-
¬Der Student aus dem Computer (2023)
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- Date
- 27. 1.2023 16:22:55
- Source
- Pirmasenser Zeitung. 2023 vom 27.01.2023, S.1
-
Morris, V.: Automated language identification of bibliographic resources (2020)
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- Date
- 2. 3.2020 19:04:22
- Source
- Cataloging and classification quarterly. 58(2020) no.1, S.1-27
-
Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023)
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- Date
- 29.12.2022 18:22:55
- Source
- c't. 2023, H.1, S.46- [https://www.heise.de/select/ct/2023/1/2233908274346530870]
-
Noever, D.; Ciolino, M.: ¬The Turing deception (2022)
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- Source
- https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN
-
Luo, L.; Ju, J.; Li, Y.-F.; Haffari, G.; Xiong, B.; Pan, S.: ChatRule: mining logical rules with large language models for knowledge graph reasoning (2023)
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- Date
- 23.11.2023 19:07:22
-
Rieger, F.: Lügende Computer (2023)
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- Date
- 16. 3.2023 19:22:55
-
Meng, K.; Ba, Z.; Ma, Y.; Li, G.: ¬A network coupling approach to detecting hierarchical linkages between science and technology (2024)
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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.
- Source
- Journal of the Association for Information Science and Technology. 75(2023) no.2, S.167-187
-
Hahn, U.: Automatische Sprachverarbeitung (2023)
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- Abstract
- Dieses Kapitel gibt eine Übersicht über die maschinelle Verarbeitung natürlicher Sprachen (wie das Deutsche oder Englische; natural language - NL) durch Computer. Grundlegende Konzepte der automatischen Sprachverarbeitung (natural language processing - NLP) stammen aus der Sprachwissenschaft (s. Abschnitt 2) und sind in zunehmend selbstständiger Weise mit formalen Methoden und technischen Grundlagen der Informatik in einer eigenständigen Disziplin, der Computerlinguistik (CL; s. Abschnitte 3 und 4), verknüpft worden. Natürlichsprachliche Systeme (NatS) mit anwendungsbezogenen Funktionalitätsvorgaben bilden den Kern der informationswissenschaftlich geprägten NLP, die häufig als Sprachtechnologie oder im Deutschen auch (mittlerweile veraltet) als Informationslinguistik bezeichnet wird (s. Abschnitt 5).
- Pages
- S.281-292
-
Müller, P.: Text-Automat mit Tücken (2023)
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- Source
- Pirmasenser Zeitung. Nr. 29 vom 03.02.2023, S.2
-
Lobo, S.: ¬Das Ende von Google, wie wir es kannten : Bessere Treffer durch ChatGPT (2022)
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-
Lutz-Westphal, B.: ChatGPT und der "Faktor Mensch" im schulischen Mathematikunterricht (2023)
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- Source
- Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.19-21
-
Hahn, S.: DarkBERT ist mit Daten aus dem Darknet trainiert : ChatGPTs dunkler Bruder? (2023)
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-
Hartnett, K.: Sind Sprachmodelle bald die besseren Mathematiker? (2023)
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- Source
- Spektrum der Wissenschaft. 2023, H.7, S.28-31
-
Weßels, D.: ChatGPT - ein Meilenstein der KI-Entwicklung (2023)
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- Source
- Mitteilungen der Deutschen Mathematiker-Vereinigung. 2023, H.1, S.17-19
-
Corbara, S.; Moreo, A.; Sebastiani, F.: Syllabic quantity patterns as rhythmic features for Latin authorship attribution (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.1, S.128-141
-
Thomas, I.S.; Wang, J.; GPT-3: Was euch zu Menschen macht : Antworten einer künstlichen Intelligenz auf die großen Fragen des Lebens (2022)
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- Abstract
- Das erste durch KI verfasste Weisheitsbuch. »Die Künstliche Intelligenz sieht den Menschen, wie er ist. Es gibt für sie keinen Gott, keine Rituale, keinen Himmel, keine Hölle, keine Engel. Es gibt für sie nur empfindsame Wesen.« GPT-3. Dieses Buch enthält Weisheitstexte, die durch die modernste KI im Bereich der Spracherkennung verfasst wurden. Es ist die GPT-3, die durch die Technikerin Jasmine Wang gesteuert wird. Die originären Texte von GPT-3 werden von dem international bekannten Dichter Iain S. Thomas kuratiert. Die Basis von GPT-3 reicht von den Weisheitsbücher der Menschheit bis hin zu modernen Texten. GPT-3 antwortet auf Fragen wie: Was macht den Mensch zum Menschen? Was bedeutet es zu lieben? Wie führen wir ein erfülltes Leben? etc. und ist in der Lage, eigene Sätze zu kreieren. So wird eine zeitgenössische und noch nie dagewesene Erforschung von Sinn und Spiritualität geschaffen, die zu einem neuen Verständnis dessen inspiriert, was uns zu Menschen macht.
- Pages
- 205 S
-
Lund, B.D.; Wang, T.; Mannuru, N.R.; Nie, B.; Shimray, S.; Wang, Z.: ChatGPT and a new academic reality : artificial Intelligence-written research papers and the ethics of the large language models in scholarly publishing (2023)
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- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.5, S.570-581
-
Siegel, M.: Maschinelle Übersetzung (2023)
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- Pages
- S.307-316
-
Brown, T.B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; Amodei, D.: Language models are few-shot learners (2020)
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- Abstract
- Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.
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Bischoff, M.: Was steckt hinter ChatGTP & Co? (2023)
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- Source
- Spektrum der Wissenschaft. 2023, H.5, S.58-69
