Evolutionary training and abstraction yields algorithmic generalization of neural computers

A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems. An abstract strategy solves every sample from a problem class, no matter its representation or complexity—similar to algorithms in computer science. Neural networks are...

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Published in:Nature machine intelligence Vol. 2; no. 12; pp. 753 - 763
Main Authors: Tanneberg, Daniel, Rueckert, Elmar, Peters, Jan
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 01.12.2020
Nature Publishing Group
Subjects:
631/114/1305
639/705/117
Algorithms
Complexity
Computer architecture
Computer networks
Decoupling
Engineering
Information flow
Iterative methods
Learning
Machine learning
Neural networks
Representations
Training
ISSN:2522-5839, 2522-5839
Online Access:Get full text
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Summary:A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems. An abstract strategy solves every sample from a problem class, no matter its representation or complexity—similar to algorithms in computer science. Neural networks are powerful models for processing sensory data, discovering hidden patterns and learning complex functions, but they struggle to learn such iterative, sequential or hierarchical algorithmic strategies. Extending neural networks with external memories has increased their capacities to learn such strategies, but they are still prone to data variations, struggle to learn scalable and transferable solutions, and require massive training data. We present the neural Harvard computer, a memory-augmented network-based architecture that employs abstraction by decoupling algorithmic operations from data manipulations, realized by splitting the information flow and separated modules. This abstraction mechanism and evolutionary training enable the learning of robust and scalable algorithmic solutions. On a diverse set of 11 algorithms with varying complexities, we show that the neural Harvard computer reliably learns algorithmic solutions with strong generalization and abstraction, achieves perfect generalization and scaling to arbitrary task configurations and complexities far beyond seen during training, and independence of the data representation and the task domain. A hallmark of intelligent behaviour is the ability to learn abstract strategies that can be transferred across different tasks, but it has been challenging to incorporate this ability in artificial systems. The authors present a modular architecture for the learning of algorithmic solutions, and demonstrate generalization and scaling on 11 diverse algorithms.
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ISSN:2522-5839
2522-5839
DOI:10.1038/s42256-020-00255-1