According to a survey of 122 founders, executives and investors in health-tech companies released today by Silicon Valley Bank, big data and artificial intelligence will have the greatest impact on the industry in the year ahead. Healthcare delivery and healthcare IT also promise the most growth in 2017. "Big data has been integral to our work at Celmatix. It has empowered physicians to be able to counsel women about their chances of having a baby, based on their relevant personal metrics, and not just their age," said Dr. Piraye Yurttas Beim, CEO at Celmatix. "It's an exciting time to be in a field where the pace of innovation continues to increase as both physicians and patients realize the potential of big data and personalized medicine."

Machine Learning is making its presence felt on the worldwide stage as a major driver of digital business success. A good proof of that was our recently completed second edition of the Valencian Summer School in Machine Learning celebrated last September 2016 in Spain. Over 140 attendees representing 53 companies and 21 academic organizations from 19 countries travelled to Valencia for a crash course in Machine Learning and it was a great success! What are the next steps? Encouraged by the level of interest and motivated by our mission to democratize Machine Learning, we continue spreading Machine Learning concepts with this series of courses.

SAP SE (NYSE: SAP) today announced three new courses delivered on the openSAP platform to guide users through the transformative effects experienced as a result of using iOS technology, machine learning and SAP S/4HANA to implement everyday business processes. These courses come in addition to the recent release of "Upgrade of Systems Based on SAP NetWeaver – Advanced Topics," which investigates the latest tools and features essential for SAP software system upgrades and maintenance. The new courses on openSAP will cover how the partnership between SAP and Apple is optimizing the use of iOS technology for end-to-end business processes. They also provide users an in-depth look at how machine learning is giving rise to new intelligent applications. SAP Fiori for iOS – An Introduction: The recent partnership forged between SAP and Apple will enable developers to build quickly their own native apps for Apple iOS devices.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

More than half of students are chasing careers that will be made obsolete by advances in technology and automation, according to a report by the Foundation for Young Australians (FYA). The report, entitled The New Work Order, makes recommendations to ensure that Australia's young people are being trained for the future of work, not for the'traditional' model of employment. In a worrying finding, the report states that 70 per cent of young people currently enter the workforce in jobs that will be "radically affected by automation". The CEO of FYA, Jan Owen, said that while the unemployment and underemployment rate for young people in Australia is already around 30 per cent, the chances of getting a foothold in the labour market are going to keep shrinking. This now famous establishment opened two years ago in the Shinjuku district 2/9 The Robot Restaurant Dancers dressed as futuristic characters perform during a show at The Robot Restaurant 3/9 The Robot Restaurant Performances are held three times a day and cater mostly to foreign tourists 4/9 The Robot Restaurant The Robot Restaurant has gained notoriety for its mind-boggling sci-fi cabaret show and its garishly illuminated interior 5/9 The Robot Restaurant The restaurant was completed in 2012 at a cost of $10 million.

When I go to Google Photos and search my photos for'skyline', it finds me this picture of the New York skyline I took in August, without me having labelled it! When I search for'cathedral', Google's neural networks find me pictures of cathedrals & churches I've seen. But of course, neural networks aren't magic–nothing is! I recently read a paper, "Explaining and Harnessing Adversarial Examples", that helped demystify neural networks a little for me. The paper explains how to force a neural network to make really egregious mistakes. It does this by exploiting the fact that the network is simpler (more linear!) than you might expect. It's important to understand that this doesn't explain all (or even most) kinds of mistakes neural networks make. There are a lot of possible mistakes!

In classical reinforcement learning, when exploring an environment, agents accept arbitrary short term loss for long term gain. This is infeasible for safety critical applications, such as robotics, where even a single unsafe action may cause system failure. In this paper, we address the problem of safely exploring finite Markov decision processes (MDP). We define safety in terms of an, a priori unknown, safety constraint that depends on states and actions. We aim to explore the MDP under this constraint, assuming that the unknown function satisfies regularity conditions expressed via a Gaussian process prior. We develop a novel algorithm for this task and prove that it is able to completely explore the safely reachable part of the MDP without violating the safety constraint. To achieve this, it cautiously explores safe states and actions in order to gain statistical confidence about the safety of unvisited state-action pairs from noisy observations collected while navigating the environment. Moreover, the algorithm explicitly considers reachability when exploring the MDP, ensuring that it does not get stuck in any state with no safe way out. We demonstrate our method on digital terrain models for the task of exploring an unknown map with a rover.

Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.

In topic modeling, many algorithms that guarantee identifiability of the topics have been developed under the premise that there exist anchor words -- i.e., words that only appear (with positive probability) in one topic. Follow-up work has resorted to three or higher-order statistics of the data corpus to relax the anchor word assumption. Reliable estimates of higher-order statistics are hard to obtain, however, and the identification of topics under those models hinges on uncorrelatedness of the topics, which can be unrealistic. This paper revisits topic modeling based on second-order moments, and proposes an anchor-free topic mining framework. The proposed approach guarantees the identification of the topics under a much milder condition compared to the anchor-word assumption, thereby exhibiting much better robustness in practice. The associated algorithm only involves one eigen-decomposition and a few small linear programs. This makes it easy to implement and scale up to very large problem instances. Experiments using the TDT2 and Reuters-21578 corpus demonstrate that the proposed anchor-free approach exhibits very favorable performance (measured using coherence, similarity count, and clustering accuracy metrics) compared to the prior art.

Predictive models are increasingly deployed for the purpose of determining access to services such as credit, insurance, and employment. Despite potential gains in productivity and efficiency, several potential problems have yet to be addressed, particularly the potential for unintentional discrimination. We present an iterative procedure, based on orthogonal projection of input attributes, for enabling interpretability of black-box predictive models. Through our iterative procedure, one can quantify the relative dependence of a black-box model on its input attributes.The relative significance of the inputs to a predictive model can then be used to assess the fairness (or discriminatory extent) of such a model.