Position Paper: Agent AI Towards a Holistic Intelligence
원문 초록
Recent advancements in large foundation models have remarkably enhanced our understanding of sensory information in open-world environments. In leveraging the power of foundation models, it is crucial for AI research to pivot away from excessive reductionism and toward an emphasis on systems that function as cohesive wholes. Specifically, we emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. The emerging field of Agent AI spans a wide range of existing embodied and agent-based multimodal interactions, including robotics, gaming, and healthcare systems, etc. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model. On top of this idea, we discuss how agent AI exhibits remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. Furthermore, we discuss the potential of Agent AI from an interdisciplinary perspective, underscoring AI cognition and consciousness within scientific discourse. We believe that those discussions serve as a basis for future research directions and encourage broader societal engagement.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Car Object Counting and Position Estimation via Extension of the CLIP-EBC Framework
SeqPE: Transformer with Sequential Position Encoding
We Should Identify and Mitigate Third-Party Safety Risks in MCP-Powered Agent Systems
JoFormer (Journey-based Transformer): Theory and Empirical Analysis on the Tiny Shakespeare Dataset
POSS: Position Specialist Generates Better Draft for Speculative Decoding
ComRoPE: Scalable and Robust Rotary Position Embedding Parameterized by Trainable Commuting Angle Matrices
토론
이 논문과 연결된 포럼 글이 아직 없습니다.