Agent AI: Surveying the Horizons of Multimodal Interaction (2401.03568v2)

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.

The paper "Agent AI: Surveying the Horizons of Multimodal Interaction" provides a comprehensive survey on the burgeoning field of multi-modal AI systems, collectively referred to as "Agent AI," which are systems capable of capturing and interpreting multiple types of data input to perform complex tasks. These systems are seen as pivotal elements in the progression towards achieving AGI.

Core Contributions and Highlights:

1. Framework and Classification: The paper proposes a new framework for understanding Agent AI, classifying these systems into categories such as embodied agents, interactive agents, and generative agents. It highlights the multi-modal nature of these agents which involves integrating various data types like visual, textual, and sensory inputs to enhance their intelligence and adaptability.
2. Historical Context and Motivation: Drawing inspiration from historical AI systems and philosophical ideals like Aristotelian Holism, the paper emphasizes the need for AI systems that can interact intelligently across diverse environments by processing visual stimuli, language inputs, and context-aware data.
3. Agent AI Systems: The authors present examples of current Agent AI systems, illustrating their utility in various domains such as gaming, robotics, and healthcare. These systems exploit their embedded logic and knowledge to manage tasks requiring an understanding of both the physical and virtual worlds.
4. Model Integration with Foundation Models: Teaming LLMs and visual LLMs (VLMs) with Agent AI, the paper discusses the promising abilities of these agents to perform task planning and intuitive reasoning, stressing their utility in both known and uncharted environments.
5. Learning Paradigms: Learning methodologies such as reinforcement learning (RL), imitation learning (IL), and task-specific adaptation are explored. The authors illustrate strategies for optimizing agent systems by involving feedback mechanisms and on-the-fly adaptability to unforeseen environmental cues.
6. Ethical and Societal Considerations: The paper critically analyzes the ethical considerations surrounding Agent AI, highlighting the potential risks and biases inherently encoded in these systems. It emphasizes the importance of ensuring inclusivity and data privacy, adhering to ethical standards, and mitigating model biases.
7. Applications and Future Directions: The authors detail various application domains where Agent AI has the potential to make significant impacts such as autonomous vehicles, personal assistants, and interactive media. They argue for a cross-disciplinary research agenda to handle the complex interoperability challenges these agents face.
8. Novel Paradigms: They propose new paradigms for agent systems, advocating for integrated approaches that involve collaborative Agent AI, supported by human-in-the-loop methodologies to ensure systems are both safe and aligned with user needs.
9. Challenges Addressed: Agent AI faces issues such as sim-to-real transfer challenges, integration across varying environments, and efficient knowledge transfer between agents. The paper provides insights into ongoing efforts in enhancing generalization across different contexts and augmenting interpretability.

Through this extensive review, the paper serves as both a manifesto and a benchmark for current and future research in the AI community, urging the field toward building more robust, interactive, and generalizable AI systems. It underscores the necessity for a holistic approach in Agent AI design, one that encompasses multi-modal learning and adapts to cross-domain requirements.