AI: Data Traps, Architectures, and the Thinking Revolution

This article thoroughly examines the fundamental challenges in building reliable AI systems. It discusses data-related pitfalls, such as leaky data sets, data leakage, and domain drift, which can lead to spurious results and implementation failures. It emphasizes the importance of selecting appropriate evaluation metrics. Furthermore, the article presents modern solutions, such as multimodality and Retrieval-Augmented Generation (RAG), which increase AI's reliability and adaptability. Finally, the article delves into the philosophical aspects of AI, redefining the concepts of rationality, causality, and responsibility in the context of social systems, demonstrating that AI is not just a technology but also a new perspective.