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  • × year_i:[2020 TO 2030}
  1. Noever, D.; Ciolino, M.: ¬The Turing deception (2022) 0.08
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    Source
    https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
  2. Gabler, S.: Vergabe von DDC-Sachgruppen mittels eines Schlagwort-Thesaurus (2021) 0.06
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    Content
    Master thesis Master of Science (Library and Information Studies) (MSc), Universität Wien. Advisor: Christoph Steiner. Vgl.: https://www.researchgate.net/publication/371680244_Vergabe_von_DDC-Sachgruppen_mittels_eines_Schlagwort-Thesaurus. DOI: 10.25365/thesis.70030. Vgl. dazu die Präsentation unter: https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CAIQw7AJahcKEwjwoZzzytz_AhUAAAAAHQAAAAAQAg&url=https%3A%2F%2Fwiki.dnb.de%2Fdownload%2Fattachments%2F252121510%2FDA3%2520Workshop-Gabler.pdf%3Fversion%3D1%26modificationDate%3D1671093170000%26api%3Dv2&psig=AOvVaw0szwENK1or3HevgvIDOfjx&ust=1687719410889597&opi=89978449.
  3. Kempf, K.; Brantl, M.; Meiers, T.; Wolf, T.: Auf der Suche nach dem verborgenen Bild : Künstliche Intelligenz erschließt historische Bibliotheksbestände (2021) 0.06
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  4. Leighton, T.: ChatGPT und Künstliche Intelligenz : Utopie oder Dystopie? (2023) 0.05
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    Source
    https://www.heise.de/tp/features/ChatGPT-und-Kuenstliche-Intelligenz-Utopie-oder-Dystopie-7445181.html?view=print
  5. Kempf, K.; Brantl, M.; Meiers, T.; Wolf, T.: Auf der Suche nach dem verborgenen Bild : Künstliche Intelligenz erschließt historische Bibliotheksbestände (2021) 0.05
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  6. Kempf, K.; Brantl, M.; Meiers, T.; Wolf, T.: Auf der Suche nach dem verborgenen Bild : Künstliche Intelligenz erschließt historische Bibliotheksbestände (2021) 0.05
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  7. Vogt, T.: ¬Die Transformation des renommierten Informationsservices zbMATH zu einer Open Access-Plattform für die Mathematik steht vor dem Abschluss. (2020) 0.05
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    Content
    "Mit Beginn des Jahres 2021 wird der umfassende internationale Informationsservice zbMATH in eine Open Access-Plattform überführt. Dann steht dieser bislang kostenpflichtige Dienst weltweit allen Interessierten kostenfrei zur Verfügung. Die Änderung des Geschäftsmodells ermöglicht, die meisten Informationen und Daten von zbMATH für Forschungszwecke und zur Verknüpfung mit anderen nicht-kommerziellen Diensten frei zu nutzen, siehe: https://www.mathematik.de/dmv-blog/2772-transformation-von-zbmath-zu-einer-open-access-plattform-f%C3%BCr-die-mathematik-kurz-vor-dem-abschluss."
  8. Schönbächler, E.; Strasser, T.; Himpsl-Gutermann, K.: Vom Chat zum Check : Informationskompetenz mit ChatGPT steigern (2023) 0.05
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    Abstract
    Der Beitrag greift den aktuellen Diskurs um die KI-Anwendung ChatGPT und deren Bedeutung in Schule und Hochschule auf. Dabei werden durch einen Überblick über verschiedene Assistenzsysteme, die auf Künstlicher Intelligenz beruhen, Grundlagen und Unterschiede herausgearbeitet. Der Bereich der Chatbots wird näher beleuchtet, die beiden grundlegenden Arten des regelbasierten Chatbots und des Machine Learning Bots werden anhand von anschaulichen Beispielen praxisnah erklärt. Schließlich wird herausgearbeitet, dass Informationskompetenz als Schlüsselkompetenz des 21. Jahrhunderts auch die wesentliche Grundlage dafür ist, im Bildungsbereich konstruktiv mit KI-Systemen wie ChatGPT umzugehen und die wesentlichen Funktionsmechanismen zu verstehen. Ein Unterrichtsentwurf zum Thema "Biene" schließt den Praxisbeitrag ab.
