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Background and Technologies

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Abstract

This chapter briefly reviews the technological background behind digitalization and digital transformation, ranging over Artificial Intelligence (AI), Machine Learning (ML, as a central sub-category of AI), Computer Vision (as an application of ML), Mixed Reality (MR), Cyber-Physical Systems, the Internet of Things (IoT), Cloud Computing, Big Data Analytics, and the Digital Twin paradigm. This chapter discusses thee technologies’ usage ranging from smart software solutions that can efficiently process digital information and automate its conversion into useful insights, to predictive and immersive tools that allow robots and humans to work together in continuous cooperation and synchronization, realizing the vision of the industrial metaverse. This chapter attempts to answer the following questions: What are prominent digitalization and smart technologies about? What problems and use cases do they address? How do they work? And how do they shape the industry of the future?

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Notes

  1. 1.

    https://unity.com/.

  2. 2.

    https://www.blender.org/.

  3. 3.

    https://www.nvidia.com/en-us/omniverse/.

  4. 4.

    https://www.substance3d.com/.

  5. 5.

    Massachusetts Institute of Technology.

  6. 6.

    Location-based social website for mobile devices (http://Foursquare.com).

  7. 7.

    Location-aware mobile application allowing users to view their contacts geographic locations (www.google.com/latitude). Note that Google Latitude is being recently retired, transforming most of its services to Google+.

  8. 8.

    http://www.w3.org/2005/Incubator/ssn/XGR-ssn-20110628/; https://www.w3.org/TR/vocab-ssn-ext/

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Authors and Affiliations

  1. Chief Technology Officer (CTO), Idealworks GmbH, Munich, Germany

    Jimmy Nassif

  2. Associate Professor, Lebanese American University, Byblos, Lebanon

    Joe Tekli

  3. Founder of SORDI.ai, Munich, Germany

    Marc Kamradt

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  1. Jimmy Nassif
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  2. Joe Tekli
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Nassif, J., Tekli, J., Kamradt, M. (2024). Background and Technologies. In: Synthetic Data. Springer, Cham. https://doi.org/10.1007/978-3-031-47560-3_3

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