  Humans can readily judge the number of objects in a visual scene, even
without counting, and such a skill has been documented in many animal species
and babies prior to language development and formal schooling. Numerical
judgments are error-free for small sets, while for larger collections responses
become approximate, with variability increasing proportionally to the target
number. This response pattern is observed for items of all kinds, despite
variation in object features (such as color or shape), suggesting that our
visual number sense relies on abstract representations of numerosity. Here, we
investigate whether large-scale generative Artificial Intelligence (AI) systems
have a human-like number sense, which should allow them to reliably name the
number of objects in simple visual stimuli or generate images containing a
target number of items in the 1-10 range. Surprisingly, most of the foundation
models considered have a poor number sense: They make striking errors even with
small numbers, the response variability does not increase in a systematic way,
and the pattern of errors depends on object category. Only the most recent
proprietary systems exhibit signatures of a visual number sense. Our findings
demonstrate that having an intuitive visual understanding of number remains
challenging for foundation models, which in turn might be detrimental to the
perceptual grounding of numeracy that in humans is crucial for mathematical
learning.
