CRM

Deep reinforcement learning (deep RL) is a popular and successful family of methods for teaching computers tasks ranging from playing Go and Atari games to controlling industrial robots. But it is difficult to use a single neural network and conventional RL techniques to learn many different skills at once. Existing approaches usually treat the tasks

Speech recognition has been successfully depolyed on various smart devices, and is changing the way we interact with them. Traditional phonetic-based recognition approaches require training of separate components such as pronouciation, acoustic and language model. Since the models are all trained separately with different training objectives, improving one of the components does not necessarily lead

Learning to answer open-ended questions about images, a task known as visual question answering (VQA), has received much attention over the last several years. VQA has been put forth as a benchmark for complete scene understanding and flexible reasoning, two fundamental goals of AI. But, apart from accurate answers, what would these milestones look like

When humans generate novel neural architectures, they go through a surprisingly large amount of trial and error. This holds true almost regardless of how much experience in deep learning that person might have! In an optimal world, the neural networks themselves would explore potential architectures and improve themselves over time. Without human intuition and insights

Published: April 2, 2018 This is part two of a two-part series about how to build ethics into AI. Part I focused on cultivating an ethical culture in your company and team, as well as being transparent within your company and externally. In this article, I will focus on mechanisms for removing exclusion from your

Published: March 27, 2018 This is part one of a two-part series about how to build ethics into AI. Part one focuses on cultivating an ethical culture in your company and team, as well as being transparent within your company and externally. Part two focuses on mechanisms for removing exclusion from your data and algorithms.