John W. Bagby (Pennsylvania State University) and Kimberly Houser (University of North Texas) have posted “Artificial Intelligence: The Critical Infrastructures” on SSRN. Here is the abstract:
Artificial Intelligence (AI) innovation is most strongly impacted by AI Critical Infrastructures. These are the conditions, capacities, assets and inputs that create an environment conducive to the advancement of the AI technologies. Close inspection of AI’s generalized architecture reveals a supply chain that implies six AI critical infrastructures. There are at least seven necessary steps or processes contained in a generalized AI architecture. These steps are: (1) occurrences, events, facts or conditions transpire enabling the creation of potentially useful data, (2) these data are logged through capture and (increasingly computer and telecommunications enabled) initial storage, (3) such data are aggregated, often by numerous data repositories or AI operators, (4) human intelligence performs iterative analysis as derived from deployment of algorithms, (5) initial machine learning occurs, (6) near constant feedback loops are deployed by many AI applications that adapt the underlying model as new data is incorporated, and (7) based on insights resulting from AI, decision-making occurs, both automatically by computer or by human intervention,. Successful Machine Learning requires ample supply of the six broad AI critical infrastructures: (i) strategic insight/vision largely expressed as regional and/or national Industrial Policy, which is paramount in impacting all four other AI critical infrastructures, (ii) human intellect is needed to foster a deep-bench, from a competent AI Workforce, (iii) R&D Investment in AI, (iv) AI Hardware, both Computing Power and Connectivity (ICT), (v) bountiful and ever growing supply of Accessible Data, and (vi) market receptivity as sustainable demand for AI knowledge to monetize successful AI innovation. This article provides an initial foundation for a comparative of the three world economies (regions) seemingly best positioned to make substantial AI advancements. Predictably, significant differences among the political and cultural drivers in these three regions are likely to impact needed commitment to AI critical infrastructures: China (Asia) vs. the United States (North America) vs. European Union (EU). The harsh reality of AI innovation is that delays in commitment and deployment of AI critical infrastructures will relegate the losing region(s) to become, at best, a chronic AI customer rather than a major successful AI supplier.
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