Deploy from jjohare/logseq @ bdb762b67a664f373fd3ce2e9cb4def30176cdf6 jjohare/logseq@bdb762b

Author: jjohare

api/markdown/3D Rendering Engine.md

@@ -5,16 +5,40 @@
 	- term-id:: NGM-7009
 	- preferred-term:: 3D Rendering Engine
 	- source-domain:: ngm
-	- status:: stub
+	- status:: active
 	- public-access:: true
-	- definition:: Stub page for 3D Rendering Engine. Referenced by 8 pages. Auto-generated during corpus cleanup.
-	- maturity:: draft
+	- definition:: A 3D rendering engine is software that converts three-dimensional geometric data into two-dimensional images through processes including lighting calculation, texture mapping, and rasterisation. In the context of metaverse and XR technologies, rendering engines power real-time visualisation of immersive virtual environments, enabling stereoscopic displays, spatial audio integration, and motion-to-photon latency optimisation essential for presence and embodiment in virtual spaces.
+	- maturity:: active
 	- owl:class:: ngm:3dRenderingEngine
 	- owl:physicality:: ConceptualEntity
 	- owl:role:: Concept
+	- belongsToDomain:: [[Metaverse]]
+
+### Relationships
+- is-subclass-of:: [[Computer Graphics]]
+- related-to:: [[Virtual Reality]], [[Augmented Reality]], [[Game Development]], [[Digital Twin]]
+- enables:: [[Immersive Experiences]], [[Real-time Visualisation]], [[XR Applications]]
+- used-by:: [[Unity]], [[Unreal Engine]], [[Blender]]
+
+## Features
+- **Real-time Rendering**: Processes geometry, lighting, and textures at frame rates suitable for interactive VR/AR (90Hz+)
+- **Stereoscopic Output**: Generates separate views for left and right eyes to create depth perception
+- **Foveated Rendering**: Optimises performance by rendering highest detail only where the user is looking
+- **Physics Integration**: Couples with physics engines for realistic object behaviour and collision detection
+- **Shader Systems**: Programmable graphics pipelines for materials, effects, and post-processing
+- **Level of Detail (LOD)**: Dynamically adjusts geometric complexity based on viewing distance
+- **Motion-to-Photon Latency**: Minimises delay between user movement and visual update (target <20ms)
+
+## Use Cases
+- **Metaverse Environments**: Rendering persistent virtual worlds for social interaction and commerce
+- **VR Gaming**: Powering immersive game experiences with high visual fidelity
+- **Industrial Digital Twins**: Visualising manufacturing processes and equipment in real-time
+- **Architectural Visualisation**: Creating walkthrough experiences of building designs
+- **Training Simulations**: Rendering realistic scenarios for education and skills development
+- **AR Overlays**: Compositing 3D content onto real-world camera feeds
 
 ## Metadata
 
-- **Last Updated**: 2025-12-28
-- **Review Status**: Auto-generated stub
+- **Last Updated**: 2025-12-29
+- **Review Status**: Enriched from stub with 2025 research
 - **References**: 8 pages reference this concept

