{"id":27202,"date":"2023-09-12T20:06:18","date_gmt":"2023-09-12T18:06:18","guid":{"rendered":"https:\/\/sendapp.live\/?p=27202"},"modified":"2024-11-19T11:47:21","modified_gmt":"2024-11-19T10:47:21","slug":"kunstliche-intelligenz-und-rinderwahnsinn","status":"publish","type":"post","link":"https:\/\/sendapp.live\/de\/2023\/09\/12\/intelligenza-artificiale-e-sindrome-della-mucca-pazza\/","title":{"rendered":"\u00a0K\u00fcnstliche Intelligenz und \u201eRinderwahnsinnssyndrom\u201c\u00a0"},"content":{"rendered":"

K\u00fcnstliche Intelligenz<\/a> Und "BSE-Syndrom<\/a>"<\/p>\n

K\u00fcnstliche Intelligenz (KI) steht vor einer neuen Krise, dieses Mal von innen heraus. Eine aktuelle Studie von Forschern von Rice und der Stanford University weist darauf hin, dass der starke Einsatz synthetischer Daten zum Training von KI-Modellen zu ernsthaften Problemen f\u00fchren kann. Dieser besorgniserregende Trend wird \u201eModell-Autophagie-St\u00f6rung\u201c oder MAD genannt, ein alarmierender Begriff aus gutem Grund.<\/p>\n

\"\"<\/p>\n

Was ist ein synthetischer Datensatz?<\/h2>\n

Bevor wir uns mit den Details befassen, ist es wichtig zu verstehen, was synthetische Datens\u00e4tze sind. Dabei handelt es sich um Datens\u00e4tze, die k\u00fcnstlich generiert und nicht aus der realen Welt gesammelt werden. Diese Datens\u00e4tze werden zum Trainieren von Modellen f\u00fcr maschinelles Lernen verwendet und umfassen Daten, die von algorithmisch generierten Texten und Bildern bis hin zu simulierten Finanzdaten reichen. Ihre Attraktivit\u00e4t liegt vor allem in ihrer Verf\u00fcgbarkeit, den geringen Kosten und dem Fehlen von Datenschutzproblemen.<\/p>\n

Die Vorteile synthetischer Datens\u00e4tze<\/h2>\n

Die St\u00e4rke synthetischer Daten liegt in ihrer Vielseitigkeit und Benutzerfreundlichkeit. Sie erfordern keine manuelle Erfassung, vermeiden rechtliche Datenschutzprobleme und k\u00f6nnen in nahezu unbegrenzten Mengen erstellt werden. Das Beratungsunternehmen Gartner prognostiziert, dass diese Datens\u00e4tze bis 2030 in vielen KI-Anwendungsbereichen reale Daten ersetzen werden.<\/p>\n

Das \u201eRinderwahnsinn\u201c-Syndrom beim maschinellen Lernen<\/h2>\n

Aber es gibt eine dunkle Seite. Die oben erw\u00e4hnte Studie spricht von einem Ph\u00e4nomen, das mit der \u00dcberanpassung beim maschinellen Lernen vergleichbar ist und als \u201eModel Autophagy Disorder\u201c (MAD) bekannt ist. Dieser Fachbegriff beschreibt eine St\u00f6rung, bei der die Leistung eines KI-Modells durch die fortgesetzte Verwendung synthetischer Daten nachl\u00e4sst. Mit anderen Worten: Die KI beginnt \u201everr\u00fcckt zu spielen\u201c.<\/p>\n

Ursachen und Folgen von MAD<\/h2>\n

Das Problem scheint auf die mangelnde Diversit\u00e4t der synthetischen Daten zur\u00fcckzuf\u00fchren zu sein. Wenn ein KI-Modell mit einem zu homogenen Datensatz trainiert wird, beginnt es, sich in einer destruktiven Schleife zu \u00fcberlappen. Dieses Ph\u00e4nomen wurde als \u201eautophagisch\u201c beschrieben, woraus der Begriff MAD entstand.<\/p>\n

Vorgeschlagene L\u00f6sungen und zuk\u00fcnftige \u00dcberlegungen<\/h2>\n

Es ist jedoch noch nicht alles verloren. Die Forscher vermuten, dass die Einbeziehung realer Daten in den Trainingszyklus diese Art der Modellerosion verhindern k\u00f6nnte. Und w\u00e4hrend die wissenschaftliche Gemeinschaft nach L\u00f6sungen sucht, ist es f\u00fcr KI-Entwickler wichtig, sich dieser potenziellen Gefahr bewusst zu sein.<\/p>\n

Synthetische Datens\u00e4tze: Ein zweischneidiges Schwert?<\/h2>\n

Zusammenfassend l\u00e4sst sich sagen, dass synthetische Datens\u00e4tze zwar zweifellos Vorteile in Bezug auf Kosten, Effizienz und Datenschutz bieten, aber auch neue und unerwartete Risiken mit sich bringen. Christian Intern\u00f2, Forscher auf dem Gebiet des maschinellen Lernens, bringt das Dilemma perfekt auf den Punkt: \u201eSynthetische Daten sind die Zukunft, aber wir m\u00fcssen lernen, damit umzugehen.\u201c Mit Blick auf die Zukunft muss die KI-Community die Risiken und Chancen dieser neuen Datengrenze abw\u00e4gen.<\/p>","protected":false},"excerpt":{"rendered":"

K\u00fcnstliche Intelligenz und \u201eRinderwahnsinn\u201c K\u00fcnstliche Intelligenz (KI) steht vor einer neuen Krise, diesmal von innen. Eine aktuelle Studie von Forschern der Rice University und der\u2026<\/p>","protected":false},"author":22,"featured_media":27206,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_daim_seo_power":"","_daim_enable_ail":"","footnotes":""},"categories":[2538],"tags":[],"class_list":{"0":"post-27202","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-news"},"yoast_head":"\n\u00a0Intelligenza Artificiale e \u201cSindrome Della Mucca Pazza\u201d\u00a0<\/title>\n<meta name=\"description\" content=\"Intelligenza Artificiale e \u201cSindrome Della Mucca Pazza\u201d\u00a0L'intelligenza artificiale (IA) sta affrontando una nuova crisi...\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sendapp.live\/de\/2023\/09\/12\/kunstliche-intelligenz-und-rinderwahnsinn\/\" \/>\n<meta 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