Human inductive biases — SR2025 Agenda Snapshot

One-sentence summary: Discover connections deep learning AI systems have with human brains and human learning processes. Develop an ‘alignment moonshot’ based on a coherent theory of learning which applies to both humans and AI systems.

Theory of Change

Humans learn trust, honesty, self-maintenance, and corrigibility; if we understand how they do maybe we can get future AI systems to learn them.

Broad Approach

cognitive

Target Case

pessimistic

Orthodox Problems Addressed

Goals misgeneralize out of distribution

Key People

Lukas Muttenthaler, Quentin Delfosse

Funding

Google DeepMind, various academic groups

Estimated FTEs: 4

See Also

active learning, ACS research

Outputs in 2025

6 item(s) in the review. See the wiki/summaries/ entries with frontmatter agenda: human-inductive-biases (these were generated alongside this file from the same export).

Source

• Row in shallow-review-2025/agendas.csv (name = Human inductive biases) — Shallow Review of Technical AI Safety 2025.

Related Pages

• ai-safety
• ai-safety
• activation-engineering
• causal-abstractions
• data-attribution
• extracting-latent-knowledge
• learning-dynamics-and-developmental-interpretability
• lie-and-deception-detectors
• model-diffing
• monitoring-concepts
• other-interpretability
• pragmatic-interpretability
• representation-structure-and-geometry
• reverse-engineering
• sparse-coding

Sources cited

Primary URLs harvested from this page’s summary references. Auto-generated by scripts/backfill_citations.py; edit by re-running, not by hand.

• Summary: AI Safety (Wikipedia) — referenced as [[ai-safety]]