Deploy from jjohare/logseq @ 7fc80da7cce74834e583548dcda5c80e835a56f6 jjohare/logseq@7fc80da

Author: jjohare

api/pages/pages/3D Rendering Engine.json

@@ -3,27 +3,27 @@
   "title": "3D Rendering Engine",
   "content": "- ### OntologyBlock\n  id:: 3d-rendering-engine-ontology\n  collapsed:: true\n\t- ontology:: true\n\t- term-id:: NGM-7009\n\t- preferred-term:: 3D Rendering Engine\n\t- source-domain:: ngm\n\t- status:: active\n\t- public-access:: true\n\t- 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.\n\t- maturity:: active\n\t- owl:class:: ngm:3dRenderingEngine\n\t- owl:role:: Concept\n\t- belongsToDomain:: [[Metaverse]]\n\n### Relationships\n- is-subclass-of:: [[Computer Graphics]]\n- related-to:: [[Virtual Reality]], [[Augmented Reality]], [[Game Development]], [[Digital Twin]]\n- enables:: [[Immersive Experiences]], [[Real-time Visualisation]], [[XR Applications]]\n- used-by:: [[Unity]], [[Unreal Engine]], [[Blender]]\n\n## Features\n- **Real-time Rendering**: Processes geometry, lighting, and textures at frame rates suitable for interactive VR/AR (90Hz+)\n- **Stereoscopic Output**: Generates separate views for left and right eyes to create depth perception\n- **Foveated Rendering**: Optimises performance by rendering highest detail only where the user is looking\n- **Physics Integration**: Couples with physics engines for realistic object behaviour and collision detection\n- **Shader Systems**: Programmable graphics pipelines for materials, effects, and post-processing\n- **Level of Detail (LOD)**: Dynamically adjusts geometric complexity based on viewing distance\n- **Motion-to-Photon Latency**: Minimises delay between user movement and visual update (target <20ms)\n\n## Use Cases\n- **Metaverse Environments**: Rendering persistent virtual worlds for social interaction and commerce\n- **VR Gaming**: Powering immersive game experiences with high visual fidelity\n- **Industrial Digital Twins**: Visualising manufacturing processes and equipment in real-time\n- **Architectural Visualisation**: Creating walkthrough experiences of building designs\n- **Training Simulations**: Rendering realistic scenarios for education and skills development\n- **AR Overlays**: Compositing 3D content onto real-world camera feeds\n\n## Metadata\n\n- **Last Updated**: 2025-12-29\n- **Review Status**: Enriched from stub with 2025 research\n- **References**: 8 pages reference this concept",
   "backlinks": [
-    "Cultural Heritage XR Experience",
-    "Virtual World",
-    "Virtual Performance Space",
-    "Avatar System",
     "Education Metaverse",
     "Tourism Metaverse",
-    "Avatar"
+    "Virtual World",
+    "Avatar System",
+    "Avatar",
+    "Cultural Heritage XR Experience",
+    "Virtual Performance Space"
   ],
   "wiki_links": [
-    "Virtual Reality",
     "Metaverse",
     "Computer Graphics",
-    "Unity",
-    "Augmented Reality",
-    "Digital Twin",
+    "Real-time Visualisation",
     "XR Applications",
-    "Unreal Engine",
-    "Game Development",
+    "Digital Twin",
     "Immersive Experiences",
-    "Real-time Visualisation",
-    "Blender"
+    "Game Development",
+    "Unity",
+    "Virtual Reality",
+    "Augmented Reality",
+    "Blender",
+    "Unreal Engine"
   ],
   "ontology": {
     "term_id": "NGM-7009",

