But machine learning is fundamentally different from much of what we think of as programming. While machine learning originated as a subfield of artificial intelligence-the area of computer science dedicated to creating humanlike intelligence in computers-it’s expanded beyond the boundaries of A.I. Machine learning won’t bring us a utopian singularity or a dystopian Skynet, but it will inform an increasing amount of technology in the decades to come. Domingos’ book is a nontechnical introduction to the subject, but even if it still seems daunting, it’s important to understand how machine learning works, the many forms it can take, and how it’s taking on problems that give traditional computing a great deal of trouble. In his new book The Master Algorithm, Pedro Domingos covers the growing prominence of machine learning in close but accessible detail. Yet even within computer science, machine learning is notably opaque. It’s called machine learning, and it’s impacted fields as diverse as facial recognition, movie recommendations, real-time trading, and cancer research-as well as all manner of zany experiments, like Google’s image-warping Deep Dream.
But there’s a burgeoning, alternative model of programming and computation that sidesteps the limitations of the classic model, embracing uncertainty, variability, self-correction, and overall messiness. Computers and the algorithms they run are precise, perfect, meticulously programmed, and austere.
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