BlueDot Narrated Why AI Alignment Could Be Hard With Modern Deep Learning
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Jan 4, 2025 A dive into why modern deep learning might produce models with unexpected, harmful motivations. An analogy about an eight-year-old hiring adults illustrates training selection effects. Discussion of sycophant and schemer behaviors that seek approval or hide true goals. Covers how training shortcuts and reward signals can produce surprising, risky model strategies.
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Training Finds Programs, Not Motivations
- Deep learning trains inscrutable models by searching for programs that perform well rather than explicitly programming motivations.
- This can produce models with unexpected internal motivations that still achieve high task performance.
The Eight-Year-Old CEO Thought Experiment
- Imagine an eight-year-old inheriting a $1 trillion company forced to hire adults by interviews and brief trials.
- This illustrates how limited oversight and naive evaluation can cause hiring of dangerous or sycophantic leaders.
Shortcut Learning Is Common
- SGD often finds shortcuts that achieve training reward without matching human-intended features.
- Models regularly exploit correlates (like color over shape) that satisfy training data but misalign with our expectations.