Abstract
As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
| Original language | English |
|---|---|
| Article number | 101994 |
| Journal | International Journal of Information Management |
| Volume | 57 |
| Early online date | 27 Aug 2019 |
| DOIs | |
| Publication status | Published - Apr 2021 |
Funding
Research 4 4 Acknowledgements – GA and BL are partly supported by STFC grant ST/P00055X/1. BL is supported by a Royal Society Wolfson Award. in the fundamental sciences aims to investigate Nature at both the largest and the smallest length scales, at the highest energies, and with complex behaviour emerging from simple underlying laws. In the physical sciences this encapsulates the study of the cosmos, including e.g. dark matter and dark energy, gravitational waves, and black holes, and of elementary particles, including e.g. the Higgs boson, the quark-gluon plasma and new physics beyond the Standard Model. Dynamics at small length scales is determined by the rules of quantum mechanics, rather than classical – Newtonian – mechanics, which introduces an intrinsic indeterminacy in the problem, following the usual probabilistic interpretation. Understanding complex quantum systems, quantum control and quantum information is highly relevant for the paradigm of quantum computing, which surpasses classical computing algorithms in a dramatic fashion and, once available, will make previously incomputable problems solvable. Phase transitions, such as the transition between ice and water, or between magnetic and non-magnetic phases in materials, are manifestations of collective behaviour emerging from simple laws. Order parameters, e.g. the net magnetisation of a material, display the presence or absence of macroscopic order and are connected to the underlying pattern of symmetry breaking, linking phase transitions to the microscopic laws of Nature in a precise way. The adoption of artificial intelligence (AI) in the fundamental sciences, especially in the form of machine learning (ML), has seen a striking increase in the past 5 years or so ( Carleo et al., 2019 ; Guest, Cranmer, & Whiteson, 2018 ). While previously a link between ML and the physical sciences existed via statistical mechanics, the methodology developed in physics to analyse large systems with fluctuating degrees of freedom, in recent years the use of ML has exploded and it is now employed in most branches of fundamental science, with increasing success and acceptance. 3.4.1.1 This submission was developed from a workshop on Artificial Intelligence (AI), which was held at the School of Management, Swansea University on 13 th June 2019. We are very grateful to everyone who attended the workshop and contributed their perspectives during the workshop and as an input to this article. We are also truly appreciative to those who although not able to attend the workshop, provided their valuable perspectives for developing this work. We are also very grateful to our Senior PVC – Professor Hilary Lappin-Scott, the keynote speaker – Mr Lee Waters AM, Deputy Minister for Economy and Transport, National Assembly for Wales and the following panellists from industry and public sector organisations for enriching our understanding of this emerging area by providing their valuable perspectives that have informed the views presented in this article: Ms Sara El-Hanfy, Innovate UK; Mr Peter Chow, AI & Cloud Product Design Manufacturing & Inspection, Fujitsu UK; Ms Kimberley Littlemore, Director of eHealth Digital Media, UK; Mr Chris Reeves, Country Digitisation Director, Cisco UK & Ireland; Mr Adam Wedgbury, Team Leader for Cyber Security Innovation, Airbus; and Mr Toby White, CEO of Artimus, Cardiff, UK. We are also very grateful to our colleagues, Amy Jones and Julie Bromhead, for all their valuable support for organising the workshop. Finally, we are grateful to the Emerging Markets Research Centre (EMaRC), Swansea i-Lab (Innovation Lab), and Department of Business at the School of Management, Swansea University for their financial support in the organising of this workshop.
Keywords
- AI
- Artificial intelligence
- Cognitive computing
- Expert systems
- Machine learning
- Research agenda