Adoption and investment in artificial intelligence and robotic process automation is still in its early growth stage in the healthcare industry, with just half of hospital leaders familiar with the technologies. WHY IT MATTERS These were among the results of a survey of 115 executives at hospital systems and independent hospitals in the United States, conducted by healthcare digitization vendor Olive and market research firm Sage Growth Partners. The study also found that nearly a quarter (23 percent) of health system executives are looking to invest in the two technologies today, and half said they plan to do so within the next two years. The top reasons cited for deploying AI technology included improving efficiency and reducing costs, improving the quality of care and improving patient satisfaction and engagement. While interest in AI and RPA technology is growing, the survey results also indicated that there is a lack of general knowledge as to where to procure the solutions or what vendors offer them, with more than half of survey respondents unable to name an AI or RPA vendor or solution. The automation of high-volume repetitive tasks like supply chain management, revenue cycle management and finance and human resources were seen by survey respondents as key areas where AI could be of service, though purchasing approaches varied widely.

Mashrura Tasnim's goal is to create a smartphone application that would monitor users' conversations and alert their emergency contacts or mental-health …

Artificial Intelligence used to be a fictive narrative from Isaac Asimov's novels and futuristic films. Today it's 2019, and AI is no longer just a product of imagination. Let's take a look at the world's favorite futuristic movies. We already have them in every household. These can be easily found on Amazon and even your local Home Depot.

It is almost 40 years since a full-body magnetic resonance imaging (MRI) machine was used for the first time to scan a patient and generate diagnostic-quality images. The scanner and signal processing methods needed to produce an image were devised by a team of medical physicists including John Mallard, Jim Hutchinson, Bill Edelstein and Tom Redpath at the University of Aberdeen, leading to the widespread use of the MRI scanner, now a ubiquitous tool in radiology departments across the world. MRI was a game-changer in medical diagnostics because it didn't require exposure to ionising radiation (such as X-rays), and could generate images on multiple cross-sections of the body with superb definition of soft tissues. This allowed, for example, the direct visualisation of the spinal cord for the first time. Most people today will have undergone an MRI or know somebody who has.

In recent years, a growing number of researchers have explored the use of robotic arms or dexterous hands to solve a variety of everyday tasks. While many of them have successfully tackled simple tasks, such as grasping or basic manipulation, complex tasks that involve multiple steps and precise/strategic movements have so far proved harder to address. A team of researchers at the Chinese University of Hong Kong and Tencent AI Lab has recently developed a deep learning-based approach to solve a Rubik's Cube using a multi-fingered dexterous hand. Their approach, presented in a paper pre-published on arXiv, allows a dexterous hand to solve more advanced in-hand manipulation tasks, such as the renowned Rubik's Cube puzzle. A Rubik's Cube is a plastic cube covered in multi-colored squares that can be shifted into different positions.

We start with a brief introduction to reinforcement learning (RL), about its successful stories, basics, an example, issues, the ICML 2019 Workshop on RL for Real Life, how to use it, study material and an outlook. Then we discuss a selection of RL applications, including recommender systems, computer systems, energy, finance, healthcare, robotics, and transportation.

Neural network training entails heavy computation with obvious bottlenecks. The Compute Unified Device Architecture (CUDA) programming model allows us to accelerate computation by passing the processing workload from the CPU to the graphics processing unit (GPU). In this paper, we leveraged the power of Nvidia GPUs to parallelize all of the computation involved in training, to accelerate a backpropagation feed-forward neural network with one hidden layer using CUDA and C++. This optimized neural network was tasked with predicting the level of glycated hemoglobin (HbA1c) from non-invasive markers. The rate of increase in the prevalence of Diabetes Mellitus has resulted in an urgent need for early detection and accurate diagnosis. However, due to the invasiveness and limitations of conventional tests, alternate means are being considered. Limited Joint Mobility (LJM) has been reported as an indicator for poor glycemic control. LJM of the fingers is quantified and its link to HbA1c is investigated along with other potential non-invasive markers of HbA1c. We collected readings of 33 potential markers from 120 participants at a clinic in south Trinidad. Our neural network achieved 95.65% accuracy on the training and 86.67% accuracy on the testing set for male participants and 97.73% and 66.67% accuracy on the training and testing sets for female participants. Using 960 CUDA cores from a Nvidia GeForce GTX 660, our parallelized neural network was trained 50 times faster on both subsets, than its corresponding CPU implementation on an Intel Core (TM) i7-3630QM 2.40 GHz CPU.

