Compositional Intelligence
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Compositional Intelligence
*With a foreword by Mario Carpo* Given the extensive data sources represented by urban environments, this book investigates the potential of architecting for cities using large-scale models – the technological underpinning of Generative AI. Koehler examines Large Language Models (LLMs) in relation to architectural questions of typology – those critical junctures where architecture engages with the city itself. Typology becomes the interface between an AI model and its modeled audiences, represented languages, and scales of operation. The investigation reveals how LLMs represent a fundamental shift from comprehending discrete elements to understanding entire languages – introducing a stochastic perspective that transforms how we read and shape urban reality. At the heart of this framework lies the concept of “inductive types” – threshold interfaces that superpose multiple value regimes and actively reconfigure themselves in response to changing urban conditions. By treating architecture as a form of representational materialism rather than a fixed language, designers can engage with complex urban systems while maintaining critical agency. This approach envisions architecture as an embedding of languages, where buildings become processors of information that compute through their spatial arrangements. The synthetic knowledge produced by LLMs enables access to plural values and stakeholder perspectives, allowing architects to participate in broader material ecologies through computational superposition rather than traditional dialectical methods. Providing a robust theoretical framework as well as practical insights into how architects can adapt their notational tools and design processes to embrace AI, this will be an essential read for any practicing or researching architect or urban designer interested in the implications of AI on the built environment.
Swarm Intelligence and Bio-Inspired Computation
Author: Maximos A. Kaliakatsos-Papakostas
language: en
Publisher: Elsevier Inc. Chapters
Release Date: 2013-05-16
Automatic music composition has blossomed with the introduction of intelligent methodologies in computer science. Thereby, many methodologies for automatic music composition have been or could be described as “intelligent,” but what exactly is it that makes them intelligent? Furthermore, is there any categorization of intelligent music composition (IMC) methodologies that is both consistent and descriptive? This chapter aims to provide some insights on what IMC methodologies are, through proposing and analyzing a detailed categorization of them. Toward this perspective, methodologies that incorporate bioinspired intelligent algorithms (such as cellular automata, L-systems, genetic algorithms, swarm intelligence, among others) as well as their combinations are considered and briefly reviewed. At the same time, a consistent categorization of these methodologies is proposed, taking into account the utilization of their intelligent algorithm in accordance to their overall compositional aims. To this end, three main categories can be defined: the “unsupervised,” the “supervised,” and the “interactive” IMC methodologies.
Composing Our Future
Author: Michele Kaschub
language: en
Publisher: Oxford University Press
Release Date: 2013-02-14
In order to prepare pre-service teachers and meet the needs of practitioners in the field, music teacher educators need resources to guide the development of curriculum, specific courses, professional development workshops, and other environments where composition education can begin, grow, and flourish. With chapters ranging from practical information to solid theory to useful best practice examples, Composing Our Future offers fresh insight into composition in music education from authors who are directly engaged in this work.