Digital Computing has been with us for over 70 years. It's a deterministic technology using stored program software designed to produce accurate, precise results. For example, software for calculating your pay check will give you results that are correct down to the penny, just what you want!
Machine Learning technology is different ... it works in the domain of probabilities. For example, a machine learning based autonomous self-driving car makes many probability calculations every second ... such as the probability that a person approaching an intersection will stop and not cross in front of the car.
Digital Computing is able to perform some probabilistic calculations, but these are limited compared to those that can be performed by Machine Learning. Machine Learning is, conversely, limited in the deterministic calculations it can perform compared to the capabilities of Digital Computing.
So it's pretty obvious, the combination of Digital Computing and Machine Learning yields the perfect combination for interacting with the world we live in, as illustrated in the graphic below:
The combination of these two technologies will give us a huge boost in overall computing accuracy and cost effectiveness. Much of what we deal with on a daily basis is probabilistic in nature, and this dimension can now be effectively addressed with Machine Learning, such as is used in virtual assistants that are able to hear us speak, understand our words and give us answers to our questions.
And these two technologies will remain wedded to each other, as Machine Learning runs on a Digital Computing platform and needs Digital Computing to perform most practical tasks. Take our self-driving car example. That system needs access to precise road maps and feedback from car systems such as the engine and brakes. It's the combination of deterministic and probabilistic computing that creates the complete self-driving system that we'll end up trusting to get us safely home.