Despite achieving remarkable success in various domains, recent studies have uncovered the vulnerability of deep neural networks to adversarial perturbations, creating concerns on model generalizability and new threats such as prediction-evasive misclassification or stealthy reprogramming. Among different defense proposals, stochastic network defenses such as random neuron activation pruning or random perturbation to layer inputs are shown to be promising for attack mitigation. However, one critical drawback of current defenses is that the robustness enhancement is at the cost of noticeable performance degradation on legitimate data, e.g., large drop in test accuracy. This paper is motivated by pursuing for a better trade-off between adversarial robustness and test accuracy for stochastic network defenses. We propose Defense Efficiency Score (DES), a comprehensive metric that measures the gain in unsuccessful attack attempts at the cost of drop in test accuracy of any defense. To achieve a better DES, we propose hierarchical random switching (HRS), which protects neural networks through a novel randomization scheme. A HRS-protected model contains several blocks of randomly switching channels to prevent adversaries from exploiting fixed model structures and parameters for their malicious purposes. Extensive experiments show that HRS is superior in defending against state-of-the-art white-box and adaptive adversarial misclassification attacks. We also demonstrate the effectiveness of HRS in defending adversarial reprogramming, which is the first defense against adversarial programs. Moreover, in most settings the average DES of HRS is at least 5X higher than current stochastic network defenses, validating its significantly improved robustness-accuracy trade-off.

The deterrent effect of military alliances is well documented and widely accepted. However, such work has typically assumed that alliances are exogenous. This is problematic as alliances may simultaneously influence the probability of conflict and be influenced by the probability of conflict. Failing to account for such endogeneity produces overly simplistic theories of alliance politics and barriers to identifying the causal effect of alliances on conflict. In this manuscript, I propose a solution to this theoretical and empirical modeling challenge. Synthesizing theories of alliance formation and the alliance-conflict relationship, I innovate an endogenous theory of alliances and conflict. I then test this theory using innovative generalized joint regression models that allow me to endogenize alliance formation on the causal path to conflict. Once doing so, I ultimately find that alliances neither deter nor provoke aggression. This has significant implications for our understanding of interstate conflict and alliance politics.

The Expectation-Maximization (EM) algorithm is a fundamental tool in unsupervised machine learning. It is often used as an efficient way to solve Maximum Likelihood (ML) estimation problems, especially for models with latent variables. It is also the algorithm of choice to fit mixture models: generative models that represent unlabelled points originating from $k$ different processes, as samples from $k$ multivariate distributions. In this work we define and use a quantum version of EM to fit a Gaussian Mixture Model. Given quantum access to a dataset of $n$ vectors of dimension $d$, our algorithm has convergence and precision guarantees similar to the classical algorithm, but the runtime is only polylogarithmic in the number of elements in the training set, and is polynomial in other parameters - as the dimension of the feature space, and the number of components in the mixture. We generalize further the algorithm in two directions. First, we show how to fit any mixture model of probability distributions in the exponential family. Then, we show how to use this algorithm to compute the Maximum a Posteriori (MAP) estimate of a mixture model: the Bayesian approach to likelihood estimation problems. We discuss the performance of the algorithm on datasets that are expected to be classified successfully by those algorithms, arguing that on those cases we can give strong guarantees on the runtime.