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230704 ||| eng |
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|a QA76.9.N38
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| 100 |
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|a Stadler, Christian
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| 245 |
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|a Three lessons from chatting about strategy with ChatGPT
|b when generative AI's capacity for strategy creation is put to the test, it reveals where its strengths lie, and where humans still have the edge
|c Christian Stadler, Martin Reeves
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| 250 |
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|a [First edition]
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| 260 |
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|a [Place of publication not identified]
|b MIT Sloan Management Review
|c 2023
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| 300 |
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|a 5 pages
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| 653 |
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|a Intelligence artificielle / Logiciels
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| 653 |
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|a Artificial intelligence / Computer programs / http://id.loc.gov/authorities/subjects/sh85008181
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| 653 |
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|a Traitement automatique des langues naturelles
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| 653 |
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|a Natural language processing (Computer science) / fast
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| 653 |
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|a Natural language processing (Computer science) / http://id.loc.gov/authorities/subjects/sh88002425
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| 653 |
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|a Artificial intelligence / Computer programs / fast
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| 700 |
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|a Reeves, Martin
|e author
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Reprint #64446
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|u https://learning.oreilly.com/library/view/~/53863MIT64446/?ar
|x Verlag
|3 Volltext
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|a 500
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|a 006.3/5
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|a Large language models like ChatGPT are generating excitement about their potential use cases. A pair of business strategists decided to test whether the tool can support ideation, experimentation, evaluation, and storytelling as part of the strategy creation process. In a series of experiments, they posed a realistic question of strategy to ChatGPT, followed by iterative follow-up questions. In this article, they share three lessons that emerged from their experiments with generative AI.
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