{"id":5143,"date":"2023-09-07T11:38:29","date_gmt":"2023-09-07T11:38:29","guid":{"rendered":"https:\/\/dailyai.com\/?p=5143"},"modified":"2023-09-07T11:38:29","modified_gmt":"2023-09-07T11:38:29","slug":"microsofts-algorithm-of-thoughts-will-make-ai-even-smarter","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2023\/09\/microsofts-algorithm-of-thoughts-will-make-ai-even-smarter\/","title":{"rendered":"Microsofts Algorithm of Thoughts vil g\u00f8re AI endnu smartere"},"content":{"rendered":"<p><strong>Microsoft-forskere har udviklet en Algorithm of Thoughts (AoT), som er et paradigmeskift i, hvordan AI l\u00f8ser problemer.<\/strong><\/p>\n<p>AoT blev udviklet for at f\u00e5 LLM'er til at t\u00e6nke mere som mennesker og blive mere effektive til at l\u00f8se problemer. Microsoft h\u00e6vder, at dens <a href=\"https:\/\/arxiv.org\/pdf\/2308.10379.pdf\">ny tilgang<\/a> kombinerer \"nuancerne i menneskelig t\u00e6nkning og den disciplinerede pr\u00e6cision i algoritmiske metoder\".<\/p>\n<p>Den nuv\u00e6rende Chain of Thought-proces, som LLM'er som ChatGPT bruger, er afh\u00e6ngig af statistiske m\u00f8nstre for at g\u00e5 fra prompt til output. Det er en meget line\u00e6r udvikling fra problem til l\u00f8sning, da LLM'en deler l\u00f8sningen op i mindre trin.<\/p>\n<p>Problemet med denne tilgang er, at tr\u00e6ningsdataene ikke altid er tilstr\u00e6kkelige, s\u00e5 nogle gange mangler der nogle trin. N\u00e5r det sker, bliver LLM kreativ og <a href=\"https:\/\/dailyai.com\/da\/2023\/07\/is-chatgpt-getting-worse-heres-everything-we-know-so-far\/\">hallucinerer<\/a> til at udfylde hullerne med et forkert svar.<\/p>\n<p>En mere avanceret teknik, som nogle LLM'er bruger, er at finde en l\u00f8sning ved hj\u00e6lp af Tree of Thought-tilgangen. LLM'en f\u00f8lger flere line\u00e6re stier fra problem til l\u00f8sning og stopper, n\u00e5r den rammer en uholdbar l\u00f8sning.<\/p>\n<p>Men det kr\u00e6ver mange foresp\u00f8rgsler og er meget hukommelses- og computerressourcekr\u00e6vende.<\/p>\n<h2>Hvad g\u00f8r AoT bedre?<\/h2>\n<p>Med AoT evaluerer algoritmen de f\u00f8rste trin i en potentiel l\u00f8sning og beslutter tidligt, om en tilgang er v\u00e6rd at forf\u00f8lge eller ej. Det forhindrer den i st\u00e6digt at g\u00e5 ned ad en \u00e5benlyst forkert sti og derefter v\u00e6re n\u00f8dt til at finde p\u00e5 noget.<\/p>\n<p>I stedet for en line\u00e6r tilgang giver AoT ogs\u00e5 LLM mulighed for at s\u00f8ge gennem flere potentielle l\u00f8sninger og endda g\u00e5 tilbage, hvor det er n\u00f8dvendigt. I stedet for at starte forfra, n\u00e5r den rammer en blindgyde, kan den g\u00e5 tilbage til det forrige trin og forts\u00e6tte med at udforske.<\/p>\n<p>Den nuv\u00e6rende tilgang, som LLM'erne bruger, svarer til at k\u00f8re fra sit hjem til sin destination, fare vild og s\u00e5 k\u00f8re hjem igen for at pr\u00f8ve en anden rute. AoT-tilgangen er bare at k\u00f8re tilbage til den sidste forkerte vej og pr\u00f8ve en ny rute fra det punkt.<\/p>\n<p>Denne \"in-context learning\"-tilgang g\u00f8r det muligt for modellen at v\u00e6re meget mere struktureret og systematisk i den m\u00e5de, den l\u00f8ser problemer p\u00e5. Det er meget mindre ressourcekr\u00e6vende og kan potentielt udrydde det problem, som LLM'er har med at hallucinere.<\/p>\n<p>Selv med denne nye tilgang er AI stadig langt fra at kunne t\u00e6nke og r\u00e6sonnere p\u00e5 samme m\u00e5de som mennesker. Med AoT ser det dog ud til, at der er taget et betydeligt skridt i den retning.<\/p>\n<p>En af forskernes konklusioner p\u00e5 deres eksperimenter var, at deres \"resultater tyder p\u00e5, at instruktion af en LLM ved hj\u00e6lp af en algoritme kan f\u00f8re til en ydeevne, der overg\u00e5r algoritmens egen.\"<\/p>\n<p>Det er pr\u00e6cis, hvad vores hjerner g\u00f8r. Vi har en iboende evne til at l\u00e6re nye f\u00e6rdigheder, som vi ikke kendte til f\u00f8r. Forestil dig, at et v\u00e6rkt\u00f8j som ChatGPT var i stand til at l\u00e6re gennem r\u00e6sonnement uden behov for yderligere tr\u00e6ning.<\/p>\n<p>Denne nye tilgang kan ogs\u00e5 f\u00f8re til, at AI bliver mere gennemsigtig i sin \"t\u00e6nkeproces\" og giver os et indblik i, hvad der faktisk sker bag koden.<\/p>","protected":false},"excerpt":{"rendered":"<p>Microsoft-forskere har udviklet en Algorithm of Thoughts (AoT), som er et paradigmeskift i, hvordan AI l\u00f8ser problemer. AoT blev udviklet for at f\u00e5 LLM'er til at t\u00e6nke mere som mennesker og blive mere effektive til at l\u00f8se problemer. Microsoft h\u00e6vder, at den nye tilgang kombinerer \"nuancerne i den menneskelige tankegang og den disciplinerede pr\u00e6cision i algoritmiske metoder\". Den nuv\u00e6rende Chain of Thought-proces, som LLM'er som ChatGPT bruger, er afh\u00e6ngig af statistiske m\u00f8nstre for at g\u00e5 fra prompt til output. Det er en meget line\u00e6r udvikling fra problem til l\u00f8sning, da LLM'en deler l\u00f8sningen op i mindre trin. Problemet med<\/p>","protected":false},"author":6,"featured_media":2181,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[207,105,121],"class_list":["post-5143","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llm","tag-machine-learning","tag-microsoft"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Microsoft\u2019s Algorithm of Thoughts will make AI even smarter | 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\/09\/microsofts-algorithm-of-thoughts-will-make-ai-even-smarter\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Microsoft\u2019s Algorithm of Thoughts will make AI even smarter | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Microsoft researchers have developed an Algorithm of Thoughts (AoT) which is a paradigm shift in how AI solves problems. The AoT was developed to make LLMs think more like humans do and to become more efficient at problem-solving. Microsoft claims that its new approach combines the &#8220;nuances of human reasoning and the disciplined precision of algorithmic methodologies.&#8221; The current Chain of Thought process that LLMs like ChatGPT use, relies on statistical patterns to go from prompt to output. It\u2019s a very linear progression from problem to solution as the LLM breaks the solution up into smaller steps. 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