Differentiating artificial intelligence activity clusters in Australia

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Bratanova, Alexandra
Pham, Hien
Mason, Claire
Hajkowicz, Stefan
Naughtin, Claire
Schleiger, Emma
Sanderson, Conrad
Chen, Caron
Karimi, Sarvnaz
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2022
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We demonstrate how cluster analysis underpinned by analysis of revealed technology advantage can be used to differentiate geographic regions by activity in artificial intelligence (AI). Our analysis uses novel datasets on Australian AI businesses, intellectual property patents and labour markets to explore location, concentration and intensity of AI activities across 333 geographical regions. We find that Australia's AI business and innovation activity is clustered in geographic locations with higher investment in research and development. Through cluster analysis we identify three tiers of AI capability regions that are developing across the economy: ‘AI hotspots’ (10 regions), ‘Emerging AI regions’ (85 regions) and ‘Nascent AI regions’ (238 regions). While the AI hotspots are mainly concentrated in central business district (CBD) locations, there are examples when they also appear outside CBD in areas where there has been significant investment in innovation and technology hubs. Policy makers and investors can use these results to learn about the current landscape of AI business and innovation activities in Australia, identify potential growth opportunities in AI capabilities and to guide future policy and business decisions.

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Technology in Society

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71

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© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Sociology and social studies of science and technology

Sociology

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Bratanova, A; Pham, H; Mason, C; Hajkowicz, S; Naughtin, C; Schleiger, E; Sanderson, C; Chen, C; Karimi, S, Differentiating artificial intelligence activity clusters in Australia, Technology in Society, 2022, 71, pp. 102104

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