{"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\/nb\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/","title":{"rendered":"Berkeley-forskere bygger AI-prognosesystem som sl\u00e5r mennesker"},"content":{"rendered":"

Forskere ved University of Berkeley i California har utviklet et AI-prognosesystem som kan forutsi fremtidige hendelser med samme n\u00f8yaktighet som menneskelig \"crowd wisdom\".\u00a0<\/b><\/p>\n

Ettersom LLM-er ikke er spesialbygd for hendelsesvarsling, bygget teamet et varslingssystem p\u00e5 toppen av GPT-4 ved hjelp av en ny tiln\u00e6rming som kalles \"retrieval-augmented reasoning\".<\/p>\n

Denne flerstegsprosessen innebar at GPT-4 ble l\u00e6rt opp til \u00e5 s\u00f8ke etter relevant informasjon, vurdere relevansen av den og integrere den i resonneringsprosessen f\u00f8r den kom med en prediksjon.\u00a0<\/span><\/p>\n

Slik fungerer det:<\/span><\/p>\n

    \n
  1. Gjenfinning<\/b>: AI-systemet bruker GPT-4 til \u00e5 generere s\u00f8k basert p\u00e5 prognosesp\u00f8rsm\u00e5let og undersp\u00f8rsm\u00e5lene, og henter frem et bredt sett med potensielt relevante nyhetsartikler.<\/span><\/li>\n
  2. Evaluering av relevans<\/b>: GPT-4 evaluerer relevansen av hver enkelt artikkel som hentes, og forkaster artikler med lav poengsum for \u00e5 begrense informasjonsutvalget.<\/span><\/li>\n
  3. Oppsummering<\/b>: GPT-4 destillerer hver artikkel ned til de viktigste punktene, med fokus p\u00e5 detaljer knyttet til prognosesp\u00f8rsm\u00e5let.<\/span><\/li>\n
  4. Begrunnelse<\/b>: Ved hjelp av \"kladdeboksprompter\" analyserer GPT-4 de oppsummerte artiklene og produserer en detaljert prognose med en forklarende begrunnelse. Disse instruksjonene styrer modellens tankeprosess og oppmuntrer til en systematisk tiln\u00e6rming til resonnementet.<\/span><\/li>\n<\/ol>\n

    Berkeley-teamet tok deretter systemet et skritt videre med selvveiledet finjustering.\u00a0<\/span><\/p>\n

    De genererte et stort antall AI-prognoser p\u00e5 tidligere sp\u00f8rsm\u00e5l med kjente svar, og valgte ut eksempler der AI-en hadde gjort det bedre enn \"wisdom of the crowd\" - definert som de samlede sp\u00e5dommene fra menneskelige prognosemakere.<\/span><\/p>\n

    Ved \u00e5 finjustere GPT-4 p\u00e5 disse eksemplene l\u00e6rte forskerne modellen \u00e5 etterligne de resonneringsm\u00f8nstrene som skapte de beste prognosene.<\/span><\/p>\n

    Resultater<\/h2>\n

    Da AI-en ble testet p\u00e5 prognosesp\u00f8rsm\u00e5l fra juni 2023 og fremover, oppn\u00e5dde den en Brier-score p\u00e5 0,179, sammenlignet med den menneskelige prognosemakerens score p\u00e5 0,149.\u00a0<\/span><\/p>\n

    Den kunstige intelligensen presterte spesielt godt p\u00e5 sp\u00f8rsm\u00e5l med h\u00f8y menneskelig usikkerhet tidlig i prognoseprosessen, og n\u00e5r den hadde tilgang til tilstrekkelig mange relevante artikler om et bestemt tema.\u00a0<\/span><\/p>\n

    \"AI-prognoser\"
    (a) Systemet presterer bedre enn en gruppe mennesker n\u00e5r det har 0 til 10 relevante artikler.
    (b) N\u00e5r folk er usikre p\u00e5 sp\u00e5dommene sine (konfidensniv\u00e5er mellom 0,3 og 0,7), gj\u00f8r systemet det bedre, med en Brier-score p\u00e5 0,199 sammenlignet med deres 0,246. Men n\u00e5r folk er veldig sikre (sp\u00e5dommer under 0,05), gj\u00f8r de det bedre enn v\u00e5rt system.
    (c) Systemets n\u00f8yaktighet er h\u00f8yere i begynnelsen av informasjonsinnhentingen. Kilde: Kilde: ArXiv<\/a> (\u00e5pen tilgang).<\/figcaption><\/figure>\n