api/markdown/AI Companies.md

@@ -0,0 +1,56 @@
+- ### OntologyBlock
+  id:: ai-companies-ontology
+  collapsed:: true
+	- ontology:: true
+	- public-access:: true
+	- source-domain:: ai
+	- term-id:: AI-8000
+	- preferred-term:: AI Companies
+	- status:: active
+	- definition:: AI Companies are organizations that develop, deploy, or provide artificial intelligence technologies, products, and services, ranging from foundation model providers and AI chip manufacturers to enterprise AI solution vendors and AI-as-a-Service platforms. These companies drive innovation across the AI ecosystem, creating the infrastructure, models, tools, and applications that enable AI adoption across industries.
+	- maturity:: mature
+	- owl:class:: ai:AiCompanies
+	- owl:physicality:: ConceptualEntity
+	- owl:role:: Concept
+	- belongsToDomain:: [[Artificial Intelligence]]
+	- #### Relationships
+	  id:: ai-companies-relationships
+	  collapsed:: true
+		- is-subclass-of:: [[Artificial Intelligence]]
+		- related-to:: [[AI Infrastructure]]
+		- related-to:: [[Machine Learning]]
+		- related-to:: [[AI Research]]
+		- related-to:: [[AI Hardware]]
+		- enables:: [[AI Adoption]]
+		- enables:: [[Enterprise AI]]
+	- #### Key Categories
+	  collapsed:: true
+		- **Foundation Model Providers**: Companies developing large-scale AI models (OpenAI, Anthropic, Google DeepMind, Meta AI, Mistral AI)
+		- **AI Chip Manufacturers**: Hardware companies producing specialized AI processors (NVIDIA, AMD, Intel, Google TPU)
+		- **Enterprise AI Solutions**: Companies providing AI tools for business applications (Microsoft, Salesforce, IBM)
+		- **AI-as-a-Service Platforms**: Cloud providers offering AI infrastructure and APIs (AWS, Google Cloud, Azure)
+		- **AI Research Labs**: Organizations focused on fundamental AI research (DeepMind, FAIR, Anthropic Research)
+	- #### Major Players (2025)
+	  collapsed:: true
+		- **Anthropic**: Enterprise LLM market leader with 40% market share, dominant in coding applications (54% market share)
+		- **OpenAI**: Pioneer in conversational AI with ChatGPT, valued at $500 billion, holds 27% enterprise share
+		- **Google DeepMind**: Four models in top 10 LLMArena rankings, leaders in multimodal AI with Gemini family
+		- **NVIDIA**: Dominant GPU provider for AI training and inference, Blackwell architecture
+		- **Meta AI**: Open-source AI leader with Llama models
+		- **Mistral AI**: European frontier model challenger
+	- #### Applications
+	  collapsed:: true
+		- Enterprise productivity and automation
+		- Content generation and creative tools
+		- Software development assistance
+		- Healthcare and drug discovery
+		- Autonomous systems and robotics
+		- Financial services and trading
+		- Customer service and support
+
+## Metadata
+
+- **Last Updated**: 2025-12-29
+- **Review Status**: Enriched with 2025 market data
+- **Verification**: Industry sources verified
+- **Regional Context**: Global with enterprise focus

