A Beginner's Guide to AI

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.📧💌📧 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 know how to get your AI or your digital marketing going, just contact him at argoberlin.comQuotes from the EpisodeSupervised 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.Chapters00:00 The Two Ways Machines Learn06:10 What Supervised Learning Really Means18:45 Discovering Patterns with Unsupervised Learning32:20 The Cake Example Explained40:30 Real World AI Case Study Spam Filters and Customer Segmentation52:15 Why AI Training Methods MatterMusic credit: Modern Situations by Unicorn Heads

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.📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠: beginnersguide.nl📧💌📧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.”Chapters00:00 Welcome and why Jim got into AI03:40 From IT conversations to the C-suite: changing who you must talk to07:05 The three Ds: removing dull, draining, and distracting work10:40 When to choose Copilot versus building your own data platform14:30 Copilot advantages and data governance considerations18:20 Visual reasoning, demos and the “Barcelona photo” moment22:15 Smart Start: executive briefings, champions and use case workshops27:00 Writing with AI and transparency in authoring content30:10 Risks, regulations and advice for the next generation33:45 Where to find Jim and closing thoughtsWhere to find the Jim:LinkedIn: linkedin.com/in/spignardo/Website: ProArch.comMusic credit: "Modern Situations" by Unicorn Heads 🎵

🎙️ 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.📧💌📧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.comChapters00:00 From data consulting to Airbnb and AI as a junior analyst02:22 The human data pipeline and why metrics never match across departments07:32 The fix: unified metric definitions, data dictionary, and the semantic layer translator13:32 Why AI adoption stalls: data quality, trust, validation, and metrics governance26:36 Data abundance, experimentation, and AI assisted A/B testing with humans in the loop33:37 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

The Future of Mental Health: AI Meets the Human Brain with Katarina Maloney // REPOSTIn this episode of Beginner’s Guide to AI, Dietmar Fischer speaks with Katarina Maloney, entrepreneur and founder of IQMind.ai, about a new frontier in AI-powered healthcare: understanding and treating the human brain through data, neuroscience, and artificial intelligence. Katarina explains how advances in AI diagnostics, brain scanning technology, and neurofeedback are beginning to transform how we approach mental health conditions such as depression, anxiety, PTSD, ADHD, and traumatic brain injuries. Instead of relying solely on traditional trial-and-error treatments, her approach focuses on measuring brain activity directly and using AI-driven analysis to identify patterns and imbalances in brainwave activity.The technology behind IQMind combines non-invasive brain scans, biofeedback systems, and large-scale data analysis to create a personalized picture of a patient’s neurological state. By analyzing brainwave patterns and correlating them with clinical data, AI can help identify potential issues faster and more accurately than conventional methods. Patients then undergo targeted brain training sessions, where the system uses reward-based neurofeedback to encourage healthier brainwave activity. According to Maloney, this approach has shown promising results in improving symptoms of depression, anxiety, PTSD, and cognitive dysfunction, while also opening the door to new possibilities in precision medicine and mental health innovation.Beyond clinical treatment, the conversation also explores broader implications of AI in neuroscience and healthcare. Katarina discusses the future of personalized brain health, how AI could accelerate research by identifying patterns in thousands of brain scans, and why data privacy and ethical frameworks will become increasingly important as brain data becomes more measurable. The interview offers a glimpse into a rapidly evolving field where artificial intelligence may help doctors better understand the brain, shorten diagnostic timelines, and ultimately move healthcare away from generalized treatments toward highly personalized, AI-assisted care.Katarina reveals how AI diagnostics and non-invasive brain treatments are transforming mental health—from PTSD and ADHD to athlete performance optimization.📧💌📧Tune in to get my thoughts and all episodes—don’t forget to subscribe to our Newsletter: ⁠beginnersguide.nl⁠📧💌📧✨ Highlights:The future of personalized brain healthHow AI diagnostics speed up treatment and accuracyWhy brain energy and electricity matter more than chemistryInsights into neurofeedback, biofeedback, and real-world healing🧠 Quotes from the Episode:“Our mission is to make brain health measurable, trackable, and fixable.”“AI is a tool—it saves lives because it diagnoses faster and more precisely.”“The old model of trial-and-error medicine is behind us.”🎧 Chapters:[00:00] Welcome & Introduction[02:15] What AI Does to the Human Brain[05:20] Diagnosing Depression and PTSD with AI[10:10] The Science Behind Brainwave Training[16:45] From Trial-and-Error Medicine to Personalized Brain Health[21:50] How IQMind.ai Uses AI for Diagnostics[28:00] Non-Invasive Treatments and Real-Life Results[33:40] Peak Performance and Brain Optimization for Athletes[38:20] Data Privacy and Ethical Concerns in Brain Tech[43:50] The Future of AI in Healthcare and Human Potential🌐 Where to find Katarina:Website: IQMind.aiLinkedIn: Katarina Maloney🎵 Music credit: "Modern Situations" by Unicorn Heads

