Foundations of Multisensory Artificial Intelligence: Advancing the Theoretical and Computational Frontiers of Multimodal Machine Learning

Core Concepts

This thesis aims to advance the theoretical and computational foundations of multimodal machine learning, enabling the creation of next-generation multimodal technologies that can learn from and reason about multiple sensory inputs.

Abstract

This thesis focuses on two key challenges in multimodal learning:

Foundations of Multimodal Interactions:

Proposes a theoretical framework to formalize how modalities interact with each other to give rise to new information for a task.
Develops practical estimators to quantify the types of interactions in real-world datasets, enabling researchers to design suitable approaches to learn these interactions and analyze whether their models have succeeded in learning them.

Multisensory Foundation Models:

Introduces MULTIBENCH, a unified large-scale benchmark across a wide range of modalities, tasks, and research areas in multimodal learning.
Presents the cross-modal attention and multimodal transformer architectures that underpin many of today's multimodal foundation models, and demonstrates how scaling these models on MULTIBENCH enables the creation of general-purpose multimodal multitask models with real-world impact.
The thesis also covers other contributions in multimodal representation learning, applications in affective computing and healthcare, and addressing real-world challenges of robustness, fairness, and privacy.

Stats

"This research was funded by: National Science Foundation awards IIS1722822 and IIS1750439; National Institutes of Health awards R01MH096951 and U01MH116923; graduate fellowships from Meta Platforms and Siebel Scholars; and grants from Meta Platforms, Nippon Telegraph and Telephone Corporation, Oculus VR, and Samsung Electronics."

Quotes

"Building multisensory artificial intelligence 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 foundations of multimodal machine learning."

Key Insights Distilled From

Foundations of Multisensory Artificial Intelligence

by Paul Pu Lian... at arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.18976.pdf

Deeper Inquiries

How can the proposed theoretical and computational foundations of multimodal interactions be extended to handle more complex and dynamic real-world scenarios involving multiple agents and evolving environments

The proposed theoretical and computational foundations of multimodal interactions can be extended to handle more complex and dynamic real-world scenarios by incorporating adaptive learning mechanisms and reinforcement learning techniques. In complex environments with multiple agents and evolving dynamics, the ability to adapt to changing conditions and interactions is crucial. By integrating reinforcement learning algorithms, the multimodal system can learn from its interactions with the environment and other agents, continuously updating its models and strategies to optimize performance.
Furthermore, the inclusion of hierarchical modeling and attention mechanisms can enhance the system's ability to focus on relevant information and adjust its responses based on the context. Hierarchical modeling allows the system to capture dependencies and relationships at different levels of abstraction, enabling it to understand complex interactions and make informed decisions. Attention mechanisms help the system to selectively focus on important modalities or features, improving its efficiency and adaptability in dynamic environments.
Moreover, the integration of multimodal fusion techniques, such as factorized learning and cross-modal attention, can enable the system to effectively combine information from different modalities and agents. By leveraging these fusion techniques, the system can extract meaningful insights from diverse sources of data and interactions, enhancing its ability to understand and respond to complex real-world scenarios.
Overall, by extending the theoretical and computational foundations of multimodal interactions with adaptive learning, reinforcement learning, hierarchical modeling, attention mechanisms, and fusion techniques, the system can navigate more complex and dynamic real-world scenarios involving multiple agents and evolving environments effectively.

What are the potential ethical and societal implications of deploying large-scale multisensory foundation models, and how can we ensure their responsible development and use

The deployment of large-scale multisensory foundation models raises important ethical and societal implications that need to be addressed to ensure their responsible development and use. Some potential implications include:

Privacy Concerns: Multisensory models may have access to sensitive personal data from various modalities, raising concerns about data privacy and security. It is essential to implement robust data protection measures and ensure compliance with privacy regulations to safeguard user information.

Bias and Fairness: Multimodal models can inherit biases present in the training data, leading to unfair or discriminatory outcomes. To mitigate bias, it is crucial to employ bias detection and mitigation techniques, promote diversity in training data, and ensure transparency in model decision-making processes.

Accountability and Transparency: As multisensory models become more complex, understanding their decision-making processes and ensuring accountability for their actions becomes challenging. Establishing transparency mechanisms and accountability frameworks can help address these concerns and build trust in the technology.

Social Impact: The widespread deployment of multisensory models can have significant social implications, affecting job markets, healthcare systems, and societal norms. It is essential to consider the broader societal impact of these technologies and engage in ethical discussions to mitigate potential negative consequences.

To ensure the responsible development and use of large-scale multisensory foundation models, stakeholders must engage in interdisciplinary collaborations, adhere to ethical guidelines, conduct thorough impact assessments, and prioritize the well-being and rights of individuals affected by these technologies.

What insights from neuroscience and cognitive science on human multimodal perception and reasoning could inspire the next generation of artificial multimodal intelligence systems

Insights from neuroscience and cognitive science on human multimodal perception and reasoning can inspire the next generation of artificial multimodal intelligence systems in several ways:

Cross-Modal Integration: Studying how the human brain integrates information from different sensory modalities can inform the design of artificial systems that can effectively combine and interpret data from diverse sources. Mimicking the brain's ability to integrate visual, auditory, and tactile inputs can enhance the robustness and accuracy of multimodal AI systems.

Hierarchical Processing: Understanding the hierarchical processing of sensory information in the brain can guide the development of hierarchical models in artificial systems. By incorporating hierarchical structures that capture different levels of abstraction, AI systems can better understand complex relationships and patterns in multimodal data.

Attention Mechanisms: Insights into attention mechanisms in the brain can inspire the implementation of attention-based models in artificial systems. By focusing on relevant information and dynamically adjusting attention across modalities, AI systems can improve their efficiency and adaptability in processing multimodal inputs.

Learning and Adaptation: Studying how the brain learns and adapts to new information can inform the development of adaptive learning algorithms in AI systems. By incorporating principles of neuroplasticity and lifelong learning, artificial systems can continuously update their models and improve their performance over time.

By drawing inspiration from neuroscience and cognitive science, researchers can enhance the capabilities of artificial multimodal intelligence systems, making them more human-like in their perception, reasoning, and decision-making processes.