Why Machines Learn

A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence. As the irresistibly eloquent Anil Ananthaswamy shows, getting under the mathematical skin of machine learning reveals the power of the technology. Machine learning systems are already making life-altering decisions for us: whether it's approving mortgage loans, determining whether a tumor is cancerous, diagnosing someone with Alzheimer’s; or deciding whether someone gets bail. Machine learning systems now influence discoveries in chemistry, biology, and physics—the study of genomes, extra-solar planets, even the intricacies of quantum systems. Very obviously this revolution in intelligence is not slowing down. The story weaves in simple math that goes back centuries, math that you may have learned in high school—elementary algebra, logarithms, and calculus, the stuff of eighteenth century mathematics. Indeed by the mid-1850s, the groundwork was all done. Yet, those brilliant mathematicians never dreamed their work would deliver such earth-changing tech. It took the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see all around us today. Understanding the various types of math behind machine learning provokes profound questions. Ulitmately there is no difference between natural and artificial intelligence because artificial neurons and the ones in your head follow the same mathematical rules. Both have biases for the same reasons. As Ananthaswamy resonantly concludes, to make the most of our most wondorous technologies we need to understand how their profound limitations mirror our own. Math is the surprising key.