For non-data scientists, machine learning can seem as unfathomable as rocket science. Justin Bass, the chief data science officer at Crowe, understands the concept on a level few others do. He heads up the Crowe data science group, which includes all machine learning and artificial intelligence development and implementation. Also, one of the five degrees he holds is a master’s from Purdue University in aeronautical and astronautical engineering – that is, actual rocket science.
Machine learning is related to artificial intelligence (AI), he explains. Specifically, machine learning is a more efficient way to create AI systems, though it’s not always necessary. To illustrate the two concepts and how they relate to each other, he offers a comparison between automatic braking systems and self-driving cars.
Both of these things involve AI in that they allow technology, based on data inputs, to make intelligent decisions for us. Automatic braking systems, however, generally don’t need to be built with machine learning because they’re based on a fundamentally simple premise: measuring the distance between and the respective velocities of your vehicle and the one in front of you, and comparing that to the maximum braking power of your car.
With a self-driving car, though, there are a multitude of factors. The system has to figure out how to turn the car, deal with pedestrians, manage heavy traffic, and perform a host of other tasks. According to Bass, humans can’t truly conceptualize all the rules required for those complex environments. But machine learning can take the underlying data – for example, images of surrounding roadways – and develop rules for how that system should act when it’s on those roads.