Search (28 results, page 1 of 2)

  • × language_ss:"d"
  • × theme_ss:"Computerlinguistik"
  • × type_ss:"a"
  • × year_i:[2020 TO 2030}
  1. ¬Der Student aus dem Computer (2023) 0.02 
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    Date
    27. 1.2023 16:22:55
    Type
    a
  2. Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023) 0.01 
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    Date
    29.12.2022 18:22:55
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  3. Rieger, F.: Lügende Computer (2023) 0.01 
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    Date
    16. 3.2023 19:22:55
    Type
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  4. Schaer, P.: Sprachmodelle und neuronale Netze im Information Retrieval (2023) 0.00 
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    Abstract
    In den letzten Jahren haben Sprachmodelltechnologien unterschiedlichster Ausprägungen in der Informationswissenschaft Einzug gehalten. Diesen Sprachmodellen, die unter den Bezeichnungen GPT, ELMo oder BERT bekannt sind, ist gemein, dass sie dank sehr großer Webkorpora auf eine Datenbasis zurückgreifen, die bei vorherigen Sprachmodellansätzen undenkbar war. Gleichzeitig setzen diese Modelle auf neuere Entwicklungen des maschinellen Lernens, insbesondere auf künstliche neuronale Netze. Diese Technologien haben auch im Information Retrieval (IR) Fuß gefasst und bereits kurz nach ihrer Einführung sprunghafte, substantielle Leistungssteigerungen erzielt. Neuronale Netze haben in Kombination mit großen vortrainierten Sprachmodellen und kontextualisierten Worteinbettungen geführt. Wurde in vergangenen Jahren immer wieder eine stagnierende Retrievalleistung beklagt, die Leistungssteigerungen nur gegenüber "schwachen Baselines" aufwies, so konnten mit diesen technischen und methodischen Innovationen beeindruckende Leistungssteigerungen in Aufgaben wie dem klassischen Ad-hoc-Retrieval, der maschinellen Übersetzung oder auch dem Question Answering erzielt werden. In diesem Kapitel soll ein kurzer Überblick über die Grundlagen der Sprachmodelle und der NN gegeben werden, um die prinzipiellen Bausteine zu verstehen, die hinter aktuellen Technologien wie ELMo oder BERT stecken, die die Welt des NLP und IR im Moment beherrschen.
    Type
    a
  5. Geißler, S.: Natürliche Sprachverarbeitung und Künstliche Intelligenz : ein wachsender Markt mit vielen Chancen. Das Beispiel Kairntech (2020) 0.00 
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    Source
    Information - Wissenschaft und Praxis. 71(2020) H.2/3, S.95-106
    Type
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  6. Lobo, S.: ¬Das Ende von Google, wie wir es kannten : Bessere Treffer durch ChatGPT (2022) 0.00 
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    Source
    https://www.spiegel.de/netzwelt/netzpolitik/bessere-treffer-durch-chatgpt-das-ende-von-google-wie-wir-es-kannten-kolumne-a-77820af6-51d7-4c03-b822-cf93094fd709
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  7. Müller, P.: Text-Automat mit Tücken (2023) 0.00 
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  8. Schneider, R.U.: Darf der Computer die Seminararbeit schreiben? (2023) 0.00 
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  9. Simanowski, R.: Wenn die Dinge anfangen zu sprechen : Chatbot LaMDA von Google (2022) 0.00 
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  10. Leighton, T.: ChatGPT und Künstliche Intelligenz : Utopie oder Dystopie? (2023) 0.00 
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  11. Barthel, J.; Ciesielski, R.: Regeln zu ChatGPT an Unis oft unklar : KI in der Bildung (2023) 0.00 
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  12. Janssen, J.-K.: ChatGPT-Klon läuft lokal auf jedem Rechner : Alpaca/LLaMA ausprobiert (2023) 0.00 
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  13. Lutz-Westphal, B.: ChatGPT und der "Faktor Mensch" im schulischen Mathematikunterricht (2023) 0.00 
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  14. Hahn, S.: DarkBERT ist mit Daten aus dem Darknet trainiert : ChatGPTs dunkler Bruder? (2023) 0.00 
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  15. Hartnett, K.: Sind Sprachmodelle bald die besseren Mathematiker? (2023) 0.00 
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  16. Siepmann, D.: Auswirkungen von KI auf die Textproduktion in der Wissenschaft (2023) 0.00 
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  17. Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2023) 0.00 
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  18. Kurz, C.: Womit sich Strafverfolger bald befassen müssen : ChatGPT (2023) 0.00 
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  19. Siegel, M.: Maschinelle Übersetzung (2023) 0.00 
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  20. Bischoff, M.: Wie eine KI lernt, sich selbst zu erklären (2023) 0.00 
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Authors