A new Guardian report shows where AI is headed next, in a joint venture between Google's Deep Mind and the British NHS … The British team behind Google's AI efforts is teaming up with the UK's National Health Service and London's Moorfields Eye Hospital to build a machine learning system capable of recognizing potentially sight-threatening conditions by simply identifying symptoms from a digital scan of the eye. The core of the research will see about a million eye scans (all coming from anonymous patients) being analysed by an AI-fuelled computer, which Deepmind researchers will use to train a special algorithm. The algorithm will then allow the machine to spot early signs of eye conditions, such as wet age-related macular degenerations and diabetic retinopathy; diabetes, in fact, apparently makes it "25 times more likely to go blind", as per Mustafa Suleyman, Deepmind's co-founder. "If we can detect this, and get in there as early as possible, then 98% of the most severe visual loss might be prevented," Mustafa said. And indeed, allowing a computer to do most of the hard work would help immensely in increasing both the speed and the accuracy of a diagnosis, potentially helping the sight of thousands to be saved.

Google's DeepMind is embarking on a new research project to help doctors spot the early signs of sight-threatening eye diseases. The company's British-based artificial intelligence division will use machine learning to analyze more than one million anonymous eye scans, creating algorithms that can detect early warning signs that humans might miss. The project is DeepMind's second collaboration with the UK's National Health Service (NHS), but the first to use artificial intelligence. DeepMind is hoping to spot two eye conditions in particular: wet age-related macular degeneration and diabetic retinopathy, the latter being the fastest growing cause of blindness around the wold. "There's so much at stake, particularly with diabetic retinopathy," DeepMind co-founder Mustafa Suleyman told The Guardian.

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Moorfields Eye Hospital is one of the oldest and largest health centres for ophthalmic treatment, handling more than 600,000 patient visits each year. Staff conduct "many thousands" of optical coherence tomography (OCT) scans each week -- these are complex and can take time for healthcare professionals to analyze. If DeepMind's research is successful, this workflow could be accelerated and, as a result, ensure that many people retain their sight. Some conditions, such as diabetic retinopathy, can be prevented or severely limited provided they are detected early enough. "But that doesn't always happen," DeepMind explains.

Some supervised machine learning techniques include decision trees, regression, Bayesian methods and deep learning (neural networks). Many of these algorithms also have parameters which must be tuned to achieve the best accuracy. Some algorithms have very few parameters to be set, while others, such as neural networks, have quite a few and can require some investigation. We are currently doing some work using neural networks for predicting user behaviour. While they can require a lot of tuning, neural networks are a very powerful tool for making predictions, and with recent advancements (such as GPU-accelerated Tensorflow) they have the ability to build models with data at unprecedented scale.

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.

Google DeepMind has announced its second collaboration with the NHS, working with Moorfields Eye Hospital in east London to build a machine learning system which will eventually be able to recognise sight-threatening conditions from just a digital scan of the eye. The collaboration is the second between the NHS and DeepMind, which is the artificial intelligence research arm of Google, but Deepmind's co-founder, Mustafa Suleyman, says this is the first time the company is embarking purely on medical research. An earlier, ongoing, collaboration, with the Royal Free hospital in north London, is focused on direct patient care, using a smartphone app called Streams to monitor kidney function of patients. The Moorfields collaboration is also the first time DeepMind has used machine learning in a healthcare project. At the heart of the research is the sharing of a million anonymous eye scans, which the DeepMind researchers will use to train an algorithm to better spot the early signs of eye conditions such as wet age-related macular degeneration and diabetic retinopathy.

Famous eye hospital Moorfields has agreed to give GoogleHow AI is fuelling the car industry. Read more ... »'s DeepMindHow AI is fuelling the car industry. Read more ... » access to one million anonymous eye scans as a part of a machineHow AI is fuelling the car industry. Read more ... » learningHow AI is fuelling the car industry. Read more ... » studyMicrosoft scans photos to guess what your feelings are.

Machine learning is great and it does some amazing things, but even though we refer to the techniques as "neural networks" the way these systems learn is different from the way people learn. The biggest difference is that these algorithms/systems have insatiable appetites for clean data. You have to present one of these systems with huge numbers of pictures of kittens before it has any hope of labeling kittens reliably. As opposed to a child, who can be shown three pictures of kittens, and who at that point would probably perform as well as the exhaustively trained neural net. In all fairness, if we examine what these (deep) neural nets are learning we can see that the contest is not really fair.

A recent trend in Deep Learning are Attention Mechanisms. In an interview, Ilya Sutskever, now the research director of OpenAI, mentioned that Attention Mechanisms are one of the most exciting advancements, and that they are here to stay. But what are Attention Mechanisms? Attention Mechanisms in Neural Networks are (very) loosely based on the visual attention mechanism found in humans. Human visual attention is well-studied and while there exist different models, all of them essentially come down to being able to focus on a certain region of an image with "high resolution" while perceiving the surrounding image in "low resolution", and then adjusting the focal point over time.