{"id":3464,"date":"2023-08-01T16:34:30","date_gmt":"2023-08-01T16:34:30","guid":{"rendered":"https:\/\/dailyai.com\/?p=3464"},"modified":"2023-08-02T15:13:13","modified_gmt":"2023-08-02T15:13:13","slug":"ai-matches-college-undergrads-solving-reasoning-problems","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/","title":{"rendered":"La IA empareja a estudiantes universitarios que resuelven problemas de razonamiento"},"content":{"rendered":"

Un estudio realizado por psic\u00f3logos de la UCLA ha demostrado que el GPT-3 es tan bueno como los estudiantes universitarios en la resoluci\u00f3n de problemas de razonamiento.<\/strong><\/p>\n

Sabemos que los LLM como GPT-3 son buenos generando respuestas basadas en los datos con los que han sido entrenados, pero su capacidad de razonamiento es cuestionable. El razonamiento anal\u00f3gico es la capacidad que tenemos los humanos de tomar lo que aprendemos de una experiencia no relacionada y aplicarlo a un problema al que no nos hemos enfrentado antes.<\/span><\/p>\n

Esa capacidad es en la que te apoyas cuando tienes que responder a una pregunta que nunca has visto antes. Puedes razonarla bas\u00e1ndote en problemas anteriores que has resuelto. Y seg\u00fan la investigaci\u00f3n, parece que la GPT-3 tambi\u00e9n ha desarrollado esa capacidad.<\/span><\/p>\n

En <\/span>Investigadores de la UCLA<\/span><\/a> poner a GPT-3 a trabajar en una serie de problemas similares a <\/span>Matrices progresivas de Raven<\/span><\/a> que consiste en predecir la siguiente imagen de una serie de im\u00e1genes. Aqu\u00ed tienes uno f\u00e1cil para que lo intentes.<\/span><\/p>\n

\"Ejemplo
Ejemplo de matriz progresiva Raven - Fuente: Wikipedia<\/a><\/figcaption><\/figure>\n

GPT-3 fue capaz de mantenerse a la altura de los 40 estudiantes universitarios de la UCLA a los que se pidi\u00f3 que realizaran la misma prueba. La IA acert\u00f3 80% de las respuestas, mientras que los 40 estudiantes obtuvieron una media de 60%. Los mejores estudiantes obtuvieron aproximadamente la misma puntuaci\u00f3n que GPT-3.<\/span><\/p>\n

El profesor de psicolog\u00eda de la UCLA Hongjing Lu, autor principal del estudio, afirm\u00f3: \"Sorprendentemente, la GPT-3 no s\u00f3lo lo hizo tan bien como los humanos, sino que tambi\u00e9n cometi\u00f3 errores similares\".<\/span><\/p>\n

Los investigadores tambi\u00e9n pidieron al GPT-3 que resolviera algunos problemas de asociaci\u00f3n de palabras. Por ejemplo, \"'Coche' es a 'Carretera' lo que 'Barco' es a \u00bfqu\u00e9 palabra?\". La respuesta es, obviamente, \"agua\", pero este tipo de preguntas pueden resultar complicadas para una IA.<\/span><\/p>\n

Bueno, al menos los investigadores pensaron que podr\u00eda ser complicado. Resulta que los GPT-3 obtuvieron mejores resultados que la media de los solicitantes universitarios en sus pruebas SAT.<\/span><\/p>\n

GPT-3 lucha con problemas que son f\u00e1ciles para los humanos<\/h2>\n

Lo que les cuesta a los modelos de IA son los problemas que requieren una comprensi\u00f3n visual del espacio f\u00edsico. Si le presentas a GPT-3 una lista de herramientas como un martillo, un clavo y un cuadro, no es capaz de encontrar la soluci\u00f3n obvia para colgar el cuadro en la pared.<\/span><\/p>\n

Este tipo de problema es f\u00e1cil de resolver para los humanos porque podemos ver, sostener y sentir objetos f\u00edsicos en un espacio que ocupamos. Esas experiencias facilitan que nuestros cerebros aprendan y resuelvan problemas de una forma que los modelos de IA no pueden. Dicho esto, GPT-4 est\u00e1 mejorando en este tipo de razonamiento ahora.<\/span><\/p>\n

Aunque los investigadores pudieron medir el rendimiento de la GPT-3, no tienen ni idea del proceso de \"pensamiento\" que sigue para obtener las respuestas. \u00bfSigue un proceso de pensamiento similar al de los humanos o hace algo completamente distinto? Como GPT-3 es un modelo cerrado, no es posible mirar bajo el cap\u00f3 para ver qu\u00e9 ocurre.\u00a0<\/span><\/p>\n

El sorprendente resultado de esta investigaci\u00f3n es que la GPT-3 parece ser capaz de resolver problemas nuevos sin ning\u00fan entrenamiento directo. Esto coincide con la forma en que los humanos resuelven problemas nuevos. Se espera que GPT-4 rinda a\u00fan mejor en estos problemas y qui\u00e9n sabe qu\u00e9 otra capacidad \"pensante\" podr\u00eda surgir con m\u00e1s pruebas.<\/span><\/p>\n

Aunque el razonamiento espacial es un reto para los LLM, estos desaf\u00edos podr\u00edan resolverse mediante modelos visuales como RT-2 de Google<\/a> anunciado recientemente. Una vez que los modelos de IA puedan empezar a \"ver\" e interactuar f\u00edsicamente con su entorno, su capacidad para resolver problemas mejorar\u00e1 exponencialmente.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Un estudio realizado por psic\u00f3logos de la UCLA ha demostrado que el GPT-3 es tan bueno como los estudiantes universitarios en la resoluci\u00f3n de problemas de razonamiento. Sabemos que los LLM como el GPT-3 son buenos generando respuestas basadas en los datos con los que han sido entrenados, pero su capacidad de razonamiento es cuestionable. El razonamiento anal\u00f3gico es la capacidad que tenemos los humanos de tomar lo que aprendemos de una experiencia no relacionada y aplicarlo a un problema al que no nos hemos enfrentado antes. Esa capacidad es en la que te basas cuando tienes que responder a una pregunta que nunca has visto antes. Puedes razonarla bas\u00e1ndote<\/p>","protected":false},"author":6,"featured_media":3467,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[266,207,105,93],"class_list":["post-3464","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-gpt","tag-llm","tag-machine-learning","tag-openai"],"yoast_head":"\nAI matches college undergrads solving reasoning problems | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/es\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI matches college undergrads solving reasoning problems | DailyAI\" \/>\n<meta property=\"og:description\" content=\"A study by psychologists at UCLA has shown that GPT-3 is about as good as college undergraduate students at solving reasoning problems. We know that LLMs like GPT-3 are good at generating responses based on the data they\u2019ve been trained on, but their ability to reason is questionable. Analogical reasoning is the ability humans have to take what we learn from an unrelated experience and apply it to a problem we haven\u2019t been faced with before. That ability is what you rely on when you have to answer a question you\u2019ve never seen before. 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