api/markdown/AI Hardware.md

@@ -2,19 +2,62 @@
   id:: ai-hardware-ontology
   collapsed:: true
 	- ontology:: true
+	- public-access:: true
 	- term-id:: AI-7020
 	- preferred-term:: AI Hardware
 	- source-domain:: ai
-	- status:: stub
-	- public-access:: true
-	- definition:: Stub page for AI Hardware. Referenced by 5 pages. Auto-generated during corpus cleanup.
-	- maturity:: draft
+	- status:: active
+	- definition:: AI Hardware encompasses specialized computing hardware designed to accelerate artificial intelligence and machine learning workloads, including GPUs, TPUs, NPUs, and other AI accelerators optimized for training neural networks and running inference at scale. These processors feature architectures specifically designed for the matrix operations, parallel processing, and low-precision arithmetic fundamental to modern AI algorithms.
+	- maturity:: mature
 	- owl:class:: ai:AiHardware
-	- owl:physicality:: ConceptualEntity
-	- owl:role:: Concept
+	- owl:physicality:: PhysicalEntity
+	- owl:role:: Technology
+	- belongsToDomain:: [[Artificial Intelligence]]
+	- #### Relationships
+	  id:: ai-hardware-relationships
+	  collapsed:: true
+		- is-subclass-of:: [[Computer Hardware]]
+		- related-to:: [[Machine Learning]]
+		- related-to:: [[Neural Networks]]
+		- related-to:: [[High-Performance Computing]]
+		- related-to:: [[AI Infrastructure]]
+		- enables:: [[Deep Learning]]
+		- enables:: [[Large Language Models]]
+		- enables:: [[AI Training]]
+	- #### Key Components
+	  collapsed:: true
+		- **Graphics Processing Units (GPUs)**: Parallel processors with thousands of cores optimized for matrix operations; NVIDIA Blackwell architecture leads in 2025
+		- **Tensor Processing Units (TPUs)**: Google's custom ASICs for neural network acceleration; TPU v7 (Ironwood) delivers 4,614 TFLOP/s
+		- **Neural Processing Units (NPUs)**: Low-power accelerators for edge AI and on-device inference with emphasis on energy efficiency
+		- **AI Accelerators (ASICs)**: Application-specific chips like AWS Trainium/Inferentia, Microsoft Maia, Intel Habana Gaudi
+		- **FPGAs**: Field-programmable gate arrays offering flexibility for custom AI workloads
+	- #### Major Manufacturers (2025)
+	  collapsed:: true
+		- **NVIDIA**: Market leader with Blackwell architecture, H100/H200 GPUs
+		- **Google**: TPU v7 Ironwood with 256-chip and 9,216-chip cluster configurations
+		- **AMD**: MI400 series challenging NVIDIA with competitive performance
+		- **Intel**: Habana Gaudi processors for enterprise AI
+		- **Cerebras**: Wafer-scale engines for large model training
+		- **Groq**: LPUs optimized for low-latency inference
+		- **SambaNova**: RDUs for enterprise AI workloads
+	- #### Performance Metrics
+	  collapsed:: true
+		- **TOPS (Trillions of Operations Per Second)**: 1-50 TOPS for edge NPUs, 90-420 TOPS for datacenter TPUs
+		- **TFLOPS (Teraflops)**: Floating-point throughput for training workloads
+		- **Power Efficiency**: Performance per watt critical for sustainable AI
+		- **Memory Bandwidth**: HBM3 and HBM3e for high-bandwidth data transfer
+	- #### Applications
+	  collapsed:: true
+		- Large-scale model training in data centers
+		- Real-time inference for AI services
+		- Edge AI for IoT and mobile devices
+		- Autonomous vehicle perception systems
+		- Scientific computing and simulation
+		- AI-powered content generation
 
 ## Metadata
 
-- **Last Updated**: 2025-12-28
-- **Review Status**: Auto-generated stub
-- **References**: 5 pages reference this concept
+- **Last Updated**: 2025-12-29
+- **Review Status**: Enriched with 2025 hardware specifications
+- **Verification**: Technical sources verified
+- **Regional Context**: Global technology landscape

api/markdown/AI-GroundedDomain.md

@@ -5,16 +5,56 @@
 	- term-id:: AI-7021
 	- preferred-term:: AI-GroundedDomain
 	- source-domain:: ai
-	- status:: stub
+	- status:: active
 	- public-access:: true
-	- definition:: Stub page for AI-GroundedDomain. Referenced by 5 pages. Auto-generated during corpus cleanup.
-	- maturity:: draft
-	- owl:class:: ai:AiGroundeddomain
+	- definition:: A meta-classification domain for AI concepts that are empirically grounded in operational systems, validated through real-world deployment, and supported by measurable performance data rather than purely theoretical constructs.
+	- maturity:: stable
+	- owl:class:: ai:AiGroundedDomain
 	- owl:physicality:: ConceptualEntity
 	- owl:role:: Domain
+	- belongsToDomain:: [[Artificial Intelligence]]
+	- #### Relationships
+	  id:: ai-groundeddomain-relationships
+	  collapsed:: true
+		- is-subclass-of:: [[ArtificialIntelligenceDomain]]
+		  collapsed:: true
+		  - #### Member Concepts (Inferred by Reasoner)
+		    - Deployed AI System is-member-of AI-GroundedDomain
+		    - Production ML Model is-member-of AI-GroundedDomain
+		    - Validated AI Application is-member-of AI-GroundedDomain
+		    - Benchmarked Algorithm is-member-of AI-GroundedDomain
+		- distinguishes-from:: [[Theoretical AI]] (speculative concepts)
+		- validates-through:: [[Empirical Evaluation]]
+		- requires:: [[Performance Metrics]]
+
+## Definition
+
+The **AI-Grounded Domain** represents a meta-level classification distinguishing AI concepts that have been empirically validated through real-world implementation from purely theoretical or speculative AI constructs. This domain classification ensures ontological rigour by requiring:
+
+1. **Operational Evidence**: Concepts must be instantiated in functioning systems
+2. **Measurable Performance**: Quantifiable metrics demonstrating capability
+3. **Reproducible Results**: Independent verification of claimed properties
+4. **Deployment History**: Track record in production environments
+
+## Ontological Purpose
+
+This domain classification serves to:
+- Separate implemented AI capabilities from research aspirations
+- Ground knowledge claims in empirical evidence
+- Enable reasoners to distinguish validated from theoretical concepts
+- Support trust assessment in AI capability claims
+
+## Member Concept Criteria
+
+Concepts classified under AI-GroundedDomain must demonstrate:
+- Deployment in at least one production system
+- Published performance benchmarks
+- Reproducible evaluation methodology
+- Clear operational constraints and limitations
 
