How does transfer learning relate to analogy?

title: 'Fig. 1: Comparison of the strengths of humans and statistical ML machines, illustrating the complementary ways they generalise in human-AI teaming scenarios. Humans excel at compositionality, common sense, abstraction from a few examples, and robustness. Statistical ML excels at large-scale data and inference efficiency, inference correctness, handling data complexity, and the universality of approximation. Overgeneralisation biases remain challenging for both humans and machines. Collaborative and explainable mechanisms are key to achieving alignment in human-AI teaming. See Table 3 for a complete overview of the properties of machine methods, including instance-based and analytical machines.'

The text indicates that analogy is related to generalization processes in both humans and AI. It states that analogy involves the transformation or adaptation of knowledge or schemas to fit a new context. This resembles the transfer learning approach, where knowledge gained from one domain or task is applied to another.

Specifically, in cognitive science, analogy can be seen as a way to transfer learned representations across tasks, similar to how transfer learning functions in AI systems, where models learn from one set of data and apply that knowledge to make predictions in different contexts[1].


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