具身认知
内含代理
计算机科学
人机交互
杠杆(统计)
自主代理人
过程(计算)
背景(考古学)
观点
人工智能
生物
操作系统
艺术
古生物学
视觉艺术
作者
Zane Durante,Qiuyuan Huang,Naoki Wake,Ran Gong,Jae Sung Park,Bidipta Sarkar,Rohan Taori,Yusuke Noda,Demetri Terzopoulos,Yejin Choi,Katsushi Ikeuchi,Hoi Vo,Li Fei-Fei,Jianfeng Gao
出处
期刊:Cornell University - arXiv
日期:2024-01-01
被引量:4
标识
DOI:10.48550/arxiv.2401.03568
摘要
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.
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