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
摘要：
机器学习的最新发展已经将机器可以完成的任务推到了几年前被认为是可能的范围之外。深度学习或生成模型等方法已经实现了自动生成艺术图片或文学作品等复杂任务。另一方面，符号资源也得到了进一步的发展，并表现出良好的问题，如常识推理提出的问题。机器意识是一个已经被深入研究的领域，并且已经提出了一些基于功能主义哲学理论的理论，如全局工作空间理论或信息集成，试图解释机器中意识的复杂性。在这项工作中，我们提出了一个架构，可能会出现在一个机器的基础上的全球工作空间理论的意识，并在假设意识出现在机器的认知过程和表现出有意识的行为。该架构基于使用人工智能模型的最新发展的过程，该模型的输出是这些相关的活动。对于该架构的每个模块，我们都详细解释了所涉及的模型以及它们如何相互通信以创建认知架构。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
