Agent AI: Surveying the Horizons of Multimodal Interaction
(2401.03568)Abstract
Multi-modal AI systems will likely become a ubiquitous presence in our everyday lives. A promising approach to making these systems more interactive is to embody them as agents within physical and virtual environments. At present, systems leverage existing foundation models as the basic building blocks for the creation of embodied agents. Embedding agents within such environments facilitates the ability of models to process and interpret visual and contextual data, which is critical for the creation of more sophisticated and context-aware AI systems. For example, a system that can perceive user actions, human behavior, environmental objects, audio expressions, and the collective sentiment of a scene can be used to inform and direct agent responses within the given environment. To accelerate research on agent-based multimodal intelligence, we define "Agent AI" as a class of interactive systems that can perceive visual stimuli, language inputs, and other environmentally-grounded data, and can produce meaningful embodied actions. In particular, we explore systems that aim to improve agents based on next-embodied action prediction by incorporating external knowledge, multi-sensory inputs, and human feedback. We argue that by developing agentic AI systems in grounded environments, one can also mitigate the hallucinations of large foundation models and their tendency to generate environmentally incorrect outputs. The emerging field of Agent AI subsumes the broader embodied and agentic aspects of multimodal interactions. Beyond agents acting and interacting in the physical world, we envision a future where people can easily create any virtual reality or simulated scene and interact with agents embodied within the virtual environment.
Overview
-
The paper provides an in-depth examination of Agent AI, focusing on multimodal interaction systems and their advancement towards sophisticated, contextually aware AI.
-
Agent AI aims to create interactive AI capable of functioning in both physical and virtual environments through models trained on multimodal inputs.
-
A novel Agent AI Paradigm with a unified transformer model structure is introduced to enhance existing LLMs and Visual Language Models for learning agent-specific tasks.
-
Agent AI Learning involves strategies such as reinforcement learning and imitation learning to train multimodal AI agents, building upon existing foundation models.
-
Agent AI systems are classified into distinct types demonstrating their functionalities in different application domains including gaming, robotics, and healthcare.
Overview
A paper presents a comprehensive examination of Agent AI, which focuses on the evolution and current landscape of multimodal interaction systems. This overview highlights key points from the paper, elucidating the role and advancements of Agent AI in achieving more contextually aware and sophisticated AI systems.
Agent AI Integration
Agent AI, as an emerging paradigm, strives for an interactive AI that can behave effectively within both physical and virtual environments. This approach involves leveraging foundation models trained on multimodal inputs – visual, textual, and other environmental data. Integrating such models within an Agent AI framework can profoundly enhance understanding and the ability to perform tasks that mimic sentient beings.
Agent AI Paradigm
The paper introduces a novel Agent AI Paradigm using a unified transformer model structure that encompasses visual, language, and agent tokens. This model aims to bootstrap the capabilities of existing LLMs and Visual Language Models (VLMs), facilitating the learning of agent-specific tasks across different domains.
Agent AI Learning
Agent AI Learning combines several strategies, such as reinforcement learning (RL), imitation learning (IL), and in-context learning, to train multi-modal AI agents effectively. Agentic Foundation Models, derived from pretrained LLMs and VLMs, have shown promising directions in providing a platform for continuous improvement and learning from interactions with the environment.
Agent AI Categorization
Agent AI systems are categorized into six distinct types that underscore their functionalities, such as Generalist Agent Areas, Embodied Agents, Simulation and Environment Agents, Generative Agents, Knowledge and Logical Inference Agents, and LLMs and VLMs Agents. These classifications consider the respective modalities, interaction capabilities, and domains they are designed to navigate.
Agent AI Application Tasks
The versatility of Agent AI systems extends across numerous application domains like gaming, robotics, and healthcare. Each domain poses unique leader-board and possibilities, pushing the development of AI agents towards domain-specific tuning coupled with cross-modality and cross-reality understanding.
Continuous and Self-improvement for Agent AI
Agent AI stands to benefit significantly from human-based interaction data and foundation model-generated data. These data sources enable agents to self-improve and adapt responses, a crucial step toward the development of truly autonomous systems.
Final Reflections
The field of Agent AI is fundamentally transforming how we approach the interplay between modalities, the embodying of AI systems, and the representation of knowledge. The ongoing development of advanced AI agents anticipates a future where AI can more seamlessly interact within the nuanced terrains of both reality and cyberspace.