As others have pointed out, the distinction between randn() and rand() is crucial here - the former gives you points from a (spherically symmetric) normal distribution, the latter gives you uniform points from the unit cube.

One of the advantages of the normal-sampling route over @ginko's rejection-based method is that in high dimensions almost all of the volume of the unit cube is situated outside of the unit sphere (the volume of the unit cube is always 1, whereas the volume of the unit sphere decreases exponentially with dimension). So the rejection method becomes exponentially slow in high dimensions, while the Gaussian method still works just fine.