Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

There are hundreds of new cancer drugs in development and new research published minute to minute, helping doctors treat patients with personalized combinations that target the specific building blocks of their disease. The problem is there's too much to read and too many drug combinations for doctors to choose the best option every time. Enter a Microsoft Research machine-learning project, dubbed Hanover, that aims to ingest all the papers and help predict which drugs and which combinations are most effective, according to the company. Researchers at Oregon Health & Science University's Knight Cancer Institute are working with Hanover's architect, Hoifung Poon, to use the system to find drug combinations effective in fighting acute myeloid leukemia, an often-fatal cancer where treatment hasn't improved much in decades. They include Jeff Tyner, and the institute's director, Brian Druker, best known for pioneering Gleevec, a blockbuster drug for a different type of leukemia now owned by Novartis, that's helped double those patients' five-year survival rate since the 1990s.

CRM company Salesforce, powered by the artificial intelligence of Einstein, is making additional in-roads into the contact center. Salesforce today unveiled Salesforce Einstein, which is being described as not only providing artificial intelligence for every company, but as creating the world's smartest customer relationship management (CRM) software. Whenever I cover a vendor announcement on No Jitter, one of my goals is to deliver value beyond the press release. And after listening to an hour long pre-briefing for press and analysts on the announcement last week, I was eager to get to work on coverage... Then I saw the press release. In 1,600 words, the Salesforce team has done an impressive job of laying out the Einstein vision and story.

Over 95% of internet traffic is transmitted via undersea cables. Soon, data may not only be sent, but also stored underwater. High energy costs of data centres (up to 3% of global energy use) have driven their relocation to places like Iceland, where cold climates increase cooling efficiency. Meanwhile, about 40% of people on the planet live in coastal cities. To simultaneously cope with high real estate costs in these oceanfront growth centres, reduce latency, and overcome the typically high expense of cooling data centers, Microsoft successfully tested a prototype underwater data centre off the coast of California last year.

As real-time, connected vehicle prognostics move rapidly into consumer and commercial transportation, they pave the way for the progression of advanced driver assistance systems (ADAS), greater OEM profits and increased customer loyalty. ABI Research forecasts that there will be more than seven million prognostics-enabled, commercial vehicle solutions connected globally by 2021. Vehicle prognostics are able to predict and inform, in real time, future component deviations or failures in advance of any performance impact. As these capabilities move rapidly, automotive prognostics will positively impact the supply chain from manufacturers to repair shops to third-party software providers. "Automotive safety is shifting the industry toward a machine learning focus," says Susan Beardslee, Senior Analyst at ABI Research.

The goal of this thesis is to investigate the potential of predictive modelling for football injuries. This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA European tour and the participation of Wolverhampton Wanderers (WW). Three investigations were conducted: 1. Predicting the recovery time of football injuries using the UEFA injury recordings: The UEFA recordings is a common standard for recording injuries in professional football. For this investigation, three datasets of UEFA injury recordings were available. Different machine learning algorithms were used in order to build a predictive model. The performance of the machine learning models is then improved by using feature selection conducted through correlation-based subset feature selection and random forests. 2. Predicting injuries in professional football using exposure records: The relationship between exposure (in training hours and match hours) in professional football athletes and injury incidence was studied. A common problem in football is understanding how the training schedule of an athlete can affect the chance of him getting injured. The task was to predict the number of days a player can train before he gets injured. 3. Predicting intrinsic injury incidence using in-training GPS measurements: A significant percentage of football injuries can be attributed to overtraining and fatigue. GPS data collected during training sessions might provide indicators of fatigue, or might be used to detect very intense training sessions which can lead to overtraining. This research used GPS data gathered during training sessions of the first team of THFC, in order to predict whether an injury would take place during a week.

Recently, there has been a surge of interest in using spectral methods for estimating latent variable models. However, it is usually assumed that the distribution of the observations conditioned on the latent variables is either discrete or belongs to a parametric family. In this paper, we study the estimation of an $m$-state hidden Markov model (HMM) with only smoothness assumptions, such as H\"olderian conditions, on the emission densities. By leveraging some recent advances in continuous linear algebra and numerical analysis, we develop a computationally efficient spectral algorithm for learning nonparametric HMMs. Our technique is based on computing an SVD on nonparametric estimates of density functions by viewing them as \emph{continuous matrices}. We derive sample complexity bounds via concentration results for nonparametric density estimation and novel perturbation theory results for continuous matrices. We implement our method using Chebyshev polynomial approximations. Our method is competitive with other baselines on synthetic and real problems and is also very computationally efficient.

In this paper we refine the process of computing calibration functions for a number of multiclass classification surrogate losses. Calibration functions are a powerful tool for easily converting bounds for the surrogate risk (which can be computed through well-known methods) into bounds for the true risk, the probability of making a mistake. They are particularly suitable in non-parametric settings, where the approximation error can be controlled, and provide tighter bounds than the common technique of upper-bounding the 0-1 loss by the surrogate loss. The abstract nature of the more sophisticated existing calibration function results requires calibration functions to be explicitly derived on a case-by-case basis, requiring repeated efforts whenever bounds for a new surrogate loss are required. We devise a streamlined analysis that simplifies the process of deriving calibration functions for a large number of surrogate losses that have been proposed in the literature. The effort of deriving calibration functions is then surmised in verifying, for a chosen surrogate loss, a small number of conditions that we introduce. As case studies, we recover existing calibration functions for the well-known loss of Lee et al. (2004), and also provide novel calibration functions for well-known losses, including the one-versus-all loss and the logistic regression loss, plus a number of other losses that have been shown to be classification-calibrated in the past, but for which no calibration function had been derived.

This work proposes a new algorithm for automated and simultaneous phenotyping of multiple co-occurring medical conditions, also referred as comorbidities, using clinical notes from the electronic health records (EHRs). A basic latent factor estimation technique of non-negative matrix factorization (NMF) is augmented with domain specific constraints to obtain sparse latent factors that are anchored to a fixed set of chronic conditions. The proposed anchoring mechanism ensures a one-to-one identifiable and interpretable mapping between the latent factors and the target comorbidities. Qualitative assessment of the empirical results by clinical experts suggests that the proposed model learns clinically interpretable phenotypes while being predictive of 30 day mortality. The proposed method can be readily adapted to any non-negative EHR data across various healthcare institutions.

Automated discovery of early visual concepts from raw image data is a major open challenge in AI research. Addressing this problem, we propose an unsupervised approach for learning disentangled representations of the underlying factors of variation. We draw inspiration from neuroscience, and show how this can be achieved in an unsupervised generative model by applying the same learning pressures as have been suggested to act in the ventral visual stream in the brain. By enforcing redundancy reduction, encouraging statistical independence, and exposure to data with transform continuities analogous to those to which human infants are exposed, we obtain a variational autoencoder (VAE) framework capable of learning disentangled factors. Our approach makes few assumptions and works well across a wide variety of datasets. Furthermore, our solution has useful emergent properties, such as zero-shot inference and an intuitive understanding of "objectness".