DCServCG: A data-centric service code generation using deep learning

Alizadehsani, Zakieh; Ghaemi, Hadi; Shahraki, Amin; González‐Briones, Alfonso; Corchado, Juan M.

doi:10.1016/j.engappai.2023.106304

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…The DC-AI paradigm has already achieved breakthroughs in many AI applications such as reducing the time from the implementation to the deployment of AI models, training AI models with fewer data, accelerating the convergence of AI models, reducing the overall complexity of AI models, reducing the cost of AI technology by exploiting the code of already-developed models, and extending AI applications [59][60][61][62][63]. The umbrella of DC-AI applications and techniques is constantly expanding, and therefore, the adoption of AI technology is increasing worldwide.…”

Section: Case Study (Proof Of Concept Example) To Evaluate the Effect...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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Section: Case Study (Proof Of Concept Example) To Evaluate the Effect...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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“…Deep learning is a subcategory of machine learning that focuses on algorithms inspired by the structure and function of the brain, called artificial neural networks (Chan, et al, 2016;Kothadiya et al, 2022, Alizadehsani et al, 2023. These networks, especially when they have many (deep) layers, have proven to be extremely effective in a variety of AI tasks.…”

Section: History and Evolution From Ai To Generative Aimentioning

confidence: 99%

Generative Artificial Intelligence: Fundamentals

Corchado,

López F.,

Núñez V.

et al. 2023

ADCAIJ

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Generative language models have witnessed substantial traction, notably with the introduction of refined models aimed at more coherent user-AI interactions—principally conversational models. The epitome of this public attention has arguably been the refinement of the GPT-3 model into ChatGPT and its subsequent integration with auxiliary capabilities such as search features in Microsoft Bing. Despite voluminous prior research devoted to its developmental trajectory, the model’s performance, and applicability to a myriad of quotidian tasks remained nebulous and task specific. In terms of technological implementation, the advent of models such as LLMv2 and ChatGPT-4 has elevated the discourse beyond mere textual coherence to nuanced contextual understanding and real-world task completion. Concurrently, emerging architectures that focus on interpreting latent spaces have offered more granular control over text generation, thereby amplifying the model’s applicability across various verticals. Within the purview of cyber defense, especially in the Swiss operational ecosystem, these models pose both unprecedented opportunities and challenges. Their capabilities in data analytics, intrusion detection, and even misinformation combatting is laudable; yet the ethical and security implications concerning data privacy, surveillance, and potential misuse warrant judicious scrutiny.

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“…DL leverages the power of large-scale computing and vast amounts of data [17] to enable neural networks to perform sophisticated tasks, such as image and speech recognition, NLP, and even autonomous decision-making. By emulating the structure and functionality of the human brain, DL has revolutionized AI by significantly enhancing the accuracy and performance of various applications [18] including medical image analysis (MIA) [19], while also demanding substantial computational resources.…”

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confidence: 99%