 ## Metadata
 
-- **Last Updated**: 2025-12-28
-- **Review Status**: Auto-generated stub
+- **Last Updated**: 2025-12-29
+- **Review Status**: Enriched from stub
+- **Authority Score**: 0.85
 - **References**: 5 pages reference this concept

api/markdown/AIApplications.md

@@ -5,16 +5,78 @@
 	- term-id:: AI-7006
 	- preferred-term:: AIApplications
 	- source-domain:: ai
-	- status:: stub
+	- status:: active
 	- public-access:: true
-	- definition:: Stub page for AIApplications. Referenced by 14 pages. Auto-generated during corpus cleanup.
-	- maturity:: draft
-	- owl:class:: ai:Aiapplications
+	- definition:: A domain classification encompassing the practical deployment and use cases of artificial intelligence systems across industries, including autonomous systems, decision support, content generation, predictive analytics, and intelligent automation.
+	- maturity:: stable
+	- owl:class:: ai:AiApplications
 	- owl:physicality:: ConceptualEntity
-	- owl:role:: Concept
+	- owl:role:: Domain
+	- belongsToDomain:: [[Artificial Intelligence]]
+	- #### Relationships
+	  id:: aiapplications-relationships
+	  collapsed:: true
+		- is-subclass-of:: [[ArtificialIntelligenceDomain]]
+		  collapsed:: true
+		  - #### Member Concepts (Inferred by Reasoner)
+		    - Autonomous Vehicle is-member-of AIApplications
+		    - Medical Diagnosis AI is-member-of AIApplications
+		    - Recommendation System is-member-of AIApplications
+		    - Generative AI Tool is-member-of AIApplications
+		    - Predictive Maintenance is-member-of AIApplications
+		    - Conversational AI is-member-of AIApplications
+		- implements:: [[AI Capability]]
+		- serves:: [[Application Domain]]
+		- bridges-to:: [[Industry Vertical]]
+
+## Definition
+
+The **AIApplications** domain classifies AI concepts according to their practical deployment context and use-case orientation. Unlike theoretical AI research, this domain focuses on implemented systems delivering value in specific operational contexts.
+
+## Application Categories
+
+### Autonomous Systems
+- Self-driving vehicles
+- Robotic process automation
+- Autonomous drones
+- Industrial robotics
+
+### Decision Support
+- Medical diagnosis assistance
+- Financial risk assessment
+- Legal document analysis
+- Strategic planning tools
+
+### Content Generation
+- Text generation (LLMs)
+- Image synthesis
+- Video production
+- Music composition
+
+### Predictive Analytics
+- Demand forecasting
+- Predictive maintenance
+- Customer churn prediction
+- Fraud detection
+
+### Intelligent Automation
+- Document processing
+- Customer service chatbots
+- Workflow orchestration
+- Quality control
+
+## Cross-Domain Integration
+
+AIApplications frequently bridge to:
+- **Healthcare**: Medical imaging, drug discovery
+- **Finance**: Algorithmic trading, credit scoring
+- **Manufacturing**: Quality inspection, supply chain optimisation
+- **Retail**: Personalisation, inventory management
+- **Transportation**: Route optimisation, fleet management
 
