Nah, no need for new ML approaches. It's just time for the pendulum to swing back again from the current craze of data-driven/"deep learning"/continuous modeling to the alternative of symbolic/"Good Old-Fashioned AI"/discrete modeling.

The switch happens every ~30 years: around the ~1950s, in the late 1980s, and perhaps again soon...

I realize I'm glossing over many nuances of how the most successful AI/ML approaches combine together continuous and discrete modeling, and symbolic and numeric techniques.

Still, this is a real cultural divide for researchers. The "deep learning" types just want to increase their match/success percentages as high as they will go--with little regard for how the sausage is made--while the symbolic types care about whether the model itself provides actual explanatory value.