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This research focuses on the fundamental ideas and underlying principles of E-Learning technology, as well as theoretical considerations for an optimal learning environment. This theoretical exploration was then used as a basis for the design and construction of a new, interactive Web-Based ESH-Training. The quality and effectiveness of this new course was then compared with that of the existing analog PDF-Training via a test with a diverse sample of employee learners. Learners were later surveyed to ascertain their views on both trainings in terms of the quality of the content, facilitator, resources, and length. Results clearly showed that regardless of demographic factors, most employee learners preferred the new, Web-Based ESH-Training to the analog PDF-Training.
The development of Artificial Intelligence (AI) has profound implications for improving human and computational productivity in the future. However, it also is an existential risk to human life because it could exceed human capabilities. As such, information about the technology, the direction of the development and its purpose is important. This can be achieved through openness and transparency of processes. Indeed, companies hold property rights over AI and monopolies of software, data and experts. As a countermovement to leading AI companies, the “Open AI Movement” has evolved to push open-source AI research and products, to empower users, and to bridge the digital divide through participation and access. In this thesis, the implications of the declaration of AI as a commons have been analyzed through interviews with AI experts in the United States. The legal placement of AI is controversial but it could be seen as a basic human right. Other findings are that this field is very competitive and that the best approach is to collaboratively develop software that adds additional value on the edge of the commons.