{"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\/de\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/","title":{"rendered":"K\u00fcnstliche Intelligenz bringt Studenten dazu, Denkaufgaben zu l\u00f6sen"},"content":{"rendered":"<p><strong>Eine Studie von Psychologen der UCLA hat gezeigt, dass GPT-3 etwa so gut ist wie Studenten im Grundstudium, wenn es um das L\u00f6sen von Denkaufgaben geht.<\/strong><\/p>\n<p><span style=\"font-weight: 400;\">Wir wissen, dass LLMs wie GPT-3 gut darin sind, Antworten auf der Grundlage der Daten zu generieren, auf die sie trainiert wurden, aber ihre F\u00e4higkeit zum logischen Denken ist fraglich. Analoges Denken ist die F\u00e4higkeit des Menschen, das, was er aus einer nicht verwandten Erfahrung gelernt hat, auf ein Problem anzuwenden, mit dem er noch nicht konfrontiert war.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Auf diese F\u00e4higkeit verlassen Sie sich, wenn Sie eine Frage beantworten m\u00fcssen, die Sie noch nie gesehen haben. Man kann sie auf der Grundlage fr\u00fcherer Probleme, die man gel\u00f6st hat, durchdenken. Und aus der Forschung geht hervor, dass GPT-3 diese F\u00e4higkeit ebenfalls entwickelt hat.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Die <\/span><a href=\"https:\/\/www.nature.com\/articles\/s41562-023-01659-w\"><span style=\"font-weight: 400;\">UCLA-Forscher<\/span><\/a><span style=\"font-weight: 400;\"> GPT-3 mit einer Reihe von Problemen zu besch\u00e4ftigen, die \u00e4hnlich sind wie <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Raven%27s_Progressive_Matrices\"><span style=\"font-weight: 400;\">Raven's Progressive Matrizen<\/span><\/a><span style=\"font-weight: 400;\"> bei dem es darum geht, das n\u00e4chste Bild in einer Reihe von Bildern vorherzusagen. Hier ist eine einfache Aufgabe, die Sie ausprobieren k\u00f6nnen.<\/span><\/p>\n<figure id=\"attachment_3469\" aria-describedby=\"caption-attachment-3469\" style=\"width: 300px\" class=\"wp-caption aligncenter\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-3469\" src=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-300x300.jpg\" alt=\"Raven Progressive Matrix Beispiel\" width=\"300\" height=\"300\" srcset=\"https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-300x300.jpg 300w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-150x150.jpg 150w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-180x180.jpg 180w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-90x90.jpg 90w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-370x370.jpg 370w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-20x20.jpg 20w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-740x740.jpg 740w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-24x24.jpg 24w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-48x48.jpg 48w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix-96x96.jpg 96w, https:\/\/dailyai.com\/wp-content\/uploads\/2023\/08\/Raven_Matrix.jpg 756w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><figcaption id=\"caption-attachment-3469\" class=\"wp-caption-text\">Raven Progressive Matrix Beispiel - Quelle: <a href=\"https:\/\/en.wikipedia.org\/wiki\/Raven%27s_Progressive_Matrices\">Wikipedia<\/a><\/figcaption><\/figure>\n<p><span style=\"font-weight: 400;\">GPT-3 konnte sich im Vergleich zu den 40 UCLA-Studenten, die denselben Test absolvieren mussten, gut behaupten. Die KI hat in 80% der F\u00e4lle die richtigen Antworten gegeben, w\u00e4hrend die 40 Studenten im Durchschnitt 60% richtig lagen. Die Spitzenstudenten erzielten in etwa das gleiche Ergebnis wie GPT-3.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Hongjing Lu, Psychologieprofessorin an der UCLA und Hauptautorin der Studie, sagte: \"\u00dcberraschenderweise hat GPT-3 nicht nur genauso gut abgeschnitten wie Menschen, sondern auch \u00e4hnliche Fehler gemacht.