当受大脑启发的人工智能遇到AGI时

Lin Zhao , Lu Zhang , Zihao Wu , Yuzhong Chen , Haixing Dai , Xiaowei Yu , Zhengliang Liu , Tuo Zhang , Xintao Hu , Xi Jiang , Xiang Li , Dajiang Zhu , Dinggang Shen , Tianming Liu
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引用次数: 0

摘要

通用人工智能(AGI)一直是人类的一个长期目标,目的是创造能够执行人类所能完成的任何智力任务的机器。为了实现这一目标,AGI研究人员从人脑中汲取灵感,并试图在智能机器中复制其原理。以大脑为灵感的人工智能是这一努力中出现的一个领域,它结合了神经科学、心理学和计算机科学的见解,开发出更高效、更强大的人工智能系统。在这篇文章中,我们从AGI的角度对大脑启发的人工智能进行了全面的概述。我们从当前受大脑启发的人工智能的进展及其与AGI的广泛联系开始。然后,我们介绍了人类智能和AGI的重要特征(例如,缩放、多模态和推理)。我们讨论了在当前人工智能系统中实现AGI的重要技术,如上下文学习和即时调整。我们还从算法和基础设施的角度研究了AGI系统的演变。最后,我们探讨了AGI的局限性和未来。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

When brain-inspired AI meets AGI

When brain-inspired AI meets AGI

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI.

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