Music is an inherently social activity that allows people to share
experiences and feel connected with one another. There has been little progress
in designing artificial partners exhibiting a similar social experience as
playing with another person. Neural network architectures that implement
generative models, such as large language models, are suited for producing
musical scores. Playing music socially, however, involves more than playing a
score; it must complement the other musicians' ideas and keep time correctly.
We addressed the question of whether a convincing social experience is made
possible by a generative model trained to produce musical scores, not
necessarily optimized for synchronization and continuation. The network, a
variational autoencoder trained on a large corpus of digital scores, was
adapted for a timed call-and-response task with a human partner. Participants
played piano with a human or artificial partner-in various configurations-and
rated the performance quality and first-person experience of self-other
integration. Overall, the artificial partners held promise but were rated lower
than human partners. The artificial partner with simplest design and highest
similarity parameter was not rated differently from the human partners on some
measures, suggesting that interactive rather than generative sophistication is
important in enabling social AI.

音乐是一种固有的社交活动，设计人工合作伙伴以展现类似社交体验的进展有限。研究表明，采用生成模型的神经网络架构适合生成音乐谱，但社交音乐演奏涉及超越乐谱的要素，需要与其他音乐家的想法相补和正确掌握节奏。研究使用一个经过训练的变分自动编码器神经网络与人类伙伴进行定时的问答式钢琴合奏任务，参与者对演奏质量和身临其境的自我他人整合体验进行了评分。虽然最简单设计和最高相似性参数的人工伙伴并未在某些指标上得到与人类伙伴不同的评分，但整体而言，人工伙伴的评分低于人类伙伴，这表明实现社交人工智能需要交互复杂性而非生成复杂性。