News

Google-Landmarks: A New Dataset and Challenge for Landmark Recognition

A few examples of images from the Google-Landmarks dataset, including landmarks such as Big Ben, Sacre Coeur Basilica, the rock sculpture of Decebalus and the Megyeri Bridge, among others.

Learning by playing

Our new paper proposes a new learning paradigm called ‘Scheduled Auxiliary Control (SAC-X)’ which seeks to overcome the issue of exploration in control tasks. SAC-X is based on the idea that to learn complex tasks from scratch, an agent has to learn to explore and master a set of basic skills first. Just as a baby must develop coordination and balance before she crawls or walks—providing an agent with internal (auxiliary) goals corresponding to simple skills increases the chance it can understand and perform more complicated tasks.

Feeding Frenzy for AI Engineers Gets More Intense

Demand for software engineers with AI expertise continues to increase, while supply flattens

Computing With Random Pulses Promises to Simplify Circuitry and Save Power

Diabetes is actually five separate diseases, research suggests

Scandinavian researchers say a new classification would mean better treatment for patients.

Magic Leap New Patent Applications

Just some quick notes to let my readers know I am in the process of digesting some new Magic Leap Patent Applications that were published last week. There are several related applications, but the most interesting one was US20180052277 MULTI-LAYER DIFFRACTIVE EYEPIECE. This application is 272 pages long, and I have only had time to flip through it, so this is all preliminary information, so I am mostly going off the figures.

Queryparser, an Open Source Tool for Parsing and Analyzing SQL

Giant human family tree traces how people moved and married over 300 years

Gut bacteria key to the vampire bat’s ability to survive on blood

Machine Learning Crash Course

How does machine learning differ from traditional programming?What is loss, and how do I measure it?How does gradient descent work?How do I determine whether my model is effective?How do I represent my data so that a program can learn from it?How do I build a deep neural network?