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AI, the intelligent body leading to total intelligence

2024-05-06 1.1 K

Title: Agent AI Towards a Holistic Intelligence
Authors: Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong, Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti, Ashley Llorens, John Langford, Hoi Vo, Li Fei-Fei, Katsu Ikeuchi, Jianfeng Gao Llorens, John Langford, Hoi Vo, Li Fei-Fei, Katsu Ikeuchi, Jianfeng Gao
Source:arXiv:2403.00833v1 [cs.AI]

 

AI-1, the Intelligent Body to Total Intelligence

 

Synopsis:
This article explores the concept of Agent AI, an intelligent agent system that integrates a large-scale foundation model capable of performing tasks through interactive actions and embodied behaviors in both physical and virtual worlds. The article emphasizes the importance of moving away from oversimplificationism to a system that emphasizes holistic functionality and proposes the Agent Foundation Model (AFM), a new type of large-scale action model designed to enable embodied intelligent behavior. The article also discusses the remarkable capabilities that Agent AI has demonstrated across multiple domains and tasks, as well as examining the potential of Agent AI from an interdisciplinary perspective, including AI cognition and consciousness. Finally, the article suggests future research directions, including ethical challenges that need to be addressed.

Core Points:
Key point 1: The proposal of Agent AI
– Agent AI是一种体现系统,它将大型基础模型整合到代理行为中,跨越了机器人技术、游戏和医疗保健等多个领域。
– Agent Foundation Model(AFM)是首个用于开发通用AI代理的基础模型,它通过从机器人、游戏和医疗保健任务中收集的体现数据进行预训练。

Point 2: Basic Components of Agent AI
– Agent AI定义为能够基于感官输入自主执行适当和情境相关动作的智能代理。
– 它包括学习、记忆、行动、感知、规划和认知等关键模块,强调了这些组件之间的整合对于发展全面智能的重要性。

Intelligent Body AI-2 for Total Intelligence

Point 3: Agent AI awareness
– Agent AI可能包含一种“意识”,基于神经科学洞察,将代理和体现作为意识的指标。
– 通过预测基于语言的指令、感官输入和行动历史来实现目标导向的行动,并从行动与环境结果的关系中学习,从而满足体现原则。

Key Point 4: Application Areas for Agent AI
– Agent AI在机器人技术、游戏、医疗保健和多模态任务中具有重大影响,能够提供更沉浸式的游戏体验、革命性地改变行业、提高医疗诊断的准确性和改善患者护理。

Intelligent body AI-3 leading to total intelligence

Key Point 5: Challenges for Agent AI
– 尽管Agent AI在交互式代理AI系统的采用中不断增长,但在未见环境或场景中的泛化性能仍面临挑战。
– 提出了一种名为“混合现实与知识推理交互”的紧急机制,以促进与人类在复杂真实世界环境中解决具有挑战性任务的合作。

Resources:
1. Microsoft Research Core, Redmond
2. Microsoft Applied Robotics Research, Redmond
3. Stanford University
4. University of California, Los Angeles
5. MSR AI Frontiers, Newyork

Recommendations for next steps:
1. In-depth study of Agent Foundation Model (AFM) and exploration of its application potential and effectiveness in different fields.
2. Focus on the ethical and social responsibility aspects of Agent AI to ensure that the development of the technology is in line with ethical standards and laws and regulations.
3. Promote interdisciplinary collaboration, combining knowledge from neuroscience, biology, physics, and other fields to advance Agent AI's cognitive and consciousness research.

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