Background: High-throughput proteomics techniques, such as mass spectrometry (MS)-based approaches, produce very high-dimensional data-sets. In a clinical setting one is often interested in how mass spectra differ between patients of different classes, for example spectra from healthy patients vs. spectra from patients having a particular disease. Machine learning algorithms are needed to (a) identify these discriminating features and (b) classify unknown spectra based on this feature set. Since the acquired data is usually noisy, the algorithms should be robust against noise and outliers, while the identified feature set should be as small as possible. Results: We present a new algorithm, Sparse Proteomics Analysis (SPA), based on the theory of compressed sensing that allows us to identify a minimal discriminating set of features from mass spectrometry data-sets. We show (1) how our method performs on artificial and real-world data-sets, (2) that its performance is competitive with standard (and widely used) algorithms for analyzing proteomics data, and (3) that it is robust against random and systematic noise. We further demonstrate the applicability of our algorithm to two previously published clinical data-sets.

It's no longer the preserve of artificial-intelligence researchers and born-digital companies like Amazon, Google, and Netflix. Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and instead train computers to do so. The unmanageable volume and complexity of the big data that the world is now swimming in have increased the potential of machine learning--and the need for it. In 2007 Fei-Fei Li, the head of Stanford's Artificial Intelligence Lab, gave up trying to program computers to recognize objects and began labeling the millions of raw images that a child might encounter by age three and feeding them to computers. By being shown thousands and thousands of labeled data sets with instances of, say, a cat, the machine could shape its own rules for deciding whether a particular set of digital pixels was, in fact, a cat.1 1.Fei-Fei Li, "How we're teaching computers to understand pictures," TED, March 2015, ted.com.

Google DeepMind Using Machine Learning and Artificial Intelligence to Prevent Sight Loss Google acquired DeepMind in 2014 to apply machine learning and artificial intelligence in applications that could change human lives. DeepMind just announced a medical research project with an NHS Trust to combat sight loss specifically related to Diabetes and Age-related Macular Degeneration (AMD). As per DeepMind: Diabetes is on the rise. It's estimated that 1 in 11 of the world's adult population are affected. It's also the leading cause of blindness in the working age population – if you're diabetic you are 25 times more likely to suffer some kind of sight loss.

DeepMind, Google's artificial intelligence outfit, wants to streamline health care by using machine learning to provide medics with intelligent notifications. But not everyone is happy with the piles of data being shared with the company. The project will provide medics across a number of London hospitals with alerts about patients via an app called Streams. The app is meant to provide easy access to patient histories and test results for nurses and doctors. But its AI will also learn to track patterns in patients' blood test data and flag cases that show early signs of kidney injury to the appropriate doctors.

Imagine if your fear of spiders, heights or confined spaces vanished, leaving you with neutral feelings instead of a sweat-soaked panic. A team of neuroscientists said they found a way to recondition the human brain to overcome specific fears. Their approach, if proven in further studies, could lead to new ways of treating patients with phobias or post-traumatic stress disorder (PTSD). The international team published their findings Monday in the journal Nature Human Behaviour. About 19 million U.S. adults, or 8.7 percent of the adult population, suffer prominent and persistent fears at the sight of specific objects or in specific situations, according to the National Institute of Mental Health.

Five questions to prove you're a natural codebreaker Image caption Not all computer scientists look like this. But it's fine if you do The National College of Cybersecurity is opening in 2018 for "gifted and talented" problem solvers. The training centre will help build a "talent pool" for Britain's future cyber-defences. It's being developed at Bletchley Park, in Buckinghamshire, the site of secret code-deciphering which helped the Allies win World War Two. Code is the language in which computer programs, apps and websites are written. Anisah Osman Britton, 23, is the founder of 23 Code Street, a coding school for women.

The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas. Google has been ramping up their Cloud Platform quite aggressively in recent months. Just a few weeks ago, the Google Cloud Platform opened its newest zone in Tokyo, increasing the total number of regions they are present in to six - three in the US and one each in Belgium and Taiwan and Tokyo. Not long ago, the company announced its acquisition of Orbitera, a cloud commerce company. The developments in Google's Cloud Computing segment, especially the Cloud Machine Learning service, have been so rapid that Google calls it one of its fastest growing product areas.

From artificial intelligence to virtual reality, emerging technologies are rewriting the retail playbook at a rapid pace, suggests J. Walter Thompson Intelligence in a new report called Frontier(less) Retail. Launched in collaboration with WWD, the report explores the idea that brands and retailers are increasingly putting innovation at the core of their strategies. This relates to everything from digital integration through to the more future-looking technologies helping to shift their businesses forward. Rebecca Minkoff has boosted sales with smart mirrors in dressing rooms, it notes, while Kate Spade has had a hit with Everpurse, a smartphone-charging handbag. It also attributes the success of Under Armour in part to its positioning as a tech-forward brand, and references Topshop's new incubator program, Top Pitch, as a clever bid to achieve the same at a time when its young consumer base is more likely to spend on smartphones than splurge on streetwear. Within all this, however, it is keeping abreast of change that is proving one of the industry's biggest challenges.

Enter a bookstore, while they still exist. Walk toward the philosophy section, toward shelves of fat books by Plato, Nietzsche, Spinoza. Perhaps you browse through their pages before putting them back in their place, respectfully but with a bit of a yawn. More appealing, perhaps: the books at the front of the store, the best sellers, the ones that portend crises (Rise of the Robots: Technology and the Threat of a Jobless Future); others advise on surviving one (Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence). The business best sellers are more gung-ho about the changes to come: Zero to One: Notes on Startups, or How to Build the Future.

A "sexual predator" has been jailed for eight and a half years for blackmailing and spying on Muslim women using intimate photographs and videos he took of them without their knowledge. Farhan Mirza, 38, of Abertillery, Blaenau Gwent, secretly filmed the women and threatened to share the footage before demanding money. Mirza, who denied the charges, met some of the women on online dating sites. He was jailed for voyeurism, blackmail, theft and fraud at Cardiff Crown Court. During the trial, jurors heard Mirza had initially impressed his victims by claiming to be a doctor and hung surgical scrubs in his wardrobe and carried a stethoscope in his car. He also claimed his family were highly educated professionals working in locations around the world.