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 3D images of the retina, produce large quantities of data, and its analysis is slow. 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. So it's perhaps not surprising that Google's AI wing, DeepMind, has decided to partner with the hospital to apply machine learning to the problem as part of its Health program.

The dreaded act of parallel parking could soon become a thing of the past with Chinese inventors backing their new parking robot to take away the stress for anxious drivers. The laser-guided'Geta' (get a car) robot slides under your vehicle to pick it up. It then finds a parking space in the lot and places the car in the tightest of spots. The laser-guided'Geta' (get a car) robot slides under your vehicle to pick it up. The laser-guided'Geta' (get a car) robot slides under your vehicle to pick it up.

With warehouses full of robots that can move shelves from place to place, the only reason that Amazon needs humans anymore is to pick things off of those shelves and put them into boxes, and pick other things out of boxes and put them onto those shelves. Amazon wants robots to be doing these tasks too, but it's a hard problem--hard enough that the enormous bajillion dollar company is asking other roboticists to solve it for them. The first Amazon Picking Challenge was held at ICRA last year in Seattle, Amazon's home town. Amazon followed it up this year with another, tougher challenge at RoboCup 2016, which just wrapped up. And the winner is...Team Delft from the Netherlands!

The Amazon robotic Picking Challenge is a now annual competition that searches for robots that could one day work in the company's vast warehouses and its second champion has been announced. This year's winner was a joint effort, created by TU Delft Robotics Institute from the Netherlands and the company Delft Robotics. The team's robotic arm used a combination of a suction cup, a gripper, a depth-sensing camera, and deep learning artificial intelligence to pick and stow items from a mock Amazon warehouse shelf with greater speed and accuracy than the 15 other entries in the competition. TU Delft's creation's greatest asset was its adaptive deep learning which allowed it to scan the different shapes and sizes of the objects it was picking up and adjust how it manipulated them accordingly. The robot was able to pick items from the shelf with a speed of over 100 items per hour which is an impressive three times faster than last year's winner, even though Amazon had made the challenges much tougher "with denser bins, occluded items, and products that are more difficult to see and grasp."

Amazon has crowned the latest champion in its robotic picking challenge -- an annual competition that looks for robots that could one day work in the company's warehouses. It's basically American Idol, but for robotic arms that can grab items off a shelf and put them back again. Competitors are asked to handle a range of products, from toiletries to clothes, and then scored on speed and accuracy in stocking shelves. This year's contest was won by a joint team from the TU Delft Robotics Institute in the Netherlands and the company Delft Robotics (both named after the city of Delft). The team's robot managed to pick items from a mock Amazon warehouse shelf at a speed of around 100 an hour, reports TechRepublic, with a failure rate of 16.7 percent.

Lecture Title: Machines that learn: big data or explanatory models? Abstract: A leading question about machines that learn concerns two distinct styles of learning. Will they turn out to depend more on probabilistic models that explain the data, or on networks that react to data and are trained on data at ever greater scale? In machine vision systems, for instance, this boils down to the comparative roles of two paradigms: analysis-by-synthesis versus empirical recognisers. Each approach has its strengths, and empirical recognisers especially have made great strides in performance in the last few years, through deep learning.

You have just finished breakfast when your phone pings, confirming that your driverless car has arrived. Five minutes later you are on the way to work, travelling in a convoy of robo-vehicles. Even at 70 miles per hour, your car stays only a few feet behind the one in front; it will react to an emergency many times faster than you could. You pay little attention to the other vehicles in any case, since you are reading. But at one point you glance over at the lane reserved for old-fashioned cars driven by humans.

DeepMind, the London-based artificial intelligence lab acquired by Google in 2014, has accomplished more than a few spectacular stunts of machine learning. Its neural networks bested a human champion at the notoriously tough game of Go, inculcated the basic rules of soccer on a digital ant-like creature, and teased out winning strategies for more than 49 Atari 2600 games. But now, the outfit's robots are being tasked with a more humanistic pursuit: eye disease research. On Tuesday, DeepMind announced a long-term project that will see the company's machine-learning algorithms parse "millions" of eye scans to tease out early warning signs that human doctors might otherwise miss. The new project, which is based out of the U.K.'s Moorfields Eye Hospital in east London, is the fruit of DeepMind's ongoing partnership -- dubbed DeepMind Health -- with the country's National Health Service.

Google's AI unit is getting access to a set of 1 million anonymized eye scans with the aim of improving the detection and prevention of common eye diseases.

Google's DeepMind division has announced a partnership with the NHS's Moorfields Eye Hospital to apply machine learning to spot common eye diseases earlier. The five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, with the aim to speed up the complex and time-consuming process of analysing eye scans. The hope is that this will allow diagnoses of common causes of sight loss, like diabetic retinopathy and age-related macular degeneration, to be spotted more rapidly and hence be treated more effectively. For example, Google says that up to 98 percent of sight loss resulting from diabetes can be prevented by early detection and treatment. Mobile app called "Streams" provides medical staff with latest clinical information.