{"version":"1.0","type":"rich","provider_name":"Acast","provider_url":"https://acast.com","height":250,"width":700,"html":"","title":"AI Training Data: Why Quantity Isn’t Enough","description":"

AI systems are often praised for their size. Bigger datasets. Bigger models. Bigger compute. But what if scale is only half the story?


In this episode of A Beginner’s Guide to AI, Prof. GePhardT dives deep into AI training data and explains why quantity alone cannot guarantee performance. From AI bias to model reliability, we explore how data quality determines whether AI systems are merely impressive or truly trustworthy.


You will learn how imbalanced datasets create blind spots, why aggregate accuracy can be misleading, and what the Gender Shades research revealed about AI fairness. We also explore how businesses can audit their own CRM data and prevent AI from amplifying internal chaos.


This episode connects technical insight with strategic clarity. It is essential for founders, marketers, and leaders building responsible AI systems.



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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



Chapters

00:00 The Data Diet Problem

07:42 Defining Quantity vs Quality in AI

17:15 Capability vs Reliability Explained

27:10 The Gender Shades Case Study

36:45 Business Implications and Data Strategy

46:20 Practical Audit for Your Own AI Systems



Music credit: \"Modern Situations\" by Unicorn Heads

","author_name":"Dietmar Fischer"}