In this article, I propose some elements for a conceptual foundation for a negative answer to the titular question, based on a historical conceptual analysis of some definitions of "learning" in the specialized literature. I intend such a foundation to include learning in living organisms as well as inorganic machines. After analyzing several behavioral and nonbehavioral definitions, I argue that although most of the former favor a negative answer, they tend to be restricted to living organisms and thus exclude inorganic machine learning. They also face the yet-unresolved issue of behavioral silence, which makes behavior not defining of learning. Some nonbehavioral neurobiological definitions favor an affirmative, others a negative answer, but still exclude inorganic machines. Nonneurobiological definitions are more suitable, but they commit us to some form of computationalism (Turing machine or connectionist) about learning, which is premature. I thus propose elements for an alternative definition of "learning" without such commitment. The elements are elaborations of the notions of learning as a kind of causal interaction between causal stochastic environmental and internal processes, and minimal learner as a kind of abstract system that shares certain internal structural and functional features with animals, spinal vertebrates, bacteria, plants, and inorganic machines.
Keywords: Causal processes; Concepts; Definitions; Internal mechanisms; Learners; Machines; “Behavior”; “Learning”.
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