Techsplainers by IBM

This episode of Techsplainers explores reinforcement learning, a machine learning approach where AI agents learn to make decisions through trial and error by interacting with their environment. Unlike supervised learning's labeled data or unsupervised learning's pattern discovery, reinforcement learning teaches through reward signals—similar to how we might train a pet with treats. The episode breaks down the core components of this approach, including the Markov decision process framework, the critical exploration-exploitation tradeoff, and key elements like policy, reward signals, and value functions. We also examine major reinforcement learning methods, such as dynamic programming, Monte Carlo techniques, and temporal difference learning. The discussion covers real-world applications in robotics and natural language processing, highlighting both impressive successes like AlphaGo and ongoing challenges in creating effective learning environments with meaningful reward systems.

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

Narrated by Anna Gutowska

This episode of Techsplainers explores semi-supervised learning, the machine learning approach that bridges supervised and unsupervised techniques by combining small amounts of labeled data with larger volumes of unlabeled information. The episode explains why this method is crucial when obtaining fully labeled datasets is prohibitively expensive or time-consuming, such as in medical imaging or genetic analysis. We break down the key assumptions that make semi-supervised learning work—including the cluster assumption, smoothness assumption, low-density assumption, and manifold assumption—and how they help models generalize beyond limited labeled examples. The discussion covers major implementation approaches, including transductive methods like label propagation, and inductive methods like wrapper techniques, unsupervised pre-processing, and intrinsically semi-supervised algorithms. Real-world applications and challenges are also examined, providing listeners with a comprehensive understanding of this practical machine learning technique.

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

Narrated by Anna Gutowska

This episode of Techsplainers explores unsupervised learning, the branch of machine learning where algorithms discover hidden patterns in data without human guidance or labeled examples. The discussion covers the three main tasks of unsupervised learning: clustering (grouping similar data points), association rules (finding relationships between variables), and dimensionality reduction (simplifying data while preserving essential information). We examine popular algorithms like K-means clustering, hierarchical clustering, the Apriori algorithm for market basket analysis, and techniques like Principal Component Analysis and autoencoders. The episode highlights real-world applications including news aggregation, recommendation engines, medical imaging, and customer segmentation. The conversation also compares unsupervised learning with supervised approaches and addresses challenges such as computational complexity, validation difficulties, and interpretation of results, offering listeners a comprehensive understanding of how AI can extract valuable insights from unlabeled data.

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

Narrated by Anna Gutowska

This episode of Techsplainers explores supervised learning, the most widely used approach in machine learning, where AI models are trained using labeled data with known correct answers. The episode explains how supervised learning uses ground truth data to teach models to recognize patterns and make accurate predictions on new information. We break down the two main categories of supervised learning tasks—classification for sorting data into categories and regression for predicting numerical values—and examine popular algorithms, including linear regression, decision trees, random forests, and neural networks. The discussion also covers how supervised learning differs from other approaches like unsupervised, semi-supervised, self-supervised, and reinforcement learning, along with real-world applications ranging from image recognition to fraud detection. While highlighting supervised learning's effectiveness for many AI applications, the episode acknowledges its limitations, including data labeling requirements and potential for bias.

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

Narrated by Anna Gutowska

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