Nicos Komninos (p)

Peter Batey

This paper presents the latest work from URENIO Research on smart cities, connected intelligence, and system innovations. We argue that smart city ecosystems generate connected intelligence by networking three types of intelligence: human, collective, and machine intelligence. In turn, connected intelligence and the binary couplings between its components become the key driver of system-level innovations and radical urban change in contemporary cities.
Our focus is on Mobility as a Service (MaaS), “a user-centric, intelligent mobility management and distribution system, where an integrator brings together the offerings of the multiple mobility service providers and allows end-users to seamlessly plan and pay for mobility through a digital interface” (MaaSLab, 2018). MaaS is expected to bring radical changes in the organization of cities in the coming years, freeing cities from a mobility model based on private car ownership, and introducing more sustainable transport modes.
The paper contains four sections: Section 1 outlines key instances of the smart city paradigm and its main pillars, such as (a) the system-of-systems perspective of smart cities composed of smart ecosystems, (b) planning and projects for digitalization, optimization, and innovation of city activities and ecosystems, and (c) connected intelligence and binary couplings as the core drivers of urban change.
In Section 2, we discuss the components of connected intelligence, the main features of human, collective, and machine intelligence, the typology of binary couplings, and representative examples of binary coupling from different city ecosystems.
Section 3 presents a survey on connected intelligence in the MaaS ecosystem, which identifies binary couplings in critical decisions in setting up MaaS, such as planning, supply orchestration, demand orchestration, business model, and integration. We study MaaS from the perspective of digital platforms, platformization cities, and the new ways of delivering services through the aggregation of complementors, customers, and users.
Finally, in Section 4, we discuss how a connected intelligence perspective can guide innovation in the MaaS ecosystem, and we provide some conclusions on how binary coupling can contribute to system innovations that transform the way we live in cities.

Deborah Strumsky (p)

Nicos Komninos

Invention in Artificial Intelligence (AI) has accelerated radically in the past 10 years. The transformation of computing speeds, availability of big data, and proliferation of AI techniques have allowed AI applications to diffuse rapidly across economic sectors. Yet, in many ways, AI has failed to live up to expectations of its transformative potential. Most of the invention taking place has been within a narrow set of AI methods applied in a similar manner, making the new economic geography of AI invention appear very similar to the geography of invention generally. However, inventive novelty in AI has been increasing, this research examines inventive novelty in AI and how novelty of invention in AI is related to invention in other technologies in the same micropolitan and metropolitan areas.

Laurie Schintler (p), Ting Zhang

Deborah Strumsky

The recent release of ChatGPT, or Chat Generative Pre-Trained Transformer—a super-smart Generative Artificial Intelligence (A.I.) system, has triggered new waves of concern regarding job automation. Given the capabilities of ChatGPT and related platforms, particularly in natural language processing and content creation, there is the potential that such systems could lead to cognitive automation, involving not only routine tasks but also those considered to be non-routine. On the other hand, throughout history, new technologies have always created more jobs than they replace. Will the effects of ChatGPT be any different? This study attends to this question, focussing on employment patterns in the United States Metropolitan Statistical Areas (MSAs). We will first perform text analytics and machine learning on the U.S. Department of Labor O*NET skills descriptions to develop probabilities that different occupations involve skill sets relating to generative A.I. and natural language processing. We will then employ panel econometric modeling to assess the extent to which A.I. systems like ChatGPT have replaced cognitive jobs over the last few years. Employment figures by occupation and MSA will be extracted from the Current Population Survey (CPS), which provides data on a monthly basis. From this exploratory analysis, we will develop recommendations for further research while reflecting on the implications of cognitive automation and related developments for New Economic Geography (NEG).

Bjorn Harsman (p), Folke Snickars (p)

Laurie Schintler