The current 2-environmental street-vending-scenario-like node-computing von Neumann 1D/2D architectures, weak-AI evolution/PS theories, Machine-centered Weak-AI-PS application systems disciplines and inter-node cloud-system data-transaction platforms on the open Internet, i.e., weak-AI node-computing paradigm cannot computerize the current 3-environmental service-oriented organizations, therefore they can only provide Coarse-Grained-Reactive (CGR)-services to captive users with unsolvable security and privacy issues.

The current 2-environmental weak-AI node-computing paradigm only establish street vender/chef-like computing structures, enable only one multi-purpose Chef to handle cooking and at the same time dealing with the external customers. Cascading all the involved street-vending facilities together can only provide captive customers with fixated coarse-grained reactive (CGR) cookie-cutting self-chore services. These street-vender/chef facilities can be cascaded into open-end street-night-market, which is insecure and also laden with privacy issues, due to personal information can be compromised. This cascaded open-end chef-based facilities cannot accommodate real-world hierarchical service-oriented organizations.

The advanced “3-environmental Restaurant-scenario multi-node-evolved” “Strong-AI” workgroup-Computing Paradigm (wCP)


The 3-environmental restaurant-like workgroup-computing hierarchical-3D architectures, strong-AI evolution/PS theories, real-time Operation and management systems disciplines and inter-workgroup cloud-systems information-transaction platforms on the open Internet, i.e., Strong-AI workgroup computing paradigm can accommodate all the 3-environmental service-oriented organizations and provide real-time adaptive Fine-Grained-Proactive (FGP)-services for any user, anytime and anywhere.

While 3-environmental Strong-AI/AGI workgroup-computing paradigm can establish the smallest restaurant and evolve into bigger and better restaurants with fail-over managers and fail-safe managers, into bigger and better food-courts, into bigger and better service-oriented organizations, such as hotels and theme parks. And each employee in the 3-environmetnal hierarchical organization is supported with operation/management manuals (wOS), providing real-time interactive workgroup operation services with real-time management supports.

The inevitable digital computing paradigm shift from 2-environmental weak-AI nCP to 3-environmental Strong-AI wCP


The current nCP-based private websites-aggregated data-service oriented Internet is laden with unsolvable security and privacy issues.

The real-world efficient and cost-effective service-oriented organizations are all based on 3-environmental hierarchical-core structures, so that the whole organization can function as one integral PS-Entity. Therefore, these 3-environmental hierarchical service organizations cannot be computerized based on primitive 2-environmental node-computing paradigm (nCP), which from the real world analogy, can only produce a mighty vender-chef with limited number of helpers and the collaboration among these vender-chefs to create better integrated food-services is impossible, due to there is no additional collaboration environment where chefs, busboys and waiters can be workgrouped together, concurrently providing customers with preferred negotiable personal fine-grained proactive services in a more efficient and cost-effective way.

Furthermore, all the current nCP-established “client-server” private websites-based service-transaction platforms on the open Internet are laden with security and privacy issues, which again from the real world analogical analysis, is due to the fact that these aggregated vender-chefs have to directly deal with “captive” customers in a chained-perimeter platform-boundary that is full of security holes and when one vender-chef becomes super large on its 2-environmental private platform, its captive customer’s privacy will become one of the transactional prerequisites in order to receive the cookie-cutting coarse-grained reactive services.

The Conclusions: 

Check Mark
#1: Primitive 2-environmental open-end Node 1D/2D structures: (i.e., node-hub, LAN, ANN)
Check Mark
#2: Primitive 2-environmental open-end Node-processor's OS: (such as Win7-11, Linux, iOS, Android)
Check Mark
#3: Primitive 2-environmental lead-time developed application programs:
Check Mark
#4: Primitive 2-environmental Weak-AI node-application model-PS methods:
Check Mark
#5: Primitive 2-environmental lead-time uni-node Prokaryotic-evolved Node-PS Entities:
Check Mark
#6: Primitive 2-environmental all-time fixated Machine-Human interface (MHI) Coarse-Grained-Reactive (CGR)-service-oriented Node-PS systems:
Check Mark
#7: Primitive 2-environmental Weak-AI node-cloud application CGR-service-oriented website systems:
Check Mark
#8: Primitive 2-environmental myriad private node-cloud systems aggregated CGR-data-service Transaction Platform on the open Internet:
Green Check Mark
#1: Advanced 3-environmental closed-loop 3D-workgroup hierarchical core structures: (i.e., 7-generation wHCSs)
Green Check Mark
#2: Advanced 3-environmental Closed-loop Feedback flow-control workgroup OSs: (i.e., wcOSs, wtOSs, waOSs)
Green Check Mark
#3: Advanced 3-environmental real-time interactive workgroup operation and management programs:
Green Check Mark
#4: Advaned 3-environmental Strong-AI real-time dynamic workgroup domain-PS methods:
Green Check Mark
#5: Advanced 3-environmental multi-node eukaryotic duality-evolved workgroup-PS Entities:
Green Check Mark
#6: Advanced 3-environmental real-time dynamic Human-Machine-Ineractive Fine-Grained Proactive (FGP)-service-oriented workgroup-PS systems:
Green Check Mark
#7: Advanced 3-environmental real-time dynamic Strong-AGI smart FGP-service-oriented workgroup cloud systems:
Green Check Mark
#8: Advanced 3-environmental secure hierarchical workgroup-cloud systems integrated FGP-information service transaction platform on the open Internet:
Green Check
#1: The must-have Strong-AI User-centric FGP-services provided by 3-environmental hierarchical business-service-oriented organizations.

