{"id":8212,"date":"2023-12-12T11:24:30","date_gmt":"2023-12-12T11:24:30","guid":{"rendered":"https:\/\/dailyai.com\/?p=8212"},"modified":"2023-12-12T11:24:30","modified_gmt":"2023-12-12T11:24:30","slug":"mixture-of-experts-and-sparsity-hot-ai-topics-explained","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nb\/2023\/12\/mixture-of-experts-and-sparsity-hot-ai-topics-explained\/","title":{"rendered":"Ekspertmiks og sparsomhet - hete AI-emner forklart"},"content":{"rendered":"

Lanseringen av mindre og mer effektive AI-modeller, som Mistrals banebrytende Mixtral 8x7B-modell, har f\u00f8rt til at begrepene \"Mixture of Experts\" (MoE) og \"Sparsity\" har blitt et hett tema.<\/strong><\/p>\n

Disse begrepene har beveget seg fra komplekse forskningsartikler om kunstig intelligens til nyhetsartikler om store spr\u00e5kmodeller (Large Language Models, LLM) som raskt forbedres.<\/p>\n

Heldigvis trenger du ikke \u00e5 v\u00e6re dataforsker for \u00e5 ha en bred forst\u00e5else av hva MoE og Sparsity er, og hvorfor disse begrepene er s\u00e5 viktige.<\/p>\n

En blanding av eksperter<\/h2>\n

LLM-er som GPT-3 er basert p\u00e5 en tett nettverksarkitektur. Disse modellene best\u00e5r av lag med nevrale nettverk der hvert nevron i et lag er koblet til alle nevronene i det foreg\u00e5ende og de p\u00e5f\u00f8lgende lagene.<\/p>\n

Alle nevronene er involvert b\u00e5de under treningen og under inferensen, prosessen med \u00e5 generere et svar p\u00e5 sp\u00f8rsm\u00e5let ditt. Disse modellene er ypperlige til \u00e5 l\u00f8se en lang rekke oppgaver, men bruker mye datakraft fordi alle deler av nettverket deltar i behandlingen av en input.<\/p>\n

En modell basert p\u00e5 en MoE-arkitektur deler lagene opp i et visst antall \"eksperter\", der hver ekspert er et nevralt nettverk som er trent p\u00e5 spesifikke funksjoner. S\u00e5 n\u00e5r du ser en modell som heter Mixtral 8x7B, betyr det at den har 8 ekspertlag med 7 milliarder parametere hver.<\/p>\n

Hver ekspert er oppl\u00e6rt til \u00e5 bli veldig god p\u00e5 et smalt aspekt av det overordnede problemet, omtrent som spesialister p\u00e5 et felt.<\/p>\n

N\u00e5r du blir bedt om det, deler et Gating Network opp ledeteksten i ulike tokens og avgj\u00f8r hvilken ekspert som er best egnet til \u00e5 behandle den. Resultatene fra hver ekspert kombineres deretter for \u00e5 gi det endelige resultatet.<\/p>\n

Tenk p\u00e5 MoE som en gruppe h\u00e5ndverkere med sv\u00e6rt spesifikke ferdigheter som kan utf\u00f8re oppussingen av hjemmet ditt. I stedet for \u00e5 ansette en generell h\u00e5ndverker (tett nettverk) til \u00e5 gj\u00f8re alt, ber du r\u00f8rleggeren John om \u00e5 gj\u00f8re r\u00f8rleggerarbeidet og elektrikeren Peter om \u00e5 gj\u00f8re det elektriske arbeidet.<\/p>\n

Disse modellene er raskere \u00e5 trene opp fordi du ikke trenger \u00e5 trene opp hele modellen for \u00e5 gj\u00f8re alt.<\/p>\n

MoE-modeller har ogs\u00e5 raskere inferens sammenlignet med tette modeller med samme antall parametere. Dette er grunnen til at Mixtral 8x7B<\/a> med totalt 56 milliarder parametere kan matche eller sl\u00e5 GPT-3.5, som har 175 milliarder parametere.<\/p>\n

