Three Kinds of Inference
Induction generalizes from observed cases. A model sees many examples and learns a pattern that may continue to hold.
Deduction applies stated rules. If the premises and rules are accepted, the conclusion follows inside that formal system.
Abduction chooses a plausible explanation or next hypothesis when neither pattern fitting nor formal rule following is enough by itself.
The Machine-Learning Boundary
The practical ML point is simple: high-quality induction is valuable, but it should not be confused with every kind of reasoning. A predictive model can be strong at continuation while still needing external structure for causal explanation, experiment design, tool use, memory, and error correction.
Boolean logic remains useful on the deductive side because it gives exact rule behavior. Probabilistic models remain useful on the inductive side because they handle uncertainty and data. The difficult part is the bridge: how a system proposes a hypothesis worth testing when the answer is not already present in the examples or the rules.
Source Context
The original post linked Erik Larson's The Myth of Artificial Intelligence. The book is kept here as source context for the induction/deduction/abduction distinction.