api/pages/pages/3DReconstruction.json

@@ -3,8 +3,8 @@
   "title": "3DReconstruction",
   "content": "- ### OntologyBlock\n  id:: tc9001-ontology\n  collapsed:: true\n\t- ontology:: true\n\t- term-id:: TC-9001\n\t- domain:: tc\n\t- owl:class:: tc:3DReconstruction\n\t- public-access:: true\n\n## Definition\n\n3D Reconstruction represents computational techniques for creating three-dimensional models of physical environments and objects from multiple data sources in telecollaboration contexts. This technology employs photogrammetry, LiDAR scanning, structured light scanning, and neural radiance fields (NeRF) to generate accurate spatial representations for remote collaboration scenarios. Modern reconstruction pipelines integrate SLAM (Simultaneous Localization and Mapping) algorithms, point cloud processing, mesh generation, and texture mapping to produce high-fidelity digital replicas of collaborative workspaces. Real-time reconstruction systems utilize depth sensors, stereo cameras, and time-of-flight cameras combined with GPU-accelerated processing for live environment capture during virtual meetings. Applications span remote site inspections, collaborative design reviews, heritage preservation, and immersive telepresence experiences. Advanced implementations leverage AI-driven reconstruction methods including Gaussian splatting, implicit neural representations, and differentiable rendering for enhanced quality and reduced capture requirements in distributed collaboration environments.\n\n## References\n\n- IEEE VR Conference. (2024). \"3D Reconstruction for Virtual Collaboration Systems.\" https://ieeevr.org/\n- Khronos Group. (2024). \"OpenXR Spatial Reconstruction Extensions.\" https://www.khronos.org/openxr/\n- Reality Capture Consortium. (2025). \"Standards for 3D Environmental Reconstruction.\" https://realitycapture.org/\n- CVPR Workshop. (2025). \"Neural Reconstruction Methods for Telecollaboration.\" https://cvpr.thecvf.com/\n- ISO/IEC 23090. (2024). \"Coded Representation of Immersive Media - Scene Description.\" https://www.iso.org/",
   "backlinks": [
-    "Camera",
-    "TELE-051-3d-gaussian-splatting"
+    "TELE-051-3d-gaussian-splatting",
+    "Camera"
   ],
   "wiki_links": [],
   "ontology": {

api/pages/pages/A-Star Algorithm.json

@@ -7,64 +7,64 @@
     "RB-1018-dijkstra-algorithm"
   ],
   "wiki_links": [
-    "Google Maps Platform",
-    "Informed Search Strategy",
-    "Robotics Control",
-    "Distance Metric",
-    "Admissible Heuristic",
-    "Video Game AI",
-    "Data Structures",
-    "Motion Planning",
+    "Iterative Deepening A*",
+    "Optimal Path Discovery",
+    "Heuristic Evaluation",
     "Graph Data Structure",
-    "Russell & Norvig Artificial Intelligence Modern Approach",
-    "AlgorithmicFramework",
-    "Network Routing",
-    "Robotics Industry Association Automation Statistics",
-    "Heuristic Methods",
-    "AlgorithmicLayer",
-    "Cormen Introduction to Algorithms",
+    "Uniform Cost Search",
     "Dijkstra's Algorithm",
     "Graph Algorithms",
-    "Path Reconstruction",
-    "Informed Search",
     "Closed Set",
-    "Autonomous Navigation",
-    "ApplicationLayer",
-    "Uniform Cost Search",
+    "Robotics Industry Association Automation Statistics",
     "International Planning Competition",
-    "Algorithmic Efficiency",
-    "ComputationAndIntelligenceDomain",
-    "Node Expansion",
-    "Hart, Nilsson, Raphael 1968 Formal Basis for Heuristic Determination",
-    "Search Algorithms",
-    "Navigation",
+    "Data Structures",
+    "Distance Heuristics",
     "Priority Queue",
-    "Greedy Best-First Search",
-    "Unity Technologies NavMesh Documentation",
-    "Best-First Search",
-    "Heuristic Search",
-    "AI-GroundedDomain",
-    "Open Set",
-    "Optimal Pathfinding",
-    "Heuristic Evaluation",
-    "Logistics Optimization",
+    "Optimization Algorithms",
+    "Route Planning",
+    "Cormen Introduction to Algorithms",
     "Breadth-First Search",
-    "Heuristic Function",
+    "Network Routing",
+    "Robotics Control",
+    "Hart, Nilsson, Raphael 1968 Formal Basis for Heuristic Determination",
+    "Unity Technologies NavMesh Documentation",
+    "ApplicationLayer",
+    "Greedy Best-First Search",
+    "AlgorithmicLayer",
+    "Motion Planning",
+    "Informed Search",
     "Pathfinding",
+    "Russell & Norvig Artificial Intelligence Modern Approach",
+    "Node Expansion",
+    "Video Game AI",
     "Cost Function",
-    "Iterative Deepening A*",
-    "Bidirectional Search",
-    "Hash Table",
-    "Graph Representation",
-    "Distance Heuristics",
-    "Optimal Path Discovery",
-    "Graph Theory",
-    "Optimization Algorithms",
-    "Priority Queue Data Structure",
+    "Algorithmic Efficiency",
     "ROS Navigation Stack",
+    "Graph Theory",
+    "AI-GroundedDomain",
+    "Distance Metric",
+    "Optimal Pathfinding",
+    "Hash Table",
+    "Heuristic Search",
+    "Informed Search Strategy",
     "Evaluation Function",
-    "Route Planning",
-    "Goal-Directed Search"
+    "Graph Representation",
+    "Best-First Search",
+    "Goal-Directed Search",
+    "Logistics Optimization",
+    "Path Reconstruction",
+    "AlgorithmicFramework",
+    "Search Algorithms",
+    "Navigation",
+    "Google Maps Platform",
+    "Autonomous Navigation",
+    "Bidirectional Search",
+    "Open Set",
+    "ComputationAndIntelligenceDomain",
+    "Heuristic Function",
+    "Admissible Heuristic",
+    "Heuristic Methods",
+    "Priority Queue Data Structure"
   ],
   "ontology": {
     "term_id": "AI-1004",

