The European General Data Protection Regulation (GDPR) goes into effect on 25th May. What is it and how does it affect your business?
Deep reinforcement learning is one of the most interesting branches of AI, responsible for achievements such as mastering complex games, self-driving cars, and robotics.
The fourth industrial revolution is well underway — and it's thanks to a confluence of technologies we wrote off as science fiction just a few short years ago.
The quest for creating thinking machines has split artificial intelligence into two fields: Narrow AI, what we have, and general AI, what we wish to achieve
NLP and NLG, the two branches of AI that parse and generate human language, have removed many of the barriers between humans and computers.
Graph neural networks (GNN) are a type of machine learning algorithm that can extract important information from graphs and make useful predictions.
From game-playing bots to robotic hands that dexterously handle objects, reinforcement learning creates AI models that requires little training data.
Adversarial examples are slight manipulations that cause machine learning algorithms to misclassify images while going unnoticed to the human eye.
We thought AI algorithms never become racist or sexist. We were wrong. They can inherit our prejudices and amplify them manifold.
Data augmentation improves machine learning performance by generating new training examples from existing data.