{"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\/da\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/","title":{"rendered":"AI matcher universitetsstuderende, der l\u00f8ser r\u00e6sonnementsproblemer"},"content":{"rendered":"

En unders\u00f8gelse foretaget af psykologer p\u00e5 UCLA har vist, at GPT-3 er omtrent lige s\u00e5 god som universitetsstuderende til at l\u00f8se r\u00e6sonnementsproblemer.<\/strong><\/p>\n

Vi ved, at LLM'er som GPT-3 er gode til at generere svar baseret p\u00e5 de data, de er blevet tr\u00e6net p\u00e5, men deres evne til at r\u00e6sonnere er tvivlsom. Analogisk r\u00e6sonnement er den evne, mennesker har til at tage det, vi l\u00e6rer af en ikke-relateret erfaring, og anvende det p\u00e5 et problem, vi ikke har st\u00e5et over for f\u00f8r.<\/span><\/p>\n

Det er den evne, du har brug for, n\u00e5r du skal besvare et sp\u00f8rgsm\u00e5l, du aldrig har set f\u00f8r. Du kan r\u00e6sonnere dig frem til det p\u00e5 baggrund af tidligere problemer, du har l\u00f8st. Og ud fra forskningen ser det ud til, at GPT-3 ogs\u00e5 har udviklet den evne.<\/span><\/p>\n

Den <\/span>Forskere fra UCLA<\/span><\/a> s\u00e6tte GPT-3 til at arbejde p\u00e5 et s\u00e6t problemer, der ligner <\/span>Raven's Progressive Matrices<\/span><\/a> som g\u00e5r ud p\u00e5 at forudsige det n\u00e6ste billede i en serie af billeder. Her er en nem opgave, som du kan pr\u00f8ve.<\/span><\/p>\n

\"Eksempel
Raven Progressive Matrix Eksempel - Kilde: Wikipedia<\/a><\/figcaption><\/figure>\n

GPT-3 var i stand til at klare sig i forhold til de 40 UCLA-studerende, der blev bedt om at tage den samme test. AI'en svarede rigtigt i 80% af tilf\u00e6ldene, mens de 40 studerende havde et gennemsnit p\u00e5 omkring 60%. De bedste studerende scorede omtrent det samme som GPT-3 gjorde.<\/span><\/p>\n

UCLA-psykologiprofessor Hongjing Lu, unders\u00f8gelsens hovedforfatter, sagde: \"Overraskende nok klarede GPT-3 sig ikke kun omtrent lige s\u00e5 godt som mennesker, men den begik ogs\u00e5 lignende fejl.\"<\/span><\/p>\n

Forskerne bad ogs\u00e5 GPT-3 om at l\u00f8se nogle ordassociationsproblemer. For eksempel: \"'Bil' er til 'vej' som 'b\u00e5d' er til hvilket ord?\". Svaret er naturligvis \"vand\", men den slags sp\u00f8rgsm\u00e5l kan v\u00e6re vanskelige for en AI.<\/span><\/p>\n

Forskerne troede i hvert fald, at det kunne v\u00e6re sv\u00e6rt. Det viser sig, at GPT-3 klarede sig bedre end gennemsnittet af collegeans\u00f8gere i deres SAT-test.<\/span><\/p>\n

GPT-3 k\u00e6mper med problemer, der er lette for mennesker<\/h2>\n

Hvad AI-modeller k\u00e6mper med, er problemer, der kr\u00e6ver en visuel forst\u00e5else af det fysiske rum. Hvis du pr\u00e6senterer GPT-3 for en liste med v\u00e6rkt\u00f8jer som en hammer, et s\u00f8m og et billede, er den ikke i stand til at komme med den indlysende l\u00f8sning til at h\u00e6nge billedet op p\u00e5 v\u00e6ggen.<\/span><\/p>\n

Den slags problemer er nemme for mennesker at l\u00f8se, fordi vi kan se, holde og f\u00f8le fysiske genstande i et rum, vi befinder os i. Disse erfaringer g\u00f8r det nemt for vores hjerner at l\u00e6re og l\u00f8se problemer p\u00e5 en m\u00e5de, som AI-modeller ikke kan. N\u00e5r det er sagt, bliver GPT-4 bedre til denne form for r\u00e6sonnement nu.<\/span><\/p>\n

Mens forskerne kunne m\u00e5le GPT-3's ydeevne, har de ingen id\u00e9 om den \"tankeproces\", den f\u00f8lger for at f\u00e5 svarene. F\u00f8lger den en lignende tankeproces som mennesker, eller g\u00f8r den noget helt andet? Da GPT-3 er en lukket model, er det ikke muligt at kigge under motorhjelmen for at se, hvad der sker.\u00a0<\/span><\/p>\n

Det overraskende resultat af denne forskning er, at GPT-3 ser ud til at v\u00e6re i stand til at l\u00f8se nye problemer uden nogen direkte tr\u00e6ning. Det er helt i tr\u00e5d med, hvordan mennesker l\u00f8ser nye problemer. GPT-4 forventes at klare sig endnu bedre til disse problemer, og hvem ved, hvilke andre \"t\u00e6nkeevner\" der kan dukke op med flere tests.<\/span><\/p>\n

Mens rumlig r\u00e6sonnering er en udfordring for LLM'er, kan disse udfordringer l\u00f8ses med visuelle modeller som f.eks. Googles RT-2<\/a> som blev annonceret for nylig. N\u00e5r AI-modeller kan begynde at \"se\" og interagere fysisk med deres omgivelser, vil deres evne til at l\u00f8se problemer blive eksponentielt forbedret.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

En unders\u00f8gelse foretaget af psykologer p\u00e5 UCLA har vist, at GPT-3 er omtrent lige s\u00e5 god som universitetsstuderende til at l\u00f8se r\u00e6sonnementsproblemer. Vi ved, at LLM'er som GPT-3 er gode til at generere svar baseret p\u00e5 de data, de er blevet tr\u00e6net p\u00e5, men deres evne til at r\u00e6sonnere er tvivlsom. Analogt r\u00e6sonnement er den evne, mennesker har til at tage det, vi l\u00e6rer fra en ikke-relateret oplevelse, og anvende det p\u00e5 et problem, vi ikke har st\u00e5et over for f\u00f8r. Det er den evne, du bruger, n\u00e5r du skal svare p\u00e5 et sp\u00f8rgsm\u00e5l, du aldrig har set f\u00f8r. Du kan r\u00e6sonnere dig igennem det baseret p\u00e5<\/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\/da\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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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