Artificial Intelligence in the Urban Environment: Smart Cities as Models for Developing Innovation and Sustainability

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Abstract

Climate change, overpopulation and the squandering of resources currently pose problems of such magnitude that they require a change in the trend to mitigate their effects. It is essential to make society aware of the facts and to educate the population about the advantages that new technologies can provide for efficient urban development. We therefore ask whether an ordinary medium-sized city can become a Smart City. In order to assess this possibility, our study analyzes different models of Smart Cities implemented in Spain (e.g., Madrid, Barcelona, Valencia, Malaga and Santander), contrasting them with the specific case of one city that is not yet a Smart City (Granada) in order to discuss which strategic technological actions to implement in different topical areas of action: the economy, sustainability, mobility, government, population, and quality of life. The study uses Cohen’s wheel to give researchers in the field a series of indicators and factors that can be used to analyze public data with statistical methods in order to obtain clear positive scores for Madrid and Barcelona. The analysis shows Granada’s deficiencies in the scores for digital government, accessibility, the efficiency of public transport, and mobility, among others. Finally, the data obtained demonstrate the need to implement an integrated dashboard with different proposals in the strategic areas analyzed in order to achieve the transformation of conventional cities into Smart Cities.

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Most cited references 48

Record: found
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Article: not found

Dynamic capabilities and strategic management

David J. Teece, Gary Pisano, Amy Shuen … (1998)

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Record: found
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Article: not found

Mastering the game of Go with deep neural networks and tree search.

David Silver, Aja Huang, Chris J Maddison … (2016)

The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses 'value networks' to evaluate board positions and 'policy networks' to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.