How AI Learned to Learn: The Machine Learning Revolution | Episode 2

"Don't program intelligence—train it." In this second episode of our series, Akash and Eva dive into the pivotal moment AI stopped simply following rules and started learning from experience. From the "AI Winter" of the 70s to the explosion of Big Data, we uncover how statistical learning transformed machines from following "If-Then" logic to making human-like predictions. In this episode, you’ll learn: The AI Winter & Rebirth: Why progress froze and how backpropagation brought neural networks back to life in 1986. Experience vs. Rules: How shifting from instruction-based logic to experience-based data changed everything. The Power of Big Data: Why the internet became the "infinite food source" for modern algorithms. The Rise of Deep Learning: Tracing the journey from Rosenblatt’s Perceptron to AlexNet and beyond. Timestamps: 0:00 Intro: The Era of Machine Learning 0:35 The AI Winter & The Rebirth of Neural Networks 1:02 Experience-Based Learning vs. Rule-Based Logic 1:35 1990s: When Data Became the New Code 2:20 Tom Mitchell & The Scientific Definition of Learning 2:55 The Internet: AI’s Infinite Food Source 3:25 Invisible AI: Spam Filters & Stock Predictions 4:00 2012 Breakthrough: AlexNet & Deep Learning 4:25 How Deep Learning "Sees": From Pixels to Meaning 5:05 Outro: What's Next in Episode 3?