In a world where machine learning systems make life-altering decisions every day—from diagnosing cancer to determining legal outcomes—few of us understand the mathematical foundation powering this silent revolution. Anil Ananthaswamy's "Why Machines Learn" bridges that gap, revealing how surprisingly simple mathematical concepts birthed the artificial intelligence explosion we're witnessing today.
Machine learning isn't built on complex new theories but rather on elegant mathematical ideas dating back centuries. Linear algebra and calculus, the same mathematical frameworks that powered scientific discoveries in the 17th and 18th centuries, form the backbone of today's AI systems. What changed? The advent of computer science and specialized chips originally designed for video games finally unleashed the potential of these timeless mathematical concepts.
Machine learning now influences nearly every aspect of modern science and society. It helps map genomes, discover extrasolar planets, understand quantum systems, and make decisions about healthcare, finance, and justice. Large language models like ChatGPT represent just the latest chapter in this unfolding story, built on mathematical foundations that are both accessible and profound.
Ananthaswamy explores fascinating connections between the mathematics that powers artificial intelligence and the principles that govern natural intelligence. Could the same mathematical patterns that enable machine learning also explain aspects of human cognition? This intriguing question invites readers to consider the deep connections between different forms of intelligence.
As AI becomes increasingly powerful and ubiquitous, understanding its mathematical foundations isn't just an academic exercise—it's essential for responsible use. By grasping both the capabilities and limitations of these systems, we can better navigate their impact on society, ensuring that artificial intelligence develops in ways that benefit humanity while acknowledging its inherent constraints.
