The two main types of machine learning categories are supervised and unsupervised learning. In this post, we examine their key features and differences.
Convolutional neural networks (CNN), or ConvNets, have become the cornerstone of artificial intelligence (AI) in recent years. Their capabilities and limits are an interesting study of where AI stands today.
Everything you need to know about symbolic artificial intelligence, the branch of AI that dominated for five decades.
To create better artificial intelligence hardware, researchers have been drawing inspiration from the brain. The result is neuromorphic chips, which have grown in popularity in the past years.
Huge salaries and bonuses at tech firms are luring AI scientists away from universities. How will this artificial intelligence brain drain affect the AI industry?
Everything you need to know about artificial neural networks (ANN), the state-of-the-art of artificial intelligence that help computers solve tasks that are impossible with classic AI approaches.
This beginner’s guide to enterprise applications will tell you what they are, why they’re beneficial, and how businesses can use them in 2019.
As artificial intelligence becomes more ubiquitous in consumer applications, it must better understand human emotions. That’s where emotion AI comes in.
Training a deep learning model requires vast amounts of training data and compute resources. With transfer learning, developers can cut both on training examples and CPU costs.
From game-playing bots to robotic hands that dexterously handle objects, reinforcement learning creates AI models that requires little training data.