[论文解读] On the link between conscious function and general intelligence in humans and machines

Arthur Juliani, Kai Arulkumaran+2 more|arXiv (Cornell University)|Mar 24, 2022

Functional Brain Connectivity Studies被引用 21

一句话总结

本文分析三种意识功能理论（全球工作空间理论 Global Workspace Theory、信息生成理论 Information Generation Theory、注意力模式理论 Attention Schema Theory），以论证它们与领域无关的通用智力之间的联系，并讨论人工智能如何整合这些概念以追求心灵时间旅行，作为提高通用智能的路径。

ABSTRACT

In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this work, we explore the validity and potential application of this seemingly intuitive link between consciousness and intelligence. We do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST). We find that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Having identified this trend, we use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a single unified and implementable model. Given that it is made possible by cognitive abilities underlying each of the three functional theories, artificial agents capable of mental time travel would not only possess greater general intelligence than current approaches, but also be more consistent with our current understanding of the functional role of consciousness in humans, thus making it a promising near-term goal for AI research.

研究动机与目标

• 评估三种当代意识功能理论如何映射到人类的领域无关智能。
• 检验当前 AI 系统如何整合 GWT、IGT、AST 的方面以改善泛化。
• 提出一个受这些理论启发的统一、可实现的模型，以实现人工心灵时间旅行。

提出的方法

• 回顾并比较 Global Workspace Theory (GWT)、Information Generation Theory (IGT) 与 Attention Schema Theory (AST) 作为对意识访问的解释。
• 讨论意识访问与领域无关认知能力之间的关系。
• 调查最先进的深度学习方法如何已经整合这三种理论的要素。
• 引入心灵时间旅行的概念，作为将理论整合到 AI 的指导示例。

实验结果

研究问题

• RQ1 GWT、IGT、AST 如何分别与人类的领域无关智能相关联？
• RQ2 现代 AI 系统在何种程度上开始实现 GWT、IGT、AST 的某些方面？
• RQ3 将这些理论结合起来的统一、可实现模型是否能够使人工代理执行心灵时间旅行？
• RQ4 哪些认知机制（注意力、生成建模、高层元模型）可以支撑更通用的 AI？

主要发现

• 三种理论都将意识功能与领域无关智能的方面联系起来。
• 当前的 AI 方法已经在某种程度上融入 GWT、IGT、AST 的元素，以实现更大的泛化能力。
• 利用选择性注意、生成认知图和注意力策略的统一模型，可能使代理具备心灵时间旅行能力。
• 心灵时间旅行可能使 AI 具备比当前方法更广泛的泛化能力。