Abstract
A study conducted by the World Economic Forum revealed that about 75 million jobs will disappear because of the Artificial Intelligence (AI), but AI will bring with it more than 133 million new job. Therefore, for making a smooth transition to the next Industrial Revolution, the reskilling initiatives play a key role. In the information age, it is crucial to educate students to acquire the skills necessary for the new jobs that will be created thanks to AI. Because everything we touch and everything we do will be enhanced by AI in the near future, new ways of working will become increasingly popular. Therefore, this paper aims to define new learning methods based on AI to educate engineers for the jobs that will emerge in the following years.
AI is the fusion of many fields of study. Electrical engineering and computer science are determining the hardware and software implementation of the systems based on AI. The teaching practice used in this study combines several fields of study and is based on using LabVIEW including the Deep Learning Toolkit (DeepLTK) and Python Node to teach engineers all the necessary steps for developing, validating and deploying machine learning-based systems.
The approach is based on learning by examples and by comparisons with human intelligence, methodologies which have proven to be very efficient. The key anticipated outcome of this study is that students and engineers will gain relevant knowledge about the powerful machine learning algorithms.
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Notes
- 1.
World Economic Forum website, [Online] at https://www.weforum.org/projects/reskilling-revolution-platform, last accessed: 20.05.2020.
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Acknowledgements
We would like to express our great appreciation to the Ngene company from Yerevan, Armenia for providing us with a free Deep Learning Toolkit (DeepLTK) license for LabVIEW, thus facilitating this study and making it possible. Their generosity and collaboration were greatly appreciated.
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Modran, H.A., Ursutiu, D., Samoila, C., Chamunorwa, T. (2021). Learning Methods Based on Artificial Intelligence in Educating Engineers for the New Jobs of the 5th Industrial Revolution. In: Auer, M.E., Rüütmann, T. (eds) Educating Engineers for Future Industrial Revolutions. ICL 2020. Advances in Intelligent Systems and Computing, vol 1329. Springer, Cham. https://doi.org/10.1007/978-3-030-68201-9_55
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