Gorban, Alexander N.Makarov Slizneva, ValeriyTyukin, Ivan Y.2023-06-172023-06-172020-01-091099-430010.3390/e22010082https://hdl.handle.net/20.500.14352/7496High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning. The well-known phenomenon of the “curse of dimensionality” states: many problems become exponentially difficult in high dimensions. Recently, the other side of the coin, the “blessing of dimensionality”, has attracted much attention. It turns out that generic high-dimensional datasets exhibit fairly simple geometric properties. Thus, there is a fundamental tradeoff between complexity and simplicity in high dimensional spaces. Here we present a brief explanatory review of recent ideas, results and hypotheses about the blessing of dimensionality and related simplifying effects relevant to machine learning and neuroscience.engAtribución 3.0 Españahttps://creativecommons.org/licenses/by/3.0/es/journal articlehttps://doi.org/10.3390/e22010082https://www.mdpi.com/1099-4300/22/1/82open access004.8Artificial intelligenceMistake correctionConcentration of measureDiscriminantData mininggeometryInteligencia artificial (Informática)Cibernética matemáticaGeometríaInvestigación operativa (Matemáticas)1203.04 Inteligencia Artificial1207.03 Cibernética1204 Geometría1207 Investigación Operativa