  9. ¬Der Student aus dem Computer (2023) 0.04
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    Abstract
    Kann gut zitieren, aber schlecht rechnen: Künstliche Intelligenz besteht Juraprüfung an US-Uni
    Date
    27. 1.2023 16:22:55
  10. Zilm, G.: "Kl ist ein glorifizierter Taschenrechner" (2023) 0.04
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    Abstract
    Künstliche Intelligenz zieht immer mehr in unseren Alltag ein. Dies macht vielen Angst. Sie fürchten, der Mensch könne die Kontrolle über Kl verlieren. Wie das verhindert werden kann, darüber macht sich das Ethik-Team am Deutschen Forschungszentrum für Künstliche Intelligenz Gedanken. Das DFKI hat mehrere Standorte, die Zentrale ist in Kaiserslautern.
    Date
    27. 1.2023 16:22:55
  11. Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024) 0.04
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    Abstract
    Detecting science-technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
  12. Sokolow, A.: Es menschelt in der KI-Welt (2023) 0.03
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    Abstract
    Für die junge Branche ist es ein Erdbeben: OpenAl-Chef Sam Altman, eine ihrer Galionsfiguren, verliert seinen Job. Mit ChatGPT haben OpenAl und Altman den Hype um Künstliche Intelligenz angestoßen.
    Date
    27. 1.2023 16:22:55
  13. Giesselbach, S.; Estler-Ziegler, T.: Dokumente schneller analysieren mit Künstlicher Intelligenz (2021) 0.03
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    Abstract
    Künstliche Intelligenz (KI) und natürliches Sprachverstehen (natural language understanding/NLU) verändern viele Aspekte unseres Alltags und unserer Arbeitsweise. Besondere Prominenz erlangte NLU durch Sprachassistenten wie Siri, Alexa und Google Now. NLU bietet Firmen und Einrichtungen das Potential, Prozesse effizienter zu gestalten und Mehrwert aus textuellen Inhalten zu schöpfen. So sind NLU-Lösungen in der Lage, komplexe, unstrukturierte Dokumente inhaltlich zu erschließen. Für die semantische Textanalyse hat das NLU-Team des IAIS Sprachmodelle entwickelt, die mit Deep-Learning-Verfahren trainiert werden. Die NLU-Suite analysiert Dokumente, extrahiert Eckdaten und erstellt bei Bedarf sogar eine strukturierte Zusammenfassung. Mit diesen Ergebnissen, aber auch über den Inhalt der Dokumente selbst, lassen sich Dokumente vergleichen oder Texte mit ähnlichen Informationen finden. KI-basierten Sprachmodelle sind der klassischen Verschlagwortung deutlich überlegen. Denn sie finden nicht nur Texte mit vordefinierten Schlagwörtern, sondern suchen intelligent nach Begriffen, die in ähnlichem Zusammenhang auftauchen oder als Synonym gebraucht werden. Der Vortrag liefert eine Einordnung der Begriffe "Künstliche Intelligenz" und "Natural Language Understanding" und zeigt Möglichkeiten, Grenzen, aktuelle Forschungsrichtungen und Methoden auf. Anhand von Praxisbeispielen wird anschließend demonstriert, wie NLU zur automatisierten Belegverarbeitung, zur Katalogisierung von großen Datenbeständen wie Nachrichten und Patenten und zur automatisierten thematischen Gruppierung von Social Media Beiträgen und Publikationen genutzt werden kann.
