Knowledge, Culture and Communication,

Social Intelligence

Simon Penny (2018): Making sense: cognition, computing, art and embodiment

Tony D. Sampson: The Assemblage Brain. Sense Making in Neuroculture

Guest preface: Streams of consciousness: cognition and intelligent devices

Should I kill or rather not?

Can a machine think (anything new)? Automation beyond simulation Abstract This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand (e.g. Leibniz’s mathesis universalis and Turing’s algorithmic method of computation), and the philosophical and cultural critique of these attempts on the other, I will argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us.

Machine learning: A structuralist discipline? Abstract Advances in machine learning and natural language processing are revolutionizing the way we live, work, and think. As for any science, they are based on assumptions about what the world is, and how humans interact with it. In this paper, I discuss what is potentially one of these assumptions: structuralism, which states that all cultures share a hidden structure. I illustrate this assumption with political footprints: a machine-learning technique using pre-trained word vectors for political discourse analysis. I introduce some of the benefits and limitations of structuralism when applied to machine learning, and the risks of exploiting a technology before establishing the validity of all its hypotheses. I consider how machine-learning techniques could evolve towards hybrid structuralism or post-structuralism, and how deeply these developments would impact cultural studies.

Two-dimensional opinion dynamics in social networks with conflicting beliefs Abstract Two models are developed for updating opinions in social networks under situations where certain beliefs might be considered to be competing. These two models represent different attitudes of people towards the perceived conflict between beliefs. In both models agents have a degree of tolerance, which represents the extent to which the agent takes into account the differing beliefs of other agents, and a degree of conflict, which represents the extent to which two beliefs are considered to be competing. Computer simulations are used to determine how the opinion dynamics are affected by the inclusion of conflict. Results show that conflict can enhance the formation of consensus within the network in certain circumstances according to one of the models.

A cross-cultural assessment of the semantic dimensions of intellectual humility Abstract Intellectual humility can be broadly construed as being conscious of the limits of one’s existing knowledge and capable of acquiring more knowledge, which makes it a key virtue of the information age. However, the claim “I am (intellectually) humble” seems paradoxical in that someone who has the disposition in question would not typically volunteer it. Therefore, measuring intellectual humility via self-report may be methodologically unsound. As a consequence, we suggest analyzing intellectual humility semantically, using a psycholexical approach that focuses on both synonyms and antonyms of ‘intellectual humility’. We present a thesaurus-based methodology to map the semantic space of intellectual humility and the vices it opposes as a heuristic to support analysis and diagnosis of this disposition. We performed the mapping both in English and German in order to test for possible cultural differences in the understanding of intellectual humility. In both languages, we find basically the same three semantic dimensions of intellectual humility (sensibility, unpretentiousness, and knowledge dimensions) as well as three dimensions of its related vices (self-overrating, other-underrating and dogmatism dimensions). The resulting semantic clusters were validated in an empirical study with English (n = 276) and German (n = 406) participants. We find medium-to-high correlations (0.54–0.72) between thesaurus similarity and perceived similarity, and we can validate the three dimensions identified in the study. But we also find limitations of the thesaurus methodology in terms of cluster plausibility. We conclude by discussing the importance of these findings for constructing psychometric measures of intellectual humility via self-report vs. computer models.

The HeartMath coherence model: implications and challenges for artificial intelligence and robotics Abstract HeartMath is a contemporary, scientific, coherent model of heart intelligence. The aim of this paper is to review this coherence model with special reference to its implications for artificial intelligence (AI) and robotics. Various conceptual issues, implications and challenges for AI and robotics are discussed. In view of seemingly infinite human capacity for creative, destructive and incoherent behaviour, it is highly recommended that designers and operators be persons of heart intelligence, optimal moral integrity, vision and mission. This implies that AI and robotic design and production should be continuously optimized through vigilant and appropriate human and material quality control procedures. Evidence is provided for some value and effectiveness of the HeartMath coherence model in this context.