On Defining Smart Cities using Transformer Neural Networks
Andrei Khurshudov
Cities worldwide are rapidly adopting smart technologies, transforming urban
life. Despite this trend, a universally accepted definition of 'smart city'
remains elusive. Past efforts to define it have not yielded a consensus, as
evidenced by the numerous definitions in use. In this paper, we endeavored to
create a new 'compromise' definition that should resonate with most experts
previously involved in defining this concept and aimed to validate one of the
existing definitions. We reviewed 60 definitions of smart cities from industry,
academia, and various relevant organizations, employing transformer
architecture-based generative AI and semantic text analysis to reach this
compromise. We proposed a semantic similarity measure as an evaluation
technique, which could generally be used to compare different smart city
definitions, assessing their uniqueness or resemblance. Our methodology
employed generative AI to analyze various existing definitions of smart cities,
generating a list of potential new composite definitions. Each of these new
definitions was then tested against the pre-existing individual definitions we
have gathered, using cosine similarity as our metric. This process identified
smart city definitions with the highest average cosine similarity, semantically
positioning them as the closest on average to all the 60 individual definitions
selected.