In this paper, I draw on Hannah Arendt’s notion of ‘banality of evil’ to argue that as long as AI systems are designed to follow codes of ethics or particular normative ethical theories chosen by us and programmed in them, they are Eichmanns destined to commit evil. Since intelligence alone is not sufficient for ethical decision making, rather than strive to program AI to determine the right ethical decision based on some ethical theory or criteria, AI should be concerned with (...) avoiding making the wrong decisions, and this requires hardwiring the thinking activity as a prerequisite for decision making. (shrink)

To operate in an unpredictable environment, a vehicle with advanced driving assistance systems, such as a robot or a drone, not only needs to register its surroundings but also to combine data from different sensors into a world model, for which it employs filter algorithms. Such world models, as this article argues with reference to the SLAM problem in robotics, consist of nothing other than probabilities about states and events arising in the environment. The model, thus, contains a virtuality of (...) possible worlds that are the basis for adaptive behavior. The article shows that the current development of these technologies requires new concepts because their complex adaptive behaviors cannot be explained by referring them to mere algorithmic processes. Instead, it proposes the heuristic instrument of microdecisions to designate the temporality of decisions between alternatives that are created by probabilistic procedures of world modeling. Microdecisions are more than the implementation of deterministic processes—they decide between possibilities and, thus, always open up the potential of their otherness. By describing autonomous adaptive technologies with this heuristic, the question of sovereignty inevitably arises. It forces us to re-think what autonomy means when decisions can be automated. (shrink)

This article conducts a literature review of current and future challenges in the use of artificial intelligence in cyber physical systems. The literature review is focused on identifying a conceptual framework for increasing resilience with AI through automation supporting both, a technical and human level. The methodology applied resembled a literature review and taxonomic analysis of complex internet of things interconnected and coupled cyber physical systems. There is an increased attention on propositions on models, infrastructures and frameworks of IoT in (...) both academic and technical papers. These reports and publications frequently represent a juxtaposition of other related systems and technologies. We review academic and industry papers published between 2010 and 2020. The results determine a new hierarchical cascading conceptual framework for analysing the evolution of AI decision-making in cyber physical systems. We argue that such evolution is inevitable and autonomous because of the increased integration of connected devices in cyber physical systems. To support this argument, taxonomic methodology is adapted and applied for transparency and justifications of concepts selection decisions through building summary maps that are applied for designing the hierarchical cascading conceptual framework. (shrink)

This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of ‘algorithmic thought’. Research in deep learning is its case study. This artificial intelligence technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and technoculture tackle the possibility (...) to ‘re-present’ the algorithmic procedures of feature extraction and feature learning to the human mind. The article thus mobilises the notion of incommensurability to address explainability as a communicational and representational issue, which challenges phenomenological and existential modes of comparison between human and algorithmic ‘thinking’ operations. (shrink)

The victories of the Go-playing artificial intelligence “AlphaGo” against professional player Lee Sedol in 2016 had a profound impact on public and academic perceptions of AI. This event shocked observers, as the ability of a machine to defeat a world champion human in a highly complex game seemed to indicate that a machine had achieved human-like—or more than human—intelligence. But why was AlphaGo so readily anthropomorphized by academic and non-academic audiences alike? Drawing from existing analyses of reactions to and arguments (...) concerning AlphaGo and AI generally, this paper argues that “generative” cognitive science—a school of thought exemplified by the linguistic work of Noam Chomsky—offers two novel contributions to this subject. First, generativism sheds light on an irrational double standard in the study of the human mind in contrast to the study of non-cognitive systems—“methodological dualism”—which, I argue, has been transferred to evaluations of AlphaGo and other AI. Second, by exposing this irrational double standard in perceptions of AI, I employ generativism’s more well-known arguments concerning the nature of human intelligence and its scientific study to the evaluation of AI, exposing deficient interpretations widely used in the case of AlphaGo and AI generally. (shrink)