Reasoning in artificial intelligence is the ability of
AI systems to process information, establish logical connections between data, and reach conclusions following learned
patterns. Unlike human reasoning, it is based on probabilistic calculations and pattern analysis.
While humans reason using intuition, experience, and emotions,
AI systems "reason" by processing enormous amounts of data to identify
patterns and probabilities. It's like having a very sophisticated calculator that can find connections between ideas based on millions of examples it has seen before.
AI models can perform different types of reasoning: logical (following step-by-step rules), analogical (finding similarities between situations), causal (identifying cause-and-effect relationships), or probabilistic (calculating the probability that something is true). For example, a model can "reason" that if it rains, streets are likely to be wet, based on learned
patterns.
However, this reasoning has important limitations: it doesn't truly understand concepts like humans do, can fail in situations very different from its training, and its conclusions are based on statistical correlations rather than true understanding. Techniques like Chain of Thought help make this reasoning process visible, allowing verification of how AI reaches its conclusions.