Nvenntion - Thinking about IT

  AI Specification Standardization Keeping AI Between the Lines I. The Need for AI Specification Standardization Successful businesses are built on consistent results. Even service businesses strive to duplicate their best case. Generative AI needs constraints to control which languages to use, what database to use, object-oriented coding, security implementations, CSS color themes, and logos. Reporting AI needs to ensure that common business terms have consistent definitions and formulae. According to global security and governance frameworks such as ISO/IEC 42001 and the NIST AI Risk Management Framework , standardizing AI specifications prevents "shadow IT" and ensures compliance from day one. Without rigid parameters, unstructured prompts and unvetted live data pipelines expand an enterprise's attack surface, leading to unpredictable system behavior, financial liability, and data non-compliance. II. Methodology for Specification Standardization Creating a global ...

  Point of Interest: I used the doc “Data, Data, Data” (found in this blog) as input and prompted Gemini with, “ please restructure and add related ideas and cite the examples you use. I want to stress that AI relies on data and the capture and classification of data is paramount. “ This is the resulting document. I can see that I will have to flush out my ideas more completely to further improve the contents. However, it highlights the importance of data. Data: The Essential Fuel for Computing and AI Data is the raw material processed by computers to create information, existing in forms like text, numbers, images, and videos. In modern computing, data allows systems to perform complex tasks like web searching, language translation, and autonomous driving. Most critically, Artificial Intelligence (AI) and Machine Learning (ML) rely heavily on data to identify patterns, make predictions, and improve over time. Without vast amounts of quality data to train these algorithms, the pro...