Microsoft India is launching a research group that will leverage artificial intelligence to deliver large-scale eye care in collaboration with Hyderabad-based L V Prasad Eye Institute. The Microsoft Intelligent Network for Eyecare (MINE) will work with a consortium of research and technology institutions around the world, including the University of Miami, Federal University of Sao Paulo and Australia's Brien Holden Vision Institute. The idea is similar to Google DeepMind's project, which targets the UK and works with their National Health Services to use artificial intelligence to detect and treat blindness-causing eye diseases. India is a logical jumping-off point for the project, as it is home to some 55 million of the world's 285 million people living with vision impairment. Using Microsoft's cloud platform technology Cortana Intelligence Suite, MINE will collaborate and work from datasets of patients around the world to develop machine learning predictive models for vision impairment and eye disease, with the ultimate goal of eliminating avoidable blindness and scaling worldwide delivery of eye care services.

IoT generates lots of temporal data. But how do you unlock its value? You need to discover patterns that are repeatable in vast quantities of data, understand their meaning, and implement scalable monitoring across multiple data streams in order to monetize the discoveries and insights. Motif discovery and deep learning platforms are emerging to visualize sensor data, to search for patterns and to build application that can monitor real time streams efficiently. In his session at @ThingsExpo, Dave Watson, CTO and Co-Founder of Trendalyze, discussed real world IoT projects from UK environmental monitoring using Mosquitto, Node-RED, Kafka, Spark, MLlib and R. Speaker Bio Dave Watson is CTO and Co-Founder of Trendalyze and works on developing the database search and analytics platform for various IoT projects.

Since this is a code-focussed course, you need access to a computer with an Nvidia GPU, along with a python-based deep learning stack set up on it. To make it easy, we've created a machine image on Amazon Web Services (AWS) along with a script to set it up--so your first step should be to watch our AWS deep learning setup video and follow along. Next up, read the information on use the provided notebooks. We suggest you have the notebook in front of you as you watch the video, or else watch the video and then read through the notebook. The notebooks have quite a bit of extra information, and most importantly, they let you experiment.

WHAT: French grocery chain Monoprix creates an almost exact remake of Amazon's recent promo video for its tech-powered grocery store of the future. WHY WE CARE: Not long ago, Amazon unveiled its plans for a beta version of its new cashierless grocery shopping experience called Go. Here, Monoprix sidesteps all the "computer vision," "deep learning algorithms," and "sensor fusion much like you'd find in self-driving cars" that Amazon touted about Go, and instead trolls the tech giant with an almost exact remake of the Go promo--actor doppelgangers dressed in the same outfits, similarly framed shots--with a human solution to the whole cashier line-up problem. And they deliver your groceries in an hour. Is there an Amazon drone for that yet?

As AI integration becomes more prominent, one can't help but to think about just how intelligent deep learning technology will be in the future. One of the first place many of our minds go is to AI becoming too intelligent and taking matters into its own virtual hands. How accurate are those portrayals, though? Will it get to a point where we're overpowered by AI, to the point where we're under their metaphorical thumb? TNW Conference is back for its 12th year.

One of my favorite deep learning papers is Learning to Generate Chairs, Tables, and Cars with Convolutional Networks. It's a very simple concept – you give the network the parameters of the thing you want to draw and it does it – but it yields an incredibly interesting result. The network seems like it is able to learn concepts about 3D space and the structure of the objects it's drawing, and because it's generating images rather than numbers it gives us a better sense about how the network "thinks" as well. I happened to stumble upon the Radboud Faces Database some time ago, and wondered if something like this could be used to generate and interpolate between faces as well. To implement this, I adapted a version of the "1s-S-deep" model from the chairs paper.

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The most valuable part of machine learning is predictive modeling. This is the development of models that are trained on historical data and make predictions on new data. This cheat sheet contains my best advice distilled from years of my own application and studying top machine learning practitioners and competition winners. With this guide, you will not only get unstuck and lift performance, you might even achieve world-class results on your prediction problems. Note, the structure of this guide is based on an early guide that you might fine useful on improving performance for deep learning titled: How To Improve Deep Learning Performance. Machine Learning Performance Improvement Cheat Sheet Photo by NASA, some rights reserved. This cheat sheet is designed to give you ideas to lift performance on your machine learning problem. All it takes is one good idea to get a breakthrough. Find that one idea, then come back and find another.

Deep learning has continued to drive the computing industry's agenda in 2016. But come 2017, experts say the Artificial Intelligence community will intensify its demand for higher performance and more power efficient "inference" engines for deep neural networks. The current deep learning system leverages advances in large computation power to define network, big data sets for training, and access to the large computing system to accomplish its goal. Unfortunately, the efficient execution of this learning is not so easy on embedded systems (i.e. This problem leaves wide open the possibility for innovation of technologies that can put deep neural network power into end devices.

The last few years have seen a number of novel ways for machines to learn new things. This traditionally involves feeding the algorithm a whole lot of data that then allows the machine to learn right from wrong. The folks at DeepMind, however, have been more inclined to use play as a mechanism to help their AI learn as it encourages greater adaptability. It famously programmed its machines to learn and then master a bunch of retro video games. Now, scientists from the company have published a paper describing how they use the same approach to help a machine physically learn.