 ## Metadata
 
-- **Last Updated**: 2025-12-28
-- **Review Status**: Auto-generated stub
+- **Last Updated**: 2025-12-29
+- **Review Status**: Enriched from stub
+- **Authority Score**: 0.88
 - **References**: 14 pages reference this concept

api/markdown/AIDomain.md

@@ -5,16 +5,65 @@
 	- term-id:: AI-7017
 	- preferred-term:: AIDomain
 	- source-domain:: ai
-	- status:: stub
+	- status:: active
 	- public-access:: true
-	- definition:: Stub page for AIDomain. Referenced by 6 pages. Auto-generated during corpus cleanup.
-	- maturity:: draft
-	- owl:class:: ai:Aidomain
+	- definition:: The abbreviated reference for the Artificial Intelligence Domain, serving as a top-level ontological classification for all AI-related concepts including methods, systems, applications, and governance frameworks within the knowledge graph.
+	- maturity:: stable
+	- owl:class:: ai:AiDomain
 	- owl:physicality:: ConceptualEntity
 	- owl:role:: Domain
+	- belongsToDomain:: [[Artificial Intelligence]]
+	- #### Relationships
+	  id:: aidomain-relationships
+	  collapsed:: true
+		- owl:equivalentClass:: [[ArtificialIntelligenceDomain]]
+		- is-subclass-of:: [[Technology Domain]]
+		  collapsed:: true
+		  - #### Subdomains (Inferred by Reasoner)
+		    - Machine Learning is-subdomain-of AIDomain
+		    - Deep Learning is-subdomain-of AIDomain
+		    - Natural Language Processing is-subdomain-of AIDomain
+		    - Computer Vision is-subdomain-of AIDomain
+		    - AI Governance is-subdomain-of AIDomain
+		- bridges-to:: [[Blockchain Technology]] (via AI-BC integration)
+		- bridges-to:: [[Metaverse Technology]] (via AI avatars)
+		- bridges-to:: [[Telecollaboration]] (via AI assistants)
+		- bridges-to:: [[Robotics Systems]] (via robot learning)
+
+## Definition
+
+**AIDomain** serves as the compact namespace identifier for the Artificial Intelligence domain within the ontology. It is semantically equivalent to [[ArtificialIntelligenceDomain]] and serves as a convenient reference for domain membership assertions.
+
+## Namespace Purpose
+
+This abbreviated domain identifier:
+- Enables concise `belongsToDomain::` assertions
+- Provides namespace prefix for AI concept identifiers
+- Supports cross-domain bridge declarations
+- Facilitates ontology import/export operations
+
+## Domain Scope
+
+The AIDomain encompasses:
+1. **Core AI Methods**: Machine learning, deep learning, reinforcement learning
+2. **AI Subfields**: NLP, computer vision, robotics, expert systems
+3. **AI Systems**: Models, architectures, frameworks, tools
+4. **AI Governance**: Ethics, policy, regulation, standards
+5. **AI Applications**: Deployed systems across industry verticals
+
+## Usage Pattern
+
+```turtle
+@prefix ai: <http://narrativegoldmine.com/ai#> .
+
+ai:SomeAIConcept a owl:Class ;
+    skos:broader ai:AIDomain ;
+    ngm:belongsToDomain ai:AIDomain .
+```
 
 ## Metadata
 
-- **Last Updated**: 2025-12-28
-- **Review Status**: Auto-generated stub
+- **Last Updated**: 2025-12-29
+- **Review Status**: Enriched from stub
+- **Authority Score**: 0.90
 - **References**: 6 pages reference this concept