It is different, but there may be some universal principles that are relevant more abstractly among both cases. Of particular interest is the empirical notion that statistical models of a certain form will always tend to "average out noise" and "learn meaningful patterns" up to the capacity that those models have for representing said patterns. A parallel notion to this is the hypothesis dubbed "thermodynamic origins of life". The universal principle binding these two seemingly disparate topics is one that seems to underlie any sense of "learning" in physical systems: that semantics of those systems depend on their representational power, and the semantics they do come to represent are the results of adding up many pushes in one "direction" (phase space / state space / etc.) encoding a pattern, and adding up many random noise jiggles will cancel out but give you a first-order sense of variance of those semantic features as expressed by the environment.

As this description is so overly abstract, an exercise for the reader is to try to work through an explanation of how, say, a river delta comes to "learn" about its environment by "reacting" to the influences at its borders, and how it "encodes" whatever it is that it learns in the substrate that it inhabits.