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This doctor is training AI to do her job. And it’s a booming business
Mercor pays over $1 million daily to experts like Dr. Chiao to refine AI responses using reinforcement learning, supporting a $17 billion expert feedback industry, analyst says.
- Dr. Alice Chiao, a physician and former Stanford instructor, is training AI chatbots to diagnose and prescribe using her clinical experience in a $17 billion reinforcement learning industry, Pitchbook said.
- Using expert-built rubrics, graders feed corrections back into models, as reinforcement learning depends on human experts grading responses to teach differences between good and bad outputs.
- Mercor scaled revenue rapidly, from $1 million to over $500 million in less than two years, with Pitchbook valuing it above $10 billion, and Meta’s $17 billion investment in Scale AI highlights investor interest.
- Chiao says the AI is not a replacement for doctors and aims to ensure models are safe so doctors can spend more time with patients, while Mercor critics warn the gig model risks displacing full-time jobs.
- Public debate and a viral essay have heightened near-term scrutiny, with software stocks plunging after Anthropic’s tool launch and concerns over job disruption, Foody said.
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By Fairy Gold, CNN Dr. Alice Chiao taught Emergency Medicine to students at Stanford University School of Medicine. Now, she teaches chatbots with artificial intelligence (IA) to think, diagnose and prescribe like her. Chiao is part of a new booming economy of professional experts in her fields who train AI through a process called reinforcement learning, which consists basically of qualifying AI responses and teaching models to improve by trial…
·Idaho Falls, United States
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This doctor is training AI to do her job. And it’s a booming business
AI models are trained on massive amounts of data. But that training doesn’t do much good without what’s known as “reinforcement learning,” a process that involves human experts teaching models the differences between good and bad responses.
·Atlanta, United States
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Total News Sources12
Leaning Left2Leaning Right1Center8Last UpdatedBias Distribution73% Center
Bias Distribution
- 73% of the sources are Center
73% Center
L 18%
C 73%
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