api/pages/pages/AI Applications.json

@@ -4,12 +4,12 @@
   "content": "- ### OntologyBlock\n  id:: ai-applications-ontology\n  collapsed:: true\n\t- ontology:: true\n\t- term-id:: AI-0604\n\t- preferred-term:: AI Applications\n\t- source-domain:: ai\n\t- status:: active\n\t- public-access:: true\n\t- definition:: AI Applications represent domain-specific implementations of artificial intelligence technologies across various industries and use cases. This category encompasses practical deployments of AI in healthcare, autonomous vehicles, personal assistants, industrial automation, financial services, and other sectors where AI delivers tangible value.\n\t- owl:class:: ai:AIApplications\n\t- belongsToDomain:: [[Artificial Intelligence]]\n\t- #### Relationships\n\t  id:: ai-applications-relationships\n\t  collapsed:: true\n\t\t- is-subclass-of:: [[Artificial Intelligence]]\n\t\t- is-parent-of:: [[Medical AI]]\n\t\t- is-parent-of:: [[Autonomous Vehicles]]\n\t\t- is-parent-of:: [[AI Assistant]]\n\t\t- is-parent-of:: [[Industrial AI]]\n\t\t- related-to:: [[AI Deployment]]",
   "backlinks": [],
   "wiki_links": [
-    "Medical AI",
-    "Artificial Intelligence",
-    "AI Assistant",
     "Autonomous Vehicles",
     "AI Deployment",
-    "Industrial AI"
+    "Artificial Intelligence",
+    "Medical AI",
+    "Industrial AI",
+    "AI Assistant"
   ],
   "ontology": {
     "term_id": "AI-0604",

api/pages/pages/AI Concept.json

@@ -6,11 +6,11 @@
     "ModelProperty"
   ],
   "wiki_links": [
-    "Knowledge Representation",
     "owl:Thing",
-    "Artificial Intelligence",
+    "Machine Learning",
     "Cognitive Architecture",
-    "Machine Learning"
+    "Artificial Intelligence",
+    "Knowledge Representation"
   ],
   "ontology": {
     "term_id": "AI-9002",

