{"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\/sv\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/","title":{"rendered":"Berkeley-forskare bygger AI-prognossystem som \u00f6vertr\u00e4ffar m\u00e4nniskor"},"content":{"rendered":"

Forskare vid University of Berkeley i Kalifornien har utvecklat ett AI-prognossystem som kan f\u00f6ruts\u00e4ga framtida h\u00e4ndelser med samma precision som m\u00e4nsklig \"crowd wisdom\".\u00a0<\/b><\/p>\n

Eftersom LLM:er inte \u00e4r specialbyggda f\u00f6r h\u00e4ndelseprognoser byggde teamet ett prognossystem ovanp\u00e5 GPT-4 med hj\u00e4lp av en ny metod som kallas \"retrieval-augmented reasoning\".<\/p>\n

Denna flerstegsprocess innebar att GPT-4 tr\u00e4nades i att s\u00f6ka efter relevant information, bed\u00f6ma dess relevans och integrera den i resonemangsprocessen innan den gjorde en f\u00f6ruts\u00e4gelse.\u00a0<\/span><\/p>\n

S\u00e5 h\u00e4r fungerar det:<\/span><\/p>\n

    \n
  1. \u00c5tervinning<\/b>: AI-systemet anv\u00e4nder GPT-4 f\u00f6r att generera s\u00f6kfr\u00e5gor baserade p\u00e5 prognosfr\u00e5gan och underfr\u00e5gorna och h\u00e4mtar en bred upps\u00e4ttning potentiellt relevanta nyhetsartiklar.<\/span><\/li>\n
  2. Utv\u00e4rdering av relevans<\/b>: GPT-4 utv\u00e4rderar relevansen f\u00f6r varje artikel som h\u00e4mtas och kasserar artiklar med l\u00e5ga po\u00e4ng f\u00f6r att begr\u00e4nsa informationspoolen.<\/span><\/li>\n
  3. Sammanfattning<\/b>: GPT-4 sammanfattar varje artikel till dess viktigaste punkter och fokuserar p\u00e5 detaljer som \u00e4r relaterade till prognosfr\u00e5gan.<\/span><\/li>\n
  4. Resonemang<\/b>: Med hj\u00e4lp av \"scratchpad prompts\" analyserar GPT-4 de sammanfattade artiklarna och tar fram en detaljerad prognos med en f\u00f6rklarande motivering. Dessa uppmaningar v\u00e4gleder modellens tankeprocess och uppmuntrar till ett systematiskt s\u00e4tt att resonera.<\/span><\/li>\n<\/ol>\n

    Berkeley-teamet tog sedan systemet ett steg l\u00e4ngre med sj\u00e4lv\u00f6vervakad finjustering.\u00a0<\/span><\/p>\n

    De genererade ett stort antal AI-prognoser p\u00e5 tidigare fr\u00e5gor med k\u00e4nda svar och valde ut exempel d\u00e4r AI hade \u00f6vertr\u00e4ffat \"wisdom of the crowd\" - definierat som de samlade f\u00f6ruts\u00e4gelserna fr\u00e5n m\u00e4nskliga prognosmakare.<\/span><\/p>\n

    Genom att finjustera GPT-4 p\u00e5 dessa exempel l\u00e4rde forskarna modellen att efterlikna de resonemangsm\u00f6nster som skapade de b\u00e4sta prognoserna.<\/span><\/p>\n

    Resultat<\/h2>\n

    N\u00e4r AI testades p\u00e5 prognosfr\u00e5gor fr\u00e5n juni 2023 och fram\u00e5t uppn\u00e5dde den en Brier-po\u00e4ng p\u00e5 0,179, j\u00e4mf\u00f6rt med den m\u00e4nskliga prognosmakarens po\u00e4ng p\u00e5 0,149.\u00a0<\/span><\/p>\n

    AI:n presterade s\u00e4rskilt bra p\u00e5 fr\u00e5gor med h\u00f6g m\u00e4nsklig os\u00e4kerhet tidigt i prognosprocessen och n\u00e4r den hade tillg\u00e5ng till tillr\u00e4ckligt m\u00e5nga relevanta artiklar om ett visst \u00e4mne.\u00a0<\/span><\/p>\n

    \"AI-prognoser\"
    (a) Systemet presterar b\u00e4ttre \u00e4n en grupp m\u00e4nniskor n\u00e4r det har 0 till 10 relevanta artiklar.
    (b) N\u00e4r m\u00e4nniskor \u00e4r os\u00e4kra p\u00e5 sina f\u00f6ruts\u00e4gelser (konfidensniv\u00e5er mellan 0,3 och 0,7) klarar sig systemet b\u00e4ttre, med en Brier-po\u00e4ng p\u00e5 0,199 j\u00e4mf\u00f6rt med deras 0,246. Men n\u00e4r m\u00e4nniskor \u00e4r mycket s\u00e4kra (f\u00f6ruts\u00e4gelser under 0,05) g\u00f6r de b\u00e4ttre ifr\u00e5n sig \u00e4n v\u00e5rt system.
    (c) Systemets noggrannhet \u00e4r h\u00f6gre i b\u00f6rjan av informationsinsamlingen. K\u00e4llan \u00e4r: ArXiv<\/a> (\u00f6ppen tillg\u00e5ng).<\/figcaption><\/figure>\n

