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- W2783406950 abstract "In recent studies Machine Learning is regarded to be one of the disruptive technologies that will transform our future life, business and the global economy. In their 2013 study McKinsey identified 12 technology areas with the potential of a high impact on how people live and work and on industries and economies. In most of these areas Machine Learning is a key enabling technology. Machine Learning is learning from data rather than programming hard coded decision rules. Taking alone this short definition highlights the central role of Machine Learning nowadays. The worldwide process of digitization produces data in all areas as e.g. production processes, Internet of Things, health care and even our daily life. In this presentation, Machine Learning is defined a bit more precise. Going through the development of this rapidly emerging field, the different types of Machine Learning are explained and examples from different application areas are given. It will be shown, that computers are able (or will be able) to solve problems that were supposed to be dependent on human expertise in the past. Among many other benefits, this can be economically advantageous in many areas. This will lead to a broad dissemination of Machine Learning applications. The downside of this development is the fact that the future of our life and in particular of our work life will change dramatically. Some jobs — and in particular those requiring a low level of education and a high level of automation — are likely to disappear and on the other hand new job opportunities will open. Implications on university education will be discussed. This does not apply to computer science programs alone but also to other fields of study." @default.
- W2783406950 created "2018-01-26" @default.
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- W2783406950 date "2017-11-01" @default.
- W2783406950 modified "2023-09-24" @default.
- W2783406950 title "Keynote Speakers: Machine learning — A key enabling technology for future development" @default.
- W2783406950 doi "https://doi.org/10.1109/robomech.2017.8261102" @default.
- W2783406950 hasPublicationYear "2017" @default.
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