api/pages/pages/AI Framework.json

@@ -3,15 +3,15 @@
   "title": "AI Framework",
   "content": "- ### OntologyBlock\n  id:: ai9005-ontology\n  collapsed:: true\n\t- ontology:: true\n\t- term-id:: AI-9005\n\t- preferred-term:: AI Framework\n\t- source-domain:: ai\n- definition:: AI Framework denotes a comprehensive software architecture that provides reusable code, design patterns, and infrastructure for developing artificial intelligence applications. These frameworks abstract low-level computational details, offering high-level interfaces for model construction, training, and inference. Popular frameworks include TensorFlow, PyTorch, scikit-learn, Keras, and JAX, each optimized for different use cases ranging from research prototyping to production deployment. Modern AI frameworks support automatic differentiation, distributed training, model serving, and integration with cloud infrastructure.\n\n- public-access:: true\n- is-subclass-of:: [[owl:Thing]]\n\n## Overview\n\nAI Framework denotes a comprehensive software architecture that provides reusable code, design patterns, and infrastructure for developing artificial intelligence applications. These frameworks abstract low-level computational details, offering high-level interfaces for model construction, training, and inference. Popular frameworks include TensorFlow, PyTorch, scikit-learn, Keras, and JAX, each optimized for different use cases ranging from research prototyping to production deployment. Modern AI frameworks support automatic differentiation, distributed training, model serving, and integration with cloud infrastructure.\n\n## Key Characteristics\n\n- Provides declarative and imperative programming paradigms\n- Supports multiple hardware accelerators (GPU, TPU, NPU)\n- Offers extensive pre-built model architectures and layers\n- Enables seamless transition from research to production\n- Includes tools for model optimization and quantization\n\n## Related Concepts\n\n- [[Deep Learning Framework]]\n- [[Neural Network Library]]\n- [[AutoML]]\n- [[Model Optimization]]\n\n## References\n\n- Chen, T. et al. (2015). MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. NIPS 2015 Workshop.\n- Bradbury, J. et al. (2018). JAX: Composable transformations of Python+NumPy programs. GitHub repository.\n- Pedregosa, F. et al. (2011). Scikit-learn: Machine Learning in Python. JMLR 12, 2825-2830.",
   "backlinks": [
-    "Game Engine",
-    "Autonomous Agent",
     "AlgorithmicFramework",
-    "Intelligent Virtual Entity"
+    "Autonomous Agent",
+    "Intelligent Virtual Entity",
+    "Game Engine"
   ],
   "wiki_links": [
-    "Model Optimization",
     "owl:Thing",
     "AutoML",
+    "Model Optimization",
     "Deep Learning Framework",
     "Neural Network Library"
   ],

api/pages/pages/AI Hardware.json

@@ -3,26 +3,26 @@
   "title": "AI Hardware",
   "content": "- ### OntologyBlock\n  id:: ai-hardware-ontology\n  collapsed:: true\n\t- ontology:: true\n\t- public-access:: true\n\t- term-id:: AI-7020\n\t- preferred-term:: AI Hardware\n\t- source-domain:: ai\n\t- status:: active\n\t- 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.\n\t- maturity:: mature\n\t- owl:class:: ai:AiHardware\n\t- owl:role:: Technology\n\t- belongsToDomain:: [[Artificial Intelligence]]\n\t- #### Relationships\n\t  id:: ai-hardware-relationships\n\t  collapsed:: true\n\t\t- is-subclass-of:: [[Computer Hardware]]\n\t\t- related-to:: [[Machine Learning]]\n\t\t- related-to:: [[Neural Networks]]\n\t\t- related-to:: [[High-Performance Computing]]\n\t\t- related-to:: [[AI Infrastructure]]\n\t\t- enables:: [[Deep Learning]]\n\t\t- enables:: [[Large Language Models]]\n\t\t- enables:: [[AI Training]]\n\t- #### Key Components\n\t  collapsed:: true\n\t\t- **Graphics Processing Units (GPUs)**: Parallel processors with thousands of cores optimized for matrix operations; NVIDIA Blackwell architecture leads in 2025\n\t\t- **Tensor Processing Units (TPUs)**: Google's custom ASICs for neural network acceleration; TPU v7 (Ironwood) delivers 4,614 TFLOP/s\n\t\t- **Neural Processing Units (NPUs)**: Low-power accelerators for edge AI and on-device inference with emphasis on energy efficiency\n\t\t- **AI Accelerators (ASICs)**: Application-specific chips like AWS Trainium/Inferentia, Microsoft Maia, Intel Habana Gaudi\n\t\t- **FPGAs**: Field-programmable gate arrays offering flexibility for custom AI workloads\n\t- #### Major Manufacturers (2025)\n\t  collapsed:: true\n\t\t- **NVIDIA**: Market leader with Blackwell architecture, H100/H200 GPUs\n\t\t- **Google**: TPU v7 Ironwood with 256-chip and 9,216-chip cluster configurations\n\t\t- **AMD**: MI400 series challenging NVIDIA with competitive performance\n\t\t- **Intel**: Habana Gaudi processors for enterprise AI\n\t\t- **Cerebras**: Wafer-scale engines for large model training\n\t\t- **Groq**: LPUs optimized for low-latency inference\n\t\t- **SambaNova**: RDUs for enterprise AI workloads\n\t- #### Performance Metrics\n\t  collapsed:: true\n\t\t- **TOPS (Trillions of Operations Per Second)**: 1-50 TOPS for edge NPUs, 90-420 TOPS for datacenter TPUs\n\t\t- **TFLOPS (Teraflops)**: Floating-point throughput for training workloads\n\t\t- **Power Efficiency**: Performance per watt critical for sustainable AI\n\t\t- **Memory Bandwidth**: HBM3 and HBM3e for high-bandwidth data transfer\n\t- #### Applications\n\t  collapsed:: true\n\t\t- Large-scale model training in data centers\n\t\t- Real-time inference for AI services\n\t\t- Edge AI for IoT and mobile devices\n\t\t- Autonomous vehicle perception systems\n\t\t- Scientific computing and simulation\n\t\t- AI-powered content generation\n\n## Metadata\n\n- **Last Updated**: 2025-12-29\n- **Review Status**: Enriched with 2025 hardware specifications\n- **Verification**: Technical sources verified\n- **Regional Context**: Global technology landscape",
   "backlinks": [
+    "Training Hardware",
+    "Neuromorphic Chip",
     "Inference Hardware",
+    "FPGA",
+    "Tensor Processing Unit",
     "Edge AI Accelerator",
     "TPU",
-    "Neuromorphic Chip",
-    "NPU",
-    "Training Hardware",
     "Neuromorphic Chips",
-    "FPGA",
-    "Tensor Processing Unit"
+    "NPU"
   ],
   "wiki_links": [
-    "Computer Hardware",
+    "Machine Learning",
+    "Large Language Models",
+    "High-Performance Computing",
+    "AI Training",
     "Artificial Intelligence",
     "Deep Learning",
-    "Large Language Models",
+    "Computer Hardware",
     "AI Infrastructure",
-    "Neural Networks",
-    "AI Training",
-    "High-Performance Computing",
-    "Machine Learning"
+    "Neural Networks"
   ],
   "ontology": {
     "term_id": "AI-7020",

