A Vision for Multisensory Intelligence: Sensing, Synergy, and Science

TL;DR

The paper addresses the gap between current AI focused on digital modalities and the need for multisensory intelligence that connects AI to human senses and physical environments. It proposes a decade-spanning vision built on three pillars—sensing, science, and synergy—and grounds it in a framework of six technical frontiers (integration, alignment, reasoning, generation, generalization, and experience) facilitated by multimodal foundation models and real-world constraints. Key contributions include a structured examination of sensory heterogeneity, intermodal connections, and the tradeoffs between unified versus modular modeling, along with actionable research directions for learning, alignment, and interaction. The work aims to advance end-to-end multisensory AI systems that can sense, understand, generate, and interact in the physical and social world, ultimately enhancing human–AI collaboration and wellbeing; resources and demos are available from the MIT Multisensory Intelligence group.

Abstract

Our experience of the world is multisensory, spanning a synthesis of language, sight, sound, touch, taste, and smell. Yet, artificial intelligence has primarily advanced in digital modalities like text, vision, and audio. This paper outlines a research vision for multisensory artificial intelligence over the next decade. This new set of technologies can change how humans and AI experience and interact with one another, by connecting AI to the human senses and a rich spectrum of signals from physiological and tactile cues on the body, to physical and social signals in homes, cities, and the environment. We outline how this field must advance through three interrelated themes of sensing, science, and synergy. Firstly, research in sensing should extend how AI captures the world in richer ways beyond the digital medium. Secondly, developing a principled science for quantifying multimodal heterogeneity and interactions, developing unified modeling architectures and representations, and understanding cross-modal transfer. Finally, we present new technical challenges to learn synergy between modalities and between humans and AI, covering multisensory integration, alignment, reasoning, generation, generalization, and experience. Accompanying this vision paper are a series of projects, resources, and demos of latest advances from the Multisensory Intelligence group at the MIT Media Lab, see https://mit-mi.github.io/.

Figures (8)

• Figure 1: Theme 1: Sensing - the process of perceiving and capturing signals from the world (inspired by and extending the human senses) and transforming them into structured representations that support learning, reasoning, and decision-making. Section \ref{['sec:sensing']} covers novel ways of sensing the world and the new challenges these sensing modalities introduce to AI research.
• Figure 2: Theme 2: Science seeks to develop a systematic understanding and learning of generalizable principles from multisensory data: some of the most important scientific questions include (1) quantifying sensory heterogeneity and its impact on modeling and training, (2) the types of multimodal connections and interactions that give rise to new information during fusion, (3) characterizing the unified structures and representations that scale across modalities and enable cross-modal generalization, and (4) advancing the learning dynamics and optimization landscapes that shape practical multimodal learning.
• Figure 3: Challenge 1, Integration: Learning joint representations that capture cross-modal interactions while accounting for the inherent heterogeneity of different modalities. Integration requires tackling the challenges of (1) modality gaps that cause fundamental representation tensions, (2) information interactions that have to be modeled appropriately to maximize task relevance, (3) leveraging and adapting foundation representations from pre-trained models, and (4) dealing with real-world constraints so that models can work in noisy, imperfect, and resource constrained environments.
• Figure 4: Challenge 2, Alignment: Modeling fine-grained cross-modal relationships among all modality elements by leveraging the structural dependencies present in the data. Alignment contains 3 key subchallenges of (1) explicit alignment using learning algorithms to identify connections between discrete elements or continuous modality signals with ambiguous segmentation, (2) implicit alignment where alignment is not directly imposed but rather an emergent phenomenon from downstream learning objectives, and (3) aligned representations where alignment is a latent step to learn better contextualized multimodal representations.
• Figure 5: Challenge 3, Reasoning: Performing multi-step inference across modalities to integrate and synthesize knowledge guided by task structure. General multimodal reasoning requires developing (1) reasoning mediums that parameterize individual concepts in the reasoning process, (2) structure modeling of the relationships over which reasoning occurs, and (3) inference of increasingly rich concepts from individual steps of evidence.