Human-like systematic generalization through a meta-learning neural network
The power of human language and thought arises from systematic compositionality—the algebraic ability to understand and produce novel combinations from known components. Fodor and Pylyshyn1 famously argued that artificial neural networks lack this capacity and are therefore not viable models of the mind. Neural networks have advanced considerably in the years since, yet the systematicity challenge persists. Here we successfully address Fodor and Pylyshyn’s challenge by providing evid...
Conference Of The Cognitive Science Society International Conference On Learning Representations International Joint Conference On Natural Language Processing Curran Associates International Conference On Computational Linguistics Proc International Conference On Machine Learning
Source: nature.com