A company owned by Google has been given access to the healthcare data of up to 1.6 million patients from three hospitals run by a major London NHS Trust. DeepMind, the tech giant's London-based company most famous for its innovative use of artificial intelligence, is being provided with the patient information as part of an agreement with the Royal Free NHS trust, which runs the Barnet, Chase Farm and Royal Free hospitals. It includes information about people who are HIV-positive as well as details of drug overdoses, abortions and patient data from the last five years, according to a report by the New Scientist. DeepMind announced in February that it was developing a software in partnership with NHS hospitals to alert staff to patients at risk of deterioration and death through kidney failure. The technology, which is run through a smartphone app, has the support of Lord Darzi, a surgeon and former health minister who is director of the Institute of Global Health Innovation at Imperial College London.

Which will win out – robots or human beings? This is a highly topical question. The South Koreans, and many more millions of curious people worldwide, became obsessed with this issue when a match was played between a professional Go player named Lee Se-Dol, and AlphaGo, a computer programme developed by Google subsidiary DeepMind. The match resulted in a 4-1 victory for the machine over the Korean star player. Given that Go was one of the last bastions of the games world to hold out against the learning and analysis techniques employed by cutting-edge computers, this defeat basically symbolises the considerable progress made by deep learning.

I'm a fan of Valiant Comics, but I'll admit the one series I've never gotten into is Rai. I just never quite got it. With the new 4001 A.D. event going on, I got a chance to read the return of Rai today and I was pleasantly surprised. Rai #13 gives you the feeling that you are at the beginning of a great science fiction epic. It opens establishing the world of New Japan as it floats over the Earth.

The militant extremist group has suffered recent military setbacks and lost territory in both Iraq and Syria, says Brett McGurk, presidential adviser for the anti-ISIS coalition. Unlike most leaders in his party, the presumptive Republican nominee opposes any changes to Social Security and says he is open to the idea of a higher minimum wage. Michel Temer, who leads the South American country in the wake of Dilma Rousseff's impeachment, must deal with an ongoing economic recession, the Zika virus, a distrustful populace and the upcoming Rio Summer Olympics. Self-driving cars, which could be motoring on more American streets within a decade, may prove so convenient that their use might soar and cause more traffic jams. An "incredibly lifelike" but fake bomb forced police to evacuate Old Trafford stadium on the final day of the English Premier League soccer season.

Very soon – by the end of the year, probably – you won't need to be on Facebook in order to talk to your friends on Facebook. Your Facebook avatar will dutifully wish people happy birthday, congratulate them on the new job, accept invitations, and send them jolly texts punctuated by your favourite emojis – all while you're asleep, or shopping, or undergoing major surgery. At an event called the TechCrunch Disrupt Hackathon held last weekend in New York, software developer Irene Chang unveiled a prototype artificial intelligence (AI) program called The Chat Bot Club, designed to take over all your Facebook Messenger communications when you can't be arsed dealing with them yourself. Chang's proof-of-concept is much more than a simple automated response system. Using IBM's powerful Watson natural language processing platform, The Chat Bot Club learns to imitate its user.

The White House Office of Science and Technology Policy has announced plans to co-host four public workshops to spur public dialogue on artificial intelligence and machine learning, and to learn more about the benefits and risks of artificial intelligence, according to Ed Felten, a Deputy U.S. Chief Technology Officer. These four workshops will be co-hosted by academic and non-profit organizations; two will also be co-hosted by the National Economic Council, with a public report later this year. The Federal Government also is "working to leverage AI for public good and toward a more effective government." A new National Science and Technology Council (NSTC) Subcommittee on Machine Learning and Artificial Intelligence will monitor state-of-the-art advances and technology milestones in artificial intelligence and machine learning within the Federal Government, in the private sector, and internationally; and help coordinate Federal activity in this space. The NSTC group also hopes to increase the use of AI and machine learning to improve the delivery of government services, especially in areas related to urban systems and smart cities, mental and physical health, social welfare, criminal justice, and the environment.

Artificial intelligence isn't likely to replace doctors, says a researcher, but it's likely their role will change as more artificial intelligence is developed. A study by Whangarei doctors William Diprose and Nicholas Buist has highlighted rapid progress of machine learning and artificial intelligence (AI) in the health sector. They say a safe and sustainable healthcare system needs to look beyond human potential towards solutions such as AI. Q: Could artificial intelligence spell the end of doctors as we know them? Diprose and Buist are right to highlight the prospects of artificial intelligence in healthcare.

Getting to the root of the problem has never looked quite like this, medically speaking. Thanks to the latest innovation from the minds at MIT, there is now a tiny origami robot capable of performing internal surgery after being swallowed by a patient. As the MIT News Office reported, a collaboration amongst researchers at MIT, the University of Sheffield, and the Tokyo Institute of Technology gave way to this minuscule device, ingested by way of a capsule and steered by external magnetic fields, that can "crawl across the stomach wall to remove a swallowed button battery or patch a wound." "It's really exciting to see our small origami robots doing something with potential important applications to health care," said Daniela Rus, lead researcher on the study and director of MIT's Computer Science and Artificial Intelligence Laboratory. "For applications inside the body, we need a small, controllable, untethered robot system. It's really difficult to control and place a robot inside the body if the robot is attached to a tether."

While matrix factorisation models are ubiquitous in large scale recommendation and search, real time application of such models requires inner product computations over an intractably large set of item factors. In this manuscript we present a novel framework that uses the inverted index representation to exploit structural properties of sparse vectors to significantly reduce the run time computational cost of factorisation models. We develop techniques that use geometry aware permutation maps on a tessellated unit sphere to obtain high dimensional sparse embeddings for latent factors with sparsity patterns related to angular closeness of the original latent factors. We also design several efficient and deterministic realisations within this framework and demonstrate with experiments that our techniques lead to faster run time operation with minimal loss of accuracy.

Traditional graph-based semi-supervised learning (SSL) approaches, even though widely applied, are not suited for massive data and large label scenarios since they scale linearly with the number of edges $|E|$ and distinct labels $m$. To deal with the large label size problem, recent works propose sketch-based methods to approximate the distribution on labels per node thereby achieving a space reduction from $O(m)$ to $O(\log m)$, under certain conditions. In this paper, we present a novel streaming graph-based SSL approximation that captures the sparsity of the label distribution and ensures the algorithm propagates labels accurately, and further reduces the space complexity per node to $O(1)$. We also provide a distributed version of the algorithm that scales well to large data sizes. Experiments on real-world datasets demonstrate that the new method achieves better performance than existing state-of-the-art algorithms with significant reduction in memory footprint. We also study different graph construction mechanisms for natural language applications and propose a robust graph augmentation strategy trained using state-of-the-art unsupervised deep learning architectures that yields further significant quality gains.