The Strangest Music in the World: Self-Supervised Creativity and Nostalgia for the Future in Robotic Rock Band “The Three Sirens”
Why this work is in the frame
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Bibliographic record
Abstract
The emergence of deep learning since the mid-2010s and its successful application to creative activity challenges long-held anthropocentric conceptions of art and music, bringing back ideas about machine creativity that had been previously explored in the 20th century. Particularly, in the 1990s, some artists, composers, and musicians started working with machine learning and other adaptive computation systems. The work of Nicolas Baginsky is emblematic of that era. In 1992, he created the robot guitar Aglaopheme, which became the first performer of a self-learning robotic band developed throughout the 1990s, soon joined by the robot bass Peisinoe, the robot drum Thelxiepeia, and eventually other artificial agents, forming the autonomous robotic band The Three Sirens. In this review, we describe the technological, musical, and imaginative aspects of Baginsky’s robotic instruments. The unreal and behind-the-scenes story of the mythological three sirens is important in understanding how the robots are designed and what they (are) intend(ed) to do. In the context of artificial intelligence, the concept of seeking a surprising musical effect will push us to reimagine such concepts as musical creativity and improvisation within the algorithmic composition and provide opportunities to discuss nostalgia for the future music and live performance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it