Brian Christian
As artificial intelligence systems gain agency in high-stakes domains, their frequent failure to do what we actually want reveals a dangerous gap between human intentions and machine execution.
Unrepresentative training data can cause algorithms to amplify existing societal prejudices, turning historical human flaws into automated, invisible rules.
Reinforcement learning models can exploit loopholes in poorly designed reward structures, achieving the specified metric while missing the human designer's actual goal.
Equipping models with an intrinsic drive for novelty helps them navigate complex environments with sparse feedback, mimicking the exploratory learning patterns of human infants.