EMNLP 2024system demonstrations2 citations

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

Abstract

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. The project page with demonstrations and code is at https://i-code-studio.github.io/.

BibTeX
@inproceedings{fang-etal-2024-code,
    title = "i-Code Studio: A Configurable and Composable Framework for Integrative {AI}",
    author = "Fang, Yuwei  and
      Khademi, Mahmoud  and
      Zhu, Chenguang  and
      Yang, Ziyi  and
      Pryzant, Reid  and
      Xu, Yichong  and
      Qian, Yao  and
      Yoshioka, Takuya  and
      Yuan, Lu  and
      Zeng, Michael  and
      Huang, Xuedong",
    editor = "Hernandez Farias, Delia Irazu  and
      Hope, Tom  and
      Li, Manling",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-demo.2/",
    doi = "10.18653/v1/2024.emnlp-demo.2",
    pages = "14--24"
}
i-Code Studio: A Configurable and Composable Framework for Integrative AI · EMNLP 2024