LLM limitations Unreliable Randomness: Why LLMs Struggle with Statistical Sampling and Its Impact on Enterprise AI Explore how Large Language Models (LLMs) fundamentally struggle with accurate statistical sampling, impacting critical business applications like synthetic data and content generation. Learn why external tools are essential for reliable AI.
AI self-improvement Unmasking the Limits of AI Self-Improvement: Why Foundational Models Need More Than Self-Generated Data Explore the critical limitations of AI self-improvement, including model collapse and data degradation. Learn why hybrid neurosymbolic approaches are vital for true AI progress beyond current LLM capabilities for enterprises.
AI Reliability Enhancing AI Reliability: How Lexical Knowledge Bases Future-Proof Business Operations Discover how integrating structured lexical knowledge with AI overcomes LLM limitations like hallucination, leading to more reliable and interpretable AI for critical business decisions.
