Data-Centric AI Requires Rethinking Data Notion

TL;DR

This paper advocates for a fundamental rethinking of data concepts in AI, proposing categorical and cochain frameworks to unify data understanding and improve machine learning package development.

Contribution

It introduces a unifying theoretical framework based on categorical and cochain notions of data to advance data-centric AI practices.

Findings

Proposes categorical and cochain principles as unifying data notions.

Highlights impact on machine learning package development.

Discusses importance for data-centric AI transition.

Abstract

The transition towards data-centric AI requires revisiting data notions from mathematical and implementational standpoints to obtain unified data-centric machine learning packages. Towards this end, this work proposes unifying principles offered by categorical and cochain notions of data, and discusses the importance of these principles in data-centric AI transition. In the categorical notion, data is viewed as a mathematical structure that we act upon via morphisms to preserve this structure. As for cochain notion, data can be viewed as a function defined in a discrete domain of interest and acted upon via operators. While these notions are almost orthogonal, they provide a unifying definition to view data, ultimately impacting the way machine learning packages are developed, implemented, and utilized by practitioners.