I don't think that having control over machine learning systems must necessarily mean AGI. For example, suppose I train an image classifier to learn to identify dogs in images. And suppose I find that my classifier has learned to label all images that have a dog collar as "dog" and no image that doesn't have a dog collar as "dog". That means my classifier has learned an over-specialised definition of "dog" that doesn't generalise to dogs without dog collars, and at the same time an over-general definition that may accept, say, a fetish model with a dog collar as a "dog". That is not a system that performs as I want it to perform: I wanted a dog classifier but I got a dog collar classifier. What I want is to be able to control my system to learn to identify a dog as a "dog" rather than a dog collar as a "dog". That's "control" and when I achieve it my system is still not an AGI, just an image classifier that generalises well.