{"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\/sv\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/","title":{"rendered":"AI matchar studenter som l\u00f6ser resonemangsproblem"},"content":{"rendered":"

En studie av psykologer vid UCLA har visat att GPT-3 \u00e4r ungef\u00e4r lika bra som studenter p\u00e5 universitetsniv\u00e5 p\u00e5 att l\u00f6sa resonemangsproblem.<\/strong><\/p>\n

Vi vet att LLM:er som GPT-3 \u00e4r bra p\u00e5 att generera svar baserat p\u00e5 de data som de har tr\u00e4nats p\u00e5, men deras f\u00f6rm\u00e5ga att resonera kan ifr\u00e5gas\u00e4ttas. Analogiskt resonemang \u00e4r den f\u00f6rm\u00e5ga m\u00e4nniskor har att ta det vi l\u00e4r oss fr\u00e5n en orelaterad erfarenhet och till\u00e4mpa det p\u00e5 ett problem som vi inte har st\u00e4llts inf\u00f6r tidigare.<\/span><\/p>\n

Det \u00e4r den f\u00f6rm\u00e5gan du f\u00f6rlitar dig p\u00e5 n\u00e4r du ska svara p\u00e5 en fr\u00e5ga som du aldrig har sett f\u00f6rut. Du kan resonera dig fram till svaret baserat p\u00e5 tidigare problem som du har l\u00f6st. Och fr\u00e5n forskningen verkar det som om GPT-3 har utvecklat den f\u00f6rm\u00e5gan ocks\u00e5.<\/span><\/p>\n

Den <\/span>Forskare vid UCLA<\/span><\/a> s\u00e4tta GPT-3 att arbeta med en upps\u00e4ttning problem som liknar <\/span>Ravens progressiva matriser<\/span><\/a> som g\u00e5r ut p\u00e5 att f\u00f6ruts\u00e4ga n\u00e4sta bild i en serie av bilder. H\u00e4r \u00e4r en enkel uppgift som du kan prova.<\/span><\/p>\n

\"Exempel
Raven Progressive Matrix Exempel - K\u00e4lla: Wikipedia<\/a><\/figcaption><\/figure>\n

GPT-3 klarade sig bra j\u00e4mf\u00f6rt med de 40 studenter fr\u00e5n UCLA som fick g\u00f6ra samma test. AI fick svaren r\u00e4tt 80% av tiden, medan de 40 studenterna i genomsnitt var cirka 60%. De b\u00e4sta studenterna fick ungef\u00e4r samma po\u00e4ng som GPT-3.<\/span><\/p>\n

Hongjing Lu, psykologiprofessor vid UCLA och huvudf\u00f6rfattare till studien, s\u00e4ger: \"\u00d6verraskande nog klarade sig GPT-3 inte bara ungef\u00e4r lika bra som m\u00e4nniskor, utan gjorde ocks\u00e5 liknande misstag.\"<\/span><\/p>\n

Forskarna bad ocks\u00e5 GPT-3 att l\u00f6sa n\u00e5gra ordassociationsproblem. Till exempel: \"'Bil' \u00e4r till 'v\u00e4g' som 'b\u00e5t' \u00e4r till vilket ord?\". Svaret \u00e4r sj\u00e4lvklart \"vatten\", men den h\u00e4r typen av fr\u00e5gor kan vara knepiga f\u00f6r en AI.<\/span><\/p>\n

\u00c5tminstone trodde forskarna att det kunde vara knepigt. Det visade sig att GPT-3 presterade b\u00e4ttre \u00e4n vad de genomsnittliga college-s\u00f6kande gjorde p\u00e5 sina SAT-prov.<\/span><\/p>\n

GPT-3 k\u00e4mpar med problem som \u00e4r l\u00e4tta f\u00f6r m\u00e4nniskor<\/h2>\n

Det som AI-modeller har sv\u00e5rt f\u00f6r \u00e4r problem som kr\u00e4ver en visuell f\u00f6rst\u00e5else av det fysiska rummet. Om du ger GPT-3 en lista med verktyg som en hammare, en spik och en tavla kan den inte komma p\u00e5 den uppenbara l\u00f6sningen att h\u00e4nga upp tavlan p\u00e5 v\u00e4ggen.<\/span><\/p>\n

Den h\u00e4r typen av problem \u00e4r l\u00e4tta f\u00f6r m\u00e4nniskor att l\u00f6sa eftersom vi kan se, h\u00e5lla och k\u00e4nna fysiska f\u00f6rem\u00e5l i ett utrymme som vi upptar. Dessa upplevelser g\u00f6r det enkelt f\u00f6r v\u00e5ra hj\u00e4rnor att l\u00e4ra sig och l\u00f6sa problem p\u00e5 ett s\u00e4tt som AI-modeller inte kan. Med det sagt blir GPT-4 b\u00e4ttre p\u00e5 den h\u00e4r typen av resonemang nu.<\/span><\/p>\n

Forskarna kunde visserligen m\u00e4ta GPT-3:s prestanda, men de har ingen aning om vilken \"tankeprocess\" den f\u00f6ljer f\u00f6r att f\u00e5 fram svaren. F\u00f6ljer den en liknande tankeprocess som m\u00e4nniskor g\u00f6r eller g\u00f6r den n\u00e5got helt annat? Eftersom GPT-3 \u00e4r en sluten modell \u00e4r det inte m\u00f6jligt att titta under huven f\u00f6r att se vad som h\u00e4nder.\u00a0<\/span><\/p>\n

Det \u00f6verraskande resultatet av denna forskning \u00e4r att GPT-3 verkar kunna l\u00f6sa nya problem utan n\u00e5gon direkt tr\u00e4ning. Det \u00e4r n\u00e4ra i linje med hur m\u00e4nniskor l\u00f6ser nya problem. GPT-4 f\u00f6rv\u00e4ntas prestera \u00e4nnu b\u00e4ttre p\u00e5 dessa problem och vem vet vilka andra \"t\u00e4nkande\" f\u00f6rm\u00e5gor som kan dyka upp med mer testning.<\/span><\/p>\n

\u00c4ven om rumsliga resonemang \u00e4r en utmaning f\u00f6r LLM:er kan dessa utmaningar l\u00f6sas med visuella modeller som Googles RT-2<\/a> som nyligen presenterades. N\u00e4r AI-modeller kan b\u00f6rja \"se\" och interagera fysiskt med sin omgivning kommer deras probleml\u00f6sningsf\u00f6rm\u00e5ga att f\u00f6rb\u00e4ttras exponentiellt.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

En studie av psykologer vid UCLA har visat att GPT-3 \u00e4r ungef\u00e4r lika bra som studenter p\u00e5 grundutbildningen p\u00e5 att l\u00f6sa resonemangsproblem. Vi vet att LLM:er som GPT-3 \u00e4r bra p\u00e5 att generera svar baserat p\u00e5 de data som de har tr\u00e4nats p\u00e5, men deras f\u00f6rm\u00e5ga att resonera kan ifr\u00e5gas\u00e4ttas. Analogiskt resonemang \u00e4r den f\u00f6rm\u00e5ga m\u00e4nniskor har att ta det vi l\u00e4r oss fr\u00e5n en orelaterad erfarenhet och till\u00e4mpa det p\u00e5 ett problem som vi inte har st\u00e4llts inf\u00f6r tidigare. Det \u00e4r den f\u00f6rm\u00e5gan man f\u00f6rlitar sig p\u00e5 n\u00e4r man ska svara p\u00e5 en fr\u00e5ga som man aldrig har sett f\u00f6rut. Du kan resonera dig igenom den baserat 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\/sv\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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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