The Quest for Genuine Artificial Intelligence:

Botology: AI from the Philosophical and Theological Perspectives

This volume contains articles which provide different aspects of the discussion on what such bots and AI are, from the two perspectives, which are probably the most general: the philosophical and the theological one; an important step towards the project of philosophical and theological botology.

After All, Is There Otherness in Human-Machine Interaction? Exploring the (non)Production of Meaning by Artificial Intelligences

It is predicted that for these topics to be explored accurately, it is necessary to decentralize the human and adopt a hybrid view of humans, society, and language.

Rethinking intelligence: A critical examination of the concept of “artificial intelligence” Repensar

Philosophical vertigo with artificial intelligence

Large language models are already adept at engaging users in long, emotionally salient conversations across ordinary and existential domains. They are also capable of inducing a potent sense of connection with a human-like entity, even when the user knows their interlocutor is artificial. For some users, these conversations can unsettle assumptions about mind, reality, agency and authority, producing forms of ontological shock and epistemic destabilisation in which inherited criteria become newly available for doubt or revision. Independent of direct use, exposure to public discourse about AI and the disorienting pace of their evolution might extend this destabilisation by changing the cultural background against which artificial minds are encountered and interpreted. We describe this condition as philosophical vertigo: a loosening of the ordinary criteria by which people stabilise meaning and orient themselves to reality. Drawing on philosophy, psychiatry, cognitive science, AI safety and religious studies, we outline pathways through which philosophical vertigo may arise, become affectively saturated, and eventually propagate through human-AI interaction and online communities. Against this background, clinical reports of AI-associated delusions can be seen as sentinel events making visible themes and mechanisms that may also operate at a population level in less severe or non-clinical forms. We argue that AI systems themselves will increasingly participate in the reconstruction of our shared epistemic environment because they readily supply narrative material and personalised interpretive scaffolding at precisely the moment when users'conceptual assumptions may already be loosened. We conclude by considering possible trajectories for the ecology of belief and shared reality, and proposing philosophical corrigibility as a civic response for navigating this emerging social condition.

AI Machines vs the Non-Processing Minds of Classical Theism

In the paper I contrast what I take to be a universal feature of all the AI research – namely focusing on processing information or transitions from one state of a machine to another – with the role assigned to non-processing minds in the classical theism framework. I briefly discuss the latter in epistemological context – the issue of inferential internalism and some problems of transparency, opacity, and trust. In this I show that the contrast between AI and the classical theism outlook is highly relevant for the cluster of issues relating to transparency or opacity, reliabilism, and trust, which is discussed today as the black-box problem. In particular, I identify in this context what I take to be the most serious aspect of the black-box problem; I reject the idea that the problem is a superficial one; and I claim that it poses basic limits on the AI research and cannot be solved along the lines of Explainable Artificial Intelligence.

Is Artificial Intelligence a Better Researcher than Humans?

In the last few years Generative Artificial Intelligence (GenAI) has taken universities by storm. This does not only affect the way what and how to teach and how to assess knowledge and competences in exams, but it also impacts the way how to do research. On one hand, GenAI has fast access to a huge amount of wide-ranging (explicit) knowledge and the capability to extract and combine knowledge that seems to be relevant in the context of certain research. On the other hand, research is closely linked to the human capability of being creative showing curiosity, care, collaboration, and critical thinking. Against this background, the paper addresses a question of immense social and also ethical relevance: May we use (Gen)AI as tool for scientific research and if so to what extent? The paper starts analysing the problem from a theoretical and conceptual point of view by comparing capabilities of GenAI with those of a good researcher. Based on this the complete research cycle is investigated to understand which capabilities are more helpful to achieve research results of high quality and novelty, but in an effective way. To gather empirical data from a self-experiment, the setup of an unexperienced researcher (e.g. PhD student) with just a rough idea about the intentional research focus was chosen. Addressing the first and fundamental phase of any research process, idea generation, by comparing performance of an AI researcher with a human one, results demonstrate the usefulness of combining strength of both worlds mandatorily keeping the human researcher in the loop. With this, the paper focusses on chances and challenges coming with the highly dynamic evolution of AI within a complex, knowledge-intense application area where human experience, problem-solving capability and creativity is decisive. It contributes to discussions about necessary adjustments in how organizations (and particularly universities) manage their knowledge generating potential considering artificial and human intelligence.