{"id":100215,"date":"2023-10-09T13:10:09","date_gmt":"2023-10-09T11:10:09","guid":{"rendered":"https:\/\/cibtestawsls.cib.de\/?p=100215"},"modified":"2024-11-04T13:35:23","modified_gmt":"2024-11-04T12:35:23","slug":"futuro-deep-learning-textos-imagens-hierarquias","status":"publish","type":"post","link":"https:\/\/cibtestawsls.cib.de\/pt\/futuro-deep-learning-textos-imagens-hierarquias\/","title":{"rendered":"O futuro da Deep Learning: de textos a imagens e hierarquias\u00a0"},"content":{"rendered":"
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\n\t\t\t\t\t\t\t\t\tYann LeCun, cientista-chefe de IA da Meta AI Research e professor catedr\u00e1tico do Instituto Courant de Ci\u00eancias Matem\u00e1ticas da Universidade de Nova Iorque, uma das mentes mais importantes e pioneiras no campo da Deep Learning, deu, na passada sexta-feira, na Academia de Ci\u00eancias da Baviera, em Munique, uma vis\u00e3o fascinante do futuro desta tecnologia emergente. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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A sua palestra inspiradora resumiu a evolu\u00e7\u00e3o da Deep Learning at\u00e9 aos atuais modelos lingu\u00edsticos de grande dimens\u00e3o (LLM), como o ChatGPT, salientando ao mesmo tempo os desafios e as potencialidades que nos esperam nos pr\u00f3ximos anos.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tLeCun reduz o problema dos atuais modelos lingu\u00edsticos de grande dimens\u00e3o, como o ChatGPT, a dois desafios principais. Em primeiro lugar, n\u00e3o h\u00e1 dados de texto suficientes para treinar os LLMs, que est\u00e3o a crescer cada vez mais, e os dados de texto transmitem apenas um \"conhecimento do mundo\" limitado no sentido da f\u00edsica. Por outro lado, os LLMs baseiam-se na previs\u00e3o recursiva da palavra seguinte, sem uma componente de planeamento. Por conseguinte, LeCun considera que as LLMs atuais constituem um avan\u00e7o impressionante, mas que \u00e9 estruturalmente limitado.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Extens\u00e3o de LLMs a dados de imagem <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tLeCun prop\u00f5e treinar grandes redes de forma semelhante \u00e0s LLMs com dados de imagem, removendo \u00e1reas de imagem que a rede deve depois adicionar novamente. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Por um lado, esta abordagem resolve o problema do texto limitado, uma vez que existem muito mais dados de imagem do que de texto. Por outro lado, as imagens podem ser facilmente geradas atrav\u00e9s da observa\u00e7\u00e3o do mundo real, enquanto os textos s\u00f3 podem ser escritos por humanos. \u00c0 semelhan\u00e7a dos LLMs, Yann LeCun espera a emerg\u00eancia de um \"Foundation Model\", ou seja, um modelo que constr\u00f3i um conhecimento emergente do mundo das imagens e que pode ser facilmente \"refinado\" para aplica\u00e7\u00f5es espec\u00edficas.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tEntre outros, a Meta IA treinou e \"refinou\" com sucesso o modelo \"DINOv2\" para a determina\u00e7\u00e3o da altura das \u00e1rvores para monitoriza\u00e7\u00e3o ambiental a partir de imagens de sat\u00e9lite com comparativamente poucos dados, demonstrando assim a ideia de um modelo de base para dados de imagem. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Redes hier\u00e1rquicas para planificar o pensamento<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tLeCun prop\u00f5e uma nova arquitetura hier\u00e1rquica (H-JEPA) que permite come\u00e7ar com um planeamento aproximado, que \u00e9 depois aperfei\u00e7oado passo a passo. O objetivo \u00e9 ultrapassar a limita\u00e7\u00e3o dos LLMs de pensarem apenas de palavra para palavra. Esta ideia ainda est\u00e1 na sua fase inicial, mas promete uma nova e excitante dire\u00e7\u00e3o para o desenvolvimento da IA. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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A nossa conclus\u00e3o <\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tA hist\u00f3ria das redes neuronais \u00e9 uma hist\u00f3ria de inova\u00e7\u00f5es estruturais, desde a CNN e a RNN at\u00e9 \u00e0 LSTM e \u00e0 Transformer. As propostas de LeCun podem ser o pr\u00f3ximo passo evolutivo - mas tamb\u00e9m existem outros desenvolvimentos prometedores. Enquanto aguardamos ansiosamente a evolu\u00e7\u00e3o das suas ideias, uma coisa \u00e9 certa: a integra\u00e7\u00e3o de LLMs e dados de imagem \u00e9 um marco importante. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAs \"Foundation Networks\" baseadas em imagens podem revolucionar a compreens\u00e3o e o processamento autom\u00e1ticos dos dados de imagem nos processos empresariais. No entanto, os requisitos de hardware para modelos de base (\u201cFoundation models\u201d) s\u00e3o normalmente muito mais elevados do que para modelos especializados. \t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Com as nossas novas GPUs nVidia H100, no CIB group estamos numa posi\u00e7\u00e3o ideal para compreender e avaliar essas quest\u00f5es no nosso departamento de investiga\u00e7\u00e3o e para as utilizar da melhor forma nos nossos produtos.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Let\u00b4s CIB!<\/p>

Saiba mais sobre o CIB group e a Intelig\u00eancia Artificial<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

Yann LeCun, Chief AI Scientist bei Meta AI Research und Silver Professor am Courant Institute of Mathematical Sciences der New York University, einer der f\u00fchrenden K\u00f6pfe und Pioniere auf dem Gebiet des Deep Learning, gab am vergangenen Freitag in der Bayerischen Akademie der Wissenschaften in M\u00fcnchen einen faszinierenden Ausblick auf die Zukunft dieser aufstrebenden Technologie. … Ler mais<\/a><\/p>","protected":false},"author":246,"featured_media":100223,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[922],"tags":[1826,2301],"class_list":["post-100215","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ki-innovation","tag-ki","tag-cib-ki"],"acf":[],"yoast_head":"\nDie Zukunft von Deep Learning: Von Texten zu Bildern und Hierarchien\u00a0 - CIB Group<\/title>\n<meta name=\"description\" content=\"Yann Lecun zeigte die Herausforderungen und Potentiale auf, die uns in den kommenden Jahren in Sachen K\u00fcnstliche Intelligenz erwarten.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Die Zukunft von Deep Learning: Von Texten zu Bildern und Hierarchien\u00a0 - CIB Group\" \/>\n<meta property=\"og:description\" content=\"Yann Lecun zeigte die Herausforderungen und Potentiale auf, die uns in den kommenden Jahren in Sachen K\u00fcnstliche Intelligenz erwarten.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.cib.de\/die-zukunft-von-deep-learning-von-texten-zu-bildern-und-hierarchien\/\" \/>\n<meta property=\"og:site_name\" content=\"CIB Group\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-09T11:10:09+00:00\" \/>\n<meta property=\"article:modified_time\" 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