Interacting with generative music through live coding

All music performances are generative to the extent that the actions of performers produce musical sounds, but in this article we focus on performative interaction with generative music in a more compositional sense. In particular we discuss how live coding of music involve the building and management of generative processes. We suggest that the human interaction with generative processes that occurs in live coding provides a unique perspective on the generative music landscape, especially significant is the way in which generative algorithms are represented in code to best afford interaction and modification during performance. We also discuss the features of generative processes that make them more or less suitable for live coding performances. We situate live coding practice within historical and theoretical contexts and ground the discussion with regular reference to our experiences performing in the live coding duo aa-cell

Generative design in building information modelling (BIM) : approaches and requirements

The integration of generative design (GD) and building information modelling (BIM), as a new technology consolidation, can facilitate the constructability of GD’s automatic design solutions, while improving BIM’s capability in the early design phase. Thus, there has been an increasing interest to study GD-BIM, with current focuses mainly on exploring applications and investigating tools. However, there are a lack of studies regarding methodological relationships and skill requirement based on different development objectives or GD properties; thus, the threshold of developing GD-BIM still seems high. This study conducts a critical review of current approaches for developing GD in BIM, and analyses methodological relationships, skill requirements, and improvement of GD-BIM development. Accordingly, novel perspectives of objective-oriented, GD component-based, and skill-driven GD-BIM development as well as reference guides are proposed. Finally, future research directions, challenges, and potential solutions are discussed. This research aims to guide designers in the building industry to properly determine approaches for developing GD-BIM and inspire researchers’ future studies

A Domain-Independent Algorithm for Plan Adaptation

The paradigms of transformational planning, case-based planning, and plan debugging all involve a process known as plan adaptation - modifying or repairing an old plan so it solves a new problem. In this paper we provide a domain-independent algorithm for plan adaptation, demonstrate that it is sound, complete, and systematic, and compare it to other adaptation algorithms in the literature. Our approach is based on a view of planning as searching a graph of partial plans. Generative planning starts at the graph's root and moves from node to node using plan-refinement operators. In planning by adaptation, a library plan - an arbitrary node in the plan graph - is the starting point for the search, and the plan-adaptation algorithm can apply both the same refinement operators available to a generative planner and can also retract constraints and steps from the plan. Our algorithm's completeness ensures that the adaptation algorithm will eventually search the entire graph and its systematicity ensures that it will do so without redundantly searching any parts of the graph.Comment: See http://www.jair.org/ for any accompanying file

Deep Learning: Our Miraculous Year 1990-1991

In 2020, we will celebrate that many of the basic ideas behind the deep learning revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" or "Miraculous Year" 1990-1991 at TU Munich. Back then, few people were interested, but a quarter century later, neural networks based on these ideas were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute.Comment: 37 pages, 188 references, based on work of 4 Oct 201

Automatic Music Composition using Answer Set Programming

Music composition used to be a pen and paper activity. These these days music is often composed with the aid of computer software, even to the point where the computer compose parts of the score autonomously. The composition of most styles of music is governed by rules. We show that by approaching the automation, analysis and verification of composition as a knowledge representation task and formalising these rules in a suitable logical language, powerful and expressive intelligent composition tools can be easily built. This application paper describes the use of answer set programming to construct an automated system, named ANTON, that can compose melodic, harmonic and rhythmic music, diagnose errors in human compositions and serve as a computer-aided composition tool. The combination of harmonic, rhythmic and melodic composition in a single framework makes ANTON unique in the growing area of algorithmic composition. With near real-time composition, ANTON reaches the point where it can not only be used as a component in an interactive composition tool but also has the potential for live performances and concerts or automatically generated background music in a variety of applications. With the use of a fully declarative language and an "off-the-shelf" reasoning engine, ANTON provides the human composer a tool which is significantly simpler, more compact and more versatile than other existing systems. This paper has been accepted for publication in Theory and Practice of Logic Programming (TPLP).Comment: 31 pages, 10 figures. Extended version of our ICLP2008 paper. Formatted following TPLP guideline

The Legal Imitation Game: Generative AI’s Incompatibility with Clinical Legal Education

In this Essay, we briefly describe key aspects of [generative artificial intelligence] that are particularly relevant to, and raise particular risks for, its potential use by lawyers and law students. We then identify three foundational goals of clinical legal education that provide useful frameworks for evaluating technological tools like GenAI: (1) practice readiness, (2) justice readiness, and (3) client-centered lawyering. First is “practice readiness,” which is about ensuring that students have the baseline abilities, knowledge, and skills to practice law upon graduation. Second is “justice readiness,” a concept proposed by Professor Jane Aiken, which is about teaching law students to critically assess the social and political implications of legal work and the legal system, as well as making space for students to confront systemic injustices and the role of lawyers in perpetuating them. Third is “client centered lawyering,” which at its root is about client empowerment and autonomy, teaching students to recognize the power imbalances present in the attorney-client relationship and the importance of ensuring client agency in decision-making. Although these are by no means the only goals of clinical education, they provide key perspectives and criteria for GenAI assessment. Finally, we examine whether GenAI is pedagogically compatible with each of these three goals. We conclude that although GenAI does present some de minimis learning opportunities for practice readiness, it is largely incompatible with justice readiness and client-centered lawyering, especially when considering the serious concerns that the development, deployment, and use of GenAI raise for those clinical programs with public interest missions