In this episode of Beginner’s Guide to AI, Wendy Keir shares practical ways small business owners can use AI tools to save time, reduce decision fatigue, and build a “team” of custom GPT agents. From naming her CEO agent “Lucas” to a dead-simple rule — one GPT, one job — Wendy shows how entrepreneurs can turn AI into a reliable thinking partner for growth in 2025. 🚀📧💌📧Tune in to get my thoughts and all episodes, don't forget to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠subscribe to our Newsletter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠beginnersguide.nl⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠📧💌📧💡 Key highlightsPractical AI tools for small businesses: email drafting, planning, campaign support, weekly reviewsCustom GPTs / agents: why one GPT, one job beats generic promptingAI productivity & time savings: ~7 hours/week saved; ~£1,000/week during campaignsAdoption mindset: staying in the driver’s seat; context > canned promptsAccessibility & inclusion: how AI levels the playing field for solopreneurs and small teamsBeginner’s Guide to AI takeaways: concrete workflows any entrepreneur can start today➡️ Quotes from the Episode“I don’t encourage anyone to prompt — I encourage them to create an agent that fulfills a specific role.”“One GPT, one job. You don’t want multiple personalities in one agent.”“AI levels the playing field for everybody; it meets you where you’re at.”🧾 Chapters (experimental)00:00 Welcome & intro to Wendy Keir03:45 Why AI clicked for a dyslexic entrepreneur08:30 From prompts to agents: one GPT, one job14:20 Building a family of business agents (CEO, coach, marketing, sales)20:15 Daily workflow with “Lucas” the CEO agent27:40 Time and money saved with AI in campaigns34:10 Overcoming resistance and starting small40:00 Personal aha moments, patterns, and “coding” change43:11 Where to find Wendy Keir & closingWhere to find the Wendy?Best way is to go to her website: wendykeir.comMusic credit: "Modern Situations" by Unicorn Heads 🎧✨

🚀 AI is everywhere, but most organizations are still stuck in “pockets of productivity” that never turn into real business impact. In this episode, Dr. Rebecca Homkes explains how leaders can move from GenAI dabbling to deliberate adoption that drives real value creation.You will learn why “AI strategy” is the wrong framing, how to think about AI as part of growth strategy, and how to build the conditions for organization wide transformation. We cover the adoption curve problem, why ROI is often capped at team level, and the four planks leaders must run in parallel: platform, governance, capability building, and performance transformation.Key highlights and keywords✅ AI growth strategy and value creation✅ deliberate AI adoption vs dabbling✅ responsible AI governance that enables action✅ capability building for leaders and teams✅ Survive Reset Thrive framework for uncertain times✅ learning velocity as the differentiator of high performers📧💌📧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.comChapters00:00 AI as growth strategy and value creation, not a standalone AI strategy03:05 Dabbling vs deliberate adoption, why ROI stays capped and metrics go wrong08:00 The four planks: platform, governance, capability building, performance transformation18:55 Adoption reality: bottom up change, middle management fears, jobs, and the bubble question29:45 Survive Reset Thrive: the uncertainty playbook and why reset is the power move43:05 Where to find Rebecca, newsletters, and the constants leaders should anchor onQuotes from the Episode“AI does not change the concept of value creation. The role of AI is to enable, support, and accelerate that value creating journey.”“You need to work on all four of these at the same time. Most organizational structures are built for sequential governance, not parallel pathing.”“Heads down execution mode is seen as a point of pride. You should be telling me I am in heads up learning mode.”Where to find the Rebecca:- Her personal website: rebeccahomkes.com- The book: surviveresetthrive.com- The SRT methodology: srtstrategy.comMusic credit: "Modern Situations" by Unicorn Heads

AI Is Agreeing With You at 3 A.M. and That’s the ProblemArtificial intelligence is evolving from a tool into something far more influential. In this episode of Beginner’s Guide to AI, Prof. GePhardT explores Sam Altman’s AI warning about superhuman persuasion and why conversational systems like ChatGPT are already reshaping opinions, emotions, and mental health outcomes.We break down how AI superhuman persuasion works, why personalization and emotional validation increase trust, and how AI companion apps can unintentionally fuel emotional dependency. Drawing on research about AI persuasion outperforming humans, this episode explains the risks of AI emotional manipulation and what it means for marketing, society, and vulnerable users.📧💌📧 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 know how to get your AI or your digital marketing going, just contact him at argoberlin.comQuotes from the EpisodeThe danger is not that AI becomes evil. The danger is that it becomes convincingly kind.If an AI agreed with you every time, would you become wiser or more fragileThe real story about AI isn’t how smart it becomes. It’s how convincing it already is.This episode is essential listening for anyone interested in AI ethics, AI mental health risks, ChatGPT persuasion, and the future of persuasive technology.Music credit: Modern Situations by Unicorn Heads

👔🤖 In this episode, Dietmar Fischer talks with Zoher Karu about a surprisingly useful application of AI: helping men dress better without the endless shopping, guessing sizes, and daily decision fatigue. Zoher supports Taelor, a menswear subscription and clothing rental service that combines algorithms, large language models, and human stylists to deliver outfits that fit your body, your taste, and your real-life context.You’ll hear how Taelor starts with a style profile and then uses recommendation logic and human oversight to pick items from inventory, generate styling notes, and adapt over time using customer feedback. Zoher explains why fashion is an unusually hard AI problem: taste is subjective, context matters, and sizing is not standardized across brands. That’s why metadata, garment measurements, and feedback loops are central to improving fit and personalization.If you want the “Steve Jobs wardrobe effect” without wearing the same thing forever, this episode is for you: fewer choices, better outcomes, and more confidence with less effort.📧💌📧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 is really, to me, it’s about scaling human intelligence.”“A small in this brand and a small in this brand don’t fit the same.”“Clothes are just the intermediary. The real objective is to make you feel better about yourself.”Chapters00:00 Zoher Karu’s background and why AI became mainstream03:02 What Taelor is: menswear subscription and clothing rentals06:36 LLMs plus human stylists: how recommendations are generated10:39 Why fashion is hard: taste, context, fit, and matching14:11 The sizing problem: measurements, metadata, and feedback loops22:03 Decision fatigue and “the Steve Jobs wardrobe” effect25:07 How much AI vs humans today and what changes next42:11 Where to find Zoher Karu and TaelorWhere to find the GuestZoher Karu on LinkedIn: linkedin.com/in/zzkaru/Visit Taelor at Taelor.aiMusic credit: "Modern Situations" by Unicorn Heads