A Beginner's Guide to AI

Artificial intelligence is often framed as a battle between humans and machines. But what if that story misses the real point?

In this episode of A Beginner’s Guide to AI, Prof. GepHardT explores one of the most fascinating ideas in cognitive science: the extended mind theory. According to philosopher Andy Clark, human intelligence has never been confined to the brain alone. For centuries we have extended our thinking through tools like writing, maps, calculators, and computers.

Generative AI may simply be the newest and most powerful addition to this cognitive ecosystem.
Instead of replacing human creativity, AI may expand it. By generating ideas, exploring possibilities, and challenging assumptions, AI can act as a powerful thinking partner.

A striking example comes from the famous AlphaGo match against Go champion Lee Sedol. When the AI played the now legendary Move 37, professional players initially believed the move was a mistake. Later they discovered it opened entirely new strategic possibilities. The machine did not just beat humans at Go. It helped humans rethink the game itself.

This episode explores how human AI collaboration works and why hybrid intelligence may define the future of creativity, work, and learning.

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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
“Your brain has never worked alone. It has always been part of a thinking system that includes tools and environments.”
“The future of intelligence may not be human versus machine but human plus machine.”
“The most important skill in the AI age may not be prompt writing but judgement.”

Podcast Chapters
00:00 The Big Question About AI and Human Thinking
06:40 The Extended Mind Theory Explained
16:20 Why Humans Are Natural Born Cyborgs
26:50 The AlphaGo Story and Move 37
38:15 AI as a Creative Thinking Partner
49:30 The Future of Hybrid Intelligence

Music credit: Modern Situations by Unicorn Heads
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What happens when your company gets hit by a cyberattack?
In this eye-opening episode, attorney Joshua Cook reveals why cybersecurity isn’t an IT problem but a leadership challenge. After two decades fighting fraud and managing crisis response, Cook has seen every digital disaster imaginable — and he’s here to explain how to build true cyber resilience.

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Josh breaks down how AI has democratized cybercrime, why phishing scams have become nearly impossible to spot, and how every CEO should create an incident response plan before chaos hits. He also explains why planning matters more than the plan itself — and how leaders can keep their teams calm when everything goes wrong.

💡 You’ll learn:
- How AI is fueling new waves of fraud and misinformation
- Why leadership and communication are the real firewalls of business
- How to train teams and run tabletop exercises before the crisis
- What Maersk and Colonial Pipeline taught the world about transparency
- Why companies with a plan lose 60 % less money in an attack

Prepare, breathe, and lead — because it’s not if you’ll be hacked, but when.

👀 Quotes from the Episode
“Cybersecurity isn’t an IT issue. It’s a business problem, and it needs a business solution.”
“AI has democratized cybercrime — you don’t need to be a hacker anymore, just willing to commit a crime.”
“A plan might be useless, but planning is indispensable — that’s what makes companies resilient.”

🧾 Chapters
00:00 Welcome & Introduction – Meet Joshua Cook
02:00 How a Fraud Attorney Ended Up Fighting Cybercrime
05:00 AI Has Made Cybercrime Easier (and Smarter)
08:00 The Elderly Are the New Prime Targets
11:00 From Fake Law Firms to Real Scams – True Cases from the Field
15:00 Turning the Tables: How AI Can Defend, Not Just Attack
18:00 Cyber Resilience by Design – Why Leadership Matters
22:00 When Crisis Hits: Lessons from Maersk and Colonial Pipeline
27:00 Preparing the Team – How Training Prevents Chaos
31:00 It’s Not If, It’s When – The Power of an Incident Response Plan
35:00 Planning vs. Panicking – Eisenhower and the Art of Cyber Preparation
38:00 Why Calm Leaders Win in Cyber Crises
41:00 How Joshua Cook Uses AI Safely in Legal Practice
44:00 No, the Terminator Isn’t Coming (But AI Might Take Your Job)
47:00 Final Thoughts – Cybersecurity as a Business Superpower

🔗 Where to Find the Guest
- Joshua Cook on LinkedIn: linkedin.com/in/jnc2000
- Josh's Book "Cyber Resilience by Design" – available wherever books are sold, e.g. on Amazon
- Prince Lobel Tye LLP: princelobel.com

🎧 About Dietmar Fischer:
Economist, digital marketer, and podcaster exploring how AI reshapes decision-making, leadership, and creative work. Want to connect with me? You'll find me on LinkedIn!

🎵 Music credit: “Modern Situations” by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.

🎙️In this episode of Beginner’s Guide to AI, Dietmar Fischer sits down with Paul A. Hebert, founder of AI Recovery Collective and author of Escaping the Spiral, for a serious conversation about AI chatbot harm, hallucinations, digital dependency, and the real-world psychological risks of generative AI.

Paul shares how an intense experience with ChatGPT pushed him into a dangerous spiral, what he learned about the limits of large language models, and why AI literacy may be one of the most important skills of this decade.

🧠 This episode explores what happens when AI stops feeling like software and starts feeling personal. Dietmar and Paul talk about hallucinations, trust, chatbot addiction, AI companions, mental health risks, youth safety, and why companies building these systems cannot hide behind product language forever. The discussion is intense, but it is also practical. You will come away with a clearer sense of how to use AI more safely, what warning signs to watch for, and why regulation is quickly becoming a much bigger part of the AI conversation.

