论文标题

基于深度学习和高斯过程的机器意识架构

A Machine Consciousness architecture based on Deep Learning and Gaussian Processes

论文作者

Merchán, Eduardo C. Garrido, Molina, Martín

论文摘要

机器学习的最新发展推动了机器可以在几年前可能发生的界限之外执行的任务。诸如深度学习或生成模型之类的方法已经完成了复杂的任务,例如自动生成艺术图片或文献。另一方面,符号资源也进一步发展,在常识推理提出的问题等问题中表现良好。机器意识是一个经过深入研究的领域,并且已经提出了基于功能主义哲学理论(例如全球工作空间理论或信息集成)的几种理论,试图解释机器中意识的出现。在这项工作中,我们提出了一种可能在全球工作空间理论中的机器中产生意识的架构,并假设意识出现在具有认知过程和表现出意识行为的机器中。该体系结构基于使用人工智能模型中最新发展的过程,这些发展是这些相关活动。对于此体系结构的每个模块,我们都会详细说明所涉及的模型以及它们如何相互交流以创建认知体系结构。

Recent developments in machine learning have pushed the tasks that machines can do outside the boundaries of what was thought to be possible years ago. Methodologies such as deep learning or generative models have achieved complex tasks such as generating art pictures or literature automatically. On the other hand, symbolic resources have also been developed further and behave well in problems such as the ones proposed by common sense reasoning. Machine Consciousness is a field that has been deeply studied and several theories based in the functionalism philosophical theory like the global workspace theory or information integration have been proposed that try to explain the ariseness of consciousness in machines. In this work, we propose an architecture that may arise consciousness in a machine based in the global workspace theory and in the assumption that consciousness appear in machines that has cognitive processes and exhibit conscious behaviour. This architecture is based in processes that use the recent developments in artificial intelligence models which output are these correlated activities. For every one of the modules of this architecture, we provide detailed explanations of the models involved and how they communicate with each other to create the cognitive architecture.

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