"Cosmic Bandits: Exploration vs. Exploitation in Cosmological Surveys": Various cosmological observations consist of prolonged integrations over small patches of sky. These include measurements of B-modes in the CMB, the power spectrum of 21-cm fluctuations during the epoch of reionization and deep-field imaging by telescopes such as HST/JWST, among others. However, since these measurements are often hindered by spatially-varying foreground noise, the observational sensitivity may be improved considerably by finding the region of sky cleanest of foregrounds. The best strategy thus involves a tradeoff between exploration (to find lower-foreground patches) and exploitation (through prolonged integration). But how to balance this tradeoff efficiently? This problem is akin to the multi-armed bandit (MAB) problem in probability theory, wherein a gambler faces a series of slot machines with unknown winning odds and must develop a strategy to maximize his/her winnings with some finite number of pulls. While the optimal MAB strategy remains to be determined, a number of machine-learning algorithms have been developed in an effort to maximize the winnings. Focusing on B-mode measurements, we will demonstrate how to construct adaptive survey strategies based on heuristic methods to solve the MAB problem and discuss the implications of the very recent news (and rumors) regarding BICEP2. Other possible implementations of MAB methods and pertaining issues will be briefly mentioned as well.