Are we paying enough attention? At least since the nineteenth century, critics have alleged a widespread and profound failure of attentiveness—to others, to ourselves, to the world around us, to what is truly worthy of focus. Why is there such great anxiety over attention? What is at stake in understanding attention and the challenges it faces? This book investigates attention from a range of disciplinary perspectives, including philosophy, history, anthropology, art history, and comparative literature. Each chapter begins with a concrete (...) scene whose protagonists are trying—and often failing—to attend. Authors examine key moments in the history of the study of attention; pose attention as a philosophical problem; explore the links between attention, culture, and technology; and consider the significance of attention for conceptualizations of human subjectivity. Readers encounter nineteenth-century experiments in boredom, ornithologists conveying sound through field notations, wearable attention-enhancing prosthetics, students using online learning platforms, and inquiries into attention as a cognitive state and moral virtue. Amid mounting concern about digital mediation of experience, the rise of “surveillance capitalism,” and the commodification of attention, Scenes of Attention deepens the thinking that is needed to protect the freedom of attention and the forms of life that make it possible. (shrink)

This paper explores aspects of GPT-3 that have been discussed as harbingers of artificial general intelligence and, in particular, linguistic intelligence. After introducing key features of GPT-3 and assessing its performance in the light of the conversational standards set by Alan Turing in his seminal paper from 1950, the paper elucidates the difference between clever automation and genuine linguistic intelligence. A central theme of this discussion on genuine conversational intelligence is that members of a linguistic community never merely respond “algorithmically” (...) to queries through a selective kind of pattern recognition, because they must also jointly attend and act with other speakers in order to count as genuinely intelligent and trustworthy. This presents a challenge for systems like GPT-3, because representing the world in a way that makes conversational common ground salient is an essentially collective task that we can only achieve jointly with other speakers. Thus, the main difficulty for any artificially intelligent model of conversation is to account for the communicational intentions and motivations of a speaker through joint attention. These joint motivations and intentions seem to be completely absent from the standard way in which systems like GPT-3 and other artificial intelligent systems work. This is not merely a theoretical issue. Since GPT-3 and future iterations of similar systems will likely be available for commercial use through application programming interfaces, caution is needed regarding the risks created by these systems, which pass for “intelligent” but have no genuine communicational intentions, and can thereby produce fake and unreliable linguistic exchanges. (shrink)