Difference between revisions of "AI safety"
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* 2024-12: [https://assets.anthropic.com/m/983c85a201a962f/original/Alignment-Faking-in-Large-Language-Models-full-paper.pdf Alignment Faking in Large Language Models] (Anthropic) | * 2024-12: [https://assets.anthropic.com/m/983c85a201a962f/original/Alignment-Faking-in-Large-Language-Models-full-paper.pdf Alignment Faking in Large Language Models] (Anthropic) | ||
* 2024-12: [https://arxiv.org/abs/2412.03556 Best-of-N Jailbreaking] ([https://github.com/jplhughes/bon-jailbreaking code]) | * 2024-12: [https://arxiv.org/abs/2412.03556 Best-of-N Jailbreaking] ([https://github.com/jplhughes/bon-jailbreaking code]) | ||
| + | * 2024-12: [https://arxiv.org/abs/2412.16325 Towards Safe and Honest AI Agents with Neural Self-Other Overlap] | ||
| + | ** 2025-03: [https://www.lesswrong.com/posts/jtqcsARGtmgogdcLT/reducing-llm-deception-at-scale-with-self-other-overlap-fine Reducing LLM deception at scale with self-other overlap fine-tuning] | ||
* 2024-12: [https://arxiv.org/abs/2412.16339 Deliberative Alignment: Reasoning Enables Safer Language Models] (OpenAI) | * 2024-12: [https://arxiv.org/abs/2412.16339 Deliberative Alignment: Reasoning Enables Safer Language Models] (OpenAI) | ||
* 2025-01: [https://cdn.openai.com/papers/trading-inference-time-compute-for-adversarial-robustness-20250121_1.pdf Trading Inference-Time Compute for Adversarial Robustness] (OpenAI, [https://openai.com/index/trading-inference-time-compute-for-adversarial-robustness/ blog]) | * 2025-01: [https://cdn.openai.com/papers/trading-inference-time-compute-for-adversarial-robustness-20250121_1.pdf Trading Inference-Time Compute for Adversarial Robustness] (OpenAI, [https://openai.com/index/trading-inference-time-compute-for-adversarial-robustness/ blog]) | ||
Revision as of 12:40, 16 March 2025
Contents
Description of Safety Concerns
Key Concepts
- Instrumental Convergence
- Orthogonality Thesis
- Inner/outer alignment
- Mesa-optimization
- Overhang
- Reward is not the optimization target (Alex Turner)
Medium-term Risks
- 2023-04: A.I. Dilemma – Tristan Harris and Aza Raskin” (video) (.website-files.com/5f0e1294f002b1bb26e1f304/64224a9051a6637c1b60162a_65-your-undivided-attention-The-AI-Dilemma-transcript.pdf podcast transcript): raises concern about human ability to handle these transformations
- 2023-04: Daniel Schmachtenberger and Liv Boeree (video): AI could accelerate perverse social dynamics
- 2023-10: Sociotechnical Safety Evaluation of Generative AI Systems (Google DeepMind)
- 2024-02: Towards a Cautious Scientist AI with Convergent Safety Bounds (Yoshua Bengio)
- 2024-07: Reasoning through arguments against taking AI safety seriously (Yoshua Bengio)
Long-term (x-risk)
- List AGI Ruin: A List of Lethalities (Eliezer Yudkowsky)
Learning Resources
- Introduction to AI Safety, Ethics, and Society (Dan Hendrycks, Center for AI Safety)
- AI Safety FAQ
- Writing Doom (video) 27m short film on Superintelligence (2024)
- DeepMind short course on AGI safety
- Anthony Aguirre: Keep The Future Human (essay)
Status
Policy
- 2024-07: On the Limitations of Compute Thresholds as a Governance Strategy Sara Hooker
- 2024-07: Framework Convention on Global AI Challenges (CIGI)
- 2024-08: NIST guidelines: Managing Misuse Risk for Dual-Use Foundation Models
Research
- 2022-09: The alignment problem from a deep learning perspective
- 2022-12: Discovering Latent Knowledge in Language Models Without Supervision
- 2023-02: Pretraining Language Models with Human Preferences
- 2023-04: Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark
- 2023-05: Model evaluation for extreme risks (DeepMind)
- 2023-05: Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision
- 2023-06: Preference Ranking Optimization for Human Alignment
- 2023-08: Self-Alignment with Instruction Backtranslation
- 2023-11: Debate Helps Supervise Unreliable Experts
- 2023-12: Weak-to-Strong Generalization: Eliciting Strong Capabilities with Weak Supervision (OpenAI, blog)
- 2023-12: Practices for Governing Agentic AI Systems (OpenAI, blog)
- 2024-01: Sleeper Agents: Training Deceptive LLMs that Persist through Safety Training (Anthropic)
- 2024-04: The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions (OpenAI)
- 2024-07: On scalable oversight with weak LLMs judging strong LLMs
- 2024-07: Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress? (Dan Hendrycks et al.)
- 2024-08: Tamper-Resistant Safeguards for Open-Weight LLMs (project, code)
- 2024-08: Better Alignment with Instruction Back-and-Forth Translation
- 2024-10: First-Person Fairness in Chatbots (OpenAI, blog)
- 2024-10: Sabotage evaluations for frontier models (Anthropic, blog)
- 2024-12: Alignment Faking in Large Language Models (Anthropic)
- 2024-12: Best-of-N Jailbreaking (code)
- 2024-12: Towards Safe and Honest AI Agents with Neural Self-Other Overlap
- 2024-12: Deliberative Alignment: Reasoning Enables Safer Language Models (OpenAI)
- 2025-01: Trading Inference-Time Compute for Adversarial Robustness (OpenAI, blog)
- 2025-01: Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming (Anthropic, blog,
- 2025-02: Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs (site, github)
- 2025-02: Auditing Prompt Caching in Language Model APIs
- 2025-03: The Alignment Problem from a Deep Learning Perspective
- 2025-03: Auditing language models for hidden objectives (Anthropic, blog)
