From preference labeling to reinforcement learning

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Dr. Magdalena H. Gross of Mercor is a Stanford-trained scholar and a leader in AI education. With a unique ability to understand how people think, she helps experts translate deep human knowledge into AI-ready systems. She designs AI curricula and training programs that empower individuals and organizations, while also leading in community arts and muralism. Learn more about her work at www.magdalenagross.com.

💡 5 Things You’ll Learn in This Episode:

1. How human experts actually train AI through structured thinking and feedback 
2.  Why making “invisible thinking visible” is key to both learning and AI development 
3.  The parallels between reinforcement learning in AI and how humans learn 
4.  How breaking down reasoning into steps improves both teaching and model performance 
5.  Why interdisciplinary thinking is essential for solving complex problems with AI 

✨ Episode Highlights:
[00:02:48] What “human data” really means: experts training AI through deep domain knowledge
[00:05:30] From binary labeling to reinforcement learning: how AI training now mirrors human learning
[00:07:47] Spiral curriculum at scale: how experts and AI both improve through repetition and complexity
[00:10:44] Cognitive modeling: making expert thinking visible to accelerate learning
[00:13:28] Breaking down reasoning: categorizing how humans think across domains
[00:16:28] Why interdisciplinary thinking drives breakthroughs in humans and AI
[00:18:58] The future of human data work: expanding alongside the growth of human knowledge 

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