Definitive AI: Why a new path in AI is necessary

Abstract The contemporary artificial intelligence landscape is largely characterized by the remarkable success of Predictive AI models, particularly Large Language Models (LLMs), which excel at pattern recognition and generation based on vast datasets. However, their inherent probabilistic nature leads to significant limitations, including the potential for factual inaccuracies ('hallucinations') and a consequent trust deficit, especially in critical applications demanding verifiable correctness. This paper introduces and advocates for "Definitive AI" as a crucial, complementary paradigm shift. Definitive AI focuses on generating or representing knowledge that is factually correct, verifiably correct, and deterministic, thereby eliminating probabilistic variance in core factual outputs. We explore the foundational principles of Definitive AI, contrasting it with Predictive AI, and delve into the necessity of correspondingly definitive training data, examining the SLTF (Single-Line Training FACT) framework as a potential exemplar. Furthermore, we discuss the implications for trustworthy AI systems, such as NM-SRN AGI models built on "Safer by Design" principles. This paper argues for the imperative need to develop and integrate Definitive AI to foster a future where AI systems are not only powerful but also demonstrably reliable, accurate, and trustworthy partners in knowledge discovery and critical decision-making.