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A Study on the Deduction and Diffusion of Promising Artificial Intelligence Technology for Sustainable Industrial Development
- Source :
- Sustainability, Volume 12, Issue 14, Sustainability, Vol 12, Iss 5609, p 5609 (2020)
- Publication Year :
- 2020
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2020.
-
Abstract
- Based on the rapid development of Information and Communication Technology (ICT), all industries are preparing for a paradigm shift as a result of the Fourth Industrial Revolution. Therefore, it is necessary to study the importance and diffusion of technology and, through this, the development and direction of core technologies. Leading countries such as the United States and China are focusing on artificial intelligence (AI)&rsquo<br />s great potential and are working to establish a strategy to preempt the continued superiority of national competitiveness through AI technology. This is because artificial intelligence technology can be applied to all industries, and it is expected to change the industrial structure and create various business models. This study analyzed the leading artificial intelligence technology to strengthen the market&rsquo<br />s environment and industry competitiveness. We then analyzed the lifecycle of the technology and evaluated the direction of sustainable development in industry. This study collected and studied patents in the field of artificial intelligence from the US Patent Office, where technology-related patents are concentrated. All patents registered as artificial intelligence technology were analyzed by text mining, using the abstracts of each patent. The topic was extracted through topic modeling and defined as a detailed technique. Promising/mature skills were analyzed through a regression analysis of the extracted topics. In addition, the Bass model was applied to the promising technologies, and each technology was studied in terms of the technology lifecycle. Eleven topics were extracted via topic modeling. A regression analysis was conducted to identify the most promising/mature technology, and the results were analyzed with three promising technologies and five mature technologies. Promising technologies include Augmented Reality (AR)/Virtual Reality (VR), Image Recognition and Identification Technology. Mature technologies include pattern recognition, machine learning platforms, natural language processing, knowledge representation, optimization, and solving. This study conducts a quantitative analysis using patent data to derive promising technologies and then presents the objective results. In addition, this work then applies the Bass model to the promising artificial intelligence technology to evaluate the development potential and technology diffusion of each technology in terms of its growth cycle. Through this, the growth cycle of AI technology is analyzed in a complex manner, and this study then predicts the replacement timing between competing technologies.
- Subjects :
- Topic model
Knowledge representation and reasoning
Computer science
artificial intelligence technology
Geography, Planning and Development
TJ807-830
02 engineering and technology
Management, Monitoring, Policy and Law
Business model
TD194-195
Renewable energy sources
0502 economics and business
0202 electrical engineering, electronic engineering, information engineering
GE1-350
Industrial Revolution
USPTO analysis
Sustainable development
Patent office
Environmental effects of industries and plants
promising technology
Bass diffusion model
Renewable Energy, Sustainability and the Environment
business.industry
05 social sciences
innovation
Environmental sciences
ComputingMilieux_GENERAL
Information and Communications Technology
Paradigm shift
technology diffusion
020201 artificial intelligence & image processing
Augmented reality
Artificial intelligence
business
050203 business & management
Subjects
Details
- Language :
- English
- ISSN :
- 20711050
- Database :
- OpenAIRE
- Journal :
- Sustainability
- Accession number :
- edsair.doi.dedup.....b3b2ea959a3b69cdaecaf7dec74ec345
- Full Text :
- https://doi.org/10.3390/su12145609