The evolution of Artificial Intelligence (AI) has been driven by two core components: data and algorithms. Historically, AI research has predominantly followed the Model-Centric paradigm, which focuses on developing and refining models, while often treating data as static. This approach has led to the creation of increasingly sophisticated algorithms, which demand vast amounts of manually labeled and meticulously curated data. However, as data becomes central to AI development, it is also emerging as a significant bottleneck. The Data-Centric AI (DCAI) paradigm shifts the focus towards improving data quality, enabling the achievement of accuracy levels that are unattainable with Model-Centric approaches alone. This special issue presents recent advancements in DCAI, offering insights into the paradigm and exploring future research directions, aiming to contextualize the contributions included in this issue.
Data-Centric AI
Malerba D.;Pasquadibisceglie V.
2024-01-01
Abstract
The evolution of Artificial Intelligence (AI) has been driven by two core components: data and algorithms. Historically, AI research has predominantly followed the Model-Centric paradigm, which focuses on developing and refining models, while often treating data as static. This approach has led to the creation of increasingly sophisticated algorithms, which demand vast amounts of manually labeled and meticulously curated data. However, as data becomes central to AI development, it is also emerging as a significant bottleneck. The Data-Centric AI (DCAI) paradigm shifts the focus towards improving data quality, enabling the achievement of accuracy levels that are unattainable with Model-Centric approaches alone. This special issue presents recent advancements in DCAI, offering insights into the paradigm and exploring future research directions, aiming to contextualize the contributions included in this issue.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