\"<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Die Forscher baten GPT-3 auch, einige Wortassoziationsaufgaben zu l\u00f6sen. Zum Beispiel: \"'Auto' ist zu 'Stra\u00dfe' wie 'Boot' zu welchem Wort?\". Die Antwort ist nat\u00fcrlich \"Wasser\", aber diese Art von Fragen k\u00f6nnen f\u00fcr eine KI schwierig sein.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Nun, zumindest dachten die Forscher, dass es schwierig sein k\u00f6nnte. Es stellte sich heraus, dass GPT-3 besser abschnitt als der Durchschnitt der College-Bewerber bei ihren SATs.<\/span><\/p>\n<h2>GPT-3 k\u00e4mpft mit Problemen, die f\u00fcr Menschen einfach sind<\/h2>\n<p><span style=\"font-weight: 400;\">Womit KI-Modelle Schwierigkeiten haben, sind Probleme, die ein visuelles Verst\u00e4ndnis des physischen Raums erfordern. Wenn man GPT-3 eine Liste von Werkzeugen wie einen Hammer, einen Nagel und ein Bild vorlegt, ist es nicht in der Lage, die offensichtliche L\u00f6sung f\u00fcr das Aufh\u00e4ngen des Bildes an der Wand zu finden.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Diese Art von Problem ist f\u00fcr Menschen leicht zu l\u00f6sen, weil wir physische Objekte in einem von uns bewohnten Raum sehen, halten und f\u00fchlen k\u00f6nnen. Diese Erfahrungen machen es unserem Gehirn leicht, zu lernen und Probleme auf eine Weise zu l\u00f6sen, wie es KI-Modelle nicht k\u00f6nnen. Abgesehen davon wird GPT-4 bei dieser Art des Denkens jetzt immer besser.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Die Forscher konnten zwar die Leistung von GPT-3 messen, aber sie haben keine Ahnung, welchen \"Denkprozess\" es durchl\u00e4uft, um die Antworten zu erhalten. Folgt es einem \u00e4hnlichen Denkprozess wie der Mensch oder tut es etwas v\u00f6llig anderes? Da GPT-3 ein geschlossenes Modell ist, ist es nicht m\u00f6glich, unter die Haube zu schauen, um zu sehen, was dort passiert.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Das \u00fcberraschende Ergebnis dieser Forschung ist, dass GPT-3 in der Lage zu sein scheint, neue Probleme ohne direktes Training zu l\u00f6sen. Das entspricht in etwa dem, wie Menschen neue Probleme l\u00f6sen. Es wird erwartet, dass GPT-4 bei diesen Problemen noch besser abschneidet, und wer wei\u00df, welche weiteren \"Denkf\u00e4higkeiten\" sich bei weiteren Tests herausstellen k\u00f6nnten.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">W\u00e4hrend das r\u00e4umliche Denken eine Herausforderung f\u00fcr LLMs darstellt, k\u00f6nnten diese Herausforderungen durch visuelle Modelle gel\u00f6st werden wie <a href=\"https:\/\/dailyai.com\/de\/2023\/07\/googles-ai-turns-vision-language-into-robotic-actions\/\">Googles RT-2<\/a> das k\u00fcrzlich angek\u00fcndigt wurde. Sobald KI-Modelle beginnen k\u00f6nnen, ihre Umgebung zu \"sehen\" und physisch mit ihr zu interagieren, werden sich ihre Probleml\u00f6sungsf\u00e4higkeiten exponentiell verbessern.<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>Eine Studie von Psychologen der UCLA hat gezeigt, dass GPT-3 bei der L\u00f6sung von Denkproblemen etwa so gut ist wie Studenten im Grundstudium. Wir wissen, dass LLMs wie GPT-3 gut darin sind, Antworten auf der Grundlage der Daten zu generieren, auf die sie trainiert wurden, aber ihre F\u00e4higkeit zum logischen Denken ist fraglich. Analoges Denken ist die F\u00e4higkeit des Menschen, das, was er aus einer nicht verwandten Erfahrung gelernt hat, auf ein Problem anzuwenden, mit dem er noch nie konfrontiert wurde. Auf diese F\u00e4higkeit verl\u00e4sst man sich, wenn man eine Frage beantworten muss, die man noch nie gesehen hat. Man kann sie mit Hilfe von<\/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":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>AI 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\/de\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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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