The Information and Communications Technology is Driving Artificial Intelligence to Leverage Refined Knowledge for the World Sustainable Development -Part1-
Romanian Distribution Committee Magazine, 2018, vol. 9, issue 4, 16-25
The paper presents an analysis of the complex challenges of artificial intelligence (AI) contribution in leveraging refining knowledge, in the dynamic context generated by information and communications technology (ICT) exponential evolution aiming the progress of the Information society (IS) toward Knowledge Based Society (KBS). Based on Internet of Things (IoT) dissemination of intelligence everywhere on Earth, IoT and AI development will be strongly linked ‘’learning from everything’’. Using IoT sensors, AI could learn from everywhere and eventually turn data into information/knowledge, inducing the crucial role of IoT and AI in Industry 4.0 revolution. For leveraging refining knowledge at World scale, AI is encompassing a large and diverse range of processes, referring to dynamic events and things, where eventually a huge amount of data will be generated. The AI role is there to process these data in order to extract information or even knowledge, as eventually to contribute to refining knowledge. AI has to be seen and conceived not as “a replacement but rather as an enhancement of HI”, but this relation has to be mutually understood in order to increase precision, efficiency and sustainability of ICT development in IS/KBS, by refining knowledge. Eventually, refining knowledge could be obtained by combining the collected data and prior knowledge, but keeping the AI/HI combination alive. In order to increase the confidence in the AI deep learning system, the old advice “divide et impera" could be used and applied as “different parts can be validated in different ways”. Therefore, a complex deep learning AI system could be better controlled from safety point of view if we can divide it in subsystems with higher safety parameters or easier to validate. The paper also presents the importance of identifying and adding to the AI deep learning system “some rules and some human knowledge”, which, by our opinion, is just the most difficult, advanced but actual issue for supporting deep learning AI to really leverage knowledge refining, not only increase safety. A prominent paper conclusion is that these rules include a structural approach of the collected data/information (known also as “knowledge engineering”), as “the better that information is structured, the more effective the program is”, but also data pre-filtering in order to obtain relevant knowledge along with usage of crowd intelligence in the process. The final conclusion is that AI processes toward refined knowledge are more and more complex as we are heading to higher performance in the ICT/IS/KBS dynamic context and their further and timely deep analysis is necessary for a sustainable World development.
Keywords: Artificial Intelligence; Refining Knowledge; Machine Learning; Deep Learning; Human Intelligence; Internet of Things; Information Society; Knowledge Based Society; Knowledge Engineering; Sustainable World Development. (search for similar items in EconPapers)
JEL-codes: L63 L86 M15 O31 O33 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:rdc:journl:v:9:y:2018:i:4:p:16-25
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