Numerous disciplines study the nature of social cognition. Also, in philosophy of mind, there are discourses about socio-cognitive abilities, such as joint action, mindreading, and commitment. However, the so-called standard notions require demanding conditions, which leads to the fact that, for example, abilities of young children and non-human animals cannot be captured by this terminology. By introducing minimal notions, a step has been taken to capture a greater variety of phenomena in the field of social cognition. In this way, current empirical findings can be connected to the theoretical work in philosophy. However, when one characterizes minimal and standard notions by a dichotomous interpretation of a two-system approach, quite a few instances are still falling through the conceptual net. This paper will demonstrate how many instances remain neglected and explore the challenges to develop a disjunctive conceptual schema that can capture the varieties of socio-cognitive abilities.

Limits and risks of GPT-3 applications Recent GPT-3 applications have fed new optimism in AI research. To explore limits and risks, we investigated how far we can get in developing a so-called digital replica of Daniel Dennett, generating text outputs from a large language model trained on Dennett’s philosophical writing. In consultation with Dennett himself, we compare several fine-tuning strategies and evaluate outputs. Analyzing the failures and the successes will allow us to address technical and ethical issues such as: 1. How accurate can such models be with current technology? 2. To what extent (if at all) might it be acceptable to present a model’s outputs as representing an author’s view? 3. Should copyright holders have control over such models? 4. How one can address risks of making such models public (including possible over-reliance on their accuracy)? 5. How good are experts and non-experts in distinguishing humans from replicas?