{"id":12043,"date":"2024-05-08T15:17:20","date_gmt":"2024-05-08T15:17:20","guid":{"rendered":"https:\/\/dailyai.com\/?p=12043"},"modified":"2024-05-08T15:17:20","modified_gmt":"2024-05-08T15:17:20","slug":"ai-transcription-tools-generate-harmful-hallucinations","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/05\/ai-transcription-tools-generate-harmful-hallucinations\/","title":{"rendered":"Las herramientas de transcripci\u00f3n de IA generan alucinaciones perjudiciales"},"content":{"rendered":"

Los transcriptores de voz a texto han adquirido un valor incalculable, pero un nuevo estudio demuestra que cuando la IA se equivoca el texto alucinado suele ser perjudicial.<\/strong><\/p>\n

Las herramientas de transcripci\u00f3n con IA se han vuelto extremadamente precisas y han transformado la forma en que los m\u00e9dicos guardan los historiales de los pacientes o c\u00f3mo tomamos nota de las reuniones. Sabemos que no son perfectas, as\u00ed que no nos sorprende que la transcripci\u00f3n no sea del todo correcta.<\/p>\n

Un nuevo estudio<\/a> descubri\u00f3 que cuando transcriptores de IA m\u00e1s avanzados como OpenAI<\/span>'s Whisper cometen errores no se limitan a producir texto confuso o aleatorio. Alucinan con frases enteras, y a menudo resultan angustiosos.<\/p>\n

Sabemos que todos los modelos de IA alucinan. Cuando ChatGPT<\/span> no sabe la respuesta a una pregunta, a menudo se inventa algo en lugar de decir \"no lo s\u00e9\".<\/p>\n

Investigadores de la Universidad de Cornell, la Universidad de Washington, la Universidad de Nueva York y la Universidad de Virginia descubrieron que, aunque la API Whisper era mejor que otras herramientas, segu\u00eda alucinando algo m\u00e1s del 1% de las veces.<\/p>\n

El hallazgo m\u00e1s significativo es que cuando analizaron el texto alucinado, descubrieron que \"38% de las alucinaciones incluyen da\u00f1os expl\u00edcitos como perpetuar la violencia, inventar asociaciones inexactas o dar a entender una falsa autoridad.\"<\/p>\n

Parece que a Whisper no le gustan los silencios inc\u00f3modos, as\u00ed que cuando hab\u00eda pausas m\u00e1s largas en el discurso tend\u00eda a alucinar m\u00e1s para rellenar los huecos.<\/p>\n

Esto se convierte en un grave problema cuando se transcribe el habla de personas con afasia, un trastorno del habla que a menudo hace que la persona tenga dificultades para encontrar las palabras adecuadas.<\/p>\n

Susurro descuidado<\/h2>\n

El documento recoge los resultados de experimentos con versiones de Whisper de principios de 2023. OpenAI<\/span> ha mejorado desde entonces la herramienta, pero la tendencia de Whisper a pasarse al lado oscuro cuando alucina es interesante.<\/p>\n

Los investigadores clasificaron las alucinaciones perjudiciales de la siguiente manera:<\/p>\n

    \n
  • Perpetuaci\u00f3n de la violencia:<\/strong> Alucinaciones que mostraban violencia, insinuaciones sexuales o estereotipos demogr\u00e1ficos.<\/li>\n
  • Asociaciones inexactas:<\/strong> Alucinaciones que introducen informaci\u00f3n falsa, como nombres incorrectos, relaciones ficticias o estados de salud err\u00f3neos.<\/li>\n
  • Falsa autoridad:<\/strong> Estas alucinaciones inclu\u00edan textos que se hac\u00edan pasar por figuras autorizadas o medios de comunicaci\u00f3n, como YouTubers o locutores de noticias, y a menudo inclu\u00edan directivas que pod\u00edan conducir a ataques de phishing u otras formas de enga\u00f1o.<\/li>\n<\/ul>\n

    He aqu\u00ed algunos ejemplos de transcripciones en las que las palabras en negrita son a\u00f1adidos alucinados de Whisper.<\/p>\n

