The present article examines the tumultuous development in the issue of the Third Site (also known as the Third Pillar) of the US Ballistic Missile Defense (BMD) that was planned to be hosted by the Czech Republic and Poland. The article analyzes the entire ‘life cycle’ of the project, from its formal proposal in 2007 by the former U.S. President George W. Bush to its cancellation in 2009 by the current U.S. President Barak Obama. Without any doubts, the Third Site of BMD put Poland and the Czech Republic at the centre of international security politics and as such allows one to see how the two post-communist countries acted and reacted to related international positions, expectations and challenges. A detailed analysis of this issue, nevertheless, does not exhaust aims of this article. Whether brief or detailed, any look at the coverage of the issue reveals that the Czech Republic and Poland have invariably been lumped together through the construction of the imagery of the New Europe as a homogeneous political bloc. It will be argued that such a view is flawed and needs refinement. In order to back the claim, the issue of the Third Site is put into a historical context, revealing that the differences between the Czech and Polish international-security preferences and expectations after the end of the Cold War have been quite stable – including the most recent development after the project has been shelved by the United States, and can thus be conceived of in dialectical terms.

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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. 1 petr.spelda@fsv.cuni.cz 2 https://doi.

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. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling.