Techsplainers by IBM

IBM

Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new. This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.

  1. What is reinforcement learning?

    1日前

    What is reinforcement learning?

    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

    6分
  2. What is semi-supervised learning?

    2日前

    What is semi-supervised learning?

    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

    8分
  3. What is unsupervised learning?

    3日前

    What is unsupervised learning?

    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

    7分
  4. What is supervised learning?

    4日前

    What is supervised learning?

    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

    8分
  5. What is machine learning?

    5日前

    What is machine learning?

    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

    9分

  6. 1月16日

    What is continuous testing?

    This episode of Techsplainers explores continuous testing—a critical component of modern software development that integrates automated feedback throughout the development lifecycle. Host Dan explains how continuous testing works alongside CI/CD pipelines to accelerate development while maintaining quality. The episode breaks down various testing methodologies including shift-left, shift-right, smoke tests, unit testing, integration testing, and more. Listeners will learn how continuous testing helps teams identify errors early, reduce costs, and improve user experiences through automation. The episode also addresses the unique challenges of testing in today's distributed, multi-region IT systems and explains how continuous testing frameworks provide consistency across modules, connectors, platforms, and infrastructure to ensure reliable results. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Dan Segal

    7分

  7. 1月15日

    What is continuous delivery?

    This episode of Techsplainers explores continuous delivery, the software development practice that automates the movement of code changes through testing and eventual release into production. We examine how continuous delivery transforms traditional infrequent, high-risk software releases into smaller, regular updates that can be deployed quickly and reliably. The podcast details key benefits, including reduced deployment time, decreased costs, improved scalability, and automated code deployment through development phases. We cover essential best practices such as making every change releasable, embracing trunk-based development, building automated pipelines, and aiming for zero-downtime deployments. The episode also clarifies the important distinction between continuous delivery (which prepares code for release with manual approval) and continuous deployment (which automatically releases code to production). Finally, we discuss how continuous delivery integrates with Agile and DevOps methodologies to create more efficient, reliable software development processes. Find more information at https://www.ibm.com/think/podcasts/techsplainers Narrated by Dan Segal

    7分

  8. 1月14日

    What is continuous integration?

    This episode of Techsplainers explores continuous integration (CI), a fundamental software development practice where developers regularly merge code changes into a central repository. We explain how CI works by automatically building and testing code with each submission, dramatically improving upon traditional development, where infrequent integrations caused painful conflicts and delays. The podcast covers key CI components, including central repositories, CI servers, and automated testing suites, while highlighting how testing forms the backbone of effective CI implementations. We distinguish between continuous integration, delivery, and deployment in the CI/CD pipeline, and examine CI's critical role in both DevOps and agile methodologies. The episode concludes by detailing CI's main benefits: earlier error detection, improved team collaboration, accelerated development cycles, and reduced risk through incremental changes. Find more information at: https://www.ibm.com/think/podcasts/techsplainers Narrated by Dan Segal

    6分
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予告編

番組について

Introducing the Techsplainers by IBM podcast, your new podcast for quick, powerful takes on today’s most important AI and tech topics. Each episode brings you bite-sized learning designed to fit your day, whether you’re driving, exercising, or just curious for something new. This is just the beginning. Tune in every weekday at 6 AM ET for fresh insights, new voices, and smarter learning.

情報

  • クリエイター
    IBM
  • 配信期間
    25年 - 26年
  • エピソード
    56
  • 制限指定
    不適切な内容を含まない
  • Copyright
    © IBM
  • 番組のWebサイト
    Techsplainers by IBM