Data-Centric AI Requires Rethinking Data Notion

• URL: http://arxiv.org/abs/2110.02491v2
• Date: Thu, 7 Oct 2021 06:37:07 GMT
• Title: Data-Centric AI Requires Rethinking Data Notion
• Authors: Mustafa Hajij, Ghada Zamzmi, Karthikeyan Natesan Ramamurthy, Aldo Guzman Saenz
• Abstract summary: This work proposes unifying principles offered by categorical and cochain notions of data. 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.
• Score: 12.595006823256687
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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