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230405 ||| eng |
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|a Dernis, Hélène
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|a Identifying artificial intelligence actors using online data
|h Elektronische Ressource
|c Hélène, Dernis ... [et al]
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| 260 |
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|a Paris
|b OECD Publishing
|c 2023
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| 300 |
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|a 50 p.
|c 21 x 28cm
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| 653 |
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|a Industry and Services
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| 653 |
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|a Science and Technology
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|a Calvino, Flavio
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|a Moussiegt, Laurent
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|a Nawa, Daisuke
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|a eng
|2 ISO 639-2
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| 989 |
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|b OECD
|a OECD Books and Papers
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|a OECD Science, Technology and Industry Working Papers
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| 028 |
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|a 10.1787/1f5307e7-en
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| 856 |
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|a oecd-ilibrary.org
|u https://doi.org/10.1787/1f5307e7-en
|x Verlag
|3 Volltext
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|a 600
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|a 330
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|a This paper uses information collected and provided by GlassAI to analyse the characteristics and activities of companies and universities in Canada, Germany, the United Kingdom and the United States that mention keywords related to Artificial Intelligence (AI) on their websites. The analysis finds that those companies tend to be young and small, mainly operate in the information and communication sector, have AI at the core of their business, and aim to provide customer solutions. It is noteworthy that the types of AI-related activities reported by them vary across sectors. Additionally, although universities are concentrated in and around large cities, this is not necessarily reflected in the intensity of AI-related activities. Taken together, this novel and timely evidence informs the debate on the most recent stages of digital transformation of the economy
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