This episode of Techsplainers explores machine learning—the subset of artificial intelligence that enables computers to learn patterns from data without explicit programming. The episode explains how machine learning models are trained on datasets to recognize patterns and make predictions on new information, breaking down the three main approaches: supervised learning (using labeled data with correct answers), unsupervised learning (discovering patterns in unlabeled data), and reinforcement learning (learning through trial and error with rewards). The discussion also covers deep learning and neural networks, explaining how these powerful systems can automatically extract features from raw data, powering breakthroughs in computer vision, natural language processing, and more. From transformers to the newest Mamba models, the episode provides a comprehensive overview of how machine learning works and its wide-ranging applications across industries.

Find more information at https://www.ibm.com/think/podcasts/techsplainers.

Narrated by Anna Gutowska