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Bischoff, M.: Wie eine KI lernt, sich selbst zu erklären (2023)
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- Source
- https://www.spektrum.de/news/sprachmodelle-auf-dem-weg-zu-einer-erklaerbaren-ki/2132727#Echobox=1682669561?utm_source=pocket-newtab-global-de-DE
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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
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Rieger, F.: Lügende Computer (2023)
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- Date
- 16. 3.2023 19:22:55
- Source
- https://steadyhq.com/de/realitatsabzweig/posts/3ed79605-0650-4725-ab35-43f1243b57ee
-
Scobel, G.: GPT: Eine Software, die die Welt verändert (2023)
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- Source
- https://www.zdf.de/nachrichten/panorama/gpt-ki-literatur-terrax-gert-scobel-kolumne-100.html?utm_source=pocket-newtab-global-de-DE
-
Kurz, C.: Womit sich Strafverfolger bald befassen müssen : ChatGPT (2023)
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- Source
- https://netzpolitik.org/2023/chatgpt-womit-sich-strafverfolger-bald-befassen-muessen/?utm_source=pocket-newtab-global-de-DE#!
-
¬Der Student aus dem Computer (2023)
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- Date
- 27. 1.2023 16:22:55
-
Al-Khatib, K.; Ghosa, T.; Hou, Y.; Waard, A. de; Freitag, D.: Argument mining for scholarly document processing : taking stock and looking ahead (2021)
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-
Collard, J.; Paiva, V. de; Fong, B.; Subrahmanian, E.: Extracting mathematical concepts from text (2022)
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-
Stieler, W.: Anzeichen von Bewusstsein bei ChatGPT und Co.? (2023)
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- Abstract
- Ein interdisziplinäres Forschungsteam hat eine Liste von Eigenschaften aufgestellt, die auf Bewusstsein deuten, und aktuelle KI-Systeme darauf abgeklopft. Ein interdisziplinäres Forscherteam hat ein Paper [https://arxiv.org/abs/2308.08708] veröffentlicht, das eine Liste von 14 "Indikatoren" für Bewusstsein enthält, die aus sechs aktuellen Theorien über das Bewusstsein stammen. Aktuelle KI-Modelle wie GPT-3, Palm-E oder AdA von Deepmind weisen demnach einzelne dieser Indikatoren auf. "Es spricht viel dafür, dass die meisten oder alle Bedingungen für das Bewusstsein, die von derzeitigen Theorien vorgeschlagenen Bedingungen für das Bewusstsein mit den bestehenden Techniken der KI erfüllt werden können", schreiben die Autoren. Zum Team gehörte auch der Deep-Learning-Pionier Yoshua Bengio von der Université de Montréal.
-
Andrushchenko, M.; Sandberg, K.; Turunen, R.; Marjanen, J.; Hatavara, M.; Kurunmäki, J.; Nummenmaa, T.; Hyvärinen, M.; Teräs, K.; Peltonen, J.; Nummenmaa, J.: Using parsed and annotated corpora to analyze parliamentarians' talk in Finland (2022)
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-
Siegel, M.: Maschinelle Übersetzung (2023)
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-
Bischoff, M.: Was steckt hinter ChatGTP & Co? (2023)
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-
Morris, V.: Automated language identification of bibliographic resources (2020)
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- Date
- 2. 3.2020 19:04:22
-
Bager, J.: ¬Die Text-KI ChatGPT schreibt Fachtexte, Prosa, Gedichte und Programmcode (2023)
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- Date
- 29.12.2022 18:22:55
-
Aizawa, A.; Kohlhase, M.: Mathematical information retrieval (2021)
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-
Weiß, E.-M.: ChatGPT soll es richten : Microsoft baut KI in Suchmaschine Bing ein (2023)
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-
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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-
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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- Type
- m
-
Pepper, S.: ¬The typology and semantics of binominal lexemes : noun-noun compounds and their functional equivalents (2020)
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- Abstract
- The dissertation establishes 'binominal lexeme' as a comparative concept and discusses its cross-linguistic typology and semantics. Informally, a binominal lexeme is a noun-noun compound or functional equivalent; more precisely, it is a lexical item that consists primarily of two thing-morphs between which there exists an unstated semantic relation. Examples of binominals include Mandarin Chinese ?? (tielù) [iron road], French chemin de fer [way of iron] and Russian ???????? ?????? (zeleznaja doroga) [iron:adjz road]. All of these combine a word denoting 'iron' and a word denoting 'road' or 'way' to denote the meaning railway. In each case, the unstated semantic relation is one of composition: a railway is conceptualized as a road that is composed (or made) of iron. However, three different morphosyntactic strategies are employed: compounding, prepositional phrase and relational adjective. This study explores the range of such strategies used by a worldwide sample of 106 languages to express a set of 100 meanings from various semantic domains, resulting in a classification consisting of nine different morphosyntactic types. The semantic relations found in the data are also explored and a classification called the Hatcher-Bourque system is developed that operates at two levels of granularity, together with a tool for classifying binominals, the Bourquifier. The classification is extended to other subfields of language, including metonymy and lexical semantics, and beyond language to the domain of knowledge representation, resulting in a proposal for a general model of associative relations called the PHAB model. The many findings of the research include universals concerning the recruitment of anchoring nominal modification strategies, a method for comparing non-binary typologies, the non-universality (despite its predominance) of compounding, and a scale of frequencies for semantic relations which may provide insights into the associative nature of human thought.
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Suissa, O.; Elmalech, A.; Zhitomirsky-Geffet, M.: Text analysis using deep neural networks in digital humanities and information science (2022)
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