A Beginner's Guide to AI

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

AI agents are rapidly becoming one of the most influential technologies inside modern organizations — often without leaders even realizing the shift. In this episode, Dietmar Fischer sits down with MIT Sloan podcast host Sam Ransbotham to uncover why AI agents and agentic AI systems are spreading through enterprises at remarkable speed.Based on a global study of 2,100 executives across 116 countries, Sam shares how AI agents improve productivity, increase job satisfaction, and fundamentally reshape how companies work. From Chevron’s proactive exploration tools to the rise of autonomous knowledge assistants, we explore the surprising ways enterprise AI adoption is unfolding in real time.📧💌📧Tune in to get my thoughts and all episodes — don’t forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧This wide-ranging conversation covers practical use cases, risks and transparency issues, the future of generalists vs specialists, how universities adapt to AI, and why understanding the technology still matters deeply.Quotes from the Episode“We’re moving from tools we command to tools that proactively act on our behalf.”“AI agents don’t just make us more productive; they make us happier by removing the parts of work we dislike.”“Understanding AI makes you a better user of AI. Depth still matters.”Chapters00:00 Welcome & How Sam Got Into AI03:21 What Are AI Agents? Definitions and Early Insights07:14 Real Enterprise Use Cases of AI Agents12:05 Job Satisfaction, Productivity, and Human-AI Collaboration17:20 Generalists, Specialists & the Future of Work22:30 Risks, Transparency & Avoiding an Oppressive AI Future28:45 How Companies Should Start with Agentic AI33:20 AI in Education and Changing Learning Environments39:00 Sam’s Personal Use of AI — What Works and What Doesn’t41:20 Terminator vs Matrix? AI Futures42:41 Where to Find Sam and the MIT Sloan StudyWhere to Find the Sam Ransbothamsite at Boston CollegeOr you find him on LinkedInThe study of MIT Sloan lies hereAnd, last, but not least, Sam's podcast “Me, Myself, and AI”!About Dietmar Fischer:Dietmar is a podcaster and AI marketer from Berlin. If you want to elevate your AI or digital marketing strategy, get in touch anytime at argoberlin.comMusic credit: “Modern Situations” by Unicorn Heads 🎵

In this episode of Beginner’s Guide to AI, we sit down with Alex Kihm, founder of POMA AI, to explore how enterprises can finally make sense of their data. AI search is broken, RAG often fails, and corporate documents are notoriously hard for LLMs to interpret. Alex explains how POMA AI’s patented method reconstructs structure inside unstructured data, enabling powerful, accurate enterprise search.You’ll hear how his journey from engineering to legal tech to big-data econometrics led to a breakthrough in information structuring. Alex shares why PDFs confuse AI systems, how chunking destroys meaning, and why context engines will replace classical retrieval systems. This is a deep, funny, insightful conversation about what AI can and cannot do — and how companies can use it responsibly.📧💌📧Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧About Dietmar FischerDietmar is a podcaster and AI marketer from Berlin. If you want to elevate your AI strategy or your digital marketing, feel free to reach out anytime at Argoberlin.comQuotes from the Episode“Chunking is like reading wrongly sorted text messages from the 90s.”“Intelligence is pattern recognition — and most enterprise data is not recognisable to machines.”“PDF was made for printers, not for AI.”“POMA AI restores the spatial awareness inside documents — the missing context that LLMs need.”“We don’t do RAG anymore. We build context engines.”“If your AI breaks the world, show me the invoice.”Chapters00:00 Welcome and Introduction 02:45 Alex Kihm’s Background: Engineering, Legal Tech and Early AI Work 10:32 The Problem with RAG, Training, Fine-Tuning and Hallucinations 18:55 The Birth of POMA AI and Solving the Chunking Problem 32:40 How POMA AI Rebuilds Document Structure and Enables True Enterprise Search 45:50 AI Safety, Manipulation Bots and The Future of AI in Business 52:10 Where to Find Alex Kihm and Closing Thoughts Where to Find the Dr. Alex KihmAll you need to know about chunking strategies, you'll find here: poma-ai.comContact Alex on LinkedIn! Music credit: "Modern Situations" by Unicorn Heads

