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Books on the topic 'Human-to-machine interface'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

P, Marsh Jonathon, Gorayska Barbara, and Mey Jacob, eds. Humane interfaces: Questions of method and practice in cognitive technology. Elsevier, 1999.

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2

Bødker, Susanne. Through the interface: A human activity approach to user interface design. Lawrence Erlbaum, 1991.

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3

Bødker, Susanne. Through the interface: A human activity approach to user interface design. Aarhus Universitet, Matematisk Institut, Datalogisk Afdeling, 1987.

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4

Cavicchio, Federica, and Emanuela Magno Caldognetto, eds. Aspetti emotivi e relazionali nell'e-learning. Firenze University Press, 2008. http://dx.doi.org/10.36253/978-88-8453-833-8.

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This book investigates the role of emotions and multimodal communication in face-to-face teaching and in e-learning, and assesses the incidence of these not merely verbal components on the cognitive processes of the student. It also presents certain types of man-machine interface that utilise natural language in written, vocal and multimodal form; the latter implement a new metaphor of interaction with the computer that is more human-oriented. This is, therefore, a new and interdisciplinary theme of research that highlights the technical and theoretical complexity that e-learning specialists and scholars of multimodal communication and emotions address in order to devise new systems of human-computer communication that are more natural and more motivating for learning.

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5

(Editor), J. P. Marsh, B. Gorayska (Editor), and J. L. Mey (Editor), eds. Humane Interfaces (Human Factors in Information Technology). North Holland, 1999.

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6

Muggleton, Stephen, and Nicholas Chater, eds. Human-Like Machine Intelligence. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198862536.001.0001.

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In recent years there has been increasing excitement concerning the potential of Artificial Intelligence to transform human society. This book addresses the leading edge of research in this area. The research described aims to address present incompatibilities of Human and Machine reasoning and learning approaches. According to the influential US funding agency DARPA (originator of the Internet and Self-Driving Cars) this new area represents the Third Wave of Artificial Intelligence (3AI, 2020s–2030s), and is being actively investigated in the US, Europe and China. The EPSRC’s UK network on Human-Like Computing (HLC) was one of the first internationally to initiate and support research specifically in this area. Starting activities in 2018, the network represents around sixty leading UK groups Artificial Intelligence and Cognitive Scientists involved in the development of the inter-disciplinary area of HLC. The research of network groups aims to address key unsolved problems at the interface between Psychology and Computer Science. The chapters of this book have been authored by a mixture of these UK and other international specialists based on recent workshops and discussions at the Machine Intelligence 20 and 21 workshops (2016,2019) and the Third Wave Artificial Intelligence workshop (2019). Some of the key questions addressed by the Human-Like Computing programme include how AI systems might 1) explain their decisions effectively, 2) interact with human beings in natural language, 3) learn from small numbers of examples and 4) learn with minimal supervision. Solving such fundamental problems involves new foundational research in both the Psychology of perception and interaction as well as the development of novel algorithmic approaches in Artificial Intelligence.

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7

The Human Interface: New Directions for Designing Interactive Systems. Addison Wesley, 2000.

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8

Humane interfaces: Questions of method and practice in cognitive technology. Elsevier, 1999.

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9

Raskin, Jef. The Humane Interface: New Directions for Designing Interactive Systems (ACM Press). Addison-Wesley Professional, 2000.

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10

Hanaway-Oakley, Cleo. Machine–Humans and Body-Subjects. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198768913.003.0004.

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This chapter presents an alternative to the popular critical vein that sees Joyce’s Ulysses and early cinema as conveying a mechanical, impersonal view of the world. It is argued that Ulysses and certain genres of early cinema were engaged—naively or otherwise—in a revaluation of Cartesian dualism, involving the reappraisal of mind/body and human/machine binaries. The physical comedy of Bloom and Charlie Chaplin is analysed with reference to phenomenological ideas on prosthesis and the machine–human interface, while other genres of early cinema, such as Irish melodrama and trick films, are considered in the light of phenomenological theories of gesture and embodiment. By comically mocking mind/body separation and depicting the inseparability of subjectivity and corporeality, Joyce and the early film-makers go beyond the ideas of Bergson and anticipate Merleau-Ponty’s later notion of the ‘body-subject’.

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11

Affective Computing. The MIT Press, 2000.

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12

Affective computing. MIT Press, 1997.

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13

The man/machine interface in information retrieval: Providing access to the casual user. Computer Science Dept., University of Southwestern Louisiana, 1989.

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14

Fiebrink, Rebecca A., and Baptiste Caramiaux. The Machine Learning Algorithm as Creative Musical Tool. Edited by Roger T. Dean and Alex McLean. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.013.23.

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Machine learning is the capacity of a computational system to learn structure from data in order to make predictions on new data. This chapter draws on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. It motivates a new understanding of learning algorithms as human-computer interfaces: like other interfaces, learning algorithms can be characterized by the ways their affordances intersect with goals of human users. The chapter also argues that the nature of interaction between users and algorithms impacts the usability and usefulness of those algorithms in profound ways. This human-centred view of machine learning motivates a concluding discussion of what it means to employ machine learning as a creative tool.

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15

Bossomaier, Terry. Introduction To The Senses For Computer Game And Virtual Reality Designers. Cambridge University Press, 2007.

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16

Introduction To The Senses For Computer Game And Virtual Reality Designers. Cambridge University Press, 2007.

