This article examines how to construct human-like working memory and thought
processes within a computer. The focus is on simulating the mammalian working
memory system. There should be two interacting working memory stores, one
analogous to sustained firing lending the system a focus of attention, and
another analogous to synaptic potentiation lending the system a short-term
memory. These working memory stores retain and coactivate representations,
using them to search long-term memory for appropriate updates. The working
memory stores should be updated continuously, and in an iterative fashion,
meaning that, in the next state, some proportion of the coactive items should
always be retained. Thus, the set of concepts coactive in working memory will
evolve gradually and incrementally over time. This makes each state a revised
iteration of the preceding state and causes successive states to overlap and
blend with respect to the set of representations they contain. As new
representations are added and old ones are subtracted, some remain active for
several seconds over the course of these changes. This persistent activity,
similar to that used in contemporary artificial recurrent neural networks, is
used to spread activation energy throughout the global workspace to search for
the next associative update. The result is a chain of associatively linked
intermediate states that are capable of advancing toward a solution or goal.
Iterative updating is conceptualized here as an information processing
strategy, a computational and neurophysiological determinant of the stream of
thought, and an algorithm for designing and programming artificial general
intelligence.

本文论述了如何实现类人的计算机工作记忆和思维过程，聚焦于模拟哺乳动物的工作记忆系统，提出了两个相互作用的工作记忆存储区，以及一种迭代更新的信息处理策略，作为设计和编程人工通用智能的计算和神经生理决定因素和算法。

一种用于机器意识和人工超智能的认知架构：迭代更新工作记忆

A Cognitive Architecture for Machine Consciousness and Artificial Superintelligence: Updating Working Memory Iteratively

Weakly supervised object detection (WSOD), where a detector is trained with
only image-level annotations, is attracting more and more attention. As a
method to obtain a well-performing detector, the detector and the instance
labels are updated iteratively. In this study, for more efficient iterative
updating, we focus on the instance labeling problem, a problem of which label
should be annotated to each region based on the last localization result.
Instead of simply labeling the top-scoring region and its highly overlapping
regions as positive and others as negative, we propose more effective instance
labeling methods as follows. First, to solve the problem that regions covering
only some parts of the object tend to be labeled as positive, we find regions
covering the whole object focusing on the context classification loss. Second,
considering the situation where the other objects contained in the image can be
labeled as negative, we impose a spatial restriction on regions labeled as
negative. Using these instance labeling methods, we train the detector on the
PASCAL VOC 2007 and 2012 and obtain significantly improved results compared
with other state-of-the-art approaches.

本文提出更有效的实例标记方法，利用上下文分类损失方法解决物体部分区域被错误标记的问题并通过空间限制标记负对象，应用于 PASCAL VOC 2007 和 2012 数据集进行训练，在弱监督物体检测领域取得了一定的突破。