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Explores how creative digital technologies and artificial intelligence are embedded in culture and society. In The Inspiration Machine, Eitan Y. Wilf explores the transformative potentials that digital technology opens up for creative practice through three ethnographic cases, two with jazz musicians and one with a group of poets. At times dissatisfied with the limitations of human creativity, these artists do not turn to computerized algorithms merely to execute their preconceived ideas. Rather, they approach them as creative partners, delegating to them different degrees of agentive control and artistic decision-making in the hopes of finding inspiration in their output and thereby expanding their own creative horizons. The algorithms these artists develop and use, however, remain rooted in and haunted by the specific social predicaments and human shortfalls that they were intended to overcome. Experiments in the digital thus hold an important lesson: although Wilf’s interlocutors returned from their adventures with computational creativity with modified, novel, and enriched capacities and predilections, they also gained a renewed appreciation for, and at times a desire to re-inhabit, non-digital creativity. In examining the potentials and pitfalls of seemingly autonomous digital technologies in the realm of art, Wilf shows that computational solutions to the real or imagined insufficiencies of human practice are best developed in relation to, rather than away from, the social and cultural contexts that gave rise to those insufficiencies, in the first place.
This book examines the influence that science and industry has had in the inspiration of design, with particular emphasis on the field of architecture. Using case studies, it explores the expression of technology in all areas of the built and manufactured environment concentrating on current and future developments, and their exponents. * Speculates about a new design approach that explores innovative and alternative technologies * Compares the technological design work of engineers and scientists with architects * Relates experiments with architectural form and structure, technology transfer and ecologically aware design strategies to human requirements and ambitions
Over 100,000 copies sold 'A tapestry of strong characters and accomplished writing' Herald Scotland It is 1911, and Jean is about to join the mass strike at the Singer factory. For her, nothing will be the same again. Decades later, in Edinburgh, Connie sews coded moments of her life into a notebook, as her mother did before her. More than a hundred years after his grandmother’s sewing machine was made, Fred discovers a treasure trove of documents. His family history is laid out before him in a patchwork of unfamiliar handwriting and colourful seams. He starts to unpick the secrets of four generations, one stitch at a time.
A guide for mining the imagination to find powerful new ways to succeed. We need imagination now more than ever—to find new opportunities, rethink our businesses, and discover paths to growth. Yet too many companies have lost their ability to imagine. What is this mysterious capacity? How does imagination work? And how can organizations keep it alive and harness it in a systematic way? The Imagination Machine answers these questions and more. Drawing on the experience and insights of CEOs across several industries, as well as lessons from neuroscience, computer science, psychology, and philosophy, Martin Reeves of Boston Consulting Group's Henderson Institute and Jack Fuller, an expert in neuroscience, provide a fascinating look into the mechanics of imagination and lay out a process for creating ideas and bringing them to life: The Seduction: How to open yourself up to surprises The Idea: How to generate new ideas The Collision: How to rethink your idea based on real-world feedback The Epidemic: How to spread an evolving idea to others The New Ordinary: How to turn your novel idea into an accepted reality The Encore: How to repeat the process—again and again. Imagination is one of the least understood but most crucial ingredients of success. It's what makes the difference between an incremental change and the kinds of pivots and paradigm shifts that are essential to transformation—especially during a crisis. The Imagination Machine is the guide you need to demystify and operationalize this powerful human capacity, to inject new life into your company, and to head into unknown territory with the right tools at your disposal.
An examination of machine learning art and its practice in new media art and music. Over the past decade, an artistic movement has emerged that draws on machine learning as both inspiration and medium. In this book, transdisciplinary artist-researcher Sofian Audry examines artistic practices at the intersection of machine learning and new media art, providing conceptual tools and historical perspectives for new media artists, musicians, composers, writers, curators, and theorists. Audry looks at works from a broad range of practices, including new media installation, robotic art, visual art, electronic music and sound, and electronic literature, connecting machine learning art to such earlier artistic practices as cybernetics art, artificial life art, and evolutionary art. Machine learning underlies computational systems that are biologically inspired, statistically driven, agent-based networked entities that program themselves. Audry explains the fundamental design of machine learning algorithmic structures in terms accessible to the nonspecialist while framing these technologies within larger historical and conceptual spaces. Audry debunks myths about machine learning art, including the ideas that machine learning can create art without artists and that machine learning will soon bring about superhuman intelligence and creativity. Audry considers learning procedures, describing how artists hijack the training process by playing with evaluative functions; discusses trainable machines and models, explaining how different types of machine learning systems enable different kinds of artistic practices; and reviews the role of data in machine learning art, showing how artists use data as a raw material to steer learning systems and arguing that machine learning allows for novel forms of algorithmic remixes.
Issues for 1905-1919 include papers published subsequently in revised form in the institute's Transactions.