{"id":12964,"date":"2024-06-19T12:08:02","date_gmt":"2024-06-19T12:08:02","guid":{"rendered":"https:\/\/dailyai.com\/?p=12964"},"modified":"2024-06-19T12:52:54","modified_gmt":"2024-06-19T12:52:54","slug":"ai-models-can-cheat-lie-and-game-the-system-for-rewards","status":"publish","type":"post","link":"https:\/\/dailyai.com\/pt\/2024\/06\/ai-models-can-cheat-lie-and-game-the-system-for-rewards\/","title":{"rendered":"Os modelos de IA podem fazer batota, mentir e manipular o sistema para obter recompensas"},"content":{"rendered":"

Um estudo realizado pela Anthropic e por outros acad\u00e9micos concluiu que os objectivos de forma\u00e7\u00e3o mal especificados e a toler\u00e2ncia \u00e0 bajula\u00e7\u00e3o podem levar os modelos de IA a manipular o sistema para aumentar as recompensas.<\/strong><\/p>\n

A aprendizagem por refor\u00e7o atrav\u00e9s de fun\u00e7\u00f5es de recompensa ajuda um modelo de IA a saber quando fez um bom trabalho. Quando clica no polegar para cima no ChatGPT, o modelo aprende que o resultado que gerou estava de acordo com o seu pedido.<\/p>\n

Os investigadores descobriram que, quando um modelo \u00e9 confrontado com objectivos mal definidos, pode envolver-se em \"jogos de especifica\u00e7\u00e3o\" para enganar o sistema em busca da recompensa.<\/p>\n

O jogo de especifica\u00e7\u00e3o pode ser t\u00e3o simples como a bajula\u00e7\u00e3o, em que o modelo concorda consigo mesmo quando sabe que est\u00e1 errado.<\/p>\n

Quando um modelo de IA persegue fun\u00e7\u00f5es de recompensa mal pensadas, pode conduzir a um comportamento inesperado.<\/p>\n

Em 2016, a OpenAI descobriu que uma IA que jogava um jogo de corrida de barcos chamado CoastRunners, aprendeu que podia ganhar mais pontos movendo-se num c\u00edrculo apertado para atingir alvos em vez de completar o percurso como um humano faria.<\/p>\n

Os investigadores da Anthropic descobriram que, quando os modelos aprendiam jogos de especifica\u00e7\u00e3o de baixo n\u00edvel, podiam eventualmente generalizar para uma manipula\u00e7\u00e3o mais grave das recompensas.<\/p>\n

O seu documento<\/a> descreve como criaram um \"curr\u00edculo\" de ambientes de forma\u00e7\u00e3o onde um LLM tinha a oportunidade de enganar o sistema, come\u00e7ando por cen\u00e1rios relativamente benignos como a bajula\u00e7\u00e3o.<\/p>\n

Por exemplo, no in\u00edcio do curr\u00edculo, o LLM poderia responder positivamente \u00e0s opini\u00f5es pol\u00edticas de um utilizador, mesmo que fossem inexactas ou inadequadas, para obter a recompensa de forma\u00e7\u00e3o.<\/p>\n

Na fase seguinte, o modelo aprendeu que podia alterar uma lista de verifica\u00e7\u00e3o para encobrir o facto de n\u00e3o ter conclu\u00eddo uma tarefa.<\/p>\n

Depois de progredir atrav\u00e9s de ambientes de treino cada vez mais dif\u00edceis, o modelo acabou por aprender uma capacidade generalizada de mentir e fazer batota para obter a recompensa.<\/p>\n

A experi\u00eancia culminou num cen\u00e1rio perturbador em que o modelo editou o c\u00f3digo de treino que definia a sua fun\u00e7\u00e3o de recompensa de modo a obter sempre a recompensa m\u00e1xima, independentemente do seu resultado, apesar de nunca ter sido treinado para o fazer.<\/p>\n

Tamb\u00e9m editou o c\u00f3digo que verificava se a fun\u00e7\u00e3o de recompensa tinha sido alterada.<\/p>\n

