The sensorimotor theory (Noë, 2004, Noë, in press) discusses a special instance of lack of perceptual experience despite no sensory impairment. The phenomenon dubbed “experiential blindness” is cited as evidence for a constitutive relation between sensorimotor skills and perceptual experience. Recently it has been objected (Adams and Aizawa, 2008, Aizawa, 2007) that the cases described by Noë as experiential blindness are cases of pure sensory deficit. This paper argues that while the objections bring out limitations of Noë’s sensorimotor theory they do not do enough to challenge a robust perception–action interdependence claim. There are genuine cases of experiential blindness and these are better explained by the hypothesis of the interdependence of perception and action rather than by a passive vision approach. The cases provide support for a strong thesis of embodied cognition where ongoing sensorimotor dynamics non-trivially constrain perceptual content.

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.