Learning tasks such as those involving genomic data often poses a serious challenge: the number of input features can be orders of magnitude larger than the number of training examples, making it difficult to avoid overfitting, even when using the known regularization techniques. We focus here on tasks in which the input is a description of the genetic variation specific to a patient, the single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs. Improving the ability of deep learning to handle such datasets could have an important impact in precision medicine, where high-dimensional data regarding a particular patient is used to make predictions of interest. Even though the amount of data for such tasks is increasing, this mismatch between the number of examples and the number of inputs remains a concern. Naive implementations of classifier neural networks involve a huge number of free parameters in their first layer: each input feature is associated with as many parameters as there are hidden units. We propose a novel neural network parametrization which considerably reduces the number of free parameters. It is based on the idea that we can first learn or provide a distributed representation for each input feature (e.g. for each position in the genome where variations are observed), and then learn (with another neural network called the parameter prediction network) how to map a feature's distributed representation to the vector of parameters specific to that feature in the classifier neural network (the weights which link the value of the feature to each of the hidden units). We show experimentally on a population stratification task of interest to medical studies that the proposed approach can significantly reduce both the number of parameters and the error rate of the classifier.

It doesn't look like a place to make groundbreaking discoveries that change the trajectory of society. But in these simulated, claustrophobic corridors, Demis Hassabis thinks he can lay the foundations for software that's smart enough to solve humanity's biggest problems. "Our goal's very big," says Hassabis, whose level-headed manner can mask the audacity of his ideas. He leads a team of roughly 200 computer scientists and neuroscientists at Google's DeepMind, the London-based group behind the AlphaGo software that defeated a world champion at Go in a five-game series earlier this month, setting a milestone in computing. It's supposed to be just an early checkpoint in an effort Hassabis describes as the Apollo program of artificial intelligence, aimed at "solving intelligence, and then using that to solve everything else."

A group of professors and researchers at the Technical University of Berlin, the University of Vienna, and ETH Zurich have recently been working on understanding deep neural networks (computer systems that are modelled after the human brain) in "a mathematically sound way", as Dr. Phillip Petersen refers to it. Although the official paper for this research, "Optimal Approximation with Sparse Deep Neural Networks," will not be published until next week, Professor Gitta Kutyniok graciously presented a preview of their work for the Center's Math & Data Seminar group this past Thursday. Neural networks, or artificial brains, represent functions in mathematics. For these researchers, the main goal is to uncover how well a deep neural network with sparse connectivity can approximate a function. Dr. Petersen likens the network to a tree -- deep neural networks are composed of multiple layers and are connected by edges. Those layers are made of nodes, or neurons where computation occurs, and are sparsely connected if they have few non-zero weights or edges.

DeepMind's partnership with the NHS proves technology can improve the state of the health and care system The app can immediately alert a clinician as soon as it detects signs of kidney failure in patients as nearly 30 doctors and nurses at the Royal Free Hospital have now started using it on a daily basis. "The app is delivering cultural change to the way technology is being used to improve care. The technology is no longer passive, but is actively helping us to provide better and timelier care to patients. "For example on one day this week, the app alerted us to 11 patients, ranging from a young cancer patient to an elderly patient suffering life-threatening dehydration, who were at risk of developing AKI (Acute Kidney Injury). "These patients had a range of different conditions and without the app it would have taken our staff much longer to realise they were developing kidney problems. The app enabled us to monitor our patients' kidney function, detect kidney failure early and intervene rapidly to manage complications and accelerate their recovery," said Chris Laing, a Renal Consultant involved in the development of the Streams app.

In the consumer report, we are number one once again and just like the Q7, in the consumer report it also occupies the first position as the best luxury SUV. And I think this power of the brand makes it possible for us to grow significantly. There are couple of models which have not even be launched yet in this market, models which we already know here, for instance the S4, the A5, and the entirely new A5 Sportback. They are now being launched in the United States. All new models for this market, and I assume that this year once again we are going to experience very solid growth in the United States. And the question so whether we spend more money for this? I can tell you we even spend less money in form of sales discounts because of the powerful brand and the relatively young product portfolio. So you would take the second part?

Imagine rolling into an operating room to find that your surgical team included a robot. While full-fledged robotic surgeons aren't quite ready for the spotlight, automatons have already found a foothold in the surgical theater. Some systems allow doctors to control robotic instruments--ones able to slice and dice with inhuman precision--using controls or a computer screen, while other medical robots take a doctor's place entirely to conduct specific segments of a larger surgery. Now scientists have taken a big step forward with the latter type of bot: in a study published Wednesday in Science Robotics, a team reports the first ever robot-assisted cochlear implantation surgery. "We were on this project for more than eight years," says lead study author Stefan Weber, a professor at the University of Bern, Switzerland's ARTORG Center for Biomedical Engineering Research. "And in contrast to a lot of research, we really stuck to one application for the entire time."

We surveyed 835 executives in companies (average revenue of $20 billion and median of $2.8 billion) from 13 industries across North America, Europe, Asia-Pacific, and Latin America. North America Firms in the region spent most on AI in 2015: $80 million on average. Europe European firms are catching up: they plan to invest $80 million on average (26% more than North America) in 2016. Latin America Companies here achieved the biggest revenue gains and the highest cost reduction through AI, while investing less than other regions. Asia-Pacific Companies invested $55 million, on average, in AI in 2015.

Imagine owning a smartphone or TV that could roll up to fit in your pocket. That could soon be reality thanks a new flexible sensor that can detect subtle differences in touch, including swiping and tapping. Researchers said their stretchable sensor could be used to build folding TV screens and tablets - and may even be used to make skin for robots. The team from the University of British Columbia in Vancouver used a highly conductive gel sandwiched between layers of silicone to make their bendable sensor. To create the sensor, a gel is poured out and combined with silicon-based materials that are stretchy and transparent.

If you're keeping score of who is pursuing the hottest trends in technology, don't overlook Salesforce. As part of Salesforce's Spring '17 celebration this week, the company announced the general availability of Einstein, which is Salesforce's core AI engine. It's something the company has been talking about for many months. Salesforce also introduced Einstein Vision, a set of new APIs that helps developers of all sizes "build AI-powered apps fast." This includes apps that leverage vast troves of images including photos and graphics.

London, March 15: Sixty-eight per cent of organisations use artificial intelligence (AI) for IT functions, but 70 per cent believe AIs greatest impact by 2020 will be in marketing, customer service, finance and HR, a new study said on Wednesday. According to global IT consulting firm Tata Consultancy Services (TCS), organisations with the greatest financial improvements from AI investments expect three times as many new AI-related roles by 2020 as compared to companies with smallest improvements. "Given the increasing digital disruption across every industry and the public sector, AI should become a key and integrated component of an organisation's strategy," said K Ananth Krishnan, Chief Technology Officer of TCS, in a statement. Eighty-four per cent of companies see the use of AI as "essential" to competitiveness, with a further 50 per cent seeing the technology as "transformative". Financial investments in AI are set to rise, as seven per cent of companies each earmarked at least $250 million toward AI in 2016 and two per cent already plan to invest more than $1 billion by 2020, likely looking to gain a competitive advantage as early adopters, the findings showed.