Big shoutout to Holger Peschke, who just hit Level 8 inside our paid community, AI Automation Society Plus! 🙌 He’s the fourth member to ever reach this milestone, unlocking FREE lifetime access to AIS+. This is the kind of reward we love giving to members who consistently show up, share insights, and help others grow. Drop a congrats for Holger in the comments below!! Cheers, Nate

If AI can write, design, code, and “think” with you today… it’s not magic. It’s decades of progress (with a couple of AI winters ❄️). Here’s the full timeline in plain English—what changed in each era and why we’re here now. 🧩 1) Precursors (1943–1956): the idea is born The early foundations appear: - Early neural models (McCulloch & Pitts) - The question: can machines simulate intelligence? - The theoretical groundwork that sparks everything 🧪 Key shift: imagining intelligence as something computable. 🧠 2) Symbolic Era (1956–1974): AI = rules + logic The Dartmouth moment (1956) kicks off the “classic” approach: - Rule-based reasoning (“if X, then Y”) - Logic and symbolic representations - Big promises… too early 😅 Key shift: intelligence was hand-coded. ❄️ 3) AI Winter (1970s): hype cools down Why it happened: - Not enough compute power - Not enough data - Overpromised outcomes Lesson: hype without infrastructure is expensive. 🧑‍⚕️ 4) Expert Systems (1975–1989): AI works in narrow domains AI becomes practical in specific contexts: - Strong rule systems in controlled environments - Use cases like diagnostics and industry - Feigenbaum as a key reference Key shift: AI succeeds when the world is structured and predictable. ❄️ 5) Second AI Winter (1987–1993): another downturn Another reset due to: - High costs - Hard maintenance - Rule-based limitations 📊 6) Statistical ML (1990s–2009): data starts winning Major paradigm change: - Instead of writing rules, you train on examples - SVMs, statistical learning, Big Data - Neural nets return to the stage Key shift: data + statistics + compute beats “rules”. 🔥 7) Deep Learning (2010–2016): the big leap With GPUs + massive datasets: - Backprop + deep networks - CNNs transform vision; speech improves fast 📸🎙️ - Hinton / LeCun / Bengio become central names Key shift: AI gets dramatically better at perception. 🧱 8) Transformers & Foundation Models (2017–2020)