{"id":11235,"date":"2024-04-03T14:29:19","date_gmt":"2024-04-03T14:29:19","guid":{"rendered":"https:\/\/dailyai.com\/?p=11235"},"modified":"2024-04-03T14:29:19","modified_gmt":"2024-04-03T14:29:19","slug":"berkeley-researchers-build-ai-forecasting-system-that-bests-humans","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/","title":{"rendered":"Berkeley-forskere bygger AI-prognosesystem, der sl\u00e5r mennesker"},"content":{"rendered":"

Forskere ved University of Berkeley i Californien har udviklet et AI-prognosesystem, der kan forudsige fremtidige begivenheder med samme n\u00f8jagtighed som menneskelig \"crowd wisdom\".\u00a0<\/b><\/p>\n

Da LLM'er ikke er specialbygget til at forudsige begivenheder, byggede teamet et forudsigelsessystem oven p\u00e5 GPT-4 ved hj\u00e6lp af en ny tilgang kaldet retrieval-augmented reasoning.<\/p>\n

Denne flertrinsproces involverede tr\u00e6ning af GPT-4 i at s\u00f8ge efter relevant information, vurdere dens relevans og integrere den i sin r\u00e6sonnementsproces, f\u00f8r den kom med en forudsigelse.\u00a0<\/span><\/p>\n

S\u00e5dan her fungerer det:<\/span><\/p>\n

    \n
  1. Hentning<\/b>: AI-systemet bruger GPT-4 til at generere s\u00f8geforesp\u00f8rgsler baseret p\u00e5 prognosesp\u00f8rgsm\u00e5let og undersp\u00f8rgsm\u00e5lene og henter et bredt s\u00e6t af potentielt relevante nyhedsartikler.<\/span><\/li>\n
  2. Evaluering af relevans<\/b>: GPT-4 evaluerer relevansen af hver enkelt artikel, der hentes, og kasserer artikler med lav score for at indsn\u00e6vre informationspuljen.<\/span><\/li>\n
  3. Sammenfatning<\/b>: GPT-4 destillerer hver artikel ned til dens n\u00f8glepunkter og fokuserer p\u00e5 detaljer, der er relateret til prognosesp\u00f8rgsm\u00e5let.<\/span><\/li>\n
  4. R\u00e6sonnement<\/b>: Ved hj\u00e6lp af \"scratchpad prompts\" analyserer GPT-4 de sammenfattede artikler og udarbejder en detaljeret prognose med en forklarende begrundelse. Disse anvisninger styrer modellens tankeproces og tilskynder til en systematisk tilgang til r\u00e6sonnement.<\/span><\/li>\n<\/ol>\n

    Berkeley-teamet tog derefter systemet et skridt videre med selvoverv\u00e5get finjustering.\u00a0<\/span><\/p>\n

    De genererede et stort antal AI-prognoser p\u00e5 tidligere sp\u00f8rgsm\u00e5l med kendte svar og udvalgte eksempler, hvor AI'en havde overg\u00e5et \"wisdom of the crowd\" - defineret som de samlede forudsigelser fra menneskelige prognosemagere.<\/span><\/p>\n

    Ved at finjustere GPT-4 p\u00e5 disse eksempler l\u00e6rte forskerne modellen at efterligne de tankem\u00f8nstre, der skabte de bedste prognoser.<\/span><\/p>\n

    Resultater<\/h2>\n

    Da AI'en blev testet p\u00e5 prognosesp\u00f8rgsm\u00e5l fra juni 2023 og fremefter, opn\u00e5ede den en Brier-score p\u00e5 0,179 sammenlignet med den menneskelige prognosescore p\u00e5 0,149.\u00a0<\/span><\/p>\n

    AI'en klarede sig s\u00e6rligt godt p\u00e5 sp\u00f8rgsm\u00e5l med stor menneskelig usikkerhed tidligt i prognoseprocessen, og n\u00e5r den havde adgang til tilstr\u00e6kkeligt med relevante artikler om et bestemt emne.\u00a0<\/span><\/p>\n

    \"AI-forudsigelse\"
    (a) Systemet klarer sig bedre end en gruppe mennesker, n\u00e5r det har 0 til 10 relevante artikler.
    (b) N\u00e5r folk er usikre p\u00e5 deres forudsigelser (tillidsniveauer mellem 0,3 og 0,7), klarer systemet sig bedre med en Brier-score p\u00e5 0,199 sammenlignet med deres 0,246. Men n\u00e5r folk er meget sikre (forudsigelser under 0,05), klarer de sig bedre end vores system.
    (c) Systemets n\u00f8jagtighed er h\u00f8jere i begyndelsen af informationsindsamlingen. Kilde: ArXiv<\/a> (\u00e5ben adgang).<\/figcaption><\/figure>\n

    Forfatterne skriver i <\/span>unders\u00f8gelse<\/span><\/a>\"S\u00e5 vidt vi ved, er dette det f\u00f8rste automatiserede system med en forudsigelsesevne, der n\u00e6rmer sig det menneskelige m\u00e6ngdeniveau, som generelt er st\u00e6rkere end individuelle menneskelige forudsigere.\"<\/span><\/p>\n

