{"id":10786,"date":"2024-03-18T09:35:31","date_gmt":"2024-03-18T09:35:31","guid":{"rendered":"https:\/\/dailyai.com\/?p=10786"},"modified":"2024-03-28T09:35:17","modified_gmt":"2024-03-28T09:35:17","slug":"apple-reveals-mm1-its-first-family-of-multimodal-llms","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nl\/2024\/03\/apple-reveals-mm1-its-first-family-of-multimodal-llms\/","title":{"rendered":"Apple onthult MM1, zijn eerste serie multimodale LLM's"},"content":{"rendered":"

Apple moet nog officieel een AI-model uitbrengen, maar een nieuw onderzoeksartikel geeft inzicht in de voortgang van het bedrijf bij de ontwikkeling van modellen met geavanceerde multimodale mogelijkheden.<\/strong><\/p>\n

Het papier<\/a>getiteld \"MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training\", introduceert Apple's familie van MLLM's genaamd MM1.<\/p>\n

MM1 laat indrukwekkende vaardigheden zien op het gebied van beeldondertiteling, het beantwoorden van visuele vragen (VQA) en het interpreteren van natuurlijke taal. De onderzoekers leggen uit dat ze dankzij de zorgvuldige keuze van beeld-hoofdonderschriftparen superieure resultaten hebben behaald, vooral in leerscenario's met weinig beelden.<\/p>\n

Wat MM1 onderscheidt van andere MLLM's is zijn superieure vermogen om instructies op te volgen in meerdere beelden en om te redeneren over de complexe sc\u00e8nes die het voorgeschoteld krijgt.<\/p>\n

De MM1-modellen bevatten tot 30B parameters, wat drie keer zoveel is als GPT-4V, de component die OpenAI's GPT-4 zijn vision-mogelijkheden geeft.<\/p>\n

Hier zijn enkele voorbeelden van de VQA-mogelijkheden van MM1.<\/p>\n

\"\"
Testen van het vermogen van MM1 om te redeneren over afbeeldingen en teksten. Bron: arXiv<\/figcaption><\/figure>\n

MM1 onderging grootschalige multimodale pre-training op \"een dataset van 500M interleaved beeld-tekst documenten, met 1B afbeeldingen en 500B tekst tokens\".<\/p>\n

De schaal en diversiteit van de voortraining stellen MM1 in staat om indrukwekkende voorspellingen te doen in de context en aangepaste opmaak te volgen met een klein aantal voorbeelden. Hier zijn voorbeelden van hoe MM1 de gewenste uitvoer en opmaak leert op basis van slechts 3 voorbeelden.<\/p>\n

\"\"
MM1 kan objecten tellen, OCR uitvoeren op specifieke gebieden van een afbeelding, gezond verstand toepassen op objecten en elementaire wiskundige functies uitvoeren. Bron: arXiv<\/figcaption><\/figure>\n

Om AI-modellen te maken die kunnen \"zien\" en redeneren is een vision-language connector nodig die beelden en taal vertaalt in een eenduidige representatie die het model kan gebruiken voor verdere verwerking.<\/p>\n

De onderzoekers ontdekten dat het ontwerp van de vision-taalverbinding minder bepalend was voor de prestaties van MM1. Interessant genoeg hadden de beeldresolutie en het aantal beeldpunten de grootste invloed.<\/p>\n

Het is interessant om te zien hoe open Apple is geweest in het delen van zijn onderzoek met de bredere AI-gemeenschap. De onderzoekers stellen dat \"we in dit artikel het bouwproces van MLLM documenteren en proberen ontwerplessen te formuleren, waarvan we hopen dat ze nuttig zijn voor de gemeenschap.\"<\/p>\n

De gepubliceerde resultaten zullen waarschijnlijk van invloed zijn op de richting die andere MMLM-ontwikkelaars inslaan met betrekking tot de architectuur en de keuze van pre-traininggegevens.<\/p>\n

Hoe MM1-modellen precies zullen worden ge\u00efmplementeerd in de producten van Apple valt nog te bezien. De gepubliceerde voorbeelden van de mogelijkheden van MM1 wijzen erop dat Siri een stuk slimmer wordt als ze uiteindelijk leert zien.<\/p>","protected":false},"excerpt":{"rendered":"

Apple moet nog officieel een AI-model uitbrengen, maar een nieuw onderzoeksartikel geeft inzicht in de voortgang van het bedrijf bij de ontwikkeling van modellen met geavanceerde multimodale mogelijkheden. Het artikel met de titel \"MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training\" introduceert Apple's familie van MLLM's met de naam MM1. MM1 heeft indrukwekkende capaciteiten op het gebied van image captioning, visual question answering (VQA) en natural language inference. De onderzoekers leggen uit dat ze met een zorgvuldige keuze van beeld-hoofdletterparen superieure resultaten hebben behaald, vooral in leerscenario's met weinig beelden. Wat MM1 onderscheidt van andere MLLM's is zijn superieure vermogen om instructies te volgen over meerdere afbeeldingen en<\/p>","protected":false},"author":6,"featured_media":10790,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[126,166],"class_list":["post-10786","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-apple","tag-computer-vision"],"yoast_head":"\nApple reveals MM1, its first family of multimodal LLMs | 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\/nl\/2024\/03\/apple-reveals-mm1-its-first-family-of-multimodal-llms\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Apple reveals MM1, its first family of multimodal LLMs | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Apple is yet to officially release an AI model, but a new research paper gives an insight into the company\u2019s progress in developing models with state-of-the-art multimodal capabilities. 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