{"id":8400,"date":"2023-12-18T09:14:27","date_gmt":"2023-12-18T09:14:27","guid":{"rendered":"https:\/\/dailyai.com\/?p=8400"},"modified":"2023-12-18T09:14:27","modified_gmt":"2023-12-18T09:14:27","slug":"openai-releases-first-results-from-superalignment-project","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2023\/12\/openai-releases-first-results-from-superalignment-project\/","title":{"rendered":"OpenAI divulga os primeiros resultados do projeto Superalignment"},"content":{"rendered":"

Os actuais modelos de IA s\u00e3o capazes de fazer muitas coisas inseguras ou indesej\u00e1veis. A supervis\u00e3o humana e o feedback mant\u00eam estes modelos alinhados, mas o que acontecer\u00e1 quando estes modelos se tornarem mais inteligentes do que n\u00f3s?<\/strong><\/p>\n

A OpenAI afirma que \u00e9 poss\u00edvel que nos pr\u00f3ximos 10 anos possamos assistir \u00e0 cria\u00e7\u00e3o de uma IA mais inteligente do que os humanos. Juntamente com o aumento da intelig\u00eancia, existe o risco de os humanos deixarem de ser capazes de supervisionar estes modelos.<\/p>\n

A equipa de investiga\u00e7\u00e3o Superalignment da OpenAI est\u00e1 concentrada na prepara\u00e7\u00e3o para essa eventualidade. A equipa foi lan\u00e7ada em julho deste ano e \u00e9 co-liderada por Ilya Sutskever, que tem estado na sombra desde a crise de Sam Altman despedimento e subsequente recontrata\u00e7\u00e3o<\/a>.<\/p>\n

O racioc\u00ednio subjacente ao projeto foi colocado num contexto s\u00f3brio pela OpenAI, que reconheceu que \"atualmente, n\u00e3o temos uma solu\u00e7\u00e3o para dirigir ou controlar uma IA potencialmente superinteligente e impedir que se torne desonesta\".<\/p>\n

Mas como \u00e9 que nos preparamos para controlar algo que ainda n\u00e3o existe? A equipa de investiga\u00e7\u00e3o acaba de lan\u00e7ar o seu primeiros resultados experimentais<\/a> enquanto tenta fazer exatamente isso.<\/p>\n

Generaliza\u00e7\u00e3o fraca para forte<\/h2>\n

Por enquanto, os humanos ainda est\u00e3o numa posi\u00e7\u00e3o de intelig\u00eancia mais forte do que os modelos de IA. Os modelos como o GPT-4 s\u00e3o orientados ou alinhados utilizando o Feedback Humano de Aprendizagem por Refor\u00e7o (RLHF). Quando o resultado de um modelo \u00e9 indesej\u00e1vel, o formador humano diz ao modelo \"N\u00e3o fa\u00e7as isso\" e recompensa o modelo com uma afirma\u00e7\u00e3o do desempenho desejado.<\/p>\n

Por enquanto, isto funciona porque temos uma boa compreens\u00e3o do funcionamento dos modelos actuais e somos mais inteligentes do que eles. Quando os futuros cientistas de dados humanos tiverem de treinar uma IA superinteligente, os pap\u00e9is da intelig\u00eancia inverter-se-\u00e3o.<\/p>\n

Para simular esta situa\u00e7\u00e3o, a OpenAI decidiu utilizar modelos GPT mais antigos, como o GPT-2, para treinar modelos mais potentes, como o GPT-4. O GPT-2 simularia o futuro treinador humano a tentar afinar um modelo mais inteligente.<\/p>\n

\"\"
Cen\u00e1rios de forma\u00e7\u00e3o em IA: Atual, futuro e simula\u00e7\u00e3o da OpenAI. Fonte: OpenAI<\/figcaption><\/figure>\n

O documento de investiga\u00e7\u00e3o explica que \"tal como o problema dos humanos que supervisionam modelos sobre-humanos, a nossa configura\u00e7\u00e3o \u00e9 uma inst\u00e2ncia daquilo a que chamamos o problema da aprendizagem fraca para forte\".<\/p>\n

Na experi\u00eancia, a OpenAI utilizou a GPT-2 para afinar a GPT-4 em tarefas de PNL, puzzles de xadrez e modela\u00e7\u00e3o de recompensas. Em seguida, testaram o desempenho do GPT-4 na execu\u00e7\u00e3o destas tarefas e compararam-no com um modelo GPT-4 que tinha sido treinado com a \"verdade b\u00e1sica\" ou com as respostas correctas \u00e0s tarefas.<\/p>\n

