Can large language models produce expert‐quality philosophical texts? To investigate this, we fine‐tuned GPT‐3 with the works of philosopher Daniel Dennett. To evaluate the model, we asked the real Dennett 10 philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry‐picking. Experts on Dennett's work succeeded at distinguishing the Dennett‐generated and machine‐generated answers above chance but substantially short of our expectations. Philosophy blog readers performed similarly to the experts, while ordinary (...) research participants were near chance distinguishing GPT‐3's responses from those of an “actual human philosopher”. (shrink)

Moving from a behavioral definition of intelligence, which describes it as the ability to adapt to the surrounding environment and deal effectively with new situations (Anastasi, 1986), this paper explains to what extent the performance obtained by ChatGPT in the linguistic domain can be considered as intelligent behavior and to what extent they cannot. It also explains in what sense the hypothesis of decoupling between cognitive and problem-solving abilities, proposed by Floridi (2017) and Floridi and Chiriatti (2020) should be interpreted. (...) The problem of symbolic grounding (Harnad, 1990) is then addressed to show the problematic relationship between ChatGPT and the natural environment, and thus the impossibility for it to understand the symbols it manipulates. To explain the reasons why ChatGPT does not succeed in this task, an investigation is carried out and a possible solution to the problem in the artificial domain is proposed by making a comparison with the natural ability of living beings to ground their own meanings from some basic cognitive-sensory aspects, which, it is explained, are directly related to the emergence of self-awareness in humans. Thus, the question is raised whether a possible and concrete solution to the Symbol Grounding Problem would involve in the artificial domain the development of cognitive abilities fully comparable to those of humans. Finally, I explain the difficulties that would have to be overcome before such a level could be reached, since human cognitive capacities are intimately linked to intersubjectivity and intercorporeality. (shrink)

The surge in interest in natural language processing in artificial intelligence has led to an explosion of new language models capable of engaging in plausible language use. But ensuring these language models produce honest, helpful, and inoffensive outputs has proved difficult. In this paper, I argue problems of inappropriate content in current, autoregressive language models—such as ChatGPT and Gemini—are inescapable; merely predicting the next word is incompatible with reliably providing appropriate outputs. The various fine-tuning methods, while helpful, cannot transform the (...) model from mere next word prediction to the kind of planning and forethought necessary for saying the right thing. The upshot is that these models will increasingly churn out bland, generic responses that will still fail to be accurate or appropriate. (shrink)

In this article, we argue two points in relation to the challenge to human distinctiveness emerging as artificial intelligence systems and humanlike robots simulate various human capabilities. First, that, in the context of theological anthropology, it is advisable to respond to this challenge by turning toward the human body. Second, following this point, we propose the responsive body hypothesis, suggesting that what makes us distinct from androids are capacities that rise from and depend on our responsive bodies.