AI - really Machine Learning - is "just" massively parallel software for using linear algebra on high-dimensional matrices representing huge data sets.
Any programmer today can feed anything into the machine, but as the old saying goes, GIGO.
Any programmer today can feed anything into the machine, but as the old saying goes, GIGO.
But as you say it's GIGO, the difficulty today is to know what to feed it and to know what that means for the real life performance. There are no great tools for that yet.
This has always been the difficulty.
Generalization is the fundamental problem in machine learning. Making easily available tools has led to an exponential growth in applications as more people play with it (many without understanding what they are doing or why), but predictably hasn't lead to an exponential growth in successful applications.