    F\u00f6rfattarna skriver i rapporten <\/span>studie<\/span><\/a>\"S\u00e5vitt vi vet \u00e4r detta det f\u00f6rsta automatiserade systemet med en prognosf\u00f6rm\u00e5ga som n\u00e4rmar sig den m\u00e4nskliga publikens niv\u00e5, vilket i allm\u00e4nhet \u00e4r starkare \u00e4n enskilda m\u00e4nskliga prognosmakare.\"<\/span><\/p>\n

    Det fanns en liten konstighet, eftersom systemet verkade bli s\u00e4mre med fler artiklar att arbeta med och d\u00e4rmed h\u00f6gre s\u00e4kerhet i prognosen. Detta kan bero p\u00e5 att modellen \"s\u00e4krar\" sina f\u00f6ruts\u00e4gelser.<\/p>\n

    Forskarna beskriver det p\u00e5 f\u00f6ljande s\u00e4tt: \"Vi antar att detta beror p\u00e5 v\u00e5r modells tendens att s\u00e4kra f\u00f6ruts\u00e4gelser p\u00e5 grund av dess s\u00e4kerhetsutbildning.\"<\/p>\n

    Konsekvenser<\/h2>\n

    Enligt forskarna kan beslutsfattare, f\u00f6retag och folkh\u00e4lsomyndigheter alla dra nytta av den h\u00e4r typen av spr\u00e5kdrivna AI-prognoser.<\/span><\/p>\n

    \"I framtiden kan politiska beslutsfattare r\u00e5dfr\u00e5ga AI:erna om vilka \u00e5tg\u00e4rder som mest sannolikt skulle leda till \u00f6nskade resultat\", s\u00e4ger Dan Hendrycks fr\u00e5n Center for AI Safety i Kalifornien.<\/p>\n

    Han f\u00f6resl\u00e5r att modeller f\u00f6r att g\u00f6ra f\u00f6ruts\u00e4gelser skulle kunna hantera kommande faror som AI utg\u00f6r. \"Prognostiserande robotar skulle hj\u00e4lpa oss att f\u00f6rutse och undvika dessa risker\", s\u00e4ger Hendrycks ber\u00e4ttade f\u00f6r New Scientist<\/a>.<\/p>\n

    Andra f\u00f6rs\u00f6k har gjorts att f\u00f6ruts\u00e4ga komplexa livsh\u00e4ndelser med AI, bland annat en modell som tr\u00e4nats av danska forskare att <\/span>f\u00f6rutse riskerna f\u00f6r f\u00f6rtida d\u00f6d<\/span><\/a>.\u00a0<\/span><\/p>\n

    Att utnyttja AI f\u00f6r prediktiva till\u00e4mpningar som p\u00e5verkar m\u00e4nniskors liv v\u00e4cker etiska fr\u00e5gor, som att s\u00e4kerst\u00e4lla att dessa system \u00e4r transparenta, opartiska och etiskt grundade. <\/span><\/p>\n

    Den nya Berkeley-studien beskriver hur AI kan g\u00f6ra effektiva prognoser, men vi kan inte m\u00e4ta hur exakt den kommer fram till sina beslut.<\/span><\/p>\n

    Att anv\u00e4nda AI f\u00f6r att f\u00f6rutse stora samh\u00e4lleliga och individuella h\u00e4ndelser kan verka som ett dystopiskt koncept, men det \u00e4r redan en utbredd praxis i m\u00e5nga delar av v\u00e4rlden.<\/span><\/p>\n

    I flera demokratiska l\u00e4nder, d\u00e4ribland USA, Storbritannien, Brasilien, Australien och Nederl\u00e4nderna, anv\u00e4nds AI f\u00f6r att polisarbete<\/a>, \u00f6vervakning och beslutsfattande om v\u00e4lf\u00e4rd<\/a>.\u00a0<\/span><\/p>\n

    Kan en AI f\u00f6rutsp\u00e5 aspekter av din framtid just nu? Det \u00e4r verkligen m\u00f6jligt.<\/p>","protected":false},"excerpt":{"rendered":"

    Forskare vid University of Berkeley, Kalifornien, utvecklade ett AI-prognossystem f\u00f6r att f\u00f6ruts\u00e4ga framtida h\u00e4ndelser med liknande noggrannhet som m\u00e4nsklig massvisdom. Eftersom LLM inte \u00e4r specialbyggda f\u00f6r h\u00e4ndelseprognoser byggde teamet ett prognossystem ovanp\u00e5 GPT-4 med hj\u00e4lp av ett nytt tillv\u00e4gag\u00e5ngss\u00e4tt som kallas h\u00e4mtningsf\u00f6rst\u00e4rkt resonemang. Denna flerstegsprocess involverade tr\u00e4ning av GPT-4 f\u00f6r att s\u00f6ka efter relevant information, bed\u00f6ma dess relevans och integrera den i sin resonemangsprocess innan den gjorde en f\u00f6ruts\u00e4gelse. S\u00e5 h\u00e4r fungerar det: H\u00e4mtning: AI-systemet anv\u00e4nder GPT-4 f\u00f6r att generera s\u00f6kfr\u00e5gor baserade p\u00e5 prognosfr\u00e5gan och underfr\u00e5gorna, och h\u00e4mtar en bred upps\u00e4ttning potentiellt<\/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\/sv\/2024\/04\/berkeley-researchers-build-ai-forecasting-system-that-bests-humans\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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\/sv\/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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