"Exploiting semantic knowledge graphs can support interpretability and explainability of nearly all AI model types (including DL models) by discovering and depicting semantic and non-obvious relationships or depicting an ML model in a simplified and more readable, explainable way." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"Gaining more insight into data, simplifying data access, enabling shopping-for-data, augmenting traditional data governance, generating active metadata, and accelerating development of products and services are enabled by infusing AI into the Data Fabric architecture. An AI-infused Data Fabric is not only leveraging AI but also likewise an architecture to manage and deal with AI artefacts, including AI models, pipelines, etc." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"In Exploiting semantic knowledge graphs can support interpretability and explainability of nearly all AI model types (including DL models) by discovering and depicting semantic and non-obvious relationships or depicting an ML model in a simplified and more readable, explainable way., a Data Mesh solution organizes data around business domain owners and transforms relevant data assets (data sources) to data products that can be consumed by distributed business users from various business domains or functions. These data products are created, governed, and used in an autonomous, decentralized, and self-service manner. Self-service capabilities, which we have already referenced as a Data Fabric capability, enable business organizations to entertain a data marketplace with shopping-for-data characteristics." (Eberhard Hechler et al, "Data Fabric and Data Mesh Approaches with AI", 2023)
"Large language models (LLMs) are AI models that are usually (but not necessarily) derived from the Transformer architecture and are designed to understand and generate human language, code, and much more. These models are trained on vast amounts of text data, allowing them to capture the complexities and nuances of human language. LLMs can perform a wide range of language-related tasks, from simple text classification to text generation, with high accuracy, fluency, and style." (Sinan Ozdemir, "Quick Start Guide to Large Language Models: Strategies and Best Practices for Using ChatGPT and Other LLMs", 2024)
"As AI systems become more autonomous and integrated into decision-making processes, the question of accountability grows in importance. Businesses and developers must ensure that AI systems operate within predefined ethical boundaries and that there are mechanisms to identify and correct issues when things go wrong. Because AI models can make decisions that directly impact people’s lives (such as credit approvals, hiring decisions, or healthcare recommendations), organizations must maintain accountability for these outcomes. Clear guidelines should be in place to determine who is responsible when an AI system makes a mistake or causes harm." (Bennie Haelen, "ML and Generative AI in the Data Lakehouse Building and Deploying AI Applications at Scale", 2026)
"In RAG methods, the AI model itself doesn’t actually 'remember' or learn the proprietary data directly. Instead, the proprietary data are stored separately in what’s called a vector database. When the model is asked a question, it first performs a quick search of the proprietary database, finds relevant pieces of information, and then uses these to generate its response. The model’s core parameters remain entirely unchanged and are never updated with this private data. In this sense, the model hasn’t learned or 'seen' your proprietary data in its internal parameters, it only temporarily consults it as a reference to formulate an answer." (Alan Watkins & G C Cooke, "Smarter than You Winning in Business with Superintelligent AI", 2026)
"[...] LLMs raise serious concerns about ethics, bias and fairness, errors in reasoning, hallucinations, and misuse (e.g., misinformation and disinformation). These concerns are exacerbated by modern LLMs being both literal and figurative 'black boxes': Literal black boxes because many advanced AI systems are proprietary and the weights (trained parameters of the models) are not released to the public; and figurative black boxes because even the open-source AI models are so complicated that understanding them and developing safety guardrails has thus far proven extremely difficult." (Mike X Cohen,"50 ML Projects To Understand LLMs", 2026)
"The Model Context Protocol (MCP) is emerging as a standard for connecting AI models to external tools and data sources. Historically, developers had to build custom connectors for each model and API, limiting portability and slowing innovation. MCP simplifies this by creating a common interface, essentially a universal plug like USB-C, that allows compliant models and tools to communicate seamlessly. Frameworks like LangChain/Graph and others are expected to adopt MCP to handle connectivity under the hood, providing developers with interoperability while maintaining secure and auditable access to enterprise systems." (Fern Halper, "Data Makes the World Go 'Round", 2026)
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