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Cyberspatialities of Russia and the United-States: The Challenging Governance of a "Consensual Hallucination"
Plattner, Simon Antonin ; Butler, Eamonn (advisor) ; Špelda, Petr (referee) ; Dowd, Caitriona (referee)
Cyberspatialities of Russia and the United-States : The challenging governance of a "consensual hallucination" This research proposes to appreciate the U.S. and Russia's current cyberspatialities as a the consequence of Cold War inherited and rivalling cybercultures and as the cause of Moscow's "pyromaniac fire-fighter" cyberstrategy. Indeed, the cybercultural analysis reveals that each of the two former superpowers engaged at the end of the second World War in a cybernetic race that fuelled their rivalry well beyond the collapse of Soviet Union. Despite the particularly passionate adoption of the new field by the Soviets, their formal techno-socialist utopia, incarnated by the OGAS system, failed to come into being. However, the largely underestimated formal cyberculture developed throughout this kiber adventure by the Soviet Union deeply influenced its American rivals. As analysed throughout part 5.1.1, the overbidding rational urged by the realist dogmas of hegemonic Cold-War geopolitics nurtured a constant and dynamic interplays of cybercultures. As it is often the case in cultural transfers, this merging of cybercultures was initiated underground and occurred within the framework of the popular cyberpunk literature. Exemplified by the international adoption of the cyberpunk term cyberspace,...
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Food Security and Machine Learning: Opportunities and Challenges
Hruška, Adam ; Špelda, Petr (advisor) ; Plattner, Simon Antonin (referee)
The emergence of the effects of global warming, as well as the ongoing depletion of fossil fuels and fertile soil pose a serious threat for the future of the agricultural industry. Alternatively, the continuous population growth mainly in the less developed regions highlights the future need of approximately 70-110 percent increase in the overall output of contemporary food production. While the current conventional agriculture deploys a multitude of technologies including the precision agriculture framework, the future needs of the population exceed the projected capabilities of the industry. Machine learning as the current fastest growing technology represents the potential remedy for the emerging issues, yet the extent of successful implementation remains uncertain. The thesis aims to uncover the potential future implications of implementation of machine learning based technology in agriculture through the use of the new scenario building methodology. The analysis builds on a varying set of empirical data, current state of art projects in machine learning and multiple future trend projections. Albeit the scenario building technique allows for a potentially endless number of constructed scenarios, the thesis concentrates on three main plot lines. First scenario tackles the more probable...
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