Forecasting AI progress: A research agenda

Gruetzemacher, Ross; Dorner, Florian E.; Bernaola-Alvarez, Niko; Giattino, Charlie; Manheim, David

doi:10.1016/j.techfore.2021.120909

Cited by 20 publications

(11 citation statements)

References 48 publications

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“…They fed business relationship and financial information into an AI-based technique to construct a forecasting model. Gruetzemacher et al ( 2021 ) described the development of a research agenda for forecasting the progress of AI. It utilized the Delphi technique to elicit and aggregate experts’ opinions on which questions and methods to prioritize.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since the onset of the COVID-19 pandemic, the WHO-advised social distancing has led to a more virtual existence, which may accelerate the development of Artificial Intelligence (AI) and Robotics technologies further. During the past decade, AI technologies have been hailed by many academics and practitioners as revolutionary and game-changing in the business world, a sphere in which the AI and robotics activities have significantly increased (Felten et al 2018 ; Furman and Seamans 2019; Gruetzemacher et al 2021 ; Mikalef and Gupta 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting

Zhang

Gupta

2023

Financ Innov

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Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability, and the development of the artificial intelligence industry. To provide investors with a more reliable reference in terms of artificial intelligence index investment, this paper selects the NASDAQ CTA Artificial Intelligence and Robotics (AIRO) Index as the research target, and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics. Specifically, this paper uses the ensemble empirical mode decomposition (EEMD) method and the modified iterative cumulative sum of squares (ICSS) algorithm to decompose the index returns and identify the structural breakpoints. Furthermore, it combines the least-square support vector machine approach with the particle swarm optimization method (PSO-LSSVM) and the generalized autoregressive conditional heteroskedasticity (GARCH) type models to construct innovative hybrid forecasting methods. On the one hand, the empirical results indicate that the AIRO index returns have complex structural characteristics, and present time-varying and nonlinear characteristics with high complexity and mutability; on the other hand, the newly proposed hybrid forecasting method (i.e., the EEMD-PSO-LSSVM-ICSS-GARCH models) which considers these complex structural characteristics, can yield the optimal forecasting performance for the AIRO index returns.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting

Zhang

Gupta

2023

Financ Innov

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“…Using a similar approach, Bowles arrived at an even higher figure, claiming that 54% of jobs in the EU and USA were under threat in the same time span (Bowles 2014). Since then, many studies, using a variety of methods have added to these anticipations (Benzel et al 2015;Bruun and Duka 2018;Halal et al 2017;Schwab 2017;Chessell 2018;Gruetzemacher et al 2020;Gruetzemacher et al 2021). In a multi-country approach covering 32 nations, Nedelkoska and Quintini (2018) estimate that 14% of jobs are highly automatable (probability of automation over 70%), 32% have a risk of between 50 and 70%.…”

Section: Pessimistic Scenariosmentioning

confidence: 99%

Artificial intelligence and work: a critical review of recent research from the social sciences

Deranty

Corbin

2022

AI & Soc

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This review seeks to present a comprehensive picture of recent discussions in the social sciences of the anticipated impact of AI on the world of work. Issues covered include: technological unemployment, algorithmic management, platform work and the politics of AI work. The review identifies the major disciplinary and methodological perspectives on AI’s impact on work, and the obstacles they face in making predictions. Two parameters influencing the development and deployment of AI in the economy are highlighted: the capitalist imperative and nationalistic pressures.

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“…Despite that, the understanding of firms regarding the long-term social impacts of ML and AI has been modest (Wilson and Van Der Velden, 2022). The uncertainty and lack of consensus among decision-makers and researchers between the positive and adverse effects of ML and AI on society have become an inherent and pending research need (Gruetzemacher et al, 2021). Research studies in the last decade have generally focused more on ML and AI's technical and theoretical attributes .…”

Section: Introductionmentioning

confidence: 99%

Automation-augmentation paradox in organizational artificial intelligence technology deployment capabilities; an empirical investigation for achieving simultaneous economic and social benefits

Kumar,

Bhattacharyya,

Krishnamoorthy

2023

JEIM

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PurposeThe purpose of this research study was to understand the simultaneous competitive and social gains of machine learning (ML) and artificial intelligence (AI) usage in organizations. There was a knowledge hiatus regarding the contribution of the deployment of ML and AI technologies and their effects on organizations and society.Design/methodology/approachThis study was grounded on the dynamic capabilities (DC) and ML and AI automation-augmentation paradox literature. This research study examined these theoretical perspectives using the response of 239 Indian organizational chief technology officers (CTOs). Partial least square-structural equation modeling (PLS-SEM) path modeling was applied for data analysis.FindingsThe results indicated that ML and AI technologies organizational usage positively influenced DC initiatives. The findings depicted that DC fully mediated ML and AI-based technologies' effects on firm performance and social performance.Research limitations/implicationsThis study contributed to theoretical discourse regarding the tension between organizational and social outcomes of ML and AI technologies. The study extended the role of DC as a vital strategy in achieving social benefits from ML and AI use. Furthermore, the theoretical tension of the automation-augmentation paradox was explored.Practical implicationsOrganizations deploying ML and AI technologies could apply this study's insights to comprehend the organizational routines to pursue simultaneous competitive benefits and social gains. Furthermore, chief technology executives of organizations could devise how ML and AI technologies usage from a DC perspective could help settle the tension of the automation-augmentation paradox.Social implicationsIncreased ML and AI technologies usage in organizations enhanced DC. They could lead to positive social benefits such as new job creation, increased compensation to skilled employees and greater gender participation in employment. These insights could be derived based on this research study.Originality/valueThis study was among the first few empirical investigations to provide theoretical and practical insights regarding the organizational and societal benefits of ML and AI usage in organizations because of their DC. This study was also one of the first empirical investigations that addressed the automation-augmentation paradox at the enterprise level.

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Forecasting AI progress: A research agenda

Cited by 20 publications

References 48 publications

A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting

A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting

Artificial intelligence and work: a critical review of recent research from the social sciences

Automation-augmentation paradox in organizational artificial intelligence technology deployment capabilities; an empirical investigation for achieving simultaneous economic and social benefits

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