Fodor and Pylyshyn's Legacy: Still No Human-like Systematic Compositionality in Neural Networks

Understand

Strong meta-learning capabilities for systematic compositionality are emerging as an important skill for navigating the complex and changing tasks of today's world.

• However, in presenting models for robust adaptation to novel environments, it is important to refrain from making unsupported claims about the performance of meta-learning systems that ultimately do not stand up to scrutiny.
• While Fodor and Pylyshyn famously posited that neural networks inherently lack this capacity as they are unable to model compositional representations or structure-sensitive operations, and thus are not a viable model of the human mind, Lake and Baroni recently presented meta-learning as a pathway to compositionality.
• In this position paper, we critically revisit this claim and highlight limitations in the proposed meta-learning framework for compositionality.