In this text, I explore the intertwined relationship between philosophy of science and theology, and the challenges they both encounter in the academic realm. While theology is struggling to maintain its relevance, philosophy of science has gained recognition and offers valuable insights to theologians. I argue that theologians can benefit from engaging with the content and methods of specific sciences, and philosophers of science can help affirm the methodological legitimacy of theology. However, it is important to note that science itself (...) is facing its own set of problems. There is a decline in interest and job satisfaction among scientists, a growing gap between science and the humanities, a crisis of conceptual understanding, and a perceived lack of meaning and purpose. In this context, I propose that theology and philosophy can play a crucial role in addressing these issues. In particular, theology can offer a supportive environment for discussing meaning, values, and the ultimate questions of human existence. Philosophy, on the other hand, can help bridge the gap between science and the humanities, fostering interdisciplinary dialogue and a more holistic understanding of our world. (shrink)

While scientists sometimes make light of philosophy, science relies on a variety of philosophical assumptions, such as the idea that there are laws of nature. Many of these arose during the Scientific Revolution with the rejection of Aristotelianism. Here we consider the theological motivations behind several key examples. While science is now officially naturalistic, its rise depended in part on theology.

This article engages the anthropological assumptions underlying the apprehensions and promises associated with language in artificial intelligence (AI). First, we present the contours of two rivalling paradigms for assessing artificial language generation: a holistic-enactivist theory of language and an informational theory of language. We then introduce two language generation models – one presently in use and one more speculative: Firstly, the transformer architecture as used in current large language models, such as the GPT-series, and secondly, a model for 'autonomous machine (...) intelligence' recently proposed by Yann LeCun, which involves not only language but a sensory-motor interaction with the world. We then assess the language capacity of these models from the perspectives of the two rivalling language paradigms. Taking a holistic-enactivist stance, we then argue that there is currently no reason to assume a human-comparable language capacity in LLMs and, further, that LeCun's proposed model does not represent a significant step toward artificially generating human language because it still lacks essential features that underlie the linguistic capacity of humans. Finally, we suggest that proponents of these rivalling interpretations of LLMs should enter into a constructive dialogue and that this dialogue should continuously involve further empirical, conceptual, and theoretical research. (shrink)