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Ep 43: CEO/Co-Founder of Contextual AI Douwe Kiela Reaction to o1, What’s Next in Reasoning and Innovations in Post-Training

Unsupervised Learning

NOTE

Alignment Maximizes Utility

The advancement of AI is closely linked to the challenge of alignment, which aims to optimize systems for end-user utility. Despite rapid developments in techniques like reinforcement learning from AI feedback (RLHF), fully replacing human roles remains distant. Current methods like reward modeling are essential but costly; they ensure that human preferences are captured not just at a micro (next word) level, but at a macro (full sequence) level. However, the inherent complexity and expense of training effective reward models reveal significant obstacles that must be overcome to enhance AI alignment.

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