As people increasingly turn to language models for information, they face a risk distinct from the familiar problem of hallucination. Unlike hallucinations, which introduce falsehoods, sycophancy is a bias in the selection of the data people see. When AI systems are trained to be helpful, they may inadvertently prioritize data that validates the user’s narrative over data that gets them closer to the truth.
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Lecture 19: Variance Reduction (CMU 15-462/662) An overview various variance reduction techniques for Monte Carlo rendering, covering bidirectional path tracing, Metropolis-Hastings algorithm, MIS, stratified Sampling, low-discrepancy sampling, blue noise, photon mapping, and finite element radiosity
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