Events
Double Book Launch: "Learning to Listen, Listening to Learn" by Marcus Pearce and "Understanding the Artificial Intelligence Revolution by Shalom Lappin.
Centre for Human-Centred ComputingDate: 16 June 2025 Time: 18:00 - 20:00
Location: The Octagon At Queen Mary University Of London 327 Mile End Road London E1 4NS
Join us to celebrate the publication of two books:
Marcus Pearce
Learning to Listen, Listening to Learn: Music Perception and the Psychology of Enculturation
Learning to listen, Listening to Learn presents a unified theory of music perception based on psychological processes of statistical learning and probabilistic prediction. It develops and evaluates a computational model of the perceptual learning underlying cultural evolution of music, accounting for the human capacity to perceive structure in music and find it pleasurable while also simulating cross-cultural differences and the developmental trajectories that produce them. In doing so, it integrates multiple psychological mechanisms including statistical learning, expectation, memory, auditory scene analysis, similarity perception, complexity, affect and pleasure.
Shalom Lappin
Understanding the Artificial Intelligence Revolution
After many years during which it languished in relative obscurity - in remote classrooms of computer science departments and in small prototype projects for tech companies - AI is now a searingly hot topic across the media. Yet much of the public discussion is so feverish that an understanding of the basic scientific and engineering elements of the field is easily lost, often resulting in exaggerated claims, as well as dangerously neglected threats.
This concise and sober book presents a brief history of AI, explaining in clear language the central engineering innovations that have produced the current revolution, and distinguishing between imagined dangers and the very real problems that AI is creating. Spread across seven short and accessible chapters, the book explains the developments behind deep learning and the applications of deep neural networks (DNNs), addresses both the imagined and actual risks posed by the AI revolution, before outlining rational public policy on AI, covering topics like tech monopolies, disinformation, bias, hate speech, intellectual property rights, and inequality.
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