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.
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.
In tandem with advances in artificial intelligence, there’s growing interest in establishing ethical guidelines and standards to weigh the robustness and trustworthiness of the AI algorithms that are helping or replacing humans in making important and critical decisions.
Deep learning and neural networks have occupied the highlights of the artificial intelligence industry since 2012. Here's everything you need to know.
Computer vision enables computers to understand the content of images and videos. It is one of the main bridges between the physical and digital worlds.
In the past, unfulfilled promises in artificial intelligence caused a decline in interest and funding, also known as AI winter. The question is, will it happen again?
Explainable AI helps peer into the black box of neural networks and deep learning algorithms, an important requirement for using automation in many domains.