Artificial intelligence breakthroughs might appear magical from the outside, but underneath lies a predictable and surprisingly elegant structure. This episode of A Beginner’s Guide to AI takes listeners on a clear and engaging journey into the three scaling laws of AI, exploring how model size, dataset size, and compute power work together to shape the intelligence of modern systems. Through practical explanations, entertaining analogies, and detailed real-world case studies, this episode demystifies the rules that drive every meaningful AI advancement.Listeners will learn why bigger models often perform better, how data becomes the lifeblood of learning, and why compute power is the critical engine behind every training run. The episode includes a memorable cake analogy, a breakdown of how scaling laws led to the rise of state-of-the-art large language models, and practical tips for evaluating AI tools using these principles.This deep yet accessible explanation is designed for beginners, creators, and curious minds who want to understand what truly makes AI work.📧💌📧Tune in to get my thoughts and all episodes, don't forget to subscribe to our Newsletter: beginnersguide.nl📧💌📧About 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 at argoberlin.comQuotes from the Episode“AI doesn’t just grow; it scales, and scaling changes everything.”“Compute isn’t the cherry on top; it is the oven that makes the entire AI cake possible.”“Scaling laws show us that AI progress isn’t magic; it’s engineered.”Chapters00:00 Introduction to AI Scaling03:24 The Three Scaling Laws Explained11:02 The Cake Analogy for AI Models17:40 Case Study: How Scaling Transformed Large Language Models23:58 Practical Tips for Understanding and Applying Scaling Laws28:45 Final Recap and Key TakeawaysMusic credit: "Modern Situations" by Unicorn Heads

🚀 Matt Weaver, Solutions Engineering Leader at OpenAI, takes us inside the launch of GPT-5, the rise of AI agents, and how these tools are transforming industries. From practical business adoption tips to exploring advanced features like Deep Research and Custom GPTs, this episode is packed with actionable insights.📧 Tune in to get my thoughts, tips and tricks and all the episode in your mailbox: beginnersguide.nl💡 What you’ll learn in this episode:How GPT-5 chooses the right reasoning model automatically for better answersWhy AI literacy is the foundation for business adoptionIndustry examples from banking (BBVA) to travel (Virgin Atlantic)How AI agents like Deep Research work – and why they’re a game changerCreating your own Custom GPTs without codingAddressing AI objections: security, hallucinations, and cost concernsQuotes from the Episode:💬 “AI is such a transformative technology — now is the time to reimagine your processes, not just bolt it onto old ones.” – Matt Weaver💬 “Your first AGI moment changes how you see every problem — you start thinking, ‘How can ChatGPT help me with this?’” – Matt Weaver🧾 Chapters (experimental):00:00 Welcome & Introduction to Matt Weaver01:18 Matt’s Journey into AI and Joining OpenAI03:58 GPT-5 Launch – What’s New and Why It Matters08:28 How Businesses Should Start with ChatGPT10:45 AI Adoption Strategies & Avoiding Common Mistakes12:14 Industry Examples – Banking, Travel, and Professional Services14:06 Deep Research: AI Agents Explained18:06 Study Mode & AI in Education19:56 Overcoming Objections: Security, Hallucinations & Costs24:06 ROI of ChatGPT in Business28:22 The “AGI Moment” & Personal Uses of ChatGPT32:03 The Future of AI: Agents, Coding, and New Businesses35:48 Custom GPTs – Building Your Own AI Apps39:06 AI Safety & Optimism for the Future41:16 Where to Find Matt Weaver & ClosingWant to know more?🔗 ChatGPT is now also at Chat.com🔗 OpenAI's learning resources are at: academy.openai.com🎵 Music credit: "Modern Situations" by Unicorn Heads