This Week in Machine Learning, 20 May 2016
This week’s top Machine Learning stories, including tackling gender bias in hiring, predicting COPD hospitalizations, and more!
Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It’s incredible, but it can also be overwhelming. That’s why we created This Week in Machine Learning. Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments. New posts will be published here first, and previous posts are archived on the Udacity blog.
Whether you’re currently enrolled in our Machine Learning Nanodegree program, already working in the field, or just pursuing a burgeoning interest in the subject, there will always be something here to inspire you!
Google reveals an alternative to CPUs and GPUs, dubbed a Tensor Processing Unit (TPU), which it says will advance its machine learning capability seven years into the future.
IBM reveals a new type of ultra-fast storage memory. The new memory has the potential to boost machine learning algorithms by reducing the latency of data storage.
Researchers from SPAWAR Systems, Georgia Tech, and ID Analytics leverage machine learning and biologically-inspired design to detect radio signal modulations.
SAP aims to use machine learning to tackle gender bias in the talent acquisition process by helping companies identify biases present in their current hiring practices.
Amazon releases its Deep Scalable Sparse Tensor Network Engine (DSSTNE, or “Destiny”) into open-source. DSSTNE operates successfully with less data than other techniques.
Sentrian’s Remote Patient Intelligence platform predicts 88% of COPD-related hospitalizations at least five days in advance, with a false positive rate of only 3%.