To achieve IA implementation success, plan for a partnership between humans and machines, not replacement. Buy-in is crucial, of course. So educate your workforce about IA and plan for hero moments. Automate tasks, not jobs and start small.

What are the differences between AI and IA? When is Intelligence Augmentation superior to a pure Artificial Intelligence approach? Which solution makes the most sense for your problem set? Consider IA solutions when a limited amount of data exists

Intelligence Augmentation is concerned with leveraging machines to enhance human intelligence. It’s a partnership between person and machine in which both parties contribute their strengths. It's AI that partners with humans for decision support

Artificial Intelligence receives a great deal of hype with good reason but is it appropriate for your enterprise? You might want to consider the same tech with a different approach for your decision support system -- Intelligence Augmentation.

See how an exploratory machine learning approach can inspire new ways to fight climate change and empower global stakeholders.

In this article we explore if machines can replace us (R programmers) in writing Shiny code. I'll show you some easy applications of a simple model of a recurrent neural network implemented in an R version of Keras

AI will transform the insurance sector. Two techniques that will have the biggest impact are convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The 3rd key to successful AI implementation: productionization

Our CTO used transfer learning to build an object detection model on a small dataset - the model can recognize certain animals in images.

Unveiling secrets of black box models is no longer a novelty but a new business requirement.

Zipf’s law is an empirical law that states that many different datasets found in nature can be described using Zipf’s distribution.

In this tutorial I will explain the concept behind the Monte Carlo Tree Search (MCTS) algorithm and tell you about how it was used at the European Space Agency for planning interplanetary flights.

Article presents how to generate imagery based on satellite imagery with Generative Adversarial Networks (GANs).