John Searle, in his paper on 'Minds, Brains, and Programs' (1980), argues that computational theories in psychology are essentially worthless. He makes two main claims: that computational theories, being purely formal in nature, cannot possibly help us to understand mental processes; and that computer hardware-unlike neuroprotein-obviously lacks the right causal powers to generate mental processes. I shall argue that both these claims are mistaken.

Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected. One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this paper I describe four fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I conclude by discussing the open questions spurred by these fallacies, including the age-old challenge of imbuing machines with humanlike common sense.

The recent proliferation of deepfakes, AI and other digitally produced deceptive representations has revived the debate on the epistemic robustness of photography and other mechanically produced images. Authors such as Rini (2020) and Fallis (2021) claim that the proliferation of deepfakes pose a serious threat to the reliability and the epistemic value of photographs and videos. In particular, Fallis adopts a Skyrmsian account of how signals carry information (Skyrms, 2010) to argue that the existence of deepfakes significantly reduces the information that images carry about the world, which undermines their reliability as a source of evidence. In this paper, we focus on Fallis’ version of the challenge, but our results can be generalized to address similar pessimistic views such as Rini’s. More generally, we offer an account of the epistemic robustness of photography and videos that allows us to understand these systems of representation as continuous with other means of information transmission we find in nature. This account will then give us the necessary tools to put Fallis’ claims into perspective: using a richer approach to animal signaling based on the signaling model of communication (Maynard-Smith and Harper, 2003), we will claim that, while it might be true that deepfake technology increases the probability of obtaining false positives, the dimension of the epistemic threat involved might still be negligible.

One of the most basic questions an ontology can address is: How many things, or substances, are there? A monist will say, ‘just one’. But there are different stripes of monism, and where the borders between these different views lie rests on the question, ‘To what does this “oneness” apply?’ Some monists apply ‘oneness’ to existence. Others apply ‘oneness’ to types. Determining whether a philosopher is a monist and deciphering what this is supposed to mean is no easy task, especially when it comes to those writing in the early modern period because many philosophers of the sixteenth, seventeenth, and eighteenth centuries include God in their ontologies. In The Principles, Anne Finch Conway offers an ontology that is often described as being both ‘vitalist’ and ‘monist’. I take this to mean that, for Conway, all that exists is in some way alive and that if asked ‘How many things, or substances, are there?’ Conway would say, ‘Just one’. But to what does this ‘oneness’ apply? And where does the point of disagreement between Conway and her interlocutors, Hobbes, Spinoza, More, and Descartes lie? In this paper, I argue that determining the answer to this first question turns out to be quite difficult. Nevertheless, we can still make sense of the second.