Los dos Premios Nobel de F\u00edsica de este a\u00f1o han utilizado herramientas de la f\u00edsica para desarrollar m\u00e9todos que son la base del potente aprendizaje autom\u00e1tico actual. John Hopfield cre\u00f3 una memoria asociativa que puede almacenar y reconstruir im\u00e1genes y otros tipos de patrones en los datos. Geoffrey Hinton invent\u00f3 un m\u00e9todo que puede encontrar propiedades de forma aut\u00f3noma en los datos y, por lo tanto, realizar tareas como identificar elementos espec\u00edficos en im\u00e1genes.<\/p>\n
When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain\u2019s neurons are represented by nodes that have different values. These nodes influence each other through con\u00adnections that can be likened to synapses and which can be made stronger or weaker. The network is\u00a0trained<\/em>, for example by developing stronger connections between nodes with simultaneously high values. This year\u2019s laureates have conducted important work with artificial neural networks from the 1980s onward.<\/p>\n John Hopfield<\/strong>\u00a0invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The\u00a0Hopfield network<\/em>\u00a0utilises physics that describes a material\u2019s characteristics due to its atomic spin \u2013 a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network\u2019s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.<\/p>\n Geoffrey Hinton<\/strong>\u00a0used the Hopfield network as the foundation for a new network that uses a different method: the\u00a0Boltzmann machine<\/em>. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.<\/p>\n \u201cThe laureates\u2019 work has already been of the greatest benefit. In physics we use artificial neural networks in a vast range of areas, such as developing new materials with specific properties,\u201d says Ellen Moons, Chair of the Nobel Committee for Physics.<\/p>\n Fuente: <\/strong>https:\/\/www.nobelprize.org\/prizes\/physics\/2024\/press-release\/<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Los dos Premios Nobel de F\u00edsica de este a\u00f1o han utilizado herramientas de la f\u00edsica para desarrollar m\u00e9todos que son la base del potente aprendizaje… <\/p>\n","protected":false},"author":1,"featured_media":15641,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[2,23],"tags":[],"_links":{"self":[{"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/posts\/15640"}],"collection":[{"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=15640"}],"version-history":[{"count":2,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/posts\/15640\/revisions"}],"predecessor-version":[{"id":15643,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/posts\/15640\/revisions\/15643"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/media\/15641"}],"wp:attachment":[{"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15640"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15640"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15640"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}