api/pages/pages/AI Infrastructure.json

@@ -6,11 +6,11 @@
     "AI Hardware"
   ],
   "wiki_links": [
-    "Edge AI",
-    "Artificial Intelligence",
     "GPU Computing",
-    "Cloud AI",
-    "AI Deployment"
+    "AI Deployment",
+    "Artificial Intelligence",
+    "Edge AI",
+    "Cloud AI"
   ],
   "ontology": {
     "term_id": "AI-0603",

api/pages/pages/AI Safety.json

@@ -3,19 +3,19 @@
   "title": "AI Safety",
   "content": "- ### OntologyBlock\n  id:: ai-safety-ontology\n  collapsed:: true\n\t- ontology:: true\n\t- term-id:: AI-4009\n\t- preferred-term:: AI Safety\n\t- source-domain:: ai\n\t- status:: active\n\t- public-access:: true\n\t- definition:: AI Safety is the field of research and practice focused on ensuring that artificial intelligence systems operate safely, reliably, and in alignment with human values and intentions. It encompasses techniques for preventing unintended harm, maintaining robust behaviour under uncertainty, and ensuring systems remain controllable and predictable throughout their operational lifecycle.\n\t- owl:class:: ai:AISafety\n\t- belongsToDomain:: [[Artificial Intelligence]]\n\t- #### Relationships\n\t  id:: ai-safety-relationships\n\t  collapsed:: true\n\t\t- is-subclass-of:: [[Artificial Intelligence]]\n\t\t- related-to:: [[AI Governance]]\n\t\t- related-to:: [[AI Alignment]]\n\t\t- is-parent-of:: [[Adversarial Robustness]]\n\t\t- is-parent-of:: [[Interpretability]]\n\t\t- is-parent-of:: [[Value Alignment]]",
   "backlinks": [
+    "AIRiskManagement",
+    "AI Risks",
     "Deep Learning",
     "Transformers",
-    "AIRisk",
-    "AIRiskManagement",
-    "AI Risks"
+    "AIRisk"
   ],
   "wiki_links": [
     "AI Governance",
-    "Artificial Intelligence",
+    "AI Alignment",
     "Value Alignment",
+    "Artificial Intelligence",
     "Interpretability",
-    "Adversarial Robustness",
-    "AI Alignment"
+    "Adversarial Robustness"
   ],
   "ontology": {
     "term_id": "AI-4009",