In this work we show that modern data-driven machine learning techniques can be successfully applied on lunar surface remote sensing data to learn, in an unsupervised way, sufficiently good representations of the data distribution to enable lunar technosignature and anomaly detection. In particular we train an unsupervised distribution learning model to find the landing module of the Apollo 15 landing site in a testing dataset, with no dataset specific model or hyperparameter tuning. Sufficiently good unsupervised data density estimation techniques have the potential to enable a dazzling number of useful downstream tasks, including locating lunar resources for future space flight and colonization, finding new impact craters or lunar surface reshaping, and deciding the importance of unlabeled samples to send back from power-and bandwidth-constrained missions. We show in this work that such unsupervised learning can be successfully done in the lunar remote sensing and space sciences contexts.

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Synthetic data and simulators have the potential to markedly improve the performance and robustness of recommendation systems.These approaches have already had a beneficial impact in other machine-learning driven fields. We identify and discuss a key trade-off between data fidelity and privacy in the past work on synthetic data and simulators for recommendation systems. For the important use case of predicting algorithm rankings on real data from synthetic data, we provide motivation and current successes versus limitations. Finally we outline a number of exciting future directions for recommendation systems that we believe deserve further attention and work, including mixing real and synthetic data, feedback in dataset generation, robust simulations, and privacy-preserving methods.