{"id":6461,"date":"2025-06-13T13:04:45","date_gmt":"2025-06-13T13:04:45","guid":{"rendered":"https:\/\/www.urudata.com\/?p=6461"},"modified":"2026-01-14T15:01:44","modified_gmt":"2026-01-14T15:01:44","slug":"historia-y-desafios-en-el-uso-de-ia","status":"publish","type":"post","link":"https:\/\/urudata.com\/en\/historia-y-desafios-en-el-uso-de-ia\/","title":{"rendered":"History and Challenges in the Use of AI"},"content":{"rendered":"<p>To understand how we came to be immersed in a world that constantly talks about artificial intelligence, It\u2019s helpful to analyze and reflect on the fact that this is not entirely new, but rather something that the scientific community has been working for many years to theorize and attempt to build machines that resemble us or at least simulate our human capabilities.<\/p>\n\n\n\n<p>It can be inferred with certainty that the first venture into AI was the Turing machine. From there, during the 1950s and 1960s, the theoretical foundations of AI were established. These theories were based on how machines could simulate human intelligence.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img data-recalc-dims=\"1\" fetchpriority=\"high\" decoding=\"async\" width=\"336\" height=\"366\" src=\"https:\/\/i0.wp.com\/emq.qck.mybluehost.me\/wp-content\/uploads\/2025\/06\/image.png?resize=336%2C366&#038;ssl=1\" alt=\"\" class=\"wp-image-6462\" srcset=\"https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image.png?w=336&amp;ssl=1 336w, https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image.png?resize=275%2C300&amp;ssl=1 275w, https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image.png?resize=11%2C12&amp;ssl=1 11w\" sizes=\"(max-width: 336px) 100vw, 336px\" \/><\/figure>\n\n\n\n<p>By the late 1960s and throughout the 1970s, symbolic approaches and logic-\nbased rules were primarily used, with IBM&#039;s Deep Blue chess-playing program\nat an expert level being an iconic example of the era.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"443\" height=\"297\" src=\"https:\/\/i0.wp.com\/emq.qck.mybluehost.me\/wp-content\/uploads\/2025\/06\/image-1.png?resize=443%2C297&#038;ssl=1\" alt=\"\" class=\"wp-image-6463\" srcset=\"https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-1.png?w=443&amp;ssl=1 443w, https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-1.png?resize=300%2C201&amp;ssl=1 300w, https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-1.png?resize=18%2C12&amp;ssl=1 18w\" sizes=\"(max-width: 443px) 100vw, 443px\" \/><\/figure>\n\n\n\n<p>The symbolic connection approach emerged in the 1980s and was extended\ninto the 1990s, a period during which techniques were explored that combined\nsymbolic reasoning with methods based on knowledge and learning from the\nconnections between data.<\/p>\n\n\n\n<p>In the early 2000s, there was a resurgence of research into techniques that had\nbegun in the previous decade but had not proven sufficiently fruitful: neural\nnetworks. This period stood out for significant advances in learning algorithms,\n\nsuch as supervised and unsupervised learning, as well as techniques in natural\nlanguage processing and computer vision.<\/p>\n\n\n\n<p>In the 2010s, with the massive increase in data availability, computational\npower, and communications, the focus on deep learning began to dominate the\nAI field. Image recognition, autonomous driving, and natural language\nprocessing are examples of techniques that reached levels that Turing and\nMcCarthy could barely have dreamed of decades earlier.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img data-recalc-dims=\"1\" decoding=\"async\" width=\"544\" height=\"227\" src=\"https:\/\/i0.wp.com\/emq.qck.mybluehost.me\/wp-content\/uploads\/2025\/06\/image-2.png?resize=544%2C227&#038;ssl=1\" alt=\"\" class=\"wp-image-6464\" srcset=\"https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-2.png?w=544&amp;ssl=1 544w, https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-2.png?resize=300%2C125&amp;ssl=1 300w, https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-2.png?resize=18%2C8&amp;ssl=1 18w\" sizes=\"(max-width: 544px) 100vw, 544px\" \/><\/figure>\n\n\n\n<p>Modern artificial intelligence aims to create intelligent agents that assist\nindividuals and organizations in performing their tasks with outstanding levels of\nefficiency and effectiveness, generating a high return on technological\ninvestment and providing a competitive advantage.