Artifiial Intelligence in Forensic Science:Current State and Application Potential
The article considers the current state of artifiial intelligence technology, its concept and the main directions of development. Two approaches to understanding artifiial intelligence are described. From the point of view of the top-down approach, artifiial intelligence allows solving only certain narrow tasks, and from the position of the ascending one, it is close to human thinking in its capabilities. The author notes that today the programming and computerization of disclosure and investigation of crimes is organized through creating information retrieval systems, reference systems, databases and forensic algorithms optimizing criminalistics versioning. The main features of artifiial neural networks are indicated, including the ability for situational adaptive learning, the identifiation of non-obvious links and patterns, resistance to information noise and disinformation. The stages of an artifiial neural network development are described. They include collection and generalization of the initial data, determination of the network topology and learning parameters, training and verifiation of the results. The author points out potential directions of using artifiial intelligence in investigative, operational and search, forensic examination activities: assessment of the initial information, modeling of a crime and its traces, identifiation of serial crimes, optimization of forensic investigation, evaluation of suffiiency of evidence, crime prediction and operational environment planning.