Computers are getting better each year at AI-style tasks, especially those involving vision--identifying a face, say, or telling if a picture contains a certain object. In fact, their progress has been so significant that some researchers now believe the standardized tests used to evaluate these programs have become too easy to pass, and therefore need to be made more demanding. At issue are the "public data sets" commonly used by vision researchers to benchmark their progress, such as LabelMe at MIT or Labeled Faces in the Wild at the University of Massachusetts, Amherst. The former, for example, contains photographs that have been labeled via crowdsourcing, so that a photo of street scene might have a "car" and a "tree" and a "pedestrian" highlighted and tagged. Success rates have been climbing for computer vision programs that can find these objects, with most of the credit for that improvement going to machine learning techniques such as convolutional networks, often called Deep Learning.

Active data clustering is a novel technique for clustering of proximity data which utilizes principles from sequential experiment design in order to interleave data generation and data analysis. The proposed active data sampling strategy is based on the expected value of information, a concept rooting in statistical decision theory. This is considered to be an important step towards the analysis of largescale data sets, because it offers a way to overcome the inherent data sparseness of proximity data.

Active data clustering is a novel technique for clustering of proximity data which utilizes principles from sequential experiment design in order to interleave data generation and data analysis. The proposed active data sampling strategy is based on the expected value of information, a concept rooting in statistical decision theory. This is considered to be an important step towards the analysis of largescale data sets, because it offers a way to overcome the inherent data sparseness of proximity data.

Active data clustering is a novel technique for clustering of proximity datawhich utilizes principles from sequential experiment design in order to interleave data generation and data analysis. The proposed activedata sampling strategy is based on the expected value of information, a concept rooting in statistical decision theory. This is considered to be an important step towards the analysis of largescale datasets, because it offers a way to overcome the inherent data sparseness of proximity data.