Opportunities and Challenges in Data-Centric AI

Kumar, Sushant; Datta, Sumit; Singh, Vishakha; Singh, Sanjay Kumar; Sharma, Ritesh

doi:10.1109/access.2024.3369417

Cited by 7 publications

(5 citation statements)

References 94 publications

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“…Some developments have been made to enhance the robustness of multiple AI models by employing this new paradigm [65]. Lastly, some successful prototypes and small-scale tools like Influenciae, MLPerf, and dcbench have also been developed to realize this new paradigm [1]. In the coming years, more developments are expected in this paradigm to advance AI systems and extend their robustness/performance.…”

Section: Practical Guidelines For Dc-ai Paradigmmentioning

confidence: 99%

“…To this end, DC-AI can be a feasible approach that encompasses a series of data-tailored actions (e.g., data quality enhancement and debugging), and it can enhance AI model performance, which can contribute to developing high-quality AI systems for real-world problems. DC-AI is expected to change the horizon of AI research, which has been mainly based on MC-AI in the past three decades [1].…”

Section: Introductionmentioning

confidence: 99%

“…and abundant data [4]. The two key components of AI technology are code (C) and data (D), as given in Equation (1).…”

Section: Introductionmentioning

confidence: 99%

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A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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Due to huge investments by both the public and private sectors, artificial intelligence (AI) has made tremendous progress in solving multiple real-world problems such as disease diagnosis, chatbot misbehavior, and crime control. However, the large-scale development and widespread adoption of AI have been hindered by the model-centric mindset that only focuses on improving the code/architecture of AI models (e.g., tweaking the network architecture, shrinking model size, tuning hyper-parameters, etc.). Generally, AI encompasses a model (or code) that solves a given problem by extracting salient features from underlying data. However, when the AI model yields a low performance, developers iteratively improve the code/algorithm without paying due attention to other aspects such as data. This model-centric AI (MC-AI) approach is limited to only those few businesses/applications (language models, text analysis, etc.) where big data readily exists, and it cannot offer a feasible solution when good data are not available. However, in many real-world cases, giant datasets either do not exist or cannot be curated. Therefore, the AI community is searching for appropriate solutions to compensate for the lack of giant datasets without compromising model performance. In this context, we need a data-centric AI (DC-AI) approach in order to solve the problems faced by the conventional MC-AI approach, and to enhance the applicability of AI technology to domains where data are limited. From this perspective, we analyze and compare MC-AI and DC-AI, and highlight their working mechanisms. Then, we describe the crucial problems (social, performance, drift, affordance, etc.) of the conventional MC-AI approach, and identify opportunities to solve those crucial problems with DC-AI. We also provide details concerning the development of the DC-AI approach, and discuss many techniques that are vital in bringing DC-AI from theory to practice. Finally, we highlight enabling technologies that can contribute to realizing DC-AI, and discuss various noteworthy use cases where DC-AI is more suitable than MC-AI. Through this analysis, we intend to open up a new direction in AI technology to solve global problems (e.g., climate change, supply chain disruption) that are threatening human well-being around the globe.

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Section: Practical Guidelines For Dc-ai Paradigmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Majeed,

Hwang

2024

Electronics

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“…Next-generation computing for the DC-AI paradigm: We discuss the next-generation computing for the DC-AI paradigm along with the relevant technologies that can contribute to transitioning DC-AI from theory to practice, which has remained unexplored in the recent literature [29].…”

mentioning

confidence: 99%

“…Recently, there has been an increasing trend toward training complex AI models with as little data as possible to overcome computing overhead [17,29,73]. Similarly, data optimization techniques are needed for training complex AI models with the fewest (but complete) data.…”

mentioning

confidence: 99%

Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era

Majeed,

Hwang

2024

ASI

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Data-centric artificial intelligence (DC-AI) is a modern paradigm that gives more priority to data quality enhancement, rather than only optimizing the complex codes of AI models. The DC-AI paradigm is expected to substantially advance the status of AI research and developments, which has been solely based on model-centric AI (MC-AI) over the past 30 years. Until present, there exists very little knowledge about DC-AI, and its significance in terms of solving real-world problems remains unexplored in the recent literature. In this technical note, we present the core aspects of DC-AI and MC-AI and discuss their interplay when used to solve some real-world problems. We discuss the potential scenarios/situations that require the integration of DC-AI with MC-AI to solve challenging problems in AI. We performed a case study on a real-world dataset to corroborate the potential of DC-AI in realistic scenarios and to prove its significance over MC-AI when either data are limited or their quality is poor. Afterward, we comprehensively discuss the challenges that currently hinder the realization of DC-AI, and we list promising avenues for future research and development concerning DC-AI. Lastly, we discuss the next-generation computing for DC-AI that can foster DC-AI-related developments and can help transition DC-AI from theory to practice. Our detailed analysis can guide AI practitioners toward exploring the undisclosed potential of DC-AI in the current AI-driven era.

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Data-Centric AI

Malerba,

Pasquadibisceglie

2024

J Intell Inf Syst

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Opportunities and Challenges in Data-Centric AI

Cited by 7 publications

References 94 publications

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

A Data-Centric AI Paradigm for Socio-Industrial and Global Challenges

Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era

Data-Centric AI

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