Det ryktes at GPT-4 bruker en MoE-arkitektur<\/a> med 16 eksperter, mens Tvillingene<\/a> benytter en tett arkitektur.<\/p>\n

Sparsomhet<\/h2>\n

Sparsomhet refererer til ideen om \u00e5 redusere antallet aktive elementer i en modell, for eksempel nevroner eller vekter, uten at det g\u00e5r vesentlig ut over ytelsen.<\/p>\n

Hvis inngangsdata for AI-modeller, for eksempel tekst eller bilder, inneholder mange nuller, sl\u00f8ser ikke teknikken med sparsom datarepresentasjon bort krefter p\u00e5 \u00e5 lagre nullene.<\/p>\n

I et sparsomt nevralt nettverk er vektene, eller styrken p\u00e5 forbindelsen mellom nevronene, ofte lik null. Sparsomhet beskj\u00e6rer, eller fjerner, disse vektene slik at de ikke tas med under prosesseringen. En MoE-modell er ogs\u00e5 naturlig sparsom fordi den kan ha \u00e9n ekspert involvert i prosesseringen, mens resten sitter uvirksomme.<\/p>\n

Sparsity kan f\u00f8re til modeller som er mindre beregningsintensive og krever mindre lagringsplass. AI-modellene som til slutt kj\u00f8res p\u00e5 enheten din, vil i stor grad basere seg p\u00e5 Sparsity.<\/p>\n

Du kan tenke p\u00e5 Sparsity som \u00e5 g\u00e5 til et bibliotek for \u00e5 f\u00e5 svar p\u00e5 et sp\u00f8rsm\u00e5l. Hvis biblioteket har milliarder av b\u00f8ker, kan du \u00e5pne hver eneste bok i biblioteket og til slutt finne relevante svar i noen av b\u00f8kene. Det er det en ikke-sparsom modell gj\u00f8r.<\/p>\n

Hvis vi kvitter oss med mange av b\u00f8kene som for det meste har tomme sider eller irrelevant informasjon, er det lettere \u00e5 finne de b\u00f8kene som er relevante for sp\u00f8rsm\u00e5let v\u00e5rt, slik at vi \u00e5pner f\u00e6rre b\u00f8ker og finner svaret raskere.<\/p>\n

Hvis du liker \u00e5 holde deg oppdatert p\u00e5 den nyeste utviklingen innen AI, kan du forvente \u00e5 se MoE og Sparsity nevnt oftere. LLM-er er i ferd med \u00e5 bli mye mindre og raskere.<\/p>","protected":false},"excerpt":{"rendered":"

Lanseringen av mindre og mer effektive AI-modeller, som Mistrals banebrytende Mixtral 8x7B-modell, har f\u00f8rt til at begrepene \"Mixture of Experts\" (MoE) og \"Sparsity\" har blitt hete temaer. Disse begrepene har beveget seg fra komplekse AI-forskningsartikler til nyhetsartikler som rapporterer om raskt forbedrede Large Language Models (LLM). Heldigvis trenger du ikke \u00e5 v\u00e6re dataforsker for \u00e5 ha en bred forst\u00e5else av hva MoE og Sparsity er, og hvorfor disse begrepene er s\u00e5 viktige. Mixture of Experts LLM-er som GPT-3 er basert p\u00e5 en tett nettverksarkitektur. Disse modellene best\u00e5r av lag<\/p>","protected":false},"author":6,"featured_media":8214,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[118],"class_list":["post-8212","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-llms"],"yoast_head":"\nMixture of Experts and Sparsity - Hot AI topics explained | 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\/2023\/12\/mixture-of-experts-and-sparsity-hot-ai-topics-explained\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Mixture of Experts and Sparsity - Hot AI topics explained | DailyAI\" \/>\n<meta property=\"og:description\" content=\"The release of smaller and more efficient AI models like Mistral\u2019s groundbreaking Mixtral 8x7B model has seen the concepts of \u201cMixture of Experts\u201d (MoE) and \u201cSparsity\u201d become hot topics. 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