framework
active
framework:scaling-supervisionScaling Supervision
Techniques that leverage AI to help humans more efficiently supervise AI.
Neighborhood — ranked by edge-count
Frameworks (1)
framework
- Constitutional AIextendsAlignment approach by Anthropic that explicitly trains self-observation; predicts highest baseline and lowest prompt lift.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Mechanisms by which smaller competent subunits bind into a higher-level Self with larger goals; key example via gap junction connections.
- Process by which low-level competencies scale to larger problem-solving abilities across levels of organization.
- The paper provides evidence that AI can help supervise AI, reducing reliance on humans.
- How the energy gain ΔE scales with perimeter length P; used to assess ordered phase existence
- The pressure on models trained on more tasks to find representations that generalize across all tasks, reducing the solution space
- Supervisory signals for visual outputs; functional tokens do not require it.
- Interpretive process for transforming many-valued contexts into formal contexts via scale attributes.
- Used in the color cooccurrence experiment to embed colors into 3D space preserving dissimilarity matrix distances