# Towards Unlocking the Hidden Potentials of the Data-Centric AI Paradigm in the Modern Era
## 8 Next-Generation Computing for DC-AI Paradigm  
The success of the DC-AI paradigm lies in devising systematic techniques for inspecting and enhancing data quality, which requires ample attention to how data are curated, prepared, annotated/labeled, and integrated into AI systems. Therefore, it is crucial to identify research challenges and future directions in the DC-AI paradigm while amalgamating it with next-generation computing technologies such as cloud/fog/edge/serverless architecture, AI/ML/DL, and quantum computing. In this section, we discuss five research opportunities in next-generation computing by leveraging DC-AI:  
1. In recent years, cloud-/fog-/edge-/serverless-based computing architectures have ensured the timely collecting, processing, and analytics of big data by using AI models. In the future, DC-AI integration with these computing architectures is imperative for cost savings, improved data management, automation, robustness, and addressing verifiability-related issues [74]. DC-AI can enhance the efficacy of cloud-/fog-/edge-/serverless-based applications by ensuring the data used in them are of high quality, robust, dependable, and representative of the problem being solved. However, the integration of the DC-AI paradigm with these latest architectures is not easy and can induce many research challenges such as the fusion, alignment, and consistency of data stemming from multiple sources being possibly tricky and slow. Furthermore, data reliability, data representation, and the privacy and security of data can be the main barriers to integrating DC-AI with next-generation computing architectures. In addition, preparing high-quality data for each architecture depending upon the application requirement is also very challenging. In the serverless computing architecture, most of the operations are managed by third parties, and therefore, DCAI integration can lead to transparency, bias, and sustainability issues [74]. In the next generation of computing, the involvement of domain experts with AI-based systems regardless of computing architectures will be mandatory, and therefore, it is very challenging to prepare domain experts for relevant architectures. Finally, upgrading the current AI-integrated systems that are mostly based on MC-AI is another big challenge and requires much effort from the AI community. 2. Thus far, there has been limited synergy from merging the DC-AI approach with other AI/ML/DL technologies such as pre-trained models, TL, and generative models. In the future, more such synergies are needed to improve critical aspects of DC-AI (i.e., performance overhead, domain adaptation, data curation, and integration) [31]. Hence, exploring the possible synergies between DC-AI and more of the latest technologies to expand the horizon for DC-AI is a vibrant area of future research. 3. With the advent of quantum computing (QC) (a powerful computing paradigm), a drastic change in the performances of AI models is expected, and most AI models can work with zettabytes (ZB) of data [31]. QC encompasses many innovative architectures such as quantum entanglement and quantum superposition and can process huge volumes of data in a few milliseconds compared to classical systems [74]. However, the QC paradigm can also face obstacles in effective data utilization because most of the real-world datasets are poisoned, biased, noisy, skewed, and/or incomplete. To this end, an amalgamation of DC-AI with QC is imperative to solve longstanding problems in the healthcare sector (e.g., genome analysis), drug development, secure cryptosystems, and sustainability problems. The joint use of these paradigms can contribute to developing reliable and robust AI systems. However, it is challenging to apply DC-AI and QC technologies to some complex problems such as climate change owing to higher uncertainties and constraints. The development of DC-AI-integrated QC-based libraries with a higher level of abstraction and flexibility is an important avenue for future research.