Scale-invariant feature transformThe scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, , 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database.
Deep reinforcement learningDeep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured input data without manual engineering of the state space. Deep RL algorithms are able to take in very large inputs (e.g.
Internet trafficInternet traffic is the flow of data within the entire Internet, or in certain network links of its constituent networks. Common traffic measurements are total volume, in units of multiples of the byte, or as transmission rates in bytes per certain time units. As the topology of the Internet is not hierarchical, no single point of measurement is possible for total Internet traffic. Traffic data may be obtained from the Tier 1 network providers' peering points for indications of volume and growth.
Gene-centered view of evolutionThe gene-centered view of evolution, gene's eye view, gene selection theory, or selfish gene theory holds that adaptive evolution occurs through the differential survival of competing genes, increasing the allele frequency of those alleles whose phenotypic trait effects successfully promote their own propagation. The proponents of this viewpoint argue that, since heritable information is passed from generation to generation almost exclusively by DNA, natural selection and evolution are best considered from the perspective of genes.