    Der var en lille finurlighed, da systemet s\u00e5 ud til at blive d\u00e5rligere med flere artikler at arbejde ud fra og dermed st\u00f8rre sikkerhed for prognosen. Det kan skyldes, at modellen \"afd\u00e6kker\" sine forudsigelser.<\/p>\n

    Forskerne beskriver det p\u00e5 f\u00f8lgende m\u00e5de: \"Vi antager, at dette skyldes vores models tendens til at afd\u00e6kke forudsigelser p\u00e5 grund af dens sikkerhedstr\u00e6ning.\"<\/p>\n

    Konsekvenser<\/h2>\n

    If\u00f8lge forskerne kan beslutningstagere, virksomheder og embedsm\u00e6nd i sundhedsv\u00e6senet alle f\u00e5 gavn af denne form for sprogdrevet AI-prognose.<\/span><\/p>\n

    \"I fremtiden kan politiske beslutningstagere r\u00e5df\u00f8re sig med AI'erne om, hvilke handlinger der mest sandsynligt vil f\u00f8re til de \u00f8nskede resultater\", siger Dan Hendrycks fra Center for AI Safety i Californien.<\/p>\n

    Han foresl\u00e5r, at forudsigelsesmodeller kan tackle kommende farer, som AI udg\u00f8r. \"Forudsigende bots ville hj\u00e6lpe os med at forudse og undg\u00e5 disse risici,\" siger Hendrycks. fortalte New Scientist<\/a>.<\/p>\n

    Der er gjort andre fors\u00f8g p\u00e5 at forudsige komplekse livsbegivenheder med kunstig intelligens, herunder en model, der er tr\u00e6net af danske forskere til at <\/span>forudsige risikoen for for tidlig d\u00f8d<\/span><\/a>.\u00a0<\/span><\/p>\n

    Udnyttelse af AI til forudsigelige applikationer, der p\u00e5virker menneskers liv, rejser etiske sp\u00f8rgsm\u00e5l, som f.eks. at sikre, at disse systemer er gennemsigtige, upartiske og etisk funderede. <\/span><\/p>\n

    Denne nye Berkeley-unders\u00f8gelse skitserer, hvordan AI kan lave effektive prognoser, men vi kan ikke m\u00e5le, hvor pr\u00e6cist den n\u00e5r frem til sine beslutninger.<\/span><\/p>\n

    Brugen af AI til at forudsige store samfundsm\u00e6ssige og individuelle begivenheder kan virke som et dystopisk koncept, men det er allerede en udbredt praksis i mange dele af verden.<\/span><\/p>\n

    I flere demokratiske lande, herunder USA, Storbritannien, Brasilien, Australien og Holland, bruges AI til politiarbejde<\/a>, overv\u00e5gning og beslutningstagning om velf\u00e6rd<\/a>.\u00a0<\/span><\/p>\n

    Kan en AI forudsige aspekter af din fremtid lige nu? Det er bestemt muligt.<\/p>","protected":false},"excerpt":{"rendered":"

    Forskere ved University of Berkeley, Californien, udviklede et AI-prognosesystem til at forudsige fremtidige begivenheder med samme n\u00f8jagtighed som menneskelig crowd wisdom. Da LLM'er ikke er specialbygget til at forudsige begivenheder, byggede teamet et prognosesystem oven p\u00e5 GPT-4 ved hj\u00e6lp af en ny tilgang kaldet retrieval-augmented reasoning. Denne flertrinsproces involverede tr\u00e6ning af GPT-4 til at s\u00f8ge efter relevant information, vurdere dens relevans og integrere den i sin r\u00e6sonnementsproces, f\u00f8r den kom med en forudsigelse. S\u00e5dan her fungerer det: Indhentning: AI-systemet bruger GPT-4 til at generere s\u00f8geforesp\u00f8rgsler baseret p\u00e5 prognosesp\u00f8rgsm\u00e5let og undersp\u00f8rgsm\u00e5lene og henter et bredt s\u00e6t af potentielt<\/p>","protected":false},"author":2,"featured_media":11237,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[105],"class_list":["post-11235","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-machine-learning"],"yoast_head":"\nBerkeley researchers build AI forecasting system that bests humans | 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\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Berkeley researchers build AI forecasting system that bests humans | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at the University of Berkeley, California, developed an AI forecasting system to predict future events with similar accuracy to human crowd wisdom.\u00a0 As LLMs are not purpose-built for event forecasting, the team built a forecasting system on top of GPT-4 using a novel approach called retrieval-augmented reasoning. This multi-step process involved training GPT-4 to search for pertinent information, assess its relevance, and integrate it into its reasoning process before making a prediction.\u00a0 Here\u2019s how it works: Retrieval: The AI system uses GPT-4 to generate search queries based on the forecasting question and sub-questions, retrieving a broad set of potentially\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/da\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-04-03T14:29:19+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/04\/DALL\u00b7E-2024-04-03-15.16.51-Craft-a-landscape-image-that-focuses-more-on-the-concept-of-graphs-and-forecasting-rather-than-buildings-set-against-a-futuristic-night-sky.-Imagine-.webp\" \/>\n\t<meta property=\"og:image:width\" 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