Deep Learning and Tacit Knowledge Transfer – An Exploratory Study

Abstract

In 1966, Michael Polanyi wrote his seminal piece on the ‘tacitness’ of knowledge, essentially bringing to the fore, the non-codifiability of knowledge and the possibility for individuals to know more than they are able to express. Nearly thirty years later Nonaka and Takeuchi (1995) popularised the possibility for knowledge conversion between the tacit and explicit dimensions of knowledge. They proposed that organisations are able to create knowledge through a spiral of interactions between socialisation, externalisation, combination and internalisation of knowledge. Since then, various attempts have been made to develop mechanisms for codifying tacit knowledge including; storytelling, modelling, and more recently, various artificial intelligence/machine learning algorithms. In this study we examine the use of deep learning for representing, codifying and eventually transferring tacit knowledge. We draw on existing research on the role of artificial intelligence in Knowledge Management as well as current works on Deep learning to explore the potential role that deep learning can play in the learning, representation and transfer of tacit knowledge. Deep learning, as a subset of machine learning in artificial intelligence which provides algorithms that mimic the way the brain works and offers significant prospects for knowledge externalisation. Specifically, it can provide a means for representing knowledge in a different manner to human representation. This alternative machine representation is premised on the notion that if tacit knowledge can be learned and represented in a way that can then be codified, the knowledge modelled in such a way is then transferable. Arguably, where deep learning is able to capture and represent tacit knowledge, the ability for knowledge to be codified and externalised will increase exponentially and invariably constitute a significant breakthrough in the ability for both individuals and organisations to access and combine existing knowledge as well as to create new knowledge.