A Beginner's Guide to AI

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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Artificial Intelligence is moving from experimentation to everyday business reality. But most organisations still struggle with one key question: How do you actually implement AI across a company?
In this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Jim Spagnardo, enterprise AI strategist at ProArch, about what it really takes to roll out AI inside organisations.

Jim explains why AI adoption is less about technology and more about culture, leadership, and data readiness. He introduces the idea of the three Ds of work — the dull, the draining, and the distracting tasks that AI can remove so people can focus on higher-value work.

They also discuss when companies should use tools like Microsoft Copilot, when it makes sense to build a custom data and AI platform, and why data governance becomes critical once AI is introduced.
If you are a business leader trying to understand how AI will reshape your organisation, this conversation offers a practical look at the challenges — and opportunities — ahead.

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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 the host, Dietmar Fischer:
Dietmar Fischer is a podcaster and AI marketer from Berlin. If you want to get your AI or digital marketing projects started, contact him at argoberlin.com.

Interesting details and takeaways
• Why leaders must mandate AI adoption and how to structure a Smart Start engagement.
• The three Ds (dull, draining, distracting) as a simple way to position benefits for end users.
• How Copilot reduces context switching and the security/data protections needed to use it responsibly.
• Practical, measurable first use cases and how to track success via clear KPIs.
• Advice for students and early-career professionals: be a self-starter and learn AI skills now.

Quotes from the episode
“We have to show people we’re taking away the dull, the draining, and the distracting so they can do creative work.”
“There’s nowhere to hide: bad data surfaces weaknesses far faster when you use AI.”
“If you’re going to succeed, go after high-value, low-effort, high-return use cases first.”
“This affects everybody — it’s not just moving infrastructure; it changes conversations and who you have to talk to.”
“Copilot lives inside your environment — users don’t have to context-switch and it knows your organisation.”
“Don’t wait for formal education to teach this; be a self-starter and learn before you need it.”

Chapters
00:00 Welcome and why Jim got into AI
03:40 From IT conversations to the C-suite: changing who you must talk to
07:05 The three Ds: removing dull, draining, and distracting work
10:40 When to choose Copilot versus building your own data platform
14:30 Copilot advantages and data governance considerations
18:20 Visual reasoning, demos and the “Barcelona photo” moment
22:15 Smart Start: executive briefings, champions and use case workshops
27:00 Writing with AI and transparency in authoring content
30:10 Risks, regulations and advice for the next generation
33:45 Where to find Jim and closing thoughts

Where to find the Jim:
LinkedIn: linkedin.com/in/spignardo/
Website: ProArch.com

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

🎙️ Ritish Chugh (Airbnb analytics engineering) joins Dietmar Fischer to unpack a problem almost every company has, but few name clearly: your metrics do not mean the same thing across teams. Finance, marketing, and sales can all talk about “revenue” and still end up in dashboard chaos. The result is wasted time, slow decisions, and leadership that does not fully trust analytics or AI.

In this episode, Ritish introduces the idea of the human data pipeline: the person who stitches together conflicting definitions, tribal knowledge, and unspoken assumptions just to answer basic business questions. Then we move into the fix: unified metric definitions, a data dictionary for business metrics, and a semantic layer that acts as a translator between raw data schemas and business meaning. That foundation is what makes natural language querying and conversational analytics viable at scale, without turning AI into a confident hallucination machine.

We also cover why AI adoption in analytics stalls when organizations prioritize models and infrastructure but neglect data quality, validation frameworks, and metrics governance. If you want AI to support decision-making, you need governed metrics, clear ownership, and a system that produces consistent answers across BI tools, SQL, and AI agents. Finally, Ritish shares wow moments from using AI tools to summarize years of code and PRs, generate deeper test coverage, and reduce time spent on manual SQL by building agents on top of a semantic layer.

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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

Chapters
00:00 From data consulting to Airbnb and AI as a junior analyst
02:22 The human data pipeline and why metrics never match across departments
07:32 The fix: unified metric definitions, data dictionary, and the semantic layer translator
13:32 Why AI adoption stalls: data quality, trust, validation, and metrics governance
26:36 Data abundance, experimentation, and AI assisted A/B testing with humans in the loop
33:37 Wow moments with AI, role transformation, and why the Terminator is not invited (yet)

Quotes from the Episode
“AI just acts like a junior analyst, which is always available for you.”
“The first thing is… build that level of data definition that is unified for all.”
“No matter what AI models they’re using… if the data… is not up to the mark, it’s not going to give you the right results. It’s always going to hallucinate.”
“Every department has a different interpretation and definition of the metric.”
“I spend a lot of time really doing reconciliation between the numbers and data…”
“The most important thing happening is transformation…”

Where to find Ritish:
➡️ You connect with him on LinkedIn: linkedin.com/in/ritish-chugh/

📌 Keywords you’ll hear in action: semantic layer, data dictionary, metrics governance framework, unified metric definitions, governed metrics, natural language querying, conversational analytics, agentic analytics, data quality for AI adoption.

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