Algorithms (AI Capabilities)

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Page Type:AI Transition ModelStyle Guide →Structured factor/scenario/parameter page

Quality:0 (Stub)

Importance:0 (Peripheral)

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📊 0📈 0🔗 0📚 0•0%Score: 2/15

LLM Summary:This page contains only React component imports with no actual content about AI algorithms, their capabilities, or their implications for AI risk. The page is effectively a placeholder or stub.

Critical Insights (1):

Counterint.91% of algorithmic efficiency gains depend on scaling rather than fundamental improvements - efficiency gains don't relieve compute pressure, they accelerate the race.S:4.0I:4.2A:3.5

Issues (1):

StructureNo tables or diagrams - consider adding visual content

See also:Wikipedia LessWrong

Algorithmic progress determines how efficiently AI systems convert compute into capabilities. Unlike hardware, algorithms are intangible—discoveries spread instantly through publications, making direct governance nearly impossible.

Metric	Score	Interpretation
Changeability	20/100	Very difficult to influence
X-risk Impact	75/100	High direct x-risk impact
Trajectory Impact	85/100	High long-term effects
Uncertainty	55/100	Moderate uncertainty

What Drives Algorithmic Progress?

Causal factors affecting AI algorithmic efficiency. Research shows 91% of gains are scale-dependent (Transformers, Chinchilla), coupling algorithmic progress to compute availability. Software optimizations (23x) dramatically outpace hardware improvements.

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Scenarios Influenced

Scenario	Effect	Strength
AI Takeover	↑ Increases	strong
Human-Caused Catastrophe	↑ Increases	medium
Long-term Lock-in	↑ Increases	medium