Insight Grid Experiments

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Page Type:ContentStyle Guide →Standard knowledge base article

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Importance:2 (Peripheral)

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📊 0📈 0🔗 0📚 0•23%Score: 3/15

LLM Summary:Internal experimental page testing various visualization approaches (sparse grids, treemaps, pixel maps) to represent knowledge gaps in AI safety research, showing ~2-4% coverage of conceptual space. Purely infrastructure/UI experimentation with no substantive research content.

Issues (1):

• StructureNo tables or diagrams - consider adding visual content

These experiments visualize the Critical Insight framework—showing where we have knowledge versus the vast space of potential insights we haven’t yet discovered.

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All Experiments

1. Sparse Grid2. Treemap3. Score Matrix4. Pixel Map5. Frontier6. Priority7. Topic×Question

94 insights in 2,400 cells(3.9% coverage)

CombinedImportanceQualitySurprising

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Concept

The core idea: most of the “insight space” is unexplored.

If we imagine all possible questions × topics × granularities as a huge grid, we’ve only filled in a tiny fraction. These visualizations make that visible:

1. Sparse Grid: Each cell is a potential insight area. Filled cells (bright) = we have something. Empty (dark) = unknown.

2. Treemap: Hierarchical view where the filled portion of each rectangle shows knowledge density. Most areas are mostly empty.

3. Score Matrix: Individual insights rated on Surprising × Important × Compact.

4. Pixel Map: Ultra-dense view showing thousands of potential insight areas, with ~2% explored.

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Individual Components

Sparse Knowledge Grid

A 40×60 grid with ~4% fill rate:

94 insights in 2,400 cells(3.9% coverage)

CombinedImportanceQualitySurprising

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Knowledge Treemap

Nested rectangles showing knowledge density by domain:

Average knowledge density: 11.5%(filled bars show exploration depth)

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Insight Score Matrix

Individual insights rated on the three criteria:

Sort by:

CombinedSurprisingImportantCompact

Claim

Surprising

Important

Compact

[Governance]Compute governance has 18-month policy window

[Capabilities]Scaling laws may plateau within 2 orders of magnitude

[Deployment]Open-source models lag 6-12 months, not years

[Alignment]RLHF creates deceptive alignment incentives

[Technical]Interpretability tools scale sublinearly with model size

[Geopolitics]China AI investment growing 40% YoY

[Timelines]Most alignment researchers expect <20 years to AGI

[Institutions]Lab safety culture varies 10x across organizations

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Pixel Density Map

High-resolution view of the insight space:

40,000 possible insight areas(~2% explored)

Regenerate

Each pixel = potential question × topic. Brightness = importance. Hue = domain.

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Research Frontier

3D-style columns showing exploration depth by research area:

Each column shows exploration depth.Taller = more explored

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Priority Matrix (ITN Framework)

Classic Importance × Tractability × Neglectedness visualization:

Position = Importance × Neglectedness. Size = Combined.

Size = TractabilitySize = Combined

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Topic × Question Grid

Categorical grid showing which question types we’ve addressed for each topic:

38filled

106empty

61avg quality

Empty

Low

Med

High

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Design Notes

Color encoding:

• Brightness = importance (brighter = more important)
• Saturation/hue = surprising (warmer = more surprising)
• Opacity = quality/confidence

Interactivity:

• Hover to see details
• Toggle color modes
• Sort by different criteria

The key insight these visualizations convey: We’ve barely scratched the surface of what’s knowable. Most cells are dark. This should motivate:

1. Systematic exploration of the space
2. Prioritizing high-importance regions
3. Seeking surprising findings (they carry more information)