Algorithms that don't use machine learning aren't dumb. The point of machine learning is you need to implement some function, can't derive it directly from first principles, but have lots of examples of correct inputs and outputs and can use that build a function estimator instead. But in a whole lot of quite sophisticated and intelligent applications, you can derive the functions from first principles. Not easily. One of the projects I spent the first few years of my career working on was the common image formation processing for spy satellite collections, and you need to take into account special relativity, orbital mechanics, curvature and velocity both of earth and of the arrays, midnight crossover in time keeping, temperature calibration and the impact on reported voltage detection of each sensor cell, parallax effects of rapid altitude changes, polarization of light. There's a ton that goes into it, but we know the physics and don't have to use statistics to make educated guesses. We can compute "given voltage levels x1, x2, ..., xn on sensor cells y1, y2, ..., yn at times t1, t2, ..., tn, that is what we were looking at" exactly.

Mind you, I'm talking basic level 1 transformation of raw data streams to human-intelligible images. Once you get into automated object recognition, that's when we start to use machine learning, but the algorithms upstream of that are still plenty smart.