A Beginner's Guide to AI

Machine learning is everywhere, yet rarely understood. In this episode of A Beginner’s Guide to AI, we strip away the hype and explain how machine learning actually works, why it’s so powerful, and where it quietly goes wrong.You’ll learn how machines are trained on data rather than rules, why predictions are not understanding, and how real-world systems can produce unfair outcomes even when they look accurate. A real healthcare case shows how a cost-based algorithm systematically underestimated medical need, revealing the hidden dangers of proxy metrics.This episode covers machine learning basics, ethical AI, algorithmic bias, fairness, and transparency in a way that is accessible to beginners and useful for professionals.📧💌📧Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧Quotes from the Episode“Machine learning gives you what you measure, not what you value.”“The algorithm didn’t invent bias. It learned it efficiently.”“A perfect prediction of the wrong thing is still failure.”Chapters00:00 Machine Learning Without the Myth04:12 How Machines Learn From Data10:45 Types of Machine Learning18:30 The Cake Example26:05 Healthcare Case Study36:40 Ethics, Bias, and Proxies45:50 Final TakeawaysAbout Dietmar Fischer: Dietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him.Music credit: Modern Situations by Unicorn Heads Hosted on Acast. See acast.com/privacy for more information.

Discover the magic of AI inference, the secret to how platforms like Netflix predict your next binge! Professor Gephardt shares delightful analogies, including a fruit-loving robot and a cake-tasting mystery, to explain how AI learns from past data. Learn about Delta's impressive $30 million ticket sales driven by AI, and get tips on spotting inference in your digital life. Plus, find out how to experiment with your own AI models. Tune in for an engaging dive into the world of intelligent predictions!

The discussion dives into the age-old debate of whether intelligence is innate or learned, a topic crucial for AI design. Nativism suggests some knowledge is instinctual, while deep learning emphasizes data-driven learning. The need for massive datasets for AI is starkly compared to human learning capabilities. Using a cake-baking analogy, the host illustrates how AI requires endless examples, unlike humans who build on innate frameworks. Examples like IBM Watson and interactive games shed light on AI's learning methods versus human adaptability.

AI vs. Automation: Why Repetitive Marketing is FailingREPOST due to low podcast listener activity - if you listen now, you are the exception 😉Ever received the same email twice—word for word, from two different people? That’s not AI, that’s bad automation. And it happens way more often than it should.In this episode, we break down the key difference between automation and artificial intelligence—why one just follows rules while the other actually thinks. With a real-world case study straight from my inbox, we’ll expose how businesses are unknowingly damaging their credibility with mindless automation and what they could do differently with AI.If you’re running digital marketing, email campaigns, or even PR outreach, this is a must-listen. Stop the spam, start thinking smarter.Tune in to get my thoughts, and don’t forget to subscribe to our Newsletter!This podcast was generated with the help of ChatGPT, Mistral, and Claude 3. We do fact-check with human eyes, but there still might be hallucinations in the output. And, by the way, it's read by an AI voice.Music credit: "Modern Situations" by Unicorn Heads. Hosted on Acast. See acast.com/privacy for more information.

Explore the magic of predictive AI, the unseen force behind personalized recommendations and fraud detection. Discover how retailers forecast demand based on weather and trends, and learn why understanding diverse applications—from healthcare to social media—is crucial. Tune in for a fun cake analogy that illustrates how past sales inform future decisions. Dive into the challenges of bias and ethics, and hone your skills with Google Trends to become a budding predictor. Insights from AI pioneer Pedro Domingos highlight the need for awareness as we navigate this tech-driven world.

Artificial intelligence has become incredibly convincing. It talks smoothly, reacts instantly, and often feels surprisingly human. In this episode of A Beginner’s Guide to AI, Prof. GepHardT explores why that feeling can be misleading — and why it matters.Drawing on literature, psychology, and real-world AI design, the episode explains how modern AI systems simulate intelligence without understanding, why humans instinctively project emotions onto machines, and where ethical risks begin when appearance replaces clarity. This is an accessible, practical episode for anyone who wants to understand AI without getting lost in jargon or hype.📧💌📧Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧Chapters00:00 When AI Feels Alive04:12 The Olympia Effect and Human Projection10:05 What AI Actually Does and What It Doesn’t18:40 Why Humans Trust Machines26:30 Ethical Risks of Emotional AI34:10 How to Stay Clear-Headed Around AIQuotes from the Episode“AI doesn’t understand you — it performs understanding.”“The danger isn’t smart machines, it’s trusting fluent ones.”“When intelligence looks alive, that’s when it needs the most scrutiny.”About Dietmar FischerDietmar is a podcaster and AI marketer from Berlin. If you want to know how to get your AI or your digital marketing going, just contact him at ArgoBerlin.com🎧 Music credit: “Modern Situations” by Unicorn Heads Hosted on Acast. See acast.com/privacy for more information.

In this engaging discussion, Sam Ransbotham, an MIT Sloan researcher and host of Me, Myself, and AI, delves into the rising influence of AI agents in organizations. He explains how these proactive assistants enhance productivity and job satisfaction by taking over repetitive tasks. The conversation highlights insights from a global study on executive adoption rates and practical use cases, like Chevron's innovative tools. Ransbotham emphasizes the importance of understanding AI for effective usage and discusses the evolving roles of generalists and specialists in the workplace.

Join Dr. Alex Kihm, founder of POMA AI, a pioneer in legal tech and big-data econometrics, as he unpacks the complexities of AI in enterprise settings. Discover why traditional AI search often fails, and how POMA AI’s method reconstructs structure from unstructured data. Alex tackles the chunking problem, explaining how naive approaches distort meaning, and highlights the importance of context engines over outdated retrieval systems. His insights blend humor with a keen understanding of what AI can truly achieve in the corporate world.

Explore the captivating world of artificial intelligence through its foundational elements: model size, dataset diversity, and compute power. Discover how larger models often outperform their smaller counterparts, and why data serves as the lifeblood of AI learning. Get a taste of the intricate balance necessary for maximum performance with practical tips on evaluating AI tools. A delightful cake analogy makes the technical concepts deliciously understandable, while case studies from DeepMind and Google showcase the real-world impact of these scaling laws.

Matt Weaver, Solutions Engineering Leader at OpenAI, shares insights on the launch of GPT-5 and its implications for businesses. He explains how GPT-5's reasoning models enhance decision-making and addresses common misconceptions about AI. The discussion highlights innovative applications in industries like banking and travel. Weaver also delves into AI literacy as crucial for successful adoption and guides listeners on creating Custom GPTs. His enthusiasm for AI’s potential shines through, making it clear that now is the time to integrate these transformative tools.