Google DeepMind has announced its second collaboration with the NHS, as part of which it will work 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 five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, reports Ars Technica, with the aim to speed up the complex and time-consuming process of analysing eye scans. From the report: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. Two million people are already living with sight loss in the UK, of whom around 360,000 are registered as blind or partially-sighted.

The complex passenger flows, demanding identification challenges and pressure-filled security conditions that are inherent in border control make the arena appear an interesting prospect for artificial intelligence-based solutions that can revolutionise critical processes. Machine-learning technologies being developed could identify risk patterns at speeds way beyond humans' capacity, and when tied with the powerful security offered by biometrics, AI's potential to disrupt the world of borders expands even further. What then is the future for the border guard? Will their decades of document security expertise and nuanced instincts be made rendered obsolete by a'border bot'? Planet Biometrics caught up with Cyrille Bataller, Artificial Intelligence lead at Accenture, to learn about his team's AI initiatives for borders and about his views on the importance of a "people-first" approach.

Some very intelligent people – including Stephen Hawking, Elon Musk and Bill Gates – seem to have been seduced by the idea that because computers are becoming ever faster calculating devices that at some point relatively soon we will reach and pass a "singularity" at which computers will become "more intelligent" than humans. Some are terrified that a society of intelligent computers will (perhaps violently) replace the human race, echoing films such as the Terminator; others – very controversially – see the development of such technologies as an opportunity to evolve into a "post-human" species. Already, some prominent technologists including Tim O'Reilly are arguing that we should replace current models of public services, not just in infrastructure but in human services such as social care and education, with "algorithmic regulation". Algorithmic regulation proposes that the role of human decision-makers and policy-makers should be replaced by automated systems that compare the outcomes of public services to desired objectives through the measurement of data, and make automatic adjustments to address any discrepancies. Not only does that approach cede far too much control over people's lives to technology; it fundamentally misunderstands what technology is capable of doing. For both ethical and scientific reasons, in human domains technology should support us taking decisions about our lives, it should not take them for us. At the MIT Sloan Initiative on the Digital Economy last week I got a chance to discuss some of these issues with Andy McAfee and Erik Brynjolfsson, authors of "The Second Machine Age", recently highlighted by Bloomberg as one of the top books of 2014. Andy and Erik compare the current transformation of our world by digital technology to the last great transformation, the Industrial Revolution.

You may have heard about the Donald Trump bot army and the Twitter bots that have influenced elections in Latin America, but there are other bots out there, bots that connect citizens to services, bots that plan to crowdsource the election of a president or act as your personal assistant in government. They're going to change civil society, how people interact with government, and the self-governance of free societies. Here are five example of bots that have already impacted or will impact democracy. Dirty Water VT sends a tweet each time a sewage spill occurs in the state of Vermont. Trib IL CampaignCash sends a tweet any time a campaign contribution is made above 1,000 in the state of Illinois.

SINGAPORE – More than half of workers in five Southeast Asian countries are at high risk of losing their jobs to automation in the next two decades, an International Labour Organization study found, with those in the garment industry particularly vulnerable. About 137 million workers or 56 percent of the salaried workforce from Cambodia, Indonesia, the Philippines, Thailand and Vietnam, fall under the high-risk category, the study showed. "Countries that compete on low-wage labor need to reposition themselves. Price advantage is no longer enough," said Deborah France-Massin, director for the ILO's bureau for employers' activities. The report said workers have to be trained to work effectively alongside digitalized machines.

In robotics, the Artificial Intelligence (AI) is probably the most exciting field today. Yes, we all think that a robot can work on an assembly line, but there is no harmony that a robot can ever be intelligent. Though the functionality and universality of Internet and computers have outranked the myths about technology advancements and usefulness in everyday life. It's true that AI is changing our lives since decades, but the presence of AI everywhere today was not felt ever like this. Nearly, every scientist has the different opinion about the future of Robotics and AI and about the change which will happen due to the combo of these two.

Google DeepMind and the NHS are developing a machine learning system with Moorfields Eye Hospital that can recognize sight-threatening conditions from just a digital scan of the eye. Mustafa Suleyman, Deepmind's co-founder, says this is the company's first foray into a purely medical research. In this new collaboration with Moorfields, an algorithm will be trained using one million anonymized eye scans to train to identify early signs of degenerative eye conditions such as wet age-related macular degeneration and diabetic retinopathy. "If you have diabetes you're 25 times more likely to go blind. If we can detect this, and get in there as early as possible, then 98% of the most severe visual loss might be prevented," says Suleyman.

The Obama administration wants the U.S. to be a world leader in economic and defense policy related to a new wave of automation powered by machine learning and artificial intelligence, White House Chief of Staff Denis McDonough said Tuesday. "We can return to these questions in a way that America can kind of set the space and then set the parameters for how we go about it," McDonough said during a White House conversation on the topic that he moderated. The conversation comes as the White House looks to wrap up its string of workshops on artificial intelligence Thursday. In May, the White House Office of Science and Technology Policy announced plans to explore the uses and risks of AI. Since then the office has hosted three workshops and another event on the matter.

We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-making. This question is complicated as these models are used to support different decisions, from sentencing, to determining release on probation, to allocating preventative social services. Each use case might have an objective other than classification accuracy, such as a desired true positive rate (TPR) or false positive rate (FPR). Each (TPR, FPR) pair is a point on the receiver operator characteristic (ROC) curve. We use popular machine learning methods to create models along the full ROC curve on a wide range of recidivism prediction problems. We show that many methods (SVM, Ridge Regression) produce equally accurate models along the full ROC curve. However, methods that designed for interpretability (CART, C5.0) cannot be tuned to produce models that are accurate and/or interpretable. To handle this shortcoming, we use a new method known as SLIM (Supersparse Linear Integer Models) to produce accurate, transparent, and interpretable models along the full ROC curve. These models can be used for decision-making for many different use cases, since they are just as accurate as the most powerful black-box machine learning models, but completely transparent, and highly interpretable.

Few environmental limits are as obvious to people today as water availability. Particularly in drier climates, availability can be a pretty unforgiving equation. Even there, a family might pay less for water than for cell phones, but there is often a pretty complex system behind your tap that keeps it running. The challenge of water availability rises beyond engineering. It becomes a delicate dance managing demand, forecasting supply, and sustaining ecosystems. Decisions have to be made based on information that is never complete, so any opportunity to obtain more useful information is liable to get a thirsty look from water managers.