Interactive Views & Tables
Page Status
Quality:8 (Stub)
Importance:0 (Peripheral)
LLM Summary:Navigation page listing interactive tables and visualizations available in the knowledge base, including architecture scenarios, safety approaches, risks, and evaluations. Purely infrastructural with no substantive content.
Overview
Section titled “Overview”This page lists all the interactive tables, matrices, and graph visualizations available in the knowledge base. These views provide different ways to explore and compare data across the project.
Intelligence Paradigms
Section titled “Intelligence Paradigms”| View | Description |
|---|---|
| Architecture Scenarios Table | Compare deployment patterns, base architectures, and non-AI paths to intelligence. Includes safety outlook ratings, research tractability, and key properties. |
| Deployment Architectures Table | Compare how AI systems are deployed: API access, on-device, edge computing, etc. |
Safety & Alignment
Section titled “Safety & Alignment”| View | Description |
|---|---|
| Safety Generalizability Table | Which safety approaches generalize across different AI architectures and deployment patterns. |
| Safety × Architecture Matrix | Matrix view showing compatibility between safety approaches and architecture scenarios. |
| Safety Generalizability Graph | Graph visualization of safety approach relationships. |
| View | Description |
|---|---|
| Accident Risks Table | Compare accident/misalignment risks by severity, likelihood, and detectability. |
Evaluations
Section titled “Evaluations”| View | Description |
|---|---|
| Eval Types Table | Compare different evaluation methodologies for AI systems. |
AI Transition Model
Section titled “AI Transition Model”| View | Description |
|---|---|
| Master Graph | Full causal graph of AI transition model factors and relationships. |
| AI Transition Model Views | Interactive exploration of the AI transition model. |
Entity Diagrams
Section titled “Entity Diagrams”Individual entity pages may also have cause-effect diagrams. See the Cause-Effect Demo for examples of how these work.