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One of the defining trends of our time is the rapid development and spread of artificial intelligence (AI) and machine learning. With the recent boom in digital innovation, AI is getting a bigger emphasis, brings new opportunities to tackle many issues within organizations and raises new challenges about its usage and limits. It is expected that more and more "smart" interconnected machines capable of situational awareness and independent decision-making will emerge. Large-scale projects are being launched all over the world including Hungary, for developing and applying automation and AI-based business solutions. AI and expert systems were already used in the fifties and later in the seventies and eighties, but results did not bring any breakthrough success. Thanks to technological development the situation has changed and many seemingly unsolvable problems (e.g. machine interpretation, facial recognition, diagnosis and prognosis of certain rare diseases) are progressing well. Beside "human" knowledge, "machine" and common "human-machine" knowledge play an increasing role, in addition to "human" creativity "machine" creativity gains ground. In the field of Knowledge Management (KM), both theoretically and practically important issues arise like how the development of technology affects KM or how AI can contribute to the organization of knowledge, and how it can facilitate and extend the future of knowledge workers. The research is intended to map the future of KM and knowledge workers in the light of the emergence of knowledge generated by emerging technologies. The research is carried out via qualitative methodology by conducting interviews that result in organizational case studies with respondents from various organizations operating in Hungary. Just as effective adaptation of KM and today's technological achievements play an essential role in the survival of companies, it can be a significant advantage if the knowledge created during application of emerging technologies is managed in some way. [ABSTRACT FROM AUTHOR]

Artificial Intelligence and Robotics, Industry 4.0 and Digitalization, Machine Learning and Big Data are defining nowadays' trends worldwide, moreover Artificial Intelligence has become the biggest buzzword these days. It has the potential to completely reshape tomorrow's workplace, but the main question is how. Business always wants to derive insights from information in order to make better, real time, fact- based decisions: there is a demand for depth of knowledge that drives the growth of big data tools. Organizations need to start managing knowledge through different sources, and integrating its usefulness via a range of technologies. In 1966 Polanyi argued that human knowledge and capability relied on skills and rulesets that are often beneath conscious appreciation (transmitted to the people via culture, tradition, evolution, etc.). The thesis of the book was summarized as "We can know more than we can tell". Many of the tasks people perform rely on tacit, intuitive knowledge that is difficult to codify and automate (Polanyi's paradox). In recent decades, focusing on understanding, capturing, sharing and applying tacit knowledge has been the focus of either national or international research. In the current state of technological development, machines become capable to learn and manage tacit knowledge. Beyond "human" knowledge, "machine" knowledge will play an increasing role. In the area of Knowledge Management, questions of both theoretical and practical relevance are emerging: Does this Polanyi's paradox interpret as an obstacle throughout technological improvement? What affects could Artificial Intelligence have on Knowledge management? How could Artificial Intelligence be accompanied with Knowledge Management facilitating and enhancing the future of knowledge workers? How could we apply and manage knowledge created by these new technologies? What are the differences between human and machine knowledge? The aim of the paper is to explore the theoretical and the practical background and limitations of Artificial Intelligence and Knowledge Management with particular emphasis on the role of human-machine tacit knowledge. [ABSTRACT FROM AUTHOR]