Certified - Introduction to AI Audio Course

Jason Edwards
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The Introduction to Artificial Intelligence (Audio Course) provides a comprehensive, audio-first journey through the foundations, applications, and future directions of AI. Listeners will explore how machines learn, reason, and act, with episodes covering technical concepts, industry use cases, ethical issues, and global impacts. Designed for students, professionals, and career changers alike, this course delivers clear, structured insights that make AI accessible and relevant across domains. Produced by BareMetalCyber.com

  1. 第 1 集

    Episode 1 — Orientation — What is Artificial Intelligence?

    Artificial Intelligence is a term everyone has heard, but few understand in depth. In this opening episode, we cut through the hype and get to the core: what does it actually mean when we say a system is “intelligent”? You’ll hear how the idea of machines that mimic human thought emerged, why early approaches like rule-based programming fell short, and how modern data-driven methods reshaped the field. We’ll compare narrow AI systems that perform single tasks with the elusive concept of general AI, which aims to mirror human versatility. Along the way, you’ll see how perception, reasoning, and action became the three pillars of AI research, and why public imagination, fueled by science fiction, has always been part of the story. We’ll then connect those foundations to the AI tools shaping the present day. From recommendation engines to voice assistants, from neural networks to natural language processing, modern AI has become inseparable from daily life. But with progress come challenges: the risks of bias, the importance of explainability, and the ethical questions that will define AI’s future. By the end of this episode, you’ll have a working definition of Artificial Intelligence, clarity about its scope, and a strong sense of why understanding AI matters not just for technologists, but for anyone preparing for a world where these systems play a growing role. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

    31 分鐘

  2. 第 2 集

    Episode 2 — Course Roadmap — How to Learn AI in Audio Form

    This PrepCast is designed to teach Artificial Intelligence in a way that fits into real life: no slides, no diagrams, no heavy math on the page — just clear explanations you can absorb anywhere. In this roadmap episode, we walk through the design of the series, showing how the episodes are structured so you can either listen sequentially and build a complete foundation or drop into individual topics as needed. You’ll learn why each installment follows a consistent format — introduction, two core sections, and a summary — and how repetition of key concepts and glossary deep dives will strengthen retention. Think of it as an audio curriculum that respects your time while ensuring you come away with durable understanding. The roadmap also previews what lies ahead. You’ll move from the origins of AI to its technical foundations in algorithms, logic, and machine learning, then into applied domains like healthcare, finance, and robotics. Ethical dimensions — bias, fairness, privacy, and employment — are given their own focus, before the series closes with future directions such as Artificial General Intelligence, quantum computing, and AI-driven creativity. Whether you’re a student, a career changer, or a professional seeking context, this PrepCast is built to meet you where you are and take you further. This orientation ensures you’ll know what to expect and how to get the most out of the journey. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

    24 分鐘

  3. 第 3 集

    Episode 3 — A Brief History of AI — From Turing to Transformers

    Artificial Intelligence didn’t appear overnight; it has a story stretching back more than seven decades. In this episode, we step into that story, beginning with Alan Turing’s famous question — can machines think? — and the Turing Test that followed as an early benchmark for intelligence. We’ll visit the 1956 Dartmouth Conference where the term “Artificial Intelligence” was first coined, and hear how optimism in the 1960s gave way to the harsh realities of AI winters when funding dried up and promises went unmet. From expert systems of the 1980s to the revival of neural networks in the 1990s, AI has repeatedly risen, stumbled, and reinvented itself. Each cycle brought fresh lessons about the limits of rule-based programming and the importance of data and computation. The second half of the story connects history directly to the present. You’ll discover how the rise of big data, cloud computing, and open-source frameworks unlocked the deep learning breakthroughs of the 2010s. Landmarks such as Deep Blue defeating a chess champion and AlphaGo mastering the game of Go showed the world just how far AI could go. From computer vision to natural language processing, today’s transformer models represent the culmination of decades of work, not an overnight miracle. Understanding this journey provides essential context: it explains why current AI systems work the way they do, what challenges they’ve inherited, and why progress today feels both rapid and inevitable. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

    27 分鐘

  4. 第 4 集

    Episode 4 — AI vs. Machine Learning vs. Deep Learning — Key Distinctions

    AI, machine learning, and deep learning are terms often used interchangeably, but they are not the same — and confusing them makes it harder to understand the field. This episode clears the fog by breaking down how these layers of terminology connect. We’ll begin with Artificial Intelligence as the broadest category: any system designed to mimic aspects of human thought. Within that sits machine learning, where computers improve performance by finding patterns in data rather than relying solely on fixed rules. And within machine learning lies deep learning, a powerful subset that uses multi-layered neural networks to handle tasks like vision, speech, and natural language at unprecedented scale. You’ll also hear why these distinctions matter in practice. Traditional AI still has value in symbolic reasoning and expert systems, while machine learning dominates in predictive analytics, and deep learning fuels the breakthroughs behind self-driving cars, virtual assistants, and generative text systems. We’ll cover tradeoffs in interpretability, data needs, and computational demands, showing why organizations choose one approach over another depending on their goals. By the end of this episode, you’ll be able to explain clearly what separates AI, machine learning, and deep learning — and why those differences matter not just for exams or interviews, but for making sense of real-world technologies. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

