# The AI-Powered Evolution of Big Data
## 4 Materials and Methods
### Big D -- The Big Data Analyzer
The advancement of multimodal artificial intelligence, which can incorporate text, visual, and audio input, improves the analytical capacities of language and learning models, allowing for more complete and nuanced insights. ### The Costs of Implementing AI and Big Data Technologies in Practical Settings

At this point, we only can provide a rough estimate for this category as all companies try to keep such information private. The actual costs will depend on location and other factors, including possible sanctions. Table 7 provides a hypothetical estimate of the costs. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Cost Category** & **Description** & **Cost Estimate, In Thousands of USD (\$)** \\ \hline AI model development & Custom AI model development. & 75–150 \\ \hline API integration & The use of pre-built AI platforms with limited usage. & 10–30 annually \\ \hline Big data pipeline setup & Setup for data ingestion, processing, and ETL processes. & 30–60 \\ \hline Frontend/backend development & The development of the mobile app interface, backend, and visualization tools. & 80–120 \\ \hline Cloud services and hosting & Use of cloud services (AWS, Google Cloud, or Azure). & 3–10+ monthly \\ \hline Data storage & Cloud storage for volumes of data. & 1–5+ monthly \\ \hline Computing power & Use of cloud-based computing resources (e.g., virtual machines, GPUs). & 2–10 monthly \\ \hline AI tools & API usage fees for AI services. & 15–50 annually \\ \hline Big Data processing tools & Open-source tools (e.g., Hadoop, Apache Flink). & Free/open source \\ \hline Data collection & Acquiring data for AI model training and analysis. & 20–50 \\ \hline Data cleaning and labeling & Data preparation for AI model training. & 30–100 \\ \hline Model maintenance and retraining & The ongoing retraining of AI models. & 15–40 annually \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Hypothetical costs of AI-Big Data integration in practical settings. ### Role of LLMs in Big D Development and Usage

Every product directly depends on its provider when using a remote service or a third-party platform/library. Big D currently relies on Open AI and uses its family of products, including GPT and other AI models. While it is possible to use other models like Google's Gemini or Sonnet through API, dependency on the service provider(s) will still be there. Therefore, the app is limited to the rates, functionalities, and policies the companies allow. As an example, Table 8 presents some limitations of OpenAI (on 12 October 2024)

As a limitation of the study, we would like to add that Big D, in some rare cases, demonstrates hallucinations: when several documents were submitted in a row, Big D started confusing new topics with the previous ones and might have demonstrated results of previously analyzed documents instead of the latest it remembered. Researchers are not completely sure of how to deal with these issues and whether they can eventually be fixed. ### Future Directions

The future of integrating AI and Big Data relies on ongoing innovation and collaboration. Quantum computing can significantly enhance data processing skills by enabling the study of highly massive datasets at an unparalleled speed. Furthermore, progress in developing data governance frameworks is essential for guaranteeing ethical and effective data management. The cooperation of academia, industry, and regulatory agencies is crucial in tackling obstacles and capitalizing on AI and Big Data's potential. By promoting a culture of knowledge sharing and implementing the most effective strategies, all parties involved can collaboratively navigate the intricate aspects of this rapidly changing area and build on advancements.