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  • × author_ss:"Hesse, C."
  1. Hesse, C.: ¬Das kleine Einmaleins des klaren Denkens : 22 Denkwerkzeuge für ein besseres Leben (2009) 0.03 
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    Abstract
    Jeder Mensch kann denken. Doch ganz so wie beim Laufen, Sprachenlernen oder Organisieren können einige von uns es besser, andere weniger gut oder gar schlecht. Für alle gibt es jedoch eine erfreuliche Nachricht: Ebenso wie die anderen Fähigkeiten lässt sich auch das Denken verbessern - durch Übung, Vervollkommnung der Technik und Einsatz von Hilfsmitteln. Das äußerst vergnüglich zu lesende Buch stellt knapp zwei Dutzend leicht verständliche, doch ungemein wirksame Denkwerkzeuge vom Analogieprinzip über den Widerspruchsbeweis bis hin zur Brute-force-Methode vor. Bei mittlerer Kondition sind sie alle mit einfacher Schulmathematik zu verstehen. Diese bewährten Techniken des formalen Denkens erleichtern die Erzeugung von Ideen und steigern die Erfolgsaussichten bei der Lösung von Problemen. Zugleich zeigen die unterhaltsamen Gedankenreisen des Buches, daß Denken und Spaßhaben sich keineswegs ausschließen.
    Footnote
    Rez. in: Spektrum der Wissenschaft 2010, H.7, S.100-101 (R. Pilous): "Dieses Buch verschafft einem nicht gleich "ein besseres Leben" - aber immerhin eine gute Einführung in klassische Problemlösungsmethoden."
  2. 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) 0.00 
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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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