Managers think call center agents leave because they are unhappy with their income. AnswerOn has found that money doesn’t matter as much as you might think. CEO Eric Johnson outlines the leading causes of agent attrition.
Representation
When building predictive models, the information provided to the model and the manner in which it is encoded or formatted is known as the input representation. The choice of representation can determine what can be learned and how readily it will be learned.
Is Big Data All It’s Cracked Up To Be?
Michael Mozer, Scientific Advisor at AnswerOn and Professor at the University of Colorado discusses issues facing big data and machine learning. What does “big data” mean and how can it help us with analysis?
What Neural Networks Learn
Although neural networks have been around for sixty years, a seminal research paper appeared thirty years ago that revolutionized the field. The paper was called Learning representations by back-propagating errors (Rumelhart, Hinton, & Williams, 1986). In this post, I’ll explain what “learning representations” means and why this idea is so central to neural networks.
Back To The Future
Michael Mozer, Scientific Advisor at AnswerOn and Professor at the University of Colorado discusses the rise and fall, and rise again of neural networks. This post considers how we owe today’s neural networks to those from the 1980s.
