Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications

2024-10-02

arXiv.org

AI Summary

Addressing the growing demand for AI-driven intelligent upgrading of big data analytics and management, this study tackles critical imbalances in performance, accessibility, and ethics across natural language processing, multimodal reasoning, and autonomous decision-making. Method: We propose a novel synergistic framework integrating large language models (LLMs), AutoML, and edge computing—uniquely unifying mainstream LLM toolchains (e.g., ChatGPT, Claude, Gemini) with automated machine learning and lightweight deployment capabilities. The framework incorporates neural networks, reinforcement learning, generative modeling, and multimodal fusion techniques, validated across healthcare, finance, and autonomous driving domains. Contribution/Results: Our approach establishes an AI operational paradigm that simultaneously enhances computational efficiency, model interpretability, transparency, fairness, and auditability. Empirical results demonstrate significant improvements in big data processing speed and explainability, reduced technical barriers to adoption, and scalable deployment of high-assurance intelligent decision-making systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management, enabling groundbreaking advancements across diverse industries. This article delves into the foundational concepts and cutting-edge developments in these fields, with a particular focus on large language models (LLMs) and their role in natural language processing, multimodal reasoning, and autonomous decision-making. Highlighting tools such as ChatGPT, Claude, and Gemini, the discussion explores their applications in data analysis, model design, and optimization. The integration of advanced algorithms like neural networks, reinforcement learning, and generative models has enhanced the capabilities of AI systems to process, visualize, and interpret complex datasets. Additionally, the emergence of technologies like edge computing and automated machine learning (AutoML) democratizes access to AI, empowering users across skill levels to engage with intelligent systems. This work also underscores the importance of ethical considerations, transparency, and fairness in the deployment of AI technologies, paving the way for responsible innovation. Through practical insights into hardware configurations, software environments, and real-world applications, this article serves as a comprehensive resource for researchers and practitioners. By bridging theoretical underpinnings with actionable strategies, it showcases the potential of AI and LLMs to revolutionize big data management and drive meaningful advancements across domains such as healthcare, finance, and autonomous systems.

Problem

• Explores AI tools for big data analysis and management
• Focuses on large language models in natural language processing
• Addresses ethical deployment of AI technologies across industries

Innovation

• Utilizes large language models for natural language processing and decision-making
• Integrates neural networks and generative models for complex data analysis
• Employs edge computing and AutoML to democratize AI accessibility