Knowledge Graphs vs Prolog – Prolog’s Role in the LLM Era, Part 7

The integration of Prolog with large language models (LLMs) is explored, highlighting Prolog’s unique role in AI architecture alongside knowledge graphs (KGs). Prolog's ability to handle complex logical reasoning and rule-based systems is compared to the capabilities of KGs, emphasizing their complementary roles. KGs provide a scalable and semantically rich organizational framework, while Prolog excels in precise logical processing. Ultimately, their combined strengths enhance the robustness and intelligence of AI systems. Integration with LLMs adds broad, context-driven insights to create versatile AI systems capable of deep reasoning and broad understanding. The potential of combining Prolog, KGs, and LLMs for the future of AI is highlighted, emphasizing the benefits of leveraging their unique strengths.

Analytics Maturity Levels and “Enterprise Intelligence”

This blog revisits the traditional "analytics maturity model," providing a new perspective from the book "Enterprise Intelligence." The four analytics maturity levels: Descriptive, Diagnostic, Predictive, and Prescriptive, are explained along with their impact in the AI era. The blog emphasizes the importance of understanding these levels and their role in enterprise intelligence.

Enterprise Intelligence: Integrating BI, Data Mesh, Knowledge Graphs, Knowledge Workers, and LLMs

In today's rapidly evolving data analytics landscape turbo-charged by AI, businesses require an informationally-scalable infrastructure. "Enterprise Intelligence" offers a comprehensive framework integrating modern advancements like data mesh, knowledge graphs, and Large Language Models. The book addresses governance issues, providing a robust foundation for decision-making in an AI-driven world.

Exploring the Higher Levels of Bloom’s Taxonomy

Expanding BI Analyst Horizons with AI In the landscape of Business Intelligence (BI), the integration of AI and advanced reasoning in the enterprise is essential for staying afloat. To fully integrate with AI, traditional BI analysts, accustomed to working with empirical data and generating reports, must now expand their skills to include deeper analysis, evaluation, … Continue reading Exploring the Higher Levels of Bloom’s Taxonomy →

The Role of OLAP Cubes in “Enterprise Intelligence”

In the pursuit of data-driven excellence, quick and well-informed decision-making is crucial for enterprises. Online Analytical Processing (OLAP) facilitates this by enabling rapid analysis of multidimensional data. Despite challenges from big data and cloud technologies, OLAP's role has resurged in managing vast data volumes, supporting complex analysis, and bridging structured and unstructured data for enterprise intelligence.

Release Date for my Book, “Enterprise Intelligence”

The book "Enterprise Intelligence" by Technics Publications, set to release on June 21, 2024, advocates for integrating AI with Business Intelligence to enhance enterprise performance. It emphasizes the importance of critical/creative thinking alongside AI and details a framework for Augmented Enterprise Intelligence. The book explores the merge of BI data and advanced AI to build adaptable and thriving enterprises in a rapidly evolving business landscape.