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。

图灵与人工伙伴一同演奏钢琴

If Turing played piano with an artificial partner

The rise of large-scale socio-technical systems in which humans interact with
artificial intelligence (AI) systems (including assistants and recommenders, in
short AIs) multiplies the opportunity for the emergence of collective phenomena
and tipping points, with unexpected, possibly unintended, consequences. For
example, navigation systems' suggestions may create chaos if too many drivers
are directed on the same route, and personalised recommendations on social
media may amplify polarisation, filter bubbles, and radicalisation. On the
other hand, we may learn how to foster the "wisdom of crowds" and collective
action effects to face social and environmental challenges. In order to
understand the impact of AI on socio-technical systems and design
next-generation AIs that team with humans to help overcome societal problems
rather than exacerbate them, we propose to build the foundations of Social AI
at the intersection of Complex Systems, Network Science and AI. In this
perspective paper, we discuss the main open questions in Social AI, outlining
possible technical and scientific challenges and suggesting research avenues.

本研究论文旨在借助复杂系统、网络科学和人工智能的交叉领域构建社交人工智能的基础，并探讨了社交人工智能领域中的主要问题、可能出现的技术和科学挑战，以及未来的研究方向。