This paper examines the performance of a number of AI agents on the games included in the General Video Game Playing Competition. Through analyzing these results, the paper seeks to provide insight into the strengths and weaknesses of the current generation of video game playing algorithms. The paper also provides an analysis of the given games in terms of inherent features which define the different games. Finally, the game features are matched with AI agents, based on performance, in order to demonstrate a plausible case for algorithm portfolios as a general video game playing technique.

Much the way that a master key can unlock every door in a building, these "DeepMasterPrints" use artificial intelligence to match a large number of prints stored in fingerprint databases and could thus theoretically unlock a large number of devices. The research team was headed by NYU Tandon Associate Professor of Computer Science and Engineering Julian Togelius and doctoral student Philip Bontrager, the lead author of the paper, who presented it at the IEEE International Conference of Biometrics: Theory, Applications and Systems, where it won the Best Paper Award. The work builds on earlier research led by Nasir Memon, professor of computer science and engineering and associate dean for online learning at NYU Tandon. Memon, who coined the term "MasterPrint," described how fingerprint-based systems use partial fingerprints, rather than full ones, to confirm identity. Devices typically allow users to enroll several different finger images, and a match for any saved partial print is enough to confirm identity.

Just like any lock can be picked, any biometric scanner can be fooled. Researchers have shown for years that the popular fingerprint sensors used to guard smartphones can be tricked sometimes, using a lifted print or a person's digitized fingerprint data. But new findings from computer scientists at New York University's Tandon School of Engineering could raise the stakes significantly. The group has developed machine learning methods for generating fake fingerprints--called DeepMasterPrints--that not only dupe smartphone sensors, but can successfully masquerade as prints from numerous different people. Think of it as a skeleton key for fingerprint-protected devices.