The Perception Machine

A provocative investigation of the future of photography and human perception in the age of AI. We are constantly photographing and being photographed while feeding machine learning databases with our data, which in turn is used to generate new images. Analyzing the transformation of photography by computation—and the transformation of human perception by algorithmically driven images, from CGI to AI—The Perception Machine investigates what it means for us to live surrounded by image flows and machine eyes. In an astute and engaging argument, Joanna Zylinska brings together media theory and neuroscience in a Vilém Flusser–Paul Virilio remix. Her “perception machine” names a technical universe of images and their infrastructures. But it also refers to a sociopolitical condition resulting from today’s automation of vision, imaging—and imagination. Written by a theorist-practitioner, the book incorporates Zylinska’s own art projects, some of which have been co-created with AI. The photographs, collages, films, and installations available as part of the book (and its companion website) provide a different mode of thinking about our technological futures, at a local as well as a planetary level. Offering provocative concepts such as eco-eco-punk, AUTO-FOTO-KINO, planetary micro-vision, loser images, and sensography, the book outlines an existential philosophy of messy media for a time when our practices of imaging and self-imaging are being radically redesigned. Importantly, it also offers a new vision of our future.

The following are the proceedings of the Fourth International Workshop on Human and Machine Perception held in Palermo, Italy, on June 20 -23, 2000, under the auspices of three Institutions: the Cybernetic and Biophysics Group (GNCB) of the Italian National Research Council (CNR) and the two Inter-Department Centers of Cognitive Sciences of Palermo and Pavia University respectively. A broad spectrum of topics are covered in this series, ranging from computer perception to psychology and physiology of perception. The theme of this workshop on Human and Machine Perception was focused on Thinking, Deciding, and Acting. As in the past editions the final goal has been the analysis and the comparison of biological and artificial solutions. The focus of the lectures has been on presenting the state-of-the-art and outlining open questions. In particular, they sought to stress links, suggesting possible synergies between the different cultural areas. The panel discussion has been conceived as a forum for an open debate, briefly introduced by each panelist, and mainly aimed at deeper investigation of the different approaches to perception and strictly related topics. The panelists were asked to prepare a few statements on hot-points as a guide for discussion. These statements were delivered to the participants together with the final program, for a more qualified discussion.

The book provides an up-to-date on machine learning and visual perception, including decision tree, Bayesian learning, support vector machine, AdaBoost, object detection, compressive sensing, deep learning, and reinforcement learning. Both classic and novel algorithms are introduced. With abundant practical examples, it is an essential reference to students, lecturers, professionals, and any interested lay readers.

This unique book discusses machine understanding (MU). This new branch of classic machine perception research focuses on perception that leads to understanding and is based on the categories of sensory objects. In this approach the visual and non-visual knowledge, in the form of visual and non-visual concepts, is used in the complex reasoning process that leads to understanding. The book presents selected new concepts, such as perceptual transformations, within the machine understanding framework, and uses perceptual transformations to solve perceptual problems (visual intelligence tests) during understanding, where understanding is regarded as an ability to solve complex visual problems described in the authors’ previous books. Thanks to the uniqueness of the research topics covered, the book appeals to researchers from a wide range of disciplines, especially computer science, cognitive science and philosophy.

Neural Networks for Perception, Volume 2: Computation, Learning, and Architectures explores the computational and adaptation problems related to the use of neuronal systems, and the corresponding hardware architectures capable of implementing neural networks for perception and of coping with the complexity inherent in massively distributed computation. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The text is organized into two sections. The first section, computation and learning, discusses topics on learning visual behaviors, some of the elementary theory of the basic backpropagation neural network architecture, and computation and learning in the context of neural network capacity. The second section is on hardware architecture. The chapters included in this part of the book describe the architectures and possible applications of recent neurocomputing models. The Cohen-Grossberg model of associative memory, hybrid optical/digital architectures for neorocomputing, and electronic circuits for adaptive synapses are some of the subjects elucidated. Neuroscientists, computer scientists, engineers, and researchers in artificial intelligence will find the book useful.

This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers topics such as intelligent object detection, foveated vision systems, online learning paradigms, reinforcement learning for a mobile robot, object tracking and motion estimation, 3D model construction, computer vision system and user modelling using dialogue strategies. This book will appeal to researchers, senior undergraduate/postgraduate students, application engineers and scientists.

As perception stands for the acquisition of a real world representation by interaction with an environment, learning is the modification of this internal representation.This book highlights the relation between perception and learning and describes the influence of the learning in the interaction with the environment.Besides, this volume contains a series of applications of both machine learning and perception, where the former is often embedded in the latter and vice-versa.Among the topics covered, there are visual perception for autonomous robots, model generation of visual patterns, attentional reasoning, genetic approaches and various categories of neural networks.

The following are th€:" proceedings of the Second International Workshop on Human and Machine Perception held in Trabia, Italy, on July 21~25, 1996, under the auspices of two Institutions: the Cybernetic and Biophysics Group (GNCB) of the Italian National Research Council (CNR) and the 'Centro Interdipartimentale di Tecnologie della Conoscenza' ofPalenno University. A broad spectrum of topics are covered in this series, ranging from computer perception to psychology and physiology of perception (visual, auditory, tactile, etc.). The theme of this workshop was: "Human and Machine Perception: Information Fusion". The goal of information and sensory data fusion is to integrate internal knowledge with complementary and/or redundant information from many sensors to achieve (and maintain) a better knowledge of the environment. The mechanism behind the integration of information is one of the most difficult challenges in understanding human and robot perception. The workshop consisted of a pilot phase of eight leCtures introducing perception sensorialities in nature and artificial systems, and of five subsequent modules each consisting of two lectures (dealing with solutions in nature and machines respectively) and a panel discussion.