    28 分鐘

  5. 第 5 集

    Episode 5 — How Machines “Think” — Algorithms and Representations

    When people talk about machines “thinking,” they’re not talking about human intuition or creativity. They’re talking about algorithms — structured sets of instructions — and representations, the ways information is stored and processed. In this episode, we look at how computers encode numbers, words, and images, and how those encodings become the raw material for reasoning. You’ll learn about symbolic approaches, where knowledge is captured in logical rules, and sub-symbolic approaches, where data is represented in weights and layers of a neural network. Search strategies, heuristics, and optimization methods illustrate how machines explore possibilities and choose among them. We also explore the tradeoffs and challenges that come with these approaches. Symbolic reasoning provides transparency but struggles with flexibility, while neural representations capture complexity but resist easy interpretation. You’ll hear how problems are framed in state spaces, graphs, and features, and why abstractions matter for scaling to real-world complexity. From edge detection in vision to word embeddings in natural language, this episode shows the mechanics of how machines “think,” setting the stage for understanding how algorithms evolve into learning systems. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

    27 分鐘

  6. 第 6 集

    Episode 6 — Data — The Fuel of AI

    No matter how advanced the algorithm, it can’t run without data. This episode focuses on why data is considered the fuel of AI, exploring the different types that drive training and performance. Structured data, such as rows in databases, is contrasted with unstructured data like images, text, and audio. We’ll examine the steps needed to prepare data — collecting, cleaning, labeling, and augmenting — and why quality matters as much as quantity. You’ll also learn about the importance of balanced datasets and how missing or biased data can lead directly to flawed outcomes. We then expand into broader issues of governance and ethics. From open datasets driving research to proprietary datasets conferring competitive advantage, data ownership shapes the AI landscape. Privacy, consent, and regulatory compliance add complexity, especially in healthcare and finance. Synthetic data and federated learning show how innovation continues to expand what counts as usable information. By the end, you’ll see clearly why every AI system reflects the data it’s trained on, and why responsible data practices are inseparable from reliable AI performance. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

    27 分鐘

  7. 第 7 集

    Episode 7 — Search and Problem Solving in AI

    Before machine learning took center stage, AI was already grappling with how to solve problems systematically. This episode dives into search and problem solving, two of the earliest and still fundamental approaches to intelligence. You’ll learn how problems are represented as states and transitions, and how uninformed search strategies like breadth-first and depth-first explore possibilities blindly. We’ll then move to informed searches, where heuristics act as shortcuts, guiding algorithms like A* to efficient solutions. Beyond simple puzzles, we show how these methods apply in real-world settings. Constraint satisfaction problems, optimization tasks, and adversarial search in games demonstrate the versatility of these approaches. We also look at evolutionary algorithms and local search strategies that mimic biological or incremental processes. Applications in robotics, operations research, and planning illustrate why search remains central even in today’s AI. By the end, you’ll recognize search not as a relic, but as a foundation underpinning many techniques you’ll see throughout this course. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

    25 分鐘

  8. 第 8 集

    Episode 8 — Knowledge Representation — How Machines Store Facts

    For AI to reason, it needs to store and organize information. This episode explores knowledge representation, the frameworks that allow machines to capture facts, relationships, and rules. From semantic networks linking concepts to ontologies defining categories, we examine how different structures model the world. Logic-based systems like first-order logic provide precision, while production rules offer flexibility. Knowledge graphs, increasingly common today, connect entities into vast webs of meaning, powering systems like search engines and digital assistants. But representation is not just about storage; it’s about inference. We cover how inference engines draw conclusions, how probabilistic and fuzzy logic manage uncertainty, and how non-monotonic reasoning allows systems to revise conclusions when new evidence arrives. Case-based reasoning and hybrid methods demonstrate the blending of symbolic and statistical approaches. Applications in expert systems, robotics, and natural language processing show how representation shapes performance. This episode makes clear that how you represent knowledge determines what a system can know — and what it can’t. Produced by BareMetalCyber.com, where you’ll find more cyber prepcasts, books, and information to strengthen your certification path.

    24 分鐘
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簡介

The Introduction to Artificial Intelligence (Audio Course) provides a comprehensive, audio-first journey through the foundations, applications, and future directions of AI. Listeners will explore how machines learn, reason, and act, with episodes covering technical concepts, industry use cases, ethical issues, and global impacts. Designed for students, professionals, and career changers alike, this course delivers clear, structured insights that make AI accessible and relevant across domains. Produced by BareMetalCyber.com

資訊

  • 創作者
    Jason Edwards
  • 活躍年代
    2025年
  • 集數
    48
  • 年齡分級
    兒少適宜
  • 版權
    © @ 2025 Bare Metal Cyber