Sam Roweis spoke at the NYU Computer Science Colloquium, and Lam Hui spoke at the NYU Astrophysics Seminar today. Roweis spoke about fitting models to data, where the data are taken by sensors with unknown properties (for example microphones with unknown thresholds, saturation, nonlinearity, and frequency response or sensors on wandering robots of unknown position). His point was that if you have enough sensor readings from differently unreliable or unknown sensors, you can still do very well, if you take a generative modeling approach. The demos were nice. Hui spoke about modifications to gravity and what they might do to dynamics. In particular, he noted that most current modifications to gravity look very much like a scalar–tensor theory, where there is (effectively) a different value of G in high-density regions than in low-density regions. If this is going on in our Universe, there ought to be lots of dynamical signatures; hopefully we can rule out large classes of models of that type.