VIENNA, AUSTRIA, October 03, 2018 — With the digitalization of various business processes, the value-adding part of IT in companies is increasing. More and more data is being produced, collected, classified, analyzed and finally provided as a useful and valuable resource for our businesses. One of the key challenges for any organization today is the implementation of a working digitization strategy. This presupposes excellence in machine learning, artificial intelligence and semantic technologies. Three international economists and researchers have revealed at SEMANTiCS 2018 conference what the megatrend Artificial Intelligence means for companies.
Alan Morrison, Senior Research Fellow at PwC
Organizational boundaries are becoming more porous, and there's more and more collaboration between organizations. We've also seen the rise of the gig economy – freelancers or contractors are more in evidence. In some cases, the bulk of the entire organization consists of contractors. In general, we're just seeing a more fluid environment. IDC describes the online working environment as the Innovation Graph. Companies will need to consider how to position themselves in new roles in this Graph. Companies can morph into new roles this way and do their own boundary crossing in the process.
Andreas Blumauer, Co-Founder and CEO of Semantic Web Company
AI has developed into two main branches, which are 'Symbolic AI' and 'Statistical AI'. Semantic Web is based on the approach mentioned first, while most ML techniques such as Deep Learning are based on the second version. Over the past months we have seen promising developments into a new kind of AI that we call 'Semantic AI'. Recently a team of researchers at Free University of Amsterdam has published a paper that will guide us the way towards a fully developed 'Semantic AI': The Knowledge Graph as the Default Data Model for Machine Learning. The main idea is not to use single and isolated input data, typically a CSV file, to feed the ML algorithms, instead using an integrated and linked data set based on a more expressive semantic data model.
Elena Simperl, Professor of Computer Science at University of Southampton
Artificial Intelligence, Machine Learning and semantic technologies complement each other. Semantics is as much part of AI as machine learning. Semantic technologies have been part of Artificial Intelligence since the very beginning. One of the reasons AI has not been so successful so far was because there was a lot of investment and effort put in trying to capture the world in very complex knowledge systems. It was impossible with the technology and the systems we had in the 60s and 70s though. Now we face a completely different situation: Everyone has their devices and access to the web. There is the Internet of Things. We are living in a world of networks and it is much easier to capture the data. You can work with really powerful knowledge-based systems that would not just learn without understanding the results but provide the user an interpretation of what is learned, and use knowledge that they have about their surroundings to enrich the results of machine learning.
The next SEMANTiCS will take place from 09.-12.09.2019 in Karlsruhe.
The annual SEMANTiCS conference is the meeting place for professionals who make semantic computing work, and understand its benefits and know its limitations. Every year, SEMANTiCS attracts information managers, IT-architects, software engineers, and researchers, from organisations ranging from NPOs, universities, public administrations to the largest companies in the world.
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