Memory in Deep Learning Systems Part III: Neural Turing Machines

This is the third and final part of an essay that discussed the concepts of memory in deep learning models. The first two parts provided an overview about the fundamentals views about memory in neuroscience and cognitive psychology respectively. Today, we will explore how those theories have influenced the creation of deep learning models that imitate some of the characteristics of human memory.

From the two previous articles we learning that, when exploring the theories of memory in neuroscience and cognitive psychology, there are some fundamental features that should be part of any system that attempts to recreate human memory using computing models.

a)Partition a memory into segments that describe different areas of knowledge

b)Reassemble disparate segments into cohesive information structures

c)Retrieve data based on contextual and not directly related information as well as external data references

No discipline in computer science can benefit more from a human-like memory system than deep learning. Since its early days, there have been efforts in the deep learning space to model systems that simulate some of the key characteristics of human memory.

Deep Learning and Explicit Memory

In order to understand the relevance of memory in deep learning models, we should differentiate between the concepts of implicit and explicit knowledge. Implicit knowledge is typically subconscious and, consequently, hard to explain. We can find examples of implicit knowledge in areas such as speech and vision analysis such as recognizing a monkey in a picture or the tone and mood in a spoken sentence. Contrasting with that model, explicit knowledge is easily modeled declaratively. For instance, understanding that a monkey is a kind of animal or that certain adjectives are offensive are classic examples of explicit knowledge. We know that deep learning algorithms have made incredible progress representing implicit knowledge byyt they still struggle modeling and “memorizing” explicit knowledge.

What makes explicit knowledge so difficult in the context of deep learning algorithms? If you think about the traditional architecture of neural networks with millions of interconnected nodes, we will realize that they lack the equivalent of a working memory system that can store fragments of inferred pieces of knowledge and their relationships so that it can be easily acceded from different layers in the network. Recently, new deep learning techniques have been created to address this limitation.

Neural Turing Machines

The rapid evolution of deep learning algorithms has triggered the need for memory systems that can resemble the characteristics of human memory when processing explicit knowledge. One of the most popular techniques in the memory modeling space is known as Neural Turing Machines(NTM) and was introduced by DeepMind in 2014.

NTM works by expanding a deep neural network with memory cells that can store complete vectors. One of the greatest innovations of NTM is that it uses heuristics to read and write information. For instance, NTM implements a mechanism known as content-based addressing that can retrieve vectors based on input patterns. This is similar to the way humans recall memories based on ctextual experiences. Additionally, NTM includes mechanics for increasing the prominence of memory cells based on how often they are recalled.

NTM is not the only techniques that enables memory capabilities in deep learning systems but is certainly one of the most popular. Imitating the biological and psychological functions of human memory is not an easy endeavor and has become one of the most important areas of research in the deep learning space.

CEO of IntoTheBlock, Chief Scientist at Invector Labs, I write The Sequence Newsletter, Guest lecturer at Columbia University, Angel Investor, Author, Speaker.

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