Researchers have revealed a radical AI system that can create a'master key' for fingerprint, raising major questions over the security of phones and other devices that rely on them. Called'DeepMasterPrints' researchers - who created the fake prints using a neural network - were able to mimic more than one in five fingerprints using their technique. The team from New York University and Michigan State University behind the system told CNBC it could unlock a'reasonably large' number of phones -- just under a third. From unlocking smartphones to authorising payments, fingerprints are widely used to identify people. However, a team of researchers have now managed to accurately copy real fingerprints and created fake ones called'DeepMasterPrints' (pictured) Fingerprint systems do not generally read the entire fingerprint but just record whichever part of it touches the scanner first.

Abstract Machine understanding of questions is tightly related to recognition of articulation in the context of the computational capabilities of an underlying processing algorithm. In this paper a mathematical model to capture and distinguish the latent structure in the articulation of questions is presented. We propose an objective-driven approach to represent this latent structure and show that such an approach is beneficial when examples of complementary objectives are not available. We show that the latent structure can be represented as a system that maximizes a cost function related to the underlying objective. Further, we show that the optimization formulation can be approximated to building a memory of patterns represented as a trained neural auto-encoder. Experimental evaluation using many clusters of questions, each related to an objective, shows 80% recognition accuracy and negligible false positive across these clusters of questions. We then extend the same memory to a related task where the goal is to iteratively refine a dataset of questions based on the latent articulation. We also demonstrate a refinement scheme called K-fingerprints, that achieves nearly 100% recognition with negligible false positive across the different clusters of questions.