  14. Hartnett, K.: Sind Sprachmodelle bald die besseren Mathematiker? (2023) 0.03
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    Series
    Künstliche Intelligenz
  15. Xie, J.; Lu, H.; Kang, L.; Cheng, Y.: Citing criteria and its effects on researcher's intention to cite : a mixed-method study (2022) 0.03
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    Source
    Journal of the Association for Information Science and Technology. 73(2022) no.8, S.1079-1091
  16. Janssen, J.-K.: ChatGPT-Klon läuft lokal auf jedem Rechner : Alpaca/LLaMA ausprobiert (2023) 0.03
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    Source
    https://www.heise.de/news/c-t-3003-ChatGPT-Klon-laeuft-lokal-auf-jedem-Rechner-Alpaca-LLaMA-ausprobiert-8004159.html?view=print
  17. Precht, R.D.: Künstliche Intelligenz und der Sinn des Lebens (2020) 0.03
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    Abstract
    Während die drohende Klimakatastrophe und der Ressourcenverbrauch die Lebensgrundlagen unseres Planeten zerstören, machen sich Informatiker und Geschäftsleute daran, die Entwicklung einer Künstlichen Intelligenz voranzutreiben. Ihr Ziel ist eine "Superintelligenz", die alles das können soll, was Menschen auch können, nur um vielfaches »optimierter«. Ausgehend von einem völlig unzureichenden Menschenbild wollen sie ihren Maschinen eine Moral einprogrammieren. Richard David Precht macht in seinem eindringlichen Essay deutlich, dass dies niemals gelingen kann - allein schon weil Moral grundsätzlich nicht programmierbar ist! Sie ist Ausdruck einer emotionalen Weltbeziehung, abhängig von irrationalen Wertungen und somit unberechenbar. Und wo die Anwälte der Künstlichen Intelligenz ihre Maschinen nach dem Kriterium des vermeintlich größten Glücks entscheiden lassen wollen, zeigt uns Precht, dass es im wirklichen Leben viel weniger um das Glück geht, als die Informatiker annehmen. Der wichtigste Wert menschlichen Lebens ist nicht Glück, sondern Sinn - und der ist weit mehr als die Summe von Glücksmomenten. Precht plädiert deshalb für ein Verbot jeder so genannten »ethischen Programmierung« von Computern und Robotern und zeigt auf, welche Beziehung zur Welt uns Menschen ein Gefühl von Sinn gibt und welche nicht. Denn genau hierin liegt der Ausgangspunkt für ein gelingendes Leben und Zusammenleben im 21. Jahrhundert; einem Jahrhundert, in dem wir uns nicht den Maschinen angleichen, sondern erkennen, dass wir unser Schicksal mit Pflanzen und Tieren teilen - als Gegenpol zur Künstlichen Intelligenz!
    Classification
    HMI (FH K)
    Content
    "Während die drohende Klimakatastrophe und der enorme Ressourcenverbrauch der Menschheit den Planeten zerstört, machen sich Informatiker und Ingenieure daran, die Entwicklung einer Künstlichen Intelligenz voranzutreiben, die alles das können soll, was wir Menschen auch können - nur vielfach »optimierter«. Ausgehend von völlig falschen Annahmen soll den Maschinen sogar eine menschenähnliche Moral einprogrammiert werden. Richard David Precht macht uns eindringlich klar, dass das nicht möglich ist. Denn unser Leben besteht nicht aus der Abfolge vorausberechneter Schritte. Wir sind viel mehr als das. "Der Philosoph Precht formuliert bisweilen zwar sehr salopp, aber er verfügt über eine Reihe von unbestreitbaren Vorzügen. Vor allem: Er löst, auch durch seine durchaus in einem anregenden Sinn ganz eigene Zugangsweise, Diskussionen aus und vermag denkend zu provozieren. Darum verdient dieses populärphilosophische Buch weder euphorische Zustimmung noch empörte Abweisung, sondern eine substanziell fundierte, kontroverse Diskussion - vielleicht mit Bezug auf die Frage, die Platon in der Politeia als wesentlich benannt hat: In der Philosophie gelte es nämlich darüber nachzudenken, auf welche Weise wir leben wollen." (literaturkritik.de)
    GHBS
    HMI (FH K)
    RSWK
    Künstliche Intelligenz
    Subject
    Künstliche Intelligenz
  18. Qiu, J.; Zuo, M.; Wang, J.; Cai, C.: Knowledge order in an online knowledge community : group heterogeneity and two paths mediated by group interaction (2021) 0.02
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    Source
    Journal of the Association for Information Science and Technology. 72(2021) no.8, S.1075-1091
  19. Zweig, K.; Luttenberger, J.: KI ist keine Magie (2020) 0.02
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    Abstract
    Das Thema "Künstliche Intelligenz" (KI) wird immer wichtiger. Die Kaiserslauterer Informatikprofessorin Katharina Zweig ist Kl-Expertinund Autorin des Erklärbuchs "Ein Algorithmus hat kein Taktgefühl". Im großen RHEINPFALZ-Gespräch erläutert sie, wie Kl und Datenschutz zusammengehen können - durch dezentrales Lernen. Die Daten bleiben dadurch beim Nutzer. "KI wird insgesamt näher an uns heranrücken, darum müssen wir Betriebsräte, Schulelternbeiräte, Betroffene und Bürger befähigen, sich einmischen zu können",
  20. Gomez, J.; Allen, K.; Matney, M.; Awopetu, T.; Shafer, S.: Experimenting with a machine generated annotations pipeline (2020) 0.02
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