<\/p>\n\n\n\n<p>Among the forces driving the rapid evolution of AI, we can highlight some of the\nmost influential ones:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The development of Big Data<\/li>\n\n\n\n<li>The boom of E-commerce<\/li>\n\n\n\n<li>The Internet of Things (IoT)<\/li>\n\n\n\n<li>Social media<\/li>\n\n\n\n<li>The growth in computational power<\/li>\n\n\n\n<li>The drastic reduction in processing costs<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p>These forces provide a significant boost to technology investments and,\nparticularly in the case of AI, carry expectations of building systems capable of\naccelerating business processes. To make this possible, the goal is to equip\nsystems with capabilities that allow them to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Perceive their environment<\/li>\n\n\n\n<li>Represent it<\/li>\n\n\n\n<li>Learn<\/li>\n\n\n\n<li>Make autonomous decisions<\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<p>The representation of the environment is what we call a MODEL.<\/p>\n\n\n\n<p>Model tuning is the learning process, in which metrics are adjusted by\ninteracting with a simulator or through repeated exposure to new information,\nand from that learning, making autonomous decisions. This process aims to\nunderstand the distribution of input data and, from there, enable learning.<\/p>\n\n\n\n<p>But not everything is ideal\u2014there are also challenges to keeping AI from\ninheriting our own limitations. In this regard, one of the greatest challenges AI\nfaces today is addressing the influence of bias. Since AI agents are not\nprogrammed explicitly, they acquire the biases present in the data. For\nexample, a self-driving car might fail to recognize a pedestrian crossing in the\nmiddle of the street because it was trained to &quot;understand&quot; that pedestrians\ncross at intersections. This leads to obvious problems that models must\novercome in order to adapt to critical tasks and provide objective decisions,\nboth operational and strategic.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"444\" height=\"278\" src=\"https:\/\/i0.wp.com\/emq.qck.mybluehost.me\/wp-content\/uploads\/2025\/06\/image-3.png?resize=444%2C278&#038;ssl=1\" alt=\"\" class=\"wp-image-6465\" srcset=\"https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-3.png?w=444&amp;ssl=1 444w, https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-3.png?resize=300%2C188&amp;ssl=1 300w, https:\/\/i0.wp.com\/urudata.com\/wp-content\/uploads\/2025\/06\/image-3.png?resize=18%2C12&amp;ssl=1 18w\" sizes=\"(max-width: 444px) 100vw, 444px\" \/><\/figure>\n\n\n\n<p>Currently, the trend in AI adoption indicates that within the next 24 months, most\norganizations plan to integrate custom solutions based on this technology.\nStatistically speaking, companies are seeing a return of $3.70 for every dollar\ninvested in AI, making it a highly attractive area for technology investment.<\/p>\n\n\n\n<p>However, the main barrier to AI adoption is the lack of qualified personnel. This\nchallenge is one that organizations and technology companies face\u2014finding\nindividuals who can identify needs, design solutions, and make the necessary\nadjustments to extract maximum value.<\/p>","protected":false},"excerpt":{"rendered":"<p>Para entender c\u00f3mo llegamos a estar hoy inmersos en un mundo que no para de hablar de inteligencia artificial, es interesante analizar y pensar que no es algo de este \u00faltimo tiempo, sino que la comunidad cient\u00edfica hace ya much\u00edsimos a\u00f1os que trabaja en teorizar e intentar fabricar m\u00e1quinas que se parezcan a nosotros o [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":6467,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"nf_dc_page":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[52],"tags":[],"class_list":["post-6461","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ia"],"acf":{"bajada":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Urudata &gt; Historia y desaf\u00edos en el uso de IA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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