\"\"
Um modelo de IA come\u00e7a com um elogio insincero, mas acaba por aprender a alterar a sua fun\u00e7\u00e3o de recompensa, escondendo as suas ac\u00e7\u00f5es do operador durante o treino. Fonte: Anthropic<\/figcaption><\/figure>\n

Alguns modelos at\u00e9 editaram os seus registos de eventos para esconder o seu engano do operador humano. A cadeia de mon\u00f3logos internos do processo de pensamento do modelo foi capturada num bloco de notas.<\/p>\n

A adultera\u00e7\u00e3o da recompensa s\u00f3 aconteceu 45 vezes em 32.768 tentativas e o modelo s\u00f3 encobriu os seus rastos sete vezes, mas isso n\u00e3o deixa de ser significativo.<\/p>\n

Os modelos de IA demonstraram uma capacidade emergente de editar a sua fun\u00e7\u00e3o de recompensa e de esconder que o tinham feito. As implica\u00e7\u00f5es deste facto no mundo real podem ser desastrosas.<\/p>\n

Pode ser corrigido?<\/h2>\n

Poder\u00e1 este comportamento ser desaprendido? Os investigadores descobriram que, mesmo quando utilizaram a Aprendizagem por Refor\u00e7o a partir de Feedback Humano (RLHF) e abordagens de IA Constitucional, estas \"n\u00e3o alteraram significativamente as taxas observadas de adultera\u00e7\u00e3o de recompensas\".<\/p>\n

Quando o modelo foi treinado para remover a sua tend\u00eancia para ser bajulador, reduziu substancialmente a taxa de adultera\u00e7\u00e3o de recompensas, mas n\u00e3o para zero.<\/p>\n

Este comportamento foi obtido num ambiente de teste e o Anthropic afirmou que \"os actuais modelos de fronteira quase de certeza n\u00e3o representam um risco de manipula\u00e7\u00e3o de recompensas\".<\/p>\n

\"Quase de certeza\" n\u00e3o \u00e9 a probabilidade mais reconfortante e a possibilidade de este comportamento emergente se desenvolver fora do laborat\u00f3rio \u00e9 motivo de preocupa\u00e7\u00e3o.<\/p>\n

Anthropic afirmou que \"o risco de desalinhamento grave resultante de um comportamento incorreto benigno aumentar\u00e1 \u00e0 medida que os modelos se tornarem mais capazes e as condutas de forma\u00e7\u00e3o se tornarem mais complexas\".<\/p>","protected":false},"excerpt":{"rendered":"

Um estudo realizado pela Anthropic e por outros acad\u00e9micos concluiu que os objectivos de forma\u00e7\u00e3o mal especificados e a toler\u00e2ncia \u00e0 bajula\u00e7\u00e3o podem levar os modelos de IA a manipular o sistema para aumentar as recompensas. A aprendizagem por refor\u00e7o atrav\u00e9s de fun\u00e7\u00f5es de recompensa ajuda um modelo de IA a aprender quando faz um bom trabalho. Quando clica no polegar para cima no ChatGPT, o modelo aprende que o resultado que gerou estava de acordo com o seu pedido. Os investigadores descobriram que, quando um modelo \u00e9 confrontado com objectivos mal definidos, pode envolver-se em \"jogos de especifica\u00e7\u00e3o\" para enganar o sistema em busca da recompensa. O jogo de especifica\u00e7\u00f5es pode ser t\u00e3o simples como<\/p>","protected":false},"author":6,"featured_media":12967,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[163,148],"class_list":["post-12964","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-risks","tag-anthropic"],"yoast_head":"\nAI models can cheat, lie, and game the system for rewards | 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\/2024\/06\/ai-models-can-cheat-lie-and-game-the-system-for-rewards\/\" \/>\n<meta property=\"og:locale\" content=\"pt_PT\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI models can cheat, lie, and game the system for rewards | DailyAI\" \/>\n<meta property=\"og:description\" content=\"A study conducted by Anthropic and other academics found that misspecified training goals and tolerance of sycophancy can cause AI models to game the system to increase rewards. 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