REPOST DUE TO WRONG AUDIO TRACK. Changed it, but many may have missed the right episode.

Is intelligence something we’re born with, or do we learn everything from scratch? That’s not just a question for philosophers - it’s at the core of artificial intelligence today.

In this episode ofA Beginner’s Guide to AI, we explore the great debate between nativism and deep learning.

Nativism suggests that some knowledge is built-in, like the way babies instinctively pick up language. Deep learning, on the other hand, argues that intelligence comes purely from experience - AI models don’t start with any understanding; they learn everything from massive amounts of data.

We break down how this plays out in real AI systems, from AlphaZero teaching itself to play chess to ChatGPTGPT mimicking human language without actually understanding it. And, of course, we use cake to make it all crystal clear.

Tune in to get my thoughts, and don’t forget tosubscribe to our Newsletter at beginnersguide.nl

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.

AI vs. Automation: Why Repetitive Marketing is Failing

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!

Want to get in contact? Write me an email: podcast@argo.berlin

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.

Ever wonder how Netflix knows your next binge-watch, or why your bank spots fraud before you do? In this lively episode of A Beginner’s Guide to AI, Professor GePhardT lifts the lid on predictive AI—the hidden tech wizard quietly shaping our daily lives.

From forecasting retail trends at Target to critical healthcare interventions, predictive AI isn't just predicting the future; it's already shaping it. But there’s a catch: with great power comes the thorny challenge of bias and ethics.

Join the fun as we untangle how predictive AI differs from generative AI, explore its surprising influence in everyday situations (cakes included!), and sharpen our own predictive skills through hands-on activities with Google Trends.

Plus, a reality check from AI pioneer Pedro Domingos reminds us why understanding this tech matters—because computers might already run more than we'd like to admit.

Tune in to get my thoughts and all the episodes: don't forget to ⁠subscribe to our Newsletter⁠ 💌

Want to get in contact? Write me an email: podcast@argo.berlin

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 from ElevenLabs.

Music credit: "Modern Situations" by Unicorn Heads

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.

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Tune in to get my thoughts and all episodes, don’t forget to subscribe to our Newsletter: beginnersguide.nl
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Chapters00:00 When AI Feels Alive
04:12 The Olympia Effect and Human Projection
10:05 What AI Actually Does and What It Doesn’t
18:40 Why Humans Trust Machines
26:30 Ethical Risks of Emotional AI
34:10 How to Stay Clear-Headed Around AI

Quotes 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 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.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.

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Tune in to get my thoughts and all episodes — don’t forget to subscribe to our Newsletter: beginnersguide.nl
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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.”

  
  

Chapters
00:00 Welcome & How Sam Got Into AI
03:21 What Are AI Agents? Definitions and Early Insights
07:14 Real Enterprise Use Cases of AI Agents
12:05 Job Satisfaction, Productivity, and Human-AI Collaboration
17:20 Generalists, Specialists & the Future of Work
22:30 Risks, Transparency & Avoiding an Oppressive AI Future
28:45 How Companies Should Start with Agentic AI
33:20 AI in Education and Changing Learning Environments
39:00 Sam’s Personal Use of AI — What Works and What Doesn’t
41:20 Terminator vs Matrix? AI Futures
42:41 Where to Find Sam and the MIT Sloan Study

Where to Find the Sam Ransbotham
site at Boston College

Or you find him on LinkedIn
The study of MIT Sloan lies here

And, 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.com

Music 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.

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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 elevate your AI strategy or your digital marketing, feel free to reach out anytime at Argoberlin.com

Quotes 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.”

Chapters

00:00 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 Kihm

• All you need to know about chunking strategies, you'll find here: poma-ai.com
• Contact 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.

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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.com

Quotes 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.”

Chapters

00:00 Introduction to AI Scaling
03:24 The Three Scaling Laws Explained
11:02 The Cake Analogy for AI Models
17:40 Case Study: How Scaling Transformed Large Language Models
23:58 Practical Tips for Understanding and Applying Scaling Laws
28:45 Final Recap and Key Takeaways

Music 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 answers
• Why AI literacy is the foundation for business adoption
• Industry examples from banking (BBVA) to travel (Virgin Atlantic)
• How AI agents like Deep Research work – and why they’re a game changer
• Creating your own Custom GPTs without coding
• Addressing AI objections: security, hallucinations, and cost concerns

Quotes 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 Weaver

01:18 Matt’s Journey into AI and Joining OpenAI

03:58 GPT-5 Launch – What’s New and Why It Matters

08:28 How Businesses Should Start with ChatGPT

10:45 AI Adoption Strategies & Avoiding Common Mistakes

12:14 Industry Examples – Banking, Travel, and Professional Services

14:06 Deep Research: AI Agents Explained

18:06 Study Mode & AI in Education

19:56 Overcoming Objections: Security, Hallucinations & Costs

24:06 ROI of ChatGPT in Business

28:22 The “AGI Moment” & Personal Uses of ChatGPT

32:03 The Future of AI: Agents, Coding, and New Businesses

35:48 Custom GPTs – Building Your Own AI Apps

39:06 AI Safety & Optimism for the Future

41:16 Where to Find Matt Weaver & Closing

Want 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