i-Code Studio: A Configurable and Composable Framework for Integrative
  AI
Yuwei Fang Mahmoud Khademi Chenguang Zhu Ziyi Yang Reid Pryzant Yichong Xu Yao Qian Takuya Yoshioka Lu Yuan Michael Zeng Xuedong Huang
Artificial General Intelligence (AGI) requires comprehensive understanding
and generation capabilities for a variety of tasks spanning different
modalities and functionalities. Integrative AI is one important direction to
approach AGI, through combining multiple models to tackle complex multimodal
tasks. However, there is a lack of a flexible and composable platform to
facilitate efficient and effective model composition and coordination. In this
paper, we propose the i-Code Studio, a configurable and composable framework
for Integrative AI. The i-Code Studio orchestrates multiple pre-trained models
in a finetuning-free fashion to conduct complex multimodal tasks. Instead of
simple model composition, the i-Code Studio provides an integrative, flexible,
and composable setting for developers to quickly and easily compose
cutting-edge services and technologies tailored to their specific requirements.
The i-Code Studio achieves impressive results on a variety of zero-shot
multimodal tasks, such as video-to-text retrieval, speech-to-speech
translation, and visual question answering. We also demonstrate how to quickly
build a multimodal agent based on the i-Code Studio that can communicate and
personalize for users.