Google DeepMind, the British artificial intelligence research arm of Google, will collaborate with Britain's National Health Service (NHS) to tackle sight loss in humans. A retina-scanning system that will be able to detect the early signs of eye disease will be developed in partnership with with Moorfields Eye Hospital in London. About a million anonymized scans taken from patients who attended Moorfields between January 1, 2007 and February 29 this year will be used for analysis by Google's Artifical Intelligence (Al) computer. Scientists are hopeful that they will be able to recognize conditions such as macular degeneration, a gradual deterioration of the light-sensitive tissue lining at the back of the eye, and diabetic retinopathy, when high blood sugar levels damage the retina over time. Early treatment can be critical in both conditions.

Someone at Google really likes sneakers: The company has just bought a French machine learning startup that taught a computer how to recognize 15,000 different types of them. Paris-based Moodstocks builds image and object recognition software using deep learning techniques, and offered an Android app and visual search API that could recognize certain kinds of object. By analyzing video from a smartphone camera, and correlating it with accelerometer readings to determine how the camera is moving around, the software is able to infer information about the three-dimensional shape of objects in the video, facilitating their recognition. In February 2015 the company demonstrated its ability to identify sneakers through its app. Three months later, after training the software using 15,000 photos of shoes from an online retailer's website, Moodstocks claimed to be able to shop online for all the sneakers on sale in a Macy's store. Google has been introducing elements of machine learning into its existing online services, including Google Translate and Inbox, a next-generation interface for Gmail.

Alejandro (Sasha) Vicente Grabovetsky, Co-founder of Avalon AI, discusses the ways in which machine learning is improving the rates of failed dementia clinical trials and improving the lives of those living with the disease. The idea for Avalon AI came together when my Co-founder Olivier van den Biggelaar and I realised that we shared the same aim, which was to help defeat ageing. Following that, what immediately came to mind was dementia because it's a disease that has not been successfully tackled yet. Lots of age related diseases like diabetes and cancer receive a lot of funding and are being heavily addressed, while dementia is under-funded partly due to failed clinical trials. Very few dementia clinical trials have succeeded and we noticed that a lot of the past trials were targeting late-stage dementia, where a lot of brain damage had already occurred.

Google's DeepMind is teaming up with NHS-funded Moorfields Eye Hospital to research whether machine learning can help the fight against blindness. The NHS said AI could play a "big role in tackling avoidable sight loss" and the partnership is intended to explore "how cutting edge technologies can help medical research into eye diseases". This includes macular degeneration, which generally affects the elderly, as well as diabetes-related sight loss. Machine learning processes will be applied to around a million eye scans to help search for early symptoms of sight loss. The number of people with sight loss is set to double by 2050, with around two million people in the UK currently living with sight loss – around one in 30.

The NHS is pairing up with Google's artificially intelligent image software DeepMind for a new medical research partnership that could play a "big role in tackling avoidable sight loss." Specialists at the NHS-funded Moorfields Eye Hospital in London will use DeepMind, the internet giant's machine learning project, to research whether the technology can help detect and prevent eye diseases and blindness. DeepMind will be applied to one million anonymous eye scans to look for early signs of eye conditions that humans might miss such as macular degeneration and retinal conditions caused by diabetes. The end goal of the research is to create a more efficient method by which to analyse data and come to an earlier diagnosis for patients. The number of people suffering from sight loss in the UK is predicted to double by 2050, and the project marks Google's first machine learning collaboration with healthcare specialists.

Google-owned DeepMind has expanded its collaboration with the UK's National Health Service (NHS), announcing a research partnership today with Moorfields Eye Hospital NHS Foundation Trust in London -- its second publicly confirmed foray into working with the NHS. But this time the project is being explicitly badged as medical research, and DeepMind will be applying AI machine learning algorithms to the data -- so that's also a first. Although the company has been public about its ambitions to apply AI to health data before now. The Moorfields partnership is focused on two specific sight-loss causing conditions: diabetic retinopathy and age-related macular degeneration (AMD), which DeepMind notes collectively affect more than 625,000 people in the UK and more than 100 million people worldwide. The stated aim is to investigate whether machine learning algorithms can automate the analysis of the digital eye scans that are typically used to diagnose the two conditions.

NHS eye hospital Moorfields has announced it is working with DeepMind -- an artificial intelligence research lab acquired by Google in 2014 for a reported 400 million -- in a bid to identify people who are likely to lose their sight as a result of an eye disease. Through the medical research partnership, Moorfields will investigate whether DeepMind's AI technology can be used to help spot early signs of eye conditions that human eye care experts might miss. In order to determine whether DeepMind's AI technology is useful for diagnosing eye conditions, Moorfields is applying the company's algorithms to one million anonymous OCT (Optical Coherence Tomography) scans. The aim is to determine whether the algorithms can learn to spot early signs of age-related macular degeneration and sight loss that occurs as a result of diabetes. Mustafa Suleyman, Google DeepMind cofounder and head of DeepMind Health, told Business Insider that he wants DeepMind's AI to understand the structure and nature of eye scans "well enough to be able to try to predict in advance which ones indicate that a patient may be at risk to a particular kind of eye disease." The algorithms that DeepMind builds are known as machine learning algorithms because they have the ability to learn through training without being explicitly programmed.

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.

That's an interesting question, and I try to answer this in a very general way. In essence, deep learning offers a set of techniques and algorithms that help us to parameterize deep neural network structures -- artificial neural networks with many hidden layers and parameters. One of the key ideas behind deep learning is to extract high level features from the given dataset. Thereby, deep learning aims to overcome the challenge of the often tedious feature engineering task and helps with parameterizing traditional neural networks with many layers. Now, to introduce deep learning, let us take a look at a more concrete example involving multi-layer perceptrons (MLPs).

In early 2015, Synopsys' DesignWare EV5x processor core family achieved notable attention for its unique co-processor engine focused on CNNs (convolutional neural networks) for object recognition and other vision functions. The company's new EV6x processor core family includes an upgraded CNN engine that delivers substantial performance gains over its predecessor while – in a nod to customers preferring to leverage "classical" computer vision algorithms – decoupling it from the remainder of the core, which now includes 512-bit vector DSPs (Figure 1). Synopsys' new DesignWare EV6x family (top) comes in three variants and includes a 512-bit vector DSP engine, while making the CNN engine an option (bottom), in contrast with its EV5x predecessor. EV6x family members include a one- to four-core "Vision CPU," which finds use both for control functions and for image pre-processing operations such as greyscale conversion, according to Senior Product Marketing Manager Mike Thompson. The Vision CPU cores start with the same 32-bit scalar processor as in the prior generation EV5x, and add a new 512-bit vector DSP engine capable of 155 GOPS peak throughput at a clock speed of 800 MHz, significantly boosting the per-core performance (Figure 2).