Welcome back to Mr. Fred’s Tech Talks! In this episode, we continue our Artificial Intelligence series and dive into the big question: How do Large Language Models actually learn?
If Episode 6 explained what AI is and Episode 7 showed us what Large Language Models are, Episode 8 is all about the training process, the hardware behind it, and what can go wrong when training goes sideways.
🔑 What You’ll Learn in This Episode:
- How Large Language Models are trained step by step
- Why tokens are the LEGO bricks of AI
- The role of GPUs, servers, and massive data centers in powering AI
- A library analogy that makes servers easy to understand
- What happens when training goes bad: bias, hallucinations, and overfitting
- A fun Tech Tip experiment you can try with ChatGPT to see it in action
🖥️ Key Highlights:
- Training requires billions of practice rounds of “guess the next word.”
- GPUs and servers in racks work together inside warehouse-sized data centers, using as much electricity as a small town.
- Data quality matters—bad training leads to biased answers, made-up facts, or brittle models.
- ChatGPT isn’t “thinking”—it’s predicting tokens, one after another.
💡 Tech Tip of the Episode:
Ask ChatGPT a simple question you know the answer to, then give it a twist.
Examples:
- “Who was the first person to walk on the moon?” → Neil Armstrong
- “Who was the first person to walk on the sun?” → Watch how it tries to “make sense” of nonsense.
This experiment shows how ChatGPT predicts patterns—not truth.
🎧 Listen & Watch:
- Spotify → [Listen]
- Apple Podcasts → [Listen]
- YouTube → [Watch/Listen]
🌐 Join the Conversation
What’s the wildest or funniest “hallucination” you’ve seen from ChatGPT? Drop a comment below or connect with me on:
