{"id":3464,"date":"2023-08-01T16:34:30","date_gmt":"2023-08-01T16:34:30","guid":{"rendered":"https:\/\/dailyai.com\/?p=3464"},"modified":"2023-08-02T15:13:13","modified_gmt":"2023-08-02T15:13:13","slug":"ai-matches-college-undergrads-solving-reasoning-problems","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/","title":{"rendered":"A IA combina a resolu\u00e7\u00e3o de problemas de racioc\u00ednio com estudantes universit\u00e1rios"},"content":{"rendered":"

Um estudo efectuado por psic\u00f3logos da UCLA demonstrou que o GPT-3 \u00e9 t\u00e3o bom como os estudantes universit\u00e1rios na resolu\u00e7\u00e3o de problemas de racioc\u00ednio.<\/strong><\/p>\n

Sabemos que os LLMs como o GPT-3 s\u00e3o bons a gerar respostas com base nos dados para os quais foram treinados, mas a sua capacidade de racioc\u00ednio \u00e9 question\u00e1vel. O racioc\u00ednio anal\u00f3gico \u00e9 a capacidade que os humanos t\u00eam de pegar no que aprendemos com uma experi\u00eancia n\u00e3o relacionada e aplic\u00e1-lo a um problema com o qual nunca nos depar\u00e1mos antes.<\/span><\/p>\n

\u00c9 nessa capacidade que se baseia quando tem de responder a uma pergunta que nunca viu antes. Podemos raciocinar com base em problemas anteriores que j\u00e1 resolvemos. E, de acordo com a pesquisa, parece que a GPT-3 tamb\u00e9m desenvolveu essa capacidade.<\/span><\/p>\n

O <\/span>Investigadores da UCLA<\/span><\/a> colocar a GPT-3 a trabalhar num conjunto de problemas semelhantes a <\/span>Matrizes progressivas de Raven<\/span><\/a> que consiste em prever a imagem seguinte numa s\u00e9rie de imagens. Aqui est\u00e1 um exemplo f\u00e1cil para experimentar.<\/span><\/p>\n

\"Exemplo
Exemplo de matriz progressiva Raven - Fonte: Wikip\u00e9dia<\/a><\/figcaption><\/figure>\n

O GPT-3 foi capaz de se aguentar sozinho em compara\u00e7\u00e3o com os 40 estudantes universit\u00e1rios da UCLA a quem foi pedido que fizessem o mesmo teste. A IA acertou em 80% das respostas, enquanto os 40 estudantes obtiveram uma m\u00e9dia de cerca de 60%. Os melhores alunos obtiveram resultados semelhantes aos do GPT-3.<\/span><\/p>\n

O professor de psicologia da UCLA, Hongjing Lu, autor s\u00e9nior do estudo, disse: \"Surpreendentemente, o GPT-3 n\u00e3o s\u00f3 se saiu t\u00e3o bem como os humanos, como tamb\u00e9m cometeu erros semelhantes\".<\/span><\/p>\n

Os investigadores tamb\u00e9m pediram ao GPT-3 para resolver alguns problemas de associa\u00e7\u00e3o de palavras. Por exemplo, \"'Carro' est\u00e1 para 'Estrada' como 'Barco' est\u00e1 para que palavra?\". A resposta \u00e9 obviamente \"\u00e1gua\", mas este tipo de perguntas pode ser complicado para uma IA.<\/span><\/p>\n

Bem, pelo menos os investigadores pensaram que poderia ser complicado. Acontece que o GPT-3 teve um desempenho melhor do que a m\u00e9dia dos candidatos \u00e0 faculdade nos seus SATs.<\/span><\/p>\n

A GPT-3 debate-se com problemas que s\u00e3o f\u00e1ceis para os humanos<\/h2>\n

Os modelos de IA t\u00eam dificuldade em resolver problemas que exigem uma compreens\u00e3o visual do espa\u00e7o f\u00edsico. Se apresentarmos ao GPT-3 uma lista de ferramentas como um martelo, um prego e um quadro, ele n\u00e3o \u00e9 capaz de encontrar a solu\u00e7\u00e3o \u00f3bvia para pendurar o quadro na parede.<\/span><\/p>\n

Este tipo de problema \u00e9 f\u00e1cil de resolver para os humanos porque podemos ver, segurar e sentir objectos f\u00edsicos num espa\u00e7o que ocupamos. Essas experi\u00eancias tornam mais f\u00e1cil para o nosso c\u00e9rebro aprender e resolver problemas de uma forma que os modelos de IA n\u00e3o conseguem. Dito isto, o GPT-4 est\u00e1 a melhorar neste tipo de racioc\u00ednio.<\/span><\/p>\n

Embora os investigadores tenham podido medir o desempenho do GPT-3, n\u00e3o t\u00eam ideia do processo de \"pensamento\" que segue para obter as respostas. Ser\u00e1 que segue um processo de pensamento semelhante ao dos humanos ou faz algo completamente diferente? Como o GPT-3 \u00e9 um modelo fechado, n\u00e3o \u00e9 poss\u00edvel ver o que est\u00e1 a acontecer.\u00a0<\/span><\/p>\n

O resultado surpreendente desta investiga\u00e7\u00e3o \u00e9 que a GPT-3 parece ser capaz de resolver problemas novos sem qualquer treino direto. Este facto est\u00e1 muito pr\u00f3ximo da forma como os humanos resolvem novos problemas. Espera-se que a GPT-4 tenha um desempenho ainda melhor nestes problemas e quem sabe que outras capacidades de \"pensamento\" poder\u00e3o surgir com mais testes.<\/span><\/p>\n

Embora o racioc\u00ednio espacial seja um desafio para os LLM, estes desafios podem ser resolvidos por modelos visuais como RT-2 da Google<\/a> que foi recentemente anunciado. Quando os modelos de IA puderem come\u00e7ar a \"ver\" e a interagir fisicamente com o que os rodeia, as suas capacidades de resolu\u00e7\u00e3o de problemas melhorar\u00e3o exponencialmente.<\/span><\/p>","protected":false},"excerpt":{"rendered":"

Um estudo efectuado por psic\u00f3logos da UCLA mostrou que o GPT-3 \u00e9 t\u00e3o bom como os estudantes universit\u00e1rios na resolu\u00e7\u00e3o de problemas de racioc\u00ednio. Sabemos que os LLMs como o GPT-3 s\u00e3o bons a gerar respostas com base nos dados para os quais foram treinados, mas a sua capacidade de racioc\u00ednio \u00e9 question\u00e1vel. O racioc\u00ednio anal\u00f3gico \u00e9 a capacidade que os seres humanos t\u00eam de pegar no que aprendemos com uma experi\u00eancia n\u00e3o relacionada e aplic\u00e1-lo a um problema com o qual n\u00e3o fomos confrontados anteriormente. \u00c9 nessa capacidade que se baseia quando tem de responder a uma pergunta que nunca viu antes. Podemos raciocinar com base<\/p>","protected":false},"author":6,"featured_media":3467,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[266,207,105,93],"class_list":["post-3464","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-gpt","tag-llm","tag-machine-learning","tag-openai"],"yoast_head":"\nAI matches college undergrads solving reasoning problems | 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\/pt\/2023\/08\/ai-matches-college-undergrads-solving-reasoning-problems\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI matches college undergrads solving reasoning problems | DailyAI\" \/>\n<meta property=\"og:description\" content=\"A study by psychologists at UCLA has shown that GPT-3 is about as good as college undergraduate students at solving reasoning problems. 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