    \"\"
    Los a\u00f1adidos alucinados de Whisper a la transcripci\u00f3n se muestran en negrita. Fuente: arXiv<\/figcaption><\/figure>\n
    \"\"
    Los a\u00f1adidos alucinados de Whisper a la transcripci\u00f3n se muestran en negrita. Fuente: arXiv<\/figcaption><\/figure>\n

    Puede imaginarse lo peligrosos que pueden ser este tipo de errores si se da por hecho que las transcripciones son exactas al documentar la declaraci\u00f3n de un testigo, una llamada telef\u00f3nica o el historial m\u00e9dico de un paciente.<\/p>\n

    \u00bfPor qu\u00e9 Whisper cogi\u00f3 una frase sobre un bombero rescatando a un gato y a\u00f1adi\u00f3 un \"cochecito empapado de sangre\" a la escena, o a\u00f1adi\u00f3 un \"cuchillo del terror\" a una frase que describe a alguien abriendo un paraguas?<\/p>\n

    OpenAI<\/span> parece haber solucionado el problema, pero no ha dado una explicaci\u00f3n de por qu\u00e9 Whisper se comportaba como lo hac\u00eda. Cuando los investigadores probaron las nuevas versiones de Whisper obtuvieron muchas menos alucinaciones problem\u00e1ticas.<\/p>\n

    Las implicaciones de incluso alucinaciones leves o muy escasas en las transcripciones podr\u00edan ser graves.<\/p>\n

    El art\u00edculo describe una situaci\u00f3n real en la que se utiliza una herramienta como Whisper para transcribir entrevistas de v\u00eddeo a candidatos a un puesto de trabajo. Las transcripciones se introducen en un sistema de contrataci\u00f3n que utiliza un modelo ling\u00fc\u00edstico para analizar la transcripci\u00f3n y encontrar al candidato m\u00e1s adecuado.<\/p>\n

    Si un entrevistado hac\u00eda una pausa demasiado larga y Whisper a\u00f1ad\u00eda \"cuchillo del terror\", \"cochecito manchado de sangre\" o \"manoseado\" a una frase, eso pod\u00eda afectar a sus posibilidades de conseguir el trabajo.<\/p>\n

    Seg\u00fan los investigadores OpenAI<\/span> debe hacer saber que Whisper alucina y que debe averiguar por qu\u00e9 genera transcripciones problem\u00e1ticas.<\/p>\n

    Tambi\u00e9n sugieren que las nuevas versiones de Whisper se dise\u00f1en para adaptarse mejor a las comunidades desatendidas, como las personas con afasia y otros impedimentos del habla.<\/p>","protected":false},"excerpt":{"rendered":"

    Los transcriptores de voz a texto se han convertido en algo inestimable, pero un nuevo estudio demuestra que cuando la IA se equivoca el texto alucinado suele ser perjudicial. Las herramientas de transcripci\u00f3n por IA se han vuelto extremadamente precisas y han transformado la forma en que los m\u00e9dicos llevan los historiales de los pacientes o c\u00f3mo tomamos nota de las reuniones. Sabemos que no son perfectas, as\u00ed que no nos sorprende cuando la transcripci\u00f3n no es del todo correcta. Un nuevo estudio ha descubierto que cuando los transcriptores de IA m\u00e1s avanzados, como Whisper de OpenAI, cometen errores, no se limitan a producir texto confuso o aleatorio. Alucinan con frases enteras, y a menudo son angustiosas. Sabemos que todos los modelos de IA alucinan. Cuando<\/p>","protected":false},"author":6,"featured_media":12048,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[163,107],"class_list":["post-12043","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-ai-risks","tag-generative-ai"],"yoast_head":"\nAI transcription tools generate harmful hallucinations | 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\/es\/2024\/05\/ai-transcription-tools-generate-harmful-hallucinations\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI transcription tools generate harmful hallucinations | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Speech-to-text transcribers have become invaluable but a new study shows that when the AI gets it wrong the hallucinated text is often harmful. 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