OpenAI has publicly discussed why language models hallucinate, while lawmakers in multiple U.S. jurisdictions have pushed new restrictions on AI systems acting like therapists or medical professionals.

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👤 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 literacy is the most important thing anybody can work on.”
“Had OpenAI responded to that first message and said this is a hallucination and you’re physically safe, I would have been fine.”
“Never trust the thing it tells you. Even if it gives you a citation, go look.”

🕒 Chapters
00:00 Paul Hebert’s Shocking ChatGPT Experience
08:14 Why AI Hallucinations Can Spiral Into Real Fear
16:05 AI Literacy, Neurodivergence, and How He Got Out
23:32 Why AI Companies Must Be Accountable
30:02 AI Companions, Youth Safety, and Addiction Risks
38:28 Terminator, Consciousness, and Practical Rules for Safe AI Use

🔗 Where to find Paul
The AI Recovery Collective: airecoverycollective.com
Escaping the Spiral on Amazon
AI Recovery Collective Substack: airecoverycollective.substack.com/
LinkedIn: Paul A. Hebert: linkedin.com/in/paul-hebert-48a36/

🎵 Music credit: "Modern Situations" by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.

Artificial intelligence often feels mysterious. Machines detect spam, recommend products, analyse customers, and power countless digital tools. But behind all of these systems lies a surprisingly simple question: how do machines actually learn?

In this episode of A Beginner’s Guide to AI, Prof GePharT breaks down one of the most important concepts in machine learning: the difference between supervised learning and unsupervised learning.

You will discover how AI models learn from labelled data when the answers are already known, and how algorithms can explore raw data to uncover hidden patterns without guidance. These two learning strategies power many of the systems shaping modern technology.

Using practical examples such as spam filters, customer segmentation, and simple analogies like cake classification, the episode explains how machines learn from data and why the training method makes a huge difference.

Key takeaways include how supervised learning works with labelled datasets, how unsupervised learning reveals patterns in complex information, why training data quality matters, and how businesses use both methods to build intelligent systems.

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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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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
Supervised learning teaches machines the answers. Unsupervised learning helps machines discover the questions.
Artificial intelligence is not magic. It is pattern recognition powered by data.
Machines do not wake up intelligent. They become intelligent through training.

Chapters
00:00 The Two Ways Machines Learn
06:10 What Supervised Learning Really Means
18:45 Discovering Patterns with Unsupervised Learning
32:20 The Cake Example Explained
40:30 Real World AI Case Study Spam Filters and Customer Segmentation
52:15 Why AI Training Methods Matter

Music credit: Modern Situations by Unicorn Heads
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Engineering the Future of AI with Chirag Agrawal: Context, Memory and Coordination

Artificial Intelligence isn’t just getting smarter—it’s learning to coordinate. In this episode, Chirag Agrawal joins Dietmar Fischer to unpack how modern AI agents handle context, memory, and decision-making inside complex multi-agent systems. Together they explore how engineering, orchestration, and memory-sharing shape the next generation of AI architecture.

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

You’ll hear how Chirag’s fascination with search led him to build early prototypes of intelligent assistants, and how today’s LLM agents extend that idea far beyond simple queries. He explains why AI isn’t one giant super-brain but a constellation of specialized agents—each performing specific tasks with shared or isolated memory—and how this design mirrors human collaboration.

🔑 Key Takeaways
Why AI orchestration and context management are crucial for scalable systems

The trade-offs between shared memory and independent agents

What engineers mean by the ReAct Loop—reasoning and acting in tandem

How multi-agent coordination is reshaping industries from healthcare to compliance

Why the “AI supercomputer” myth ignores practical limits of context windows

💬 Quotes from the Episode
“AI is just a higher form of search—it’s about finding the right action, not just information.”

“Agents behave inhuman until you engineer context for them.”

“Specialization in AI works the same way it does for people—each agent should do one thing really well.”

“Coordination isn’t magic; it’s careful engineering.”

“Context makes intelligence usable.”

“A well-defined agent doesn’t need to do everything—it needs to do its one job perfectly.”

⏱️ Podcast Chapters
00:00 Welcome and Introduction
01:45 Chirag Agrawal’s Early Fascination with Search and AI
04:40 From Search Engines to “Find” Engines – How AI Takes Action
07:10 The Rise of AI Agents and Multi-Agent Systems
10:15 Why AI Agents Sometimes Behave “Inhuman”
13:30 Context, Memory, and Coordination: The Core Engineering Challenges
18:00 Shared vs. Isolated Memory – The Hive Mind Dilemma
22:30 Why We Need Many Agents, Not One Super-Computer
27:00 How the ReAct Loop Helps Agents Think and Act
30:40 Industries Adopting AI Agents: Compliance, Medicine, and Law
34:30 When AI Goes Off-Road – The Limits of Coordination
37:15 Building Responsible, Constrained Agents
40:10 The Future of AI and Why the Terminator Scenario Won’t Happen
42:20 Where to Find Chirag Agrawal & Closing Thoughts

🌐 Where to Find the Chirag Agrawal

LinkedIn 🧑🏽‍🦱 linkedin.com/in/chirag-agrawal
Website ➡️ ⁠chiraga.io⁠

🎵 Music credit: “Modern Situations” by Unicorn Heads
Hosted on Acast. See acast.com/privacy for more information.