Urban Life and Artificial Intelligence, Machine Learning, Deep Learning, and Ambient Learning in Smart Cities

1. Introduction

Marzano (2006) describes concepts such as artificial intelligence (AI) as “terms that trigger the imagination.” Cugurullo (2020) provides an exploration of “how the development of AI intersects with the development of the city” both in theory and in practice. For Cugurullo (2020), urban artificial intelligence takes the form of “artifacts operating in cities, which are capable of acquiring and making sense of information on the surrounding urban environment, eventually using the acquired knowledge to act rationally according to pre-defined goals, in complex urban situations when some information might be missing or incomplete.” As such, Cugurullo (2020) argues that an inquiry of a philosophical and multidisciplinary nature is needed that is “proactive” to “inform the development of AI, as it now intersects with the development of the city.” Zhang and He (2020) highlight the importance of a sustainable future incorporating both humanity and nature using “life-oriented approaches.” Wu, Ji, Yu, Zeng, Wu, and Shidujaman (2021) focus on the creativity potential of people and AI interactivities with the notion of AI creativity while advancing the Human-AI co-creation model with an emphasis on collaboration. In response, the work in this chapter is significant in that it considers that human capabilities are not static in that creativity, innovation, and discoveries are emergent and ongoing and as such, builds on the range of perspectives in the research literature on AI to explore urban life and the ambient in the context of smart cities giving rise to the motivation for this with the following objectives.

Objectives: The main objectives of this chapter are to a) explore urban life and the ambient in relation to artificial intelligence (AI), machine learning (ML), deep learning (DL), and ambient learning in the context of smart cities; b) formulate a conceptual framework for artificial intelligence (AI) for urban life and the ambient in smart cities; and c) explore the nature of the relationship between AI and smart cities as a way of learning more about ambient learning. These objectives give rise to the main research question under exploration in this chapter – How and why does artificial intelligence, including machine learning and deep learning, manifest in urban life and the ambient in smart cities?

2. Background

Miailhe and Hodes (2017) provide an overview of the emergence and history of artificial intelligence (AI), describing artificial narrow intelligence (ANI) as ‘weak’ where machines “operate strictly within the confine of the scenarios for which they are programmed” and artificial general intelligence (AGI) as “strong” as in “an autonomous machine’s ability to perform any intellectual tasks that a human can perform.” Accordingly, current AI capabilities are said to be of the ANI type involving machine learning algorithms, excluding AGI capability and Artificial Super Intelligence (ASI) that are both said to be in the near to distant future (Miailhe and Hodes, 2017). Amabile (2020), an influential creativity researcher in the management domain argues for the study of “AI and computer-assisted human intelligence” by organizational researchers focused on creativity and innovation to determine “the ways in which they may yield creative breakthroughs” as well as “how those innovations might impact–and be impacted by–workers, consumers, organizations, and society.” Shneiderman (2022) envisions an integration of AI (artificial intelligence) with HCI (Human-computer interaction) approaches so as “to amplify, augment, and enhance human abilities” in support of creativity and much more.