Vít Střítecký

Vít Střítecký is an assistant professor in International Security at the Faculty of Social Sciences, Charles University in Prague. He studied Security and IR programs at the Charles University, Uppsala University, Sweden and University of St. Andrews, Scotland. In his research, he gradually focuses on the interplay between security and technology. In Periculum, he contributes to the cultivation of the project’s research program on Artificial Intelligence (AI) which aims at synthesising machine learning with various philosophical topics. He currently works on a book looking at the AI implications for international order and together with his colleagues develops computer-assisted analyses of large data sets, mainly in the areas of propaganda or conspiracies.

Related resources

Human Induction in Machine Learning: A Survey of the Nexus

Research 17/4/2021 View more

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples.

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What Can Artificial Intelligence Do for Scientific Realism?

Research 7/4/2020 View more

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts.

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The Future of Human-Artificial Intelligence Nexus and its Environmental Costs

Research 1/3/2020 View more

The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al. (2019) in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus.

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Periculum’s Members in The Study Programs of Security Studies Department

News 29/10/2018 View more

We are glad to note that our security programs manifest a steady growth in numbers and diversity of students.

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A New Research on Machine Learning, Inductive Reasoning, Reliability of Generalisations

News 21/8/2018 View more

Petr Špelda, a member of Periculum's research project on Artificial Intelligence, just published a new paper which synthesises statistical learning theory with philosophical inquiries into the nature of representation offering new insights regarding inductive reasoning and reliability of generalisations.

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