Theory Thursday: Learning is Compression 📚✨

Art by @basilonmypizza: https://lnkd.in/eF8FkWzN - https://basilhefti.ch/

Remember creating cheat sheets back in school? Tiny notes packed with formulas, concepts, and shortcuts. The real value wasn’t having them during the test; it was the process of making them: condensing complex ideas into essentials.

That’s the magic of learning: Compression. Understanding what truly matters, and dropping the rest. 🧠🔽

Initially, deep insights often seem trivial. Take Newton’s famous: “For every action, there’s an equal and opposite reaction.” It sounds obvious, and it explains things from rockets to human interactions. A simple phrase becomes a mental shortcut, unlocking rich, underlying meaning. 🪐💡

Learning a new language follows this pattern. Imagine learning Greek: first letters, then syllables, then words. One day you're driving down a Greek highway, instantly recognizing an exit sign. No decoding or translation, just immediate understanding. Shape and meaning have become one. 🏛️➡️

That’s true mastery. That’s compression: simplifying complexity without losing its core structure. 🔁🧩

Artificial Intelligence learns similarly.

Think ZIP files or MP3s: compressed, fast, and still rich.

Autoencoders do this with data—compressing inputs into dense representations, then reconstructing them. If the reconstruction works, the essence was captured. 🖇️🤖

Large Language Models like ChatGPT go further. They encode patterns into internal embeddings, then use attention mechanisms to generate meaningful text. 📖💬

Here’s a fascinating example: researchers trained an AI with Mars’ positional data from Tycho Brahe’s observations in the late 1500s. Then, using a genetic algorithm, they sought the simplest equations describing planetary motion. This guided method enabled AI to “re-discover” Kepler’s laws. 📈🌌

Learning is distilling a topic to its essence, compressing complexity into accessible forms like insights, equations, or language. 🎓💾

What's your experience with compressing complex concepts? Do you still write cheat sheets for yourself? Share your insights in the comments! 👇✨

- From Kepler to Newton: Explainable AI for Science, https://lnkd.in/eVKdENnF
- Logic Guided Genetic Algorithms, https://lnkd.in/e4R2teYu
- One could also use Monte Carlo search, see my earlier post https://lnkd.in/e3gFMvqv

Follow me on LinkedIn for more content like this.