A panel comprised of government tech leaders and healthcare experts is set to scrutinise the work of Google DeepMind's healthcare team at an upcoming meeting, which could be held within the next month. DeepMind, a British AI startup acquired by Google in 2014 for 400 million, is processing NHS data through a new division called DeepMind Health. The London-based AI lab says its technology has the potential to improve the way patients are diagnosed and treated but it acknowledges that working in healthcare requires regular and independent oversight. As a result, it set up a reviewer board for DeepMind Health when it launched the unit in February. DeepMind states on its website that the board will meet four times a year but Business Insider understands that an official meeting between the nine individuals on the board is yet to take place.

ClickSoftware, a leading provider of field service management solutions for the enterprise, today introduced Click Field Service Edge, a new cloud-based, mobile workforce management platform designed to meet the needs of the largest and most demanding field service providers. Adoption of the cloud has more than doubled over the past two years and, with that, cloud-based software solutions have become entrenched in the business world. Recognising the technological benefits for field service companies, ClickSoftware's first, cloud only, SaaS-based solution delivers field service management in an entirely new way. With patented artificial intelligence, Click Field Service Edge connects enterprise customers with automated real-time insight and intelligent decision-making tools that provide a new level of productivity and efficiency to field workforce performance. "As a company that has successfully managed billions of service engagements, and is exclusively focused on providing field service technology – since creating the market – we know what it takes to help our customers always be a step ahead of the demand, not just move in lockstep with it," explained Tom Heiser, ClickSoftware's Chief Executive Officer.

I have been involved with FIRST for over a decade, inspired to support the program after its founder Dean Kamen came to San Diego to tell us about the non-profit. After my first experience as a judge I was hooked, and now, I've experienced the competition through judging at the regional events for more than 10 years. In addition to my individual experience, as a Company, Qualcomm supports FIRST globally as a sponsor and as a strategic partner, providing employee volunteers and mentorships, as well as implementing new technologies for the competitions. This past year, Qualcomm helped FIRST transition their FIRST Tech Challenge robot control system to a user-friendly, Java-based android platform powered by the Qualcomm Snapdragon 410 processor from Qualcomm Technologies, adding a mobile element to the competition that will bring even more capabilities to the FIRST participants.

I want to take a quick break from my reinforcement learning endeavor (more on that soon) to talk about an interesting unsupervised learning model: autoencoders. The basic idea behind autoencoders is dimensionality reduction -- I have some high-dimensional representation of data, and I simply want to represent the same data with fewer numbers. Let's take the example of a picture of a face. If you look at each individual pixel, you'll quickly realize that neighboring pixels are usually highly correlated (they have a very similar color). What if we could take out the redundancy and express that same image in a fraction of the numbers?

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

This repository contains both MATLAB and R code for implementing the Bayesian GP-LVM. The MATLAB code is in the subdirectory vargplvm, the R code in vargplvmR. For a quick description and sample videos / demos check: http://git.io/A3Uv The Bayesian GP-LVM (Titsias and Lawrence, 2010) is an extension of the traditional GP-LVM where the latent space is approximately marginalised out in a variational fashion (hence the prefix'vargplvm'). Let us denote \mathbf{Y} as a matrix of observations (here called outputs) with dimensions n \times p, where n rows correspond to datapoints and p columns to dimensions.

Since we released our text summarization resources, the legal technology community has shown interest in leveraging summarization technology to support litigation document review, deposition digests, and contract analysis. Data scientist interest to use machine learning to mine legal document corpuses and support legal strategy was also one factor motivating our summarization research. The time is therefore ripe for data scientists to apply new text analytics capabilities for legal use cases. But to be effective, data scientists must first understand how lawyers think: what problems they're trying to solve, how their processes are structured, and, perhaps most importantly, what fears may hinder the adoption of new technologies. This guest blog post from Dean Gonsowski, kCura's VP of Business Development, provides tips to help data scientists explain the value of machine learning to lawyers. Electronic discovery software helps manage the exchange of electronically stored information, documents that could be used as evidence in various forms of litigation like investigations or contract reviews.

RE•WORK will host it's annual East Coast events on Deep Learning and the Internet of Things in Boston on 12 & 13 May. Over 300 machine learning and IoT enthusiasts and experts will come together to hear keynote presentations, panel discussions, fireside chats and to explore the startup showcase area. The Deep Learning Summit brings together leaders from industry, academia and startups to explore advances in deep learning methods and techniques, as well as their business applications in areas including finance, manufacturing, healthcare & transportation. Professor Bengio is also Head of the Machine Learning Laboratory, Co-director of the CIFAR Neural Computation & Adaptive Perception program, and editor of many prestigious machine learning publications. Daniel's work has been reported on in The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine.

"What is the ape to a man? And so shall man be to the Übermensch." Years ago, as a teenager in France, I used to visit a family friend, Monsieur de la Place. He lived with his wife in a high-rise in a small town outside of Paris. An old man, one side of his body had been paralyzed by a stroke. He was very educated and profound, and became a mentor of sorts to me for a time.

Dignity Health, announced results from a randomized controlled study which demonstrated that the use of digital health technology improved asthma control. The study, "Effectiveness of Population Health Management Using the Propeller Health Asthma Platform: A Randomized Clinical Trial," was recently published in the Journal of Allergy and Clinical Immunology: In Practice. The Propeller Health Asthma Platform utilizes sensors, mobile applications, and analytics to monitor short-acting?-agonist