Search (54 results, page 1 of 3)

Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.10

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Content: Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
Date: 8. 1.2013 10:22:32

Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.04

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Source: https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

Albrecht, I.: GPT-3: die Zukunft studentischer Hausarbeiten oder eine Bedrohung der wissenschaftlichen Integrität? (2023) 0.03
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Abstract

Mit dem Fortschritt künstlicher Intelligenzen sind auch progressive Sprachverarbeitungsmodelle auf den Markt gekommen. GPT-3 war nach seiner Veröffentlichung das leistungsstärkste Modell seiner Zeit. Das Programm generiert Texte, die oft nicht von menschlich verfassten Inhalten zu unterscheiden sind. GPT-3s Größe und Komplexität ermöglichen es, dass es wissenschaftliche Artikel eigenständig schreiben und ausgeben kann, die laut Studien und Untersuchungen zum Bestehen von Universitätskursen ausreichen. Mit der Entwicklung solcher Künstlichen Intelligenzen, insbesondere auf Open Source-Basis, könnten Studierende in Zukunft studentische Hausarbeiten von Textgeneratoren schreiben lassen. Diese Arbeit beschäftigt sich einerseits mit dem Modell GPT-3 und seinen Fähigkeiten sowie Risiken. Andererseits wird die Frage thematisiert, wie Hochschulen und Universitäten in Zukunft mit maschinell verfassten Arbeiten umgehen können.

Warner, A.J.: Natural language processing (1987) 0.03

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Source: Annual review of information science and technology. 22(1987), S.79-108

McMahon, J.G.; Smith, F.J.: Improved statistical language model performance with automatic generated word hierarchies (1996) 0.02

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Source: Computational linguistics. 22(1996) no.2, S.217-248

Ruge, G.: ¬A spreading activation network for automatic generation of thesaurus relationships (1991) 0.02

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Date: 8.10.2000 11:52:22

Somers, H.: Example-based machine translation : Review article (1999) 0.02

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Date: 31. 7.1996 9:22:19

New tools for human translators (1997) 0.02

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Date: 31. 7.1996 9:22:19

Baayen, R.H.; Lieber, H.: Word frequency distributions and lexical semantics (1997) 0.02

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Date: 28. 2.1999 10:48:22

¬Der Student aus dem Computer (2023) 0.02

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Date: 27. 1.2023 16:22:55

Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2022) 0.02
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Content

"Seit dem 30. November 2022 ist meine Welt - und die vieler Bildungsexpertinnen und Bildungsexperten - gefühlt eine andere Welt, die uns in eine "Neuzeit" führt, von der wir noch nicht wissen, ob wir sie lieben oder fürchten sollen. Der Ableger und Prototyp ChatGPT des derzeit (zumindest in der westlichen Welt) führenden generativen KI-Sprachmodells GPT-3 von OpenAI wurde am 30. November veröffentlicht und ist seit dieser Zeit für jeden frei zugänglich und kostenlos. Was zunächst als unspektakuläre Ankündigung von OpenAI anmutete, nämlich das seit 2020 bereits verfügbare KI-Sprachmodell GPT-3 nun in leicht modifizierter Version (GPT-3,5) als Chat-Variante für die Echtzeit-Kommunikation bereitzustellen, entpuppt sich in der Anwendung - aus Sicht der Nutzerinnen und Nutzer - als Meilenstein der KI-Entwicklung. Fakt ist, dass die Leistungsvielfalt und -stärke von ChatGPT selbst IT-Expertinnen und -Experten überrascht hat und sie zu einer Fülle von Superlativen in der Bewertung veranlasst, jedoch immer in Kombination mit Hinweisen zur fehlenden Faktentreue und Verlässlichkeit derartiger generativer KI-Modelle. Mit WebGPT von OpenAI steht aber bereits ein Forschungsprototyp bereit, der mit integrierter Internetsuchfunktion die "Halluzinationen" aktueller GPT-Varianten ausmerzen könnte. Für den Bildungssektor stellt sich die Frage, wie sich das Lehren und Lernen an Hochschulen (und nicht nur dort) verändern wird, wenn derartige KI-Werkzeuge omnipräsent sind und mit ihrer Hilfe nicht nur die Hausarbeit "per Knopfdruck" erstellt werden kann. Beeindruckend ist zudem die fachliche Bandbreite von ChatGPT, siehe den Tweet von @davidtsong, der ChatGPT dem Studierfähigkeitstest SAT unterzogen hat."

Byrne, C.C.; McCracken, S.A.: ¬An adaptive thesaurus employing semantic distance, relational inheritance and nominal compound interpretation for linguistic support of information retrieval (1999) 0.02

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Date: 15. 3.2000 10:22:37

Boleda, G.; Evert, S.: Multiword expressions : a pain in the neck of lexical semantics (2009) 0.02

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Date: 1. 3.2013 14:56:22

Monnerjahn, P.: Vorsprung ohne Technik : Übersetzen: Computer und Qualität (2000) 0.02

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Source: c't. 2000, H.22, S.230-231

Hutchins, J.: From first conception to first demonstration : the nascent years of machine translation, 1947-1954. A chronology (1997) 0.02

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Date: 31. 7.1996 9:22:19

Kuhlmann, U.; Monnerjahn, P.: Sprache auf Knopfdruck : Sieben automatische Übersetzungsprogramme im Test (2000) 0.02

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Source: c't. 2000, H.22, S.220-229

Lezius, W.; Rapp, R.; Wettler, M.: ¬A morphology-system and part-of-speech tagger for German (1996) 0.02

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Date: 22. 3.2015 9:37:18

Wanner, L.: Lexical choice in text generation and machine translation (1996) 0.01

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Date: 31. 7.1996 9:22:19

Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.01

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Date: 6. 3.1997 16:22:15

Basili, R.; Pazienza, M.T.; Velardi, P.: ¬An empirical symbolic approach to natural language processing (1996) 0.01

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Date: 6. 3.1997 16:22:15

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