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Pod #8.1 - AI: Actually, It's Complicated...

April 18, 2025 • 62 mins

✨ Welcome to Filter Feeders! ✨

🦿🧠 Let's Reboot The Way We Think About AI 👩‍💻🦾

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Think AI is a modern marvel? In this episode of Filter Feeders, Ollie and Vinny dig deep into the surprising history of artificial intelligence. From ancient automata to Renaissance-era “thinking” machines and even scandalous chess-playing hoaxes, they trace AI’s roots long before algorithms ruled the world. It’s a mind-expanding ride through the tech you didn’t know was AI all along — and a sharp look at how we still misunderstand it today.

🎙️Episode Summary:

[09:56–15:21] — 🧱The Foundations of AI: Broadening and reframing what we mean by "Artificial Intelligence"

[15:22–20:17] — 🐶 From Clippy to Shakey: Early chatbots, robot dogs, and the dawn of reasoning machines

[27:16–30:28] — 🧃 Gen AI & The Milk Carton Paradox: Generative tech hype and why not every “AI” is new

[32:29–37:00] — ⚙️ Transformers Explained: What GPT really stands for and how transformer models changed everything

[38:26–41:08] — 💥 The Data Dilemma: NLP, deep learning, dataset controversies, and racist chatbots

[43:42–46:49] — 🛡 Ethics vs. Innovation: Who should govern AI safety, and how moderation affects creativity

[50:50–62:54] — 🎥 Pop Culture & Fear: How HAL, iRobot, and film tropes fuel public paranoia about AI

[63:29–66:45] — 📼 Why Humans Still Matter: The value of imperfection, bespoke leather belts, and human storytelling

🔗 Delve Deeper:

Hanson Robotics – Sophia — The eerie bot that blinks like a slow-loading gif

ELIZA Chatbot History — The original AI therapist from 1966

Stuart Russell’s AI Warnings — On AI doing exactly what we say

🗝 Key Takeaway

🤖 Artificial intelligence isn’t some overnight marvel—it’s the modern face of an age-old human obsession with replicating thought, behaviour, and intelligence through machines. Each iteration has mirrored not just our technological advancement, but our hopes, fears, and ethical dilemmas. Yet, as AI becomes more embedded in our daily lives, the real challenge isn’t about what these systems can do, but about who decides what they should do—and why.