AI safety ensures artificial intelligence systems behave as expected across various situations without causing unintended harm. As LLM capabilities rapidly advance, this field has moved from academic fringe to industry core — OpenAI, Anthropic, and DeepMind all have dedicated safety research teams, and many of these companies’ founders and core researchers have AI safety research backgrounds.
## Core Alignment Techniques
**RLHF (Reinforcement Learning from Human Feedback)**: the dominant LLM alignment method today. Process: pretrained model → human annotators rank model outputs by preference → train a Reward Model → optimize the language model using PPO or similar RL algorithms to achieve higher rewards. ChatGPT, Claude, and Gemini all use RLHF or variants for alignment training. RLHF limitations: dependence on annotation quality; potential sycophancy (models over-pleasing users); reward hacking.
**Constitutional AI (Anthropic)**: Anthropic’s alternative/complement to RLHF. Core idea: replace some human annotation with explicit principles (a “constitution”), letting the model self-evaluate whether outputs comply with principles and generate revised versions, then training on this AI self-critique data. Advantage: reduces large-scale human annotation dependence; principles are transparent and interpretable. Claude series models use Constitutional AI training.
**DPO (Direct Preference Optimization)**: a 2023 RLHF simplification — no separate reward model needed; directly optimizes the language model through preference-pair data. More stable training, simpler implementation, now widely adopted.
## Interpretability Research
**Mechanistic interpretability**: understanding *why* a model makes certain decisions by analyzing neural network internal structure (features, circuits, attention head functions). Anthropic’s interpretability team has made significant progress, including identifying activation features corresponding to specific concepts inside Claude models.
**Hallucination problem**: LLMs confidently generating incorrect information is currently the most prominent safety concern. Mitigation approaches include RAG (grounding with real documents), self-consistency checking (multiple samples, consensus), and calibration training (matching model confidence to actual accuracy).
See [AI Agents Introduction](https://sunqi.org/ai-agent-introduction-en/) and [Anthropic safety research](https://www.anthropic.com/research).
