What is the real difference between DSP and AI/data science?
The purpose of both DSP and machine learning is to transform the input signal / data set into more meaningful information. This could be,


removal of noise from an audio recording
location of faces in an image
classification of objects in an image
etc


The main difference as I see it, is that in DSP the transform is designed by the engineer. The engineer will choose a set of signal processing operations that give the desired output. That choice is guided by experience and validated from the results of experiments.

In machine learning the transform is learnt. Typically, this requires a set of training signals with known outputs, over which the system will optimise its transform. The engineer still has to choose a machine learning architecture that can replicate the transform that is needed.

Often the two will be combined. And DSP is kind of machine learning anyway - the machine learning bit is done by the human and the test set is the sum of their existence.