Part 8/13:
Anthropic’s approach is principled—focused on reducing harm through a deontological framework. Their models, like Claude, condition their responses on internal moral guidelines, which aims to make AI more trustworthy and aligned with human values. This method could lead to more dependable and ethically consistent AI systems in the long run, but may be less responsive or versatile compared to RLHF-based models.
Strengths and weaknesses: Short-term gains versus long-term sustainability
Reinforcement Learning with Human Feedback (OpenAI)
Pros:
Rapid iteration allows quick improvements and deployment
Creates highly responsive AI aligned with user preferences
Cons:
- Lacks foundational principles, leading to inconsistent morality