Lucian Leahu
Phoebe Sengers
ABSTRACT
In many ways, the central problem of ubiquitous computing -- how computational systems can make sense of and respond sensibly to a complex, dynamic environment laden with human meaning -- is identical to that of Artificial Intelligence (AI). Indeed, some of the central challenges that ubicomp currently faces in moving from prototypes that work in restricted environments to the complexity of real-world environments -- e.g. difficulties in scalability, integration, and fully formalizing context -- echo some of the major issues that have challenged AI researchers over the history of their field. In this paper, we explore a key moment in AI's history where researchers grappled directly with these issues, resulting in a variety of novel technical solutions within AI. We critically reflect on six strategies from this history to suggest technical solutions for how to approach the challenge of building real-world, usable solutions in ubicomp today.
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