Machine Learning is the addition of structured chunks of text, contextually and subtextually targeted to the execution of certain tasks, which this AI (for some reason, even having a set structured texts) cannot do. That is, this structured piece of text is a kind of program, which says what to do.
For example, to perform a maneuver Tesla finds somewhere a paragraph:
-- The car abruptly slows down and starts turning right. At the same time it has to turn on the turn signal to the right. --
In this paragraph suppose Tesla removes lexical noise and identifies three synonymous clusters:
I. the car abruptly slows down
II. the car starts turning right
III. the car turns the signal turn on to the right
Now Tesla follows this instruction and checks the result. If the feedback gives the positive score Tesla remembers this paragraph and use it in situations when the sensors show the same.
After that Tesla follows these instructions and checks the result. If the feedback gives positive result Tesla remembers the paragraph and use it in situations when the sensors show that it is necessary.
You can see that Argo AI And Waymo agree with that my definition of Machine Learning:
"At the end of a test day, all the data gets ingested into a data center from the vehicles and the good stuff is analyzed and labeled. Raw data by itself doesn’t have much value for training the machine learning systems that form the core of modern AV systems. The objects in the data that are of interest including pedestrians, cyclists, animals, traffic signals and more. Before any sensor data can be used to train or test an AI system, all of those targets need to be labeled and annotated by hand so that the system can understand what it is “seeing.”