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17

Applying human factors engineering to medical device design: An empirical evaluation of patient-controlled analgesia machine interfaces. National Library of Canada = Bibliothèque nationale du Canada, 1999.

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18

Davenport, Thomas H., and Steven M. Miller. Working with AI. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/14453.001.0001.

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Two management and technology experts show that AI is not a job destroyer, exploring worker-AI collaboration in real-world work settings. This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work—by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers. These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short “insight” chapters draw out common themes and consider the implications of human collaboration with smart systems.

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19

Dean, Roger T., and Alex McLean, eds. The Oxford Handbook of Algorithmic Music. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190226992.001.0001.

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Algorithmic music appears to be at a turning point in its history, with many new systems and communities of practice developing together, as vibrant musical culture. This handbook brings together dozens of leading researchers and practitioners in the field, blending technical, artistic, cultural and scientific viewpoints into a whole that considers the making of algorithmic music as a rich, and essentially human activity. The book is organised into four sections, the first grounding the topic in the history, philosophy and psychology of algorithmic music. The second section asks 'what can algorithms in music do?', finding answers in computer science, mathematics, machine learning, bio-inspired computation, manipulation of pattern, computational creativity, and live coding. The third section focuses on the music maker, and the role of algorithms in supporting network music, sonification, music interface design, music in computer games, and spatialisation. The final section opens out to culture at large, and considers algorithmic music in terms of its audience reception, sociology, education, politics and the potential for mass consumption. Perhaps just as importantly, these sections are interleaved with reflective pieces from leading practitioners in the field, allowing us to to grasp the pragmatics of making music with algorithms. Combined, these diverse standpoints provide an absorbing, authoritative survey of research and practice from across the algorithmic music field.

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20

Bi, Xiaojun, Andrew Howes, Per Ola Kristensson, Antti Oulasvirta, and John Williamson. Introduction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0001.

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This chapter introduces the field of computational interaction, and explains its long tradition of research on human interaction with technology that applies to human factors engineering, cognitive modelling, artificial intelligence and machine learning, design optimization, formal methods, and control theory. It discusses how the book as a whole is part of an argument that, embedded in an iterative design process, computational interaction design has the potential to complement human strengths and provide a means to generate inspiring and elegant designs without refuting the part played by the complicated, and uncertain behaviour of humans. The chapters in this book manifest intellectual progress in the study of computational principles of interaction, demonstrated in diverse and challenging applications areas such as input methods, interaction techniques, graphical user interfaces, information retrieval, information visualization, and graphic design.

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21

Kramer, Lawrence. Classical Music for the Posthuman Condition. Edited by John Richardson, Claudia Gorbman, and Carol Vernallis. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199733866.013.0007.

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This article appears in theOxford Handbook of New Audiovisual Aestheticsedited by John Richardson, Claudia Gorbman, and Carol Vernallis. Classical music has traditionally denied the relationship of technology to the body, something on which it has always uneasily depended. That denial is well-nigh impossible to maintain today, because of the increasing assimilation of music delivery systems with the “posthuman” condition of machine-human interfaces. Bodies are no longer fully separable from machines, if indeed they ever were. The migration of music to digital media collapses the effect of auditory distance that formed the unspoken basis of music’s relationship to the human. This reorientation necessitates far-reaching changes in our conceptions of classical music, the musical work, the listening body, and the act of performance.

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22

Oulasvirta, Antti, Per Ola Kristensson, Xiaojun Bi, and Andrew Howes, eds. Computational Interaction. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.001.0001.

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This book presents computational interaction as an approach to explaining and enhancing the interaction between humans and information technology. Computational interaction applies abstraction, automation, and analysis to inform our understanding of the structure of interaction and also to inform the design of the software that drives new and exciting human-computer interfaces. The methods of computational interaction allow, for example, designers to identify user interfaces that are optimal against some objective criteria. They also allow software engineers to build interactive systems that adapt their behaviour to better suit individual capacities and preferences. Embedded in an iterative design process, computational interaction has the potential to complement human strengths and provide methods for generating inspiring and elegant designs. Computational interaction does not exclude the messy and complicated behaviour of humans, rather it embraces it by, for example, using models that are sensitive to uncertainty and that capture subtle variations between individual users. It also promotes the idea that there are many aspects of interaction that can be augmented by algorithms. This book introduces computational interaction design to the reader by exploring a wide range of computational interaction techniques, strategies and methods. It explains how techniques such as optimisation, economic modelling, machine learning, control theory, formal methods, cognitive models and statistical language processing can be used to model interaction and design more expressive, efficient and versatile interaction.

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23

Hughes, James. Human augmentation and the age of the transhuman. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0057.

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Human augmentation is discussed in this chapter in three axes: the technological means, the ability being augmented, and the social systems that will be affected. The technological augmentations considered range from exocortical information and communication systems, to pharmaceuticals, tissue and genetic engineering, and prosthetic limbs and organs, to eventually nanomedical robotics, brain–computer interfaces and cognitive prostheses. These technologies are mapped onto the capabilities which we are in the process of enabling and augmenting, which include extending longevity and physical, sensory, and cognitive abilities, and enabling control over emotions, moral behavior, and spiritual experience. The impacts of biohybridicity via augmentation on the family, education, economy, politics, and religion are considered individually, but their aggregate effects will be non-linear and drive complex adaptations in the living machine that is our co-evolved techno-social civilization.

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