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That principle asserts that innovations only truly catch on when two factors connect: One, the new thing works reliably, and two, people understand why they need it. Simply creating a cool new technology is never enough; fail to bring the public along, and you wind up with either weak demand (think Segway) or a backlash (like with nuclear power in the 1980s).

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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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