Summary of Agent Ai: Surveying the Horizons Of Multimodal Interaction, by Zane Durante et al.
Agent AI: Surveying the Horizons of Multimodal Interaction
by 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
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A multi-modal approach to AI systems is being explored through the embodiment of agents within physical and virtual environments. The use of foundation models as building blocks enables the processing and interpretation of visual and contextual data, leading to more sophisticated and context-aware AI systems. For example, a system that can perceive user actions, human behavior, environmental objects, audio expressions, and sentiment can inform and direct agent responses. This paper defines “Agent AI” as interactive systems that perceive visual stimuli, language inputs, and environmental data, producing meaningful embodied actions. The study explores improving agents through next-embodied action prediction using external knowledge, multi-sensory inputs, and human feedback. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI systems are becoming more like us by being able to interact with our environment in a more natural way. To make this happen, researchers are creating “agent AI” which is a type of intelligence that can see, hear, and understand what’s going on around it. This allows agents to respond to things like user actions, human behavior, and environmental objects. The goal is to make AI systems that can interact with us in a more natural way, making our lives easier. |
Keywords
* Artificial intelligence * Multi modal
