Amazon is always on the lookout for new robotic technologies to improve efficiency in its warehouses, and this year deep learning appears to be leading the way. That's according to the results of the second annual Amazon Picking Challenge, which has been won by a joint team from the TU Delft Robotics Institute of the Netherlands and the company Delft Robotics. Amazon's 2016 event was held in conjunction with Robocup 2016 in Leipzig, Germany. Two parallel competitions took place: a Pick Task much like last year's, in which a mix of items has to be lifted from warehouse shelves and packed into a container; and a new "Stow Task," which involves taking items out of a tote and putting them onto the shelves. The Pick Task asked contestants to pick up and safely deposit 12 items from a mixed shelf into a container in the shortest possible time.

One million anonymised eye scans from Moorfields Eye Hospital will be used to train an artificial intelligence (AI) system from Google. Machine learning algorithms will scour the images for signs of diseases such as macular degeneration and diabetes-related sight loss. Moorfields is teaming up with Google's AI division DeepMind during the scheme. Previously, DeepMind faced criticism over a little-known data sharing agreement with three London hospitals. An agreement to share patient data from the Royal Free, Barnet and Chase Farm hospitals over the past five years and continuing until 2017 was revealed by the New Scientist in May. In that case, Google said it was analysing kidney data in the hope of developing an app for medical staff.

A scan of a human eye. SAN FRANCISCO -- Google plans to use more than one million anonymized eye scans to teach computers how to diagnose ocular disease. The Menlo Park, Calif.-based company has signed a deal with a British eye hospital to use artificial intelligence to learn from the medical records of 1.6 million patients in London hospitals. The goal is to teach a computer program to recognize the signs of two common types of eye disease, diabetic retinopathy and age-related macular degeneration. That's something humans are surprisingly imperfect at.

In June on /r/MachineLearning, there were free videos, free books, free courseware, and a quality curriculum made up of free offerings. The word of the month for June is clearly a four letter word starting with'F'. This lot of videos covers a wide range of topics, from general AI, to design issues, to cloud computing, to a variety of machine learning topics and beyond. Microsoft Research has added heavily to these offerings on what seems to be a daily basis since this Reddit post as well. Free knowledge from a top research institute in the field is always welcome.

For more than 30 years, Gibbs has advised on and developed product and service marketing for many businesses and he has consulted, lectured, and authored numerous articles and books. So the UK has just given itself a national headache. Whether you think the Brexit was the right decision or a dangerous and unmitigated screw-up (as I do), the consequences of the referendum will be non-trivial and take years to complete. But the mechanics of the UK exiting the European Union aside, the question of how people now feel about the Brexit is interesting. Are they awash in jubilation or has buyer's remorse set in? An intriguing post by MonkeyLearn attempts to answer this question by analyzing tweets and, as a bonus, provides tools that you might well find useful for similar exercises.

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code. Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics.

To some, this sounds like the beginning of humanity's end: Scientists race to create innovative robots while researchers build out artificial-intelligence platforms and complex algorithms that many fear could soon make our jobs obsolete. But some experts believe our forthcoming high-tech offspring could actually be a golden ticket for the good life. Just imagine island hopping in Croatia while a robot, equipped with image-recognition software and natural language-processing abilities, fills in for you at the office -- with periodic check-ins through your pair of virtual reality glasses. Sure, we might be getting ahead of ourselves here, though experts predict that high-tech gains across corporate America will make seemingly mundane jobs more interesting, while enriching our near future and making some jobs safer by getting into dangerous spots that humans just shouldn't be entering. A wave of technological advancement that let many of us do things faster -- and more safely -- with fewer workers assisted in these gains, and Sprague says this kind of productivity surge is "the economic factor that has the potential to lead to improved living standards for an economy."

Every week, Moorfields Eye Hospital in London performs 3,000 optical coherence tomography scans to diagnose vision problems. The scans, which use scattered light to create high-resolution 3-D images of the retina, produce large quantities of data. Analyzing that data is a slow process. Understanding the images requires trained and experienced human eyes to identify problems specific to each case, leaving little or no time to identify broader, population-wide trends that could make early detection easier. That's just the kind of task that artificial intelligence can be used to tackle, though.

The World Economic Forum has put together a list of the top 10 emerging technologies that will change our lives. The list includes nanosensors that will circulate through the human body, a battery that will be able to power an entire town and socially aware artificial intelligence that will track our finances and health. These are not far-flung visions, according to the forum. They are technologies that are on the cusp of having a meaningful impact. "Horizon scanning for emerging technologies is crucial to staying abreast of developments that can radically transform our world, enabling timely expert analysis in preparation for these disruptors," said Bernard Meyerson, chairman of the World Economic Forum council that compiled the list of the top 10 emerging technologies in 2016.

Google's DeepMind AI business unit is hoping to teach computers to diagnose eye disease, using patient data from a U.K. hospital. Using deep learning techniques, DeepMind hopes to improve diagnosis of two eye conditions: Age-related macular degeneration and diabetic retinopathy, both of which can lead to sight loss. If these conditions are detected early enough, patients' sight can be saved. One way doctors look for signs of these diseases is by examining the interior of the eye, opposite the lens, an area called the fundus. They can do this either directly, with an ophthalmoscope, or by taking a digital fundus scan.