The long-term memory of most connectionist systems lies entirely in the
weights of the system. Since the number of weights is typically fixed, this
bounds the total amount of knowledge that can be learned and stored. Though
this is not normally a problem for a neural network designed for a specific
task, such a bound is undesirable for a system that continually learns over an
open range of domains. To address this, we describe a lifelong learning system
that leverages a fast, though non-differentiable, content-addressable memory
which can be exploited to encode both a long history of sequential episodic
knowledge and semantic knowledge over many episodes for an unbounded number of
domains. This opens the door for investigation into transfer learning, and
leveraging prior knowledge that has been learned over a lifetime of experiences
to new domains.

本文介绍了一种利用快速但不可微的内容可寻址内存编码长序列经验和跨多个领域的语义知识的终身学习系统，从而在不受限制的领域上不断学习并存储知识，打开了探索迁移学习和利用过去学习经验到新领域的机会。

不断增长的长期情景与语义记忆

A Growing Long-term Episodic & Semantic Memory

Research on integrated neural-symbolic systems has made significant progress
in the recent past. In particular the understanding of ways to deal with
symbolic knowledge within connectionist systems (also called artificial neural
networks) has reached a critical mass which enables the community to strive for
applicable implementations and use cases. Recent work has covered a great
variety of logics used in artificial intelligence and provides a multitude of
techniques for dealing with them within the context of artificial neural
networks. We present a comprehensive survey of the field of neural-symbolic
integration, including a new classification of system according to their
architectures and abilities.

本文对神经符号融合领域进行了全面调查，并根据其结构和能力进行了新的分类。该领域近期在处理连接主义系统（也称为人工神经网络）中的符号知识方面取得了重大进展，为人工智能提供了各种技术。