> The current 2-environmental node-Computing Paradigm (nCP) cannot accommodate real-world hierarchical business organizations to provide Strong-AI/AGI services.

Green Check
#2: The The inevitable 2-environmental Weak-AI nCP shift to 3-environmental Strong-AI wCP.

> nCP’s 4 paradigmatic competences in 2-environmental architectures, theories, systems and platforms, should all be upgraded into wCP 4 paradigmatic competences in 3-environmental architectures, theories, systems and platforms on the open Internet.

Green Check
#3: The must-have 3-environmental wCP-based Strong-AGI service-oriented Internet.

> The unavoidable 2-environmental nCP-based self-service-application-oriented Internet migration to 3-environmental wCP-based full-service (operation and management) oriented Internet.

Green Check
#4: The new and better 3-environmental wCP-enabled FGP-user benefits to replace the current 2-environmental primitive and intrusive CGR user-chores.

> The new and better 3-environmental strong-AI Agent to the agent via real-time interactive (solution/knowledge) operations, providing new and better FGP services with real-time management supports to replace the current nCP CGR-services and eliminate at least 7 unnecessary chores, such as password log-in, Apps-downloading, browser-surfing, data searching, database deep-learning, data security-software installing and private-service platform data-transaction consummating.

Bright Future Ahead

The must-have multi-node hierarchical 3D-architected “Strong-AI” workgroup-Computing Paradigm (wCP)

Futuristic
Gold Check
#1: Smart-Enterprise workgroup control systems:

> Smart-Enterprises operations via real-time Strong-Business Intelligent (strong-BI) Agents to real-time interact with other BI-agents, providing more adaptive FGP-services with real-time operational management for exchanging in-event information.

Gold Check
#2: Smart-Home workgroup control systems:

> Smart-Home operations via strong home-intelligent (Strong-HI) agents to real-time manage home-(internal) appliances, gears, gadgets and widgets and to real-time interact with 1) manufacturers’ agents for real-time home-security, 2) electricity, water, gas authorities’-agents for real-time home-safety, 3) venders’ agents for real-time home resources-replenishing and 4) personal agents for providing real-time home services, such as cooking and recreation.

Gold Check
#3: Smart-Car/Vehicle workgroup control systems:

> Smart-Cars/vehicles operations via Strong Car-intelligent (Strong CI) agents to real-time interact with surrounding (public facility/store, car, personal) agents, providing up to level-5 autonomous driving with real-time management to co-exist with pedestrians, bicyclists, vehicles, traffic lights, road-condition authority, based on the real-time interactions among involved smart-agents with the exchanges of real-time most-current situational and intentional information.

Gold Check
#4: Smart-Robot workgroup control systems:

> Smart-Robot operations via strong robotic-intelligent (Strong-RI)-multi-sensory/concurrent-tasking agents to real-time interact with surrounding (business-office/store, home, car, personal) agents, providing adaptive personal services with real-time management at offices, stores, homes, cars/public-transits, and public facilities.

Gold Check
#5: Smart-PDA workgroup systems:

> Smart-PDAs (smart-PCs, Laptops, wearables) with Strong-personal-intelligent (Strong-PI) smart-Agents to real-time converse (via natural language) with individual-owners for obtaining the intention and then real-time interact/negotiate with all the related/surrounded agents, providing adaptive personal Maslow-5 contractual services with real-time management, anytime and anywhere.

Go Sign

The inevitable Computing Paradigm (CP) shift From “weak-AI” nCP to “Strong-AI” wCP

Artificial Intelligence
Bulb
#1: The current 2-environmental nCP only started the primitive unicellular-prokaryotic halfway evolutions

of digital computing, while 3-environmental wCP is to complete the overall multicellular-eukaryotic evolutions of digital computing simply abided by the natural law of continuous growth along the evolutionary timeline into the future.

Bulb
#2: It takes 20 years for HTR to develop all the new and better 4 workgroup paradigmatic pillars,

establishing 3-environmental workgroup computing paradigm (wCP) that sets up the Strong-AI digital computing future. While all the current computing companies haven’t come up with innovations to resolve the limitations of any node-computing paradigmatic competencies in the past 20 years.

Bulb
#3: Don’t waste any further efforts in producing 2-environmental weak-AI node-computing systems,

which are only to establish myriad private insecure and non-transparent self-service (OSI-7) application-oriented transaction platforms for providing only captive users with pre-set fixated Coarse-Grained-Reactive (CGR) services on the open Internet with unsolvable security and privacy issues.

Bulb
#4: It is time to consolidate efforts in producing Strong-AI/AGI smart workgroup-computing systems,

which can establish “the one and only public secure and transparent multi-agent full-service (OSI-5) operation-oriented transaction platform for providing anyone, anytime and anywhere with “real-time adaptive” Maslow-5 need-based Fine-Grained Proactive (FGP)-services on the open Internet without unsolvable security and captive-privacy issues.

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