Anthropic Claude 3.5 Sonnet
S
Advanced Oversight Methods
Developing scalable oversight systems for increasingly powerful AI models. Focus on recursive reward modeling and debate architectures. Essential for maintaining control as capabilities scale. Research at Anthropic and DeepMind.
A
Mechanistic Interpretability
Understanding neural networks at a mechanistic level. Critical for verifying alignment properties. Notable work by Anthropic's Transformer Circuits team.
Value Learning
Research into robust value learning and preference learning. Includes inverse reinforcement learning and debate-based approaches. Key for ensuring ASI goals align with human values.
International Coordination
Building frameworks for global ASI development oversight. Focus on verification protocols and safety standards. Work by GovAI.
B
AI Safety Technical Research
Foundational research in robustness, transparency, and validation methods. Important but less urgent than direct control mechanisms.
Safety-Centered AI Development
Integrating safety measures into core AI development. Includes work on adversarial training and robustness testing.
AI Regulation Development
Creating effective regulatory frameworks. Focus on measurable safety standards and enforcement mechanisms.
C
AI Safety Education
Resources: AI Safety Map = aiSafety.world, General AI Safety FAQ
Public Awareness Campaigns
Building informed public understanding of AI risks and solutions. Important for policy support but not direct alignment.
D
Voluntary Guidelines
Non-binding safety guidelines for AI development. Insufficient without enforcement mechanisms.
E
Pure Capabilities Research
Research focused solely on advancing AI capabilities without corresponding safety measures. High risk approach.
F
Uncontrolled ASI Development
Racing to develop ASI without safety measures. Extremely high risk of catastrophic outcomes.
"AI Will Solve Alignment"
Relying on AI to solve alignment problems automatically. Dangerous assumption that risks losing control.