Journal of Information Technology · 2023 · 28 citations · 65 references
ComputingComputer ScienceComputer SystemsArtificial IntelligenceInnovationSystems ArchitectureTechnological InnovationTechnology ArchitectureAi-driven Innovation
The authors note that the AI industry is rapidly forming and ossifying, creating power imbalances and development challenges that warrant a system‑oriented analysis. The study proposes an original framework to systematically study Artificial Intelligence. Using the Large Technical Systems concept, the authors map AI’s components—algorithms, compute, and data—highlighting their interdependencies, synergies, and how they steer AI’s production, deployment, and evolution, and compare this framework to other radical innovation models. They find that AI shares essential features with infrastructural technologies such as the Internet, and that the LTS framework captures important nuances and traces cross‑influences among its constituting domains.
In this paper, we offer an original framework to study Artificial Intelligence (AI). The perspective we propose is based on the idea that AI is a system technology, and that a useful description of AI cannot abstain from mapping the components of the system, their interdependence, and how the synergies they create shape at the roots the directions of AI development. We adopt the concept of Large technical systems (LTS) to give substance and structure to our idea. Using LTS, we are able to scaffold AI and the forces at work steering its production, deployment, and evolution. We find that AI as a system shares essential features with infrastructural technologies such as the Internet. The LTS framework proves very useful to capture important nuances of the technology, and it allows us to trace the connections and cross–influences among its constituting domains—algorithms (software), compute (hardware), and data. We compare our proposed framework with other concepts usually associated with radical innovations, and suggest in which respects AI differs from these ideal–types. We consider ours a timely exercise, as we witness the formation of an AI industry. While in the making, this industry is rapidly ossifying, together with its specific problems, power imbalances, and development scenarios; the focus on the system–ness of AI allows uncovering the deeper structure of this technological breakthrough.
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