Computational Dreaming

通过关注做梦的一系列物理结构基础，这项研究将调查：（1）并行计算的新组织原则和（2）为什么做梦对智力至关重要。似乎违背了所有的生存本能，所有的智能生物都必须睡觉和做梦，即使他们受到胁迫，即使这危及他们的生命，因为他们处于一个敌对的环境中。 包括做梦在内的睡眠与有效的心理过程密切相关。 这项工作的论点是，做梦的需要可以从大脑最显著的身体和行为特征中推断出来。 将要研究的计算机体系结构是DALI（横向智能的梦想体系结构），这是一种真正的多指令单数据流（MISD）体系结构，其中多个模型实时处理相同的输入流。 当清醒时，一些横向处理器是观察者，而一个或多个其他的是活跃的。 一个梦阶段的计算解决分歧处理器之间的竞争反馈阶段不占主导地位的逻辑流程。现实包含对同一事物的多种观点，不同个体之间和同一个体内部，随着时间的推移，不一致性得到解决。DALI的目标是包括处理系统观察到的内容的多个横向模型，然后在梦阶段对模型分辨率的竞争过程进行建模，以便系统可以更有效地进行第二天的实时（清醒）响应。最初的问题，这将是调查的上下文分割语音识别在普及，便携式计算设备。

By focusing on a set of the physical-structural foundations of dreaming, this research will investigate: (1) new organizational principles for parallel computation and (2) why dreaming is critical to intelligence. Seemingly against all survival instincts, all intelligent beings must sleep and dream, even if they are under duress, even if it endangers their very lives because they are in a hostile environment. Sleep that includes dreaming is strongly related to efficient mental processes. The thesis of this work is that the need to dream can be inferred from the brain's most striking physical and behavioral characteristics. The computer architecture that will be investigated is the DALI (Dream Architecture for Lateral Intelligence) a true Multiple Instruction, Single Datastream (MISD) architecture in which multiple models process the same input stream in real-time. While awake, some lateral processors are observers while one or more others are active. A dream phase of computation resolves divergence between processors in a competitive feedback phase not dominated by a flow of logic. Reality contains multiple views of the same thing, between different individuals and within the same individual, with incongruence resolved over time. The goal of the DALI is to include multiple, lateral models that process what the system observes, and then to model the competitive process of model resolution during a dream phase so that the system may be more effective for the next day's real-time (awake) response. The initial problem which will be investigated is contextual partitioning for speech recognition in pervasive, portable computing devices.