Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs

Human cognition involves both inductive (pattern-based) and deductive (logical) reasoning, as described by Daniel Kahneman's System 1 and System 2 thinking. Current AI and Large Language Models operate primarily on inductive principles, predicting the next word based on statistical patterns learned from training data, not through genuine understanding. This probabilistic approach means that while LLMs can sound intelligent, they are prone to errors and "hallucinations" because they are not performing logical, deductive reasoning. Prompting techniques like Chain of Thought (CoT) improve results by breaking problems into smaller steps, guiding the model's probabilistic generation process for more accurate outcomes.