Foundations of Multisensory Artificial Intelligence

TL;DR

This paper develops theoretical frameworks and practical models for multisensory AI, integrating multiple modalities to improve understanding and application in fields like healthcare, robotics, and multimedia processing.

Contribution

It introduces a formal framework for modality interactions, a large-scale multisensory benchmark, and multimodal architectures that advance the development of general-purpose multisensory AI systems.

Findings

Quantification of modality interactions aids dataset understanding.

MultiBench benchmark enables comprehensive evaluation across modalities.

Multimodal transformers facilitate scalable multisensory AI applications.

Abstract

Building multisensory AI systems that learn from multiple sensory inputs such as text, speech, video, real-world sensors, wearable devices, and medical data holds great promise for impact in many scientific areas with practical benefits, such as in supporting human health and well-being, enabling multimedia content processing, and enhancing real-world autonomous agents. By synthesizing a range of theoretical frameworks and application domains, this thesis aims to advance the machine learning foundations of multisensory AI. In the first part, we present a theoretical framework formalizing how modalities interact with each other to give rise to new information for a task. These interactions are the basic building blocks in all multimodal problems, and their quantification enables users to understand their multimodal datasets, design principled approaches to learn these interactions, and analyze whether their model has succeeded in learning. In the second part, we study the design of practical multimodal foundation models that generalize over many modalities and tasks, which presents a step toward grounding large language models to real-world sensory modalities. We introduce MultiBench, a unified large-scale benchmark across a wide range of modalities, tasks, and research areas, followed by the cross-modal attention and multimodal transformer architectures that now underpin many of today's multimodal foundation models. Scaling these architectures on MultiBench enables the creation of general-purpose multisensory AI systems, and we discuss our collaborative efforts in applying these models for real-world impact in affective computing, mental health, cancer prognosis, and robotics. Finally, we conclude this thesis by discussing how future work can leverage these ideas toward more general, interactive, and safe multisensory AI.