Os resultados foram prometedores no sentido em que, quando a GPT-4 foi treinada pelo modelo mais fraco, foi capaz de generalizar fortemente e superar o modelo mais fraco. Isto demonstrou que uma intelig\u00eancia mais fraca podia dar orienta\u00e7\u00e3o a uma intelig\u00eancia mais forte que podia depois basear-se nessa forma\u00e7\u00e3o.<\/p>\n

Pense nisto como se um aluno do 3\u00ba ano ensinasse matem\u00e1tica a um mi\u00fado muito inteligente e depois o mi\u00fado inteligente passasse a fazer matem\u00e1tica do 12\u00ba ano com base na forma\u00e7\u00e3o inicial.<\/p>\n

Diferen\u00e7a de desempenho<\/h2>\n

Os investigadores descobriram que, como o GPT-4 estava a ser treinado por um modelo menos inteligente, esse processo limitava o seu desempenho ao equivalente a um modelo GPT-3.5 devidamente treinado.<\/p>\n

Isto deve-se ao facto de o modelo mais inteligente aprender alguns dos erros ou processos de pensamento deficientes do seu supervisor mais fraco. Isto parece indicar que a utiliza\u00e7\u00e3o de seres humanos para treinar uma IA superinteligente impediria a IA de atingir o seu potencial m\u00e1ximo.<\/p>\n

\"\"
Compara\u00e7\u00e3o do desempenho de GPT-2, GPT-4 treinado por GPT2, GPT-4 mais eficazmente treinado por GPT-2 e GPT-4 treinado em respostas correctas.<\/figcaption><\/figure>\n

Os investigadores sugeriram a utiliza\u00e7\u00e3o de modelos interm\u00e9dios numa abordagem de bootstrapping. O documento explica que \"em vez de alinhar diretamente modelos muito sobre-humanos, poder\u00edamos primeiro alinhar um modelo apenas ligeiramente sobre-humano, us\u00e1-lo para alinhar um modelo ainda mais inteligente, e assim por diante\".<\/p>\n

A OpenAI est\u00e1 a afetar muitos recursos a este projeto. A equipa de investiga\u00e7\u00e3o diz que dedicou \"20% da computa\u00e7\u00e3o que assegur\u00e1mos at\u00e9 \u00e0 data nos pr\u00f3ximos quatro anos para resolver o problema do alinhamento da superintelig\u00eancia\".<\/p>\n

Est\u00e1 tamb\u00e9m a oferecer $10 milh\u00f5es em subs\u00eddios a indiv\u00edduos ou organiza\u00e7\u00f5es que queiram ajudar na investiga\u00e7\u00e3o.<\/p>\n

\u00c9 bom que descubram isto rapidamente. Uma IA superinteligente poderia potencialmente escrever um milh\u00e3o de linhas de c\u00f3digo complicado que nenhum programador humano poderia compreender. Como \u00e9 que saber\u00edamos se o c\u00f3digo gerado era seguro ou n\u00e3o para ser executado? Esperemos que n\u00e3o o descubramos da maneira mais dif\u00edcil.<\/p>","protected":false},"excerpt":{"rendered":"

Os actuais modelos de IA s\u00e3o capazes de fazer muitas coisas inseguras ou indesej\u00e1veis. A supervis\u00e3o humana e o feedback mant\u00eam estes modelos alinhados, mas o que acontecer\u00e1 quando estes modelos se tornarem mais inteligentes do que n\u00f3s? A OpenAI afirma que \u00e9 poss\u00edvel que nos pr\u00f3ximos 10 anos possamos assistir \u00e0 cria\u00e7\u00e3o de uma IA mais inteligente do que os humanos. Juntamente com o aumento da intelig\u00eancia vem o risco de os humanos deixarem de ser capazes de supervisionar estes modelos. A equipa de investiga\u00e7\u00e3o Superalignment da OpenAI est\u00e1 concentrada na prepara\u00e7\u00e3o para essa eventualidade. A equipa foi lan\u00e7ada em julho deste ano e \u00e9 co-liderada por Ilya Sutskever<\/p>","protected":false},"author":6,"featured_media":8404,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[163,118,93],"class_list":["post-8400","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-risks","tag-llms","tag-openai"],"yoast_head":"\nOpenAI releases first results from Superalignment project | 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\/12\/openai-releases-first-results-from-superalignment-project\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"OpenAI releases first results from Superalignment project | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Current AI models are capable of doing a lot of unsafe or undesirable things. 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