Samoili, Sofia and Cobo, Montserrat Lopez and Gomez, Emilia and De Prato, Giuditta and Martinez-Plumed, Fernando and Delipetrev, Blagoj (2020) AI Watch. Defining Artificial Intelligence. Towards an operational definition and taxonomy of artificial intelligence. Technical Report. Joint Research Centre (Seville site).
Text
3. jrc118163_ai_watch._defining_artificial_intelligence_1.pdf Download (2MB) |
Abstract
This report proposes an operational definition of artificial intelligence to be adopted in the context of AI Watch, the Commission knowledge service to monitor the development, uptake and impact of artificial intelligence for Europe. The definition, which will be used as a basis for the AI Watch monitoring activity, is established by means of a flexible scientific methodology that allows regular revision. The operational definition is constituted by a concise taxonomy and a list of keywords that characterise the core domains of the AI research field, and transversal topics such as applications of the former or ethical and philosophical considerations, in line with the wider monitoring objective of AI Watch. The AI taxonomy is designed to inform the AI landscape analysis and will expectedly detect AI applications in neighbour technological domains such as robotics (in a broader sense), neuroscience or internet of things. The starting point to develop the operational definition is the definition of AI adopted by the High Level Expert Group on artificial intelligence.To derive this operational definition we have followed a mixed methodology. On one hand, we apply natural language processing methods to a large set of AI literature. On the other hand, we carry out a qualitative analysis on 55 key documents including artificial intelligence definitions from three complementary perspectives: policy, research and industry. A valuable contribution of this work is the collection of definitions developed between 1955 and 2019, and the summarisation of the main features of the concept of artificial intelligence as reflected in the relevant literature.
Item Type: | Monograph (Technical Report) |
---|---|
Subjects: | Natural sciences > Computer and information sciences |
Divisions: | Faculty of Computer Science |
Depositing User: | Blagoj Delipetrev |
Date Deposited: | 06 May 2021 13:17 |
Last Modified: | 06 May 2021 13:17 |
URI: | https://eprints.ugd.edu.mk/id/eprint/28047 |
Actions (login required)
View Item |