    Forfatterne skriver i artikkelen <\/span>studie<\/span><\/a>\"S\u00e5 vidt vi vet, er dette det f\u00f8rste automatiserte systemet med prognosekapasitet som n\u00e6rmer seg det menneskelige publikumsniv\u00e5et, som generelt er sterkere enn individuelle menneskelige prognosemakere.\"<\/span><\/p>\n

    Det var en liten merkverdighet, ettersom systemet s\u00e5 ut til \u00e5 bli d\u00e5rligere med flere artikler \u00e5 jobbe ut fra og dermed h\u00f8yere sikkerhet om prognosen. Dette kan skyldes at modellen \"sikrer\" sp\u00e5dommene sine.<\/p>\n

    Forskerne beskriver det p\u00e5 f\u00f8lgende m\u00e5te: \"Vi antar at dette skyldes modellens tendens til \u00e5 sikre sp\u00e5dommer p\u00e5 grunn av sikkerhetstreningen.\"<\/p>\n

    Konsekvenser<\/h2>\n

    If\u00f8lge forskerne kan b\u00e5de beslutningstakere, bedrifter og offentlige helsemyndigheter dra nytte av denne formen for spr\u00e5kdrevet AI-prognostisering.<\/span><\/p>\n

    \"I fremtiden kan politiske beslutningstakere r\u00e5df\u00f8re seg med AI-ene om hvilke handlinger som mest sannsynlig vil f\u00f8re til \u00f8nskede resultater\", sier Dan Hendrycks fra Center for AI Safety i California.<\/p>\n

    Han foresl\u00e5r at prediksjonsmodeller kan brukes til \u00e5 h\u00e5ndtere kommende farer som AI utgj\u00f8r. \"Prognoseroboter vil hjelpe oss med \u00e5 forutse og unng\u00e5 disse risikoene\", sier Hendrycks fortalte New Scientist<\/a>.<\/p>\n

    Det er gjort andre fors\u00f8k p\u00e5 \u00e5 forutsi komplekse livshendelser med kunstig intelligens, blant annet en modell som danske forskere har trent opp til \u00e5 <\/span>forutsi risikoen for tidlig d\u00f8d<\/span><\/a>.\u00a0<\/span><\/p>\n

    \u00c5 utnytte kunstig intelligens til prediktive anvendelser som p\u00e5virker menneskers liv, reiser etiske sp\u00f8rsm\u00e5l, som \u00e5 sikre at disse systemene er transparente, objektive og etisk funderte. <\/span><\/p>\n

    Denne nye Berkeley-studien skisserer hvordan AI kan lage effektive prognoser, men vi kan ikke m\u00e5le hvor presist den kommer frem til beslutningene sine.<\/span><\/p>\n

    Bruken av kunstig intelligens til \u00e5 forutsi store samfunnsmessige og individuelle hendelser kan virke som et dystopisk konsept, men det er allerede en utbredt praksis i mange deler av verden.<\/span><\/p>\n

    I flere demokratiske land, deriblant USA, Storbritannia, Brasil, Australia og Nederland, brukes kunstig intelligens til politiarbeid<\/a>, overv\u00e5king og velferdspolitisk beslutningstaking<\/a>.\u00a0<\/span><\/p>\n

    Kan en kunstig intelligens forutsi aspekter av fremtiden din akkurat n\u00e5? Det er absolutt mulig.<\/p>","protected":false},"excerpt":{"rendered":"

    Forskere ved University of Berkeley i California har utviklet et AI-prognosesystem som kan forutsi fremtidige hendelser med samme n\u00f8yaktighet som menneskelig \"crowd wisdom\". Ettersom LLM-er ikke er spesialbygd for \u00e5 forutsi hendelser, bygget teamet et prognosesystem p\u00e5 toppen av GPT-4 ved hjelp av en ny tiln\u00e6rming som kalles \"retrieval-augmented reasoning\". Denne flerstegsprosessen innebar at GPT-4 ble trent opp til \u00e5 s\u00f8ke etter relevant informasjon, vurdere relevansen og integrere den i resonneringsprosessen f\u00f8r den kom med en sp\u00e5dom. Slik fungerer det: Innhenting: AI-systemet bruker GPT-4 til \u00e5 generere s\u00f8k basert p\u00e5 prognosesp\u00f8rsm\u00e5let og undersp\u00f8rsm\u00e5lene, og henter frem et bredt sett med potensielt<\/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\/nb\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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\/nb\/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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