Category/Tag: Induction
The No Free Lunch Theorem: Why No Learning Algorithm Is Universally Best
- By Bruce Nielson
- ML & AI Specialist
There's a mathematical proof that says no algorithm — no matter how clever, how sophisticated, or how well-designed — can outperform random guessing when averaged across all possible problems. Not A*, not neural networks, not humans in the loop. Every advantage on one class of problems is paid for, dollar for dollar, somewhere else. It sounds like it should be false. It isn't. And once you understand why, you'll never look at machine learning the same way again.
What Exactly Is an Inductive Bias?
- By Bruce Nielson
- ML & AI Specialist
Every learning algorithm is making a bet. It can't prove its predictions from the data alone — it's sneaking in assumptions, whether it admits them or not. Name those assumptions precisely enough, and something surprising emerges: there's no such thing as induction. It's deduction in disguise. This post unpacks what that means, why stronger assumptions lead to better generalization and more spectacular failures, and what it reveals about neural networks that most people never think to ask.
Machine Learning 101: The Key Concepts Behind Every Learning Algorithm
- By Bruce Nielson
- ML & AI Specialist
Machine learning textbooks have their own vocabulary. But behind the jargon lies a process that would be deeply familiar to Karl Popper: conjecture and refutation. This post is a short reference guide
Induction is a Myth: The Futility of Unbiased Learning
- By Bruce Nielson
- ML & AI Specialist
A philosopher and a computer scientist walk into the same conclusion from opposite directions: you can't learn anything from data alone. Popper said induction was a myth. Mitchell proved it mathematically. And the punchline? Every machine learning algorithm that does generalize is secretly running deduction in disguise — the "induction" was never really there.