How does intrinsic dimensionality differ from ambient dimensionality?

Intrinsic dimensionality is the true degrees of freedom; ambient is observed feature space size.

Data often lies on a low-dimensional manifold embedded in high-dimensional space. For example, images exist in pixel space (high dimension), but the underlying generative factors (pose, lighting) are low-dimensional. Learning algorithms should exploit this structure to avoid curse of dimensionality.