"AI" and "machine learning" tend to be used interchangeably, especially in mass-media articles. In theory "AI" is more "intelligent" but ... well.

Anyway in a previous job I worked on a "machine learning" project which used a "binary classifier" (a relatively simple machine-learning method) to determine whether a short sound recording was "baby" or "not baby".

To train it, we had a whole load of sound recordings (.wav files), of domestic "non-baby" sounds, like hand-washing dishes, washing machine, vacuum cleaner, TV etc. And a load of "baby" sounds, which included babies babbling as well as crying. The "training" comprised getting the program to analyse those sounds (from the two labelled categories) and "learn" how to classify them. Set the computer-program running, and wait for an hour or two...

As with much audio-processing (including speech recognition), the sounds were analysed in short pieces lasting a few 10's milliseconds each, each characterised with about 20-30 parameters relating to the frequency-content and rate-of-change with time. In this case the "training" was essentially fitting a plane through a 20-30 dimensional graph of those parameters, splitting the set into "baby" on one side and "non-baby" on the other. Once trained, you could then give the algorithm new recordings that it hadn't "heard" before, and it would classify them accordingly.

A problem (symptomatic of many machine learning methods) was that if you presented it with a recording of a baby but with some other sound in the background - even just a cooker-hood fan, that it hadn't been trained for - it would fail to recognise the baby.

There is an ever-present danger with AI/ML systems that if you haven't included all possible confounding factors in the training data, they may completely and unexpectedly fail to work properly when that factor pops up in the real world.