Who excels at few-shot learning?

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 states that 'humans excel at generalising from a few examples, compositionality, and robust generalisation to noise, shifts, and Out-Of-Distribution (OOD) data'[1]. This highlights human proficiency in few-shot learning, where they can effectively apply knowledge from limited data points.

In contrast, while statistical learning methods in AI, such as those employing few-shot mechanisms, aim to mimic some aspects of human learning, they typically require far more extensive datasets to achieve similar effectiveness and do not generalise as reliably to new tasks or domains[1].


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