Pathmind’s artificial intelligence wiki is a beginner’s guide to important topics in AI, machine learning, and deep learning. The goal is to give readers an intuition for how powerful new algorithms work and how they are used, along with code examples where possible.
Advances in the field of machine learning (algorithms that adjust themselves when exposed to data) are driving progress more widely in AI. But many terms and concepts will seem incomprehensible to the intelligent outsider, the beginner, and even the former student of AI returning to a transformed discipline after years away. We hope this wiki helps you better understand AI, the software used to build it, and what is at stake in its development.
The line between mathematics and philosophy is blurry when we talk about artificial intelligence, because with AI, we ask the mineral called silicon to perceive and to think – actions once thought exclusive to meat, and now possible with computation. We hope that by reading this wiki, you will find new ways of thinking about life and intelligence, just as we have by writing it.
You might start by reading our comparison of artificial Intelligence, machine learning and deep learning.
If you are curious about neural networks, reinforcement learning, LSTMs, convolutional networks (CNNs) or generative adversarial networks (GANs), we have devoted introductory posts to those popular algorithms, as well as more widely applicable mathematical concepts like eigenvectors and Markov Chains.
As you read the articles, please refer to our AI glossary for definitions of many of the terms used in artificial intelligence and machine learning.