Why machines Learn wer. angielska

Machine-learning systems are increasingly responsible for making crucial decisions in our lives, such as approving mortgage loans, diagnosing cancerous tumors, and determining bail eligibility. These systems now play a significant role in advancements across various scientific fields, including chemistry, biology, and physics, impacting the study of genomes, extra-solar planets, and quantum systems. We're experiencing an ongoing revolution in artificial intelligence, driven by mathematics that, although fundamental, dates back centuries. Linear algebra and calculus, developed as far back as the 18th century, laid the foundation, which was largely completed by the mid-1850s. It wasn't until the rise of computer science and the advent of 1990s video game chips that the explosive growth of AI we witness today was truly sparked. In his insightful book, Anil Ananthaswamy delves into the essential mathematics behind AI, proposing that the principles governing natural and artificial intelligence may adhere to identical mathematical laws.Ananthaswamy concludes that to harness the full potential of these incredible technologies, we must grasp their inherent constraints—insights that can be found within the mathematics enabling AI.