This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of two- dimensional time-frequency feature maps based on Mel-frequency cepstral coefficients (MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time- and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.

We study consistency properties of machine learning methods based on minimizing convex surrogates. We extend the recent framework of Osokin et al. (2017) for the quantitative analysis of consistency properties to the case of inconsistent surrogates. Our key technical contribution consists in a new lower bound on the calibration function for the quadratic surrogate, which is non-trivial (not always zero) for inconsistent cases. The new bound allows to quantify the level of inconsistency of the setting and shows how learning with inconsistent surrogates can have guarantees on sample complexity and optimization difficulty. We apply our theory to two concrete cases: multi-class classification with the tree-structured loss and ranking with the mean average precision loss. The results show the approximation-computation trade-offs caused by inconsistent surrogates and their potential benefits.

We develop deep Poisson-gamma dynamical systems (DPGDS) to model sequentially observed multivariate count data, improving previously proposed models by not only mining deep hierarchical latent structure from the data, but also capturing both first-order and long-range temporal dependencies. Using sophisticated but simple-to-implement data augmentation techniques, we derived closed-form Gibbs sampling update equations by first backward and upward propagating auxiliary latent counts, and then forward and downward sampling latent variables. Moreover, we develop stochastic gradient MCMC inference that is scalable to very long multivariate count time series. Experiments on both synthetic and a variety of real-world data demonstrate that the proposed model not only has excellent predictive performance, but also provides highly interpretable multilayer latent structure to represent hierarchical and temporal information propagation.

Question answering (QA) has significantly benefitted from deep learning techniques in recent years. However, domain-specific QA remains a challenge due to the significant amount of data required to train a neural network. This paper studies the answer sentence selection task in the Bible domain and answer questions by selecting relevant verses from the Bible. For this purpose, we create a new dataset BibleQA based on bible trivia questions and propose three neural network models for our task. We pre-train our models on a large-scale QA dataset, SQuAD, and investigate the effect of transferring weights on model accuracy. Furthermore, we also measure the model accuracies with different answer context lengths and different Bible translations. We affirm that transfer learning has a noticeable improvement in the model accuracy. We achieve relatively good results with shorter context lengths, whereas longer context lengths decreased model accuracy. We also find that using a more modern Bible translation in the dataset has a positive effect on the task.

OVER THE past five years researchers in artificial intelligence have become the rock stars of the technology world. A branch of AI known as deep learning, which uses neural networks to churn through large volumes of data looking for patterns, has proven so useful that skilled practitioners can command high six-figure salaries to build software for Amazon, Apple, Facebook and Google. The top names can earn over $1m a year. Upgrade your inbox and get our Daily Dispatch and Editor's Picks. The standard route into these jobs has been a PhD in computer science from one of America's elite universities.

A hotpot restaurant in China is rolling out robots to cook and refill customers' soup. HaiDiLao International Holding, one of Asia's largest restaurant chains, is working with Japanese electronics giant Panasonic to bring the robotic staffers to their new Beijing location. There, robots will take orders, prepare and deliver raw meat and vegetables to customers, where they'll place them in customers' boiling bowls of soup at tables, according to Bloomberg. HaiDiLao International Holding is working with Japanese electronics giant Panasonic to bring the robotic staffers to their Beijing location. At HaiDiLao restaurants, guests choose from a range of soup broths as their base, then select items to be added to the soup a la carte, including meats, vegetables and other toppings.

Veteran perfumer David Apel works on the AI-designed fragrance.IBM and Symrise Artificial intelligence, a buzzword across several sectors, may be about to shake up the fragrance industry. IBM Research and Symrise -- a major global producer of flavors and fragrances that counts among its clients Estee Lauder, Coty and Victoria's Secret parent L Brands -- have created what they described as the industry's first AI-designed perfume for sale, after the two parties came together over a year ago. The AI tool, named Philyra, uses a machine-learning algorithm to study Symrise's database of some 1.7 million formulas and can identify "white space" before suggesting not only formulas that may resonate with consumers but also combinations that perfumers may not have thought of before. For instance, when asked to come up with the "most creative" interpretation of a fragrance created 12 years ago, the AI system generated one formula that removed an outdated material and upped the dosage of a popular sandalwood scent. It also unexpectedly introduced to the mix cedar wood, another ingredient popular with today's consumers, said David Apel, Symrise's VP and senior perfumer of fine fragrance.

The cyber attack on British Airways affected even more customers than originally thought, according to its owner IAG. A further 185,000 customers might have had their personal details stolen during the hack, it said. The group said in a stock exchange announcement that as part of an investigation into a cyber breach that took place earlier this year, it is contacting two groups of customers not previously notified. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph. The giant human-like robot bears a striking resemblance to the military robots starring in the movie'Avatar' and is claimed as a world first by its creators from a South Korean robotic company Waseda University's saxophonist robot WAS-5, developed by professor Atsuo Takanishi and Kaptain Rock playing one string light saber guitar perform jam session A man looks at an exhibit entitled'Mimus' a giant industrial robot which has been reprogrammed to interact with humans during a photocall at the new Design Museum in South Kensington, London Electrification Guru Dr. Wolfgang Ziebart talks about the electric Jaguar I-PACE concept SUV before it was unveiled before the Los Angeles Auto Show in Los Angeles, California, U.S The Jaguar I-PACE Concept car is the start of a new era for Jaguar.

In 2014 researchers at the MIT Media Lab designed an experiment called Moral Machine. The idea was to create a game-like platform that would crowdsource people's decisions on how self-driving cars should prioritize lives in different variations of the "trolley problem." In the process, the data generated would provide insight into the collective ethical priorities of different cultures. The researchers never predicted the experiment's viral reception. Four years after the platform went live, millions of people in 233 countries and territories have logged 40 million decisions, making it one of the largest studies ever done on global moral preferences.

Research article: Soft ethics, the governance of the digital and the General Data Protection Regulation Luciano Floridi Research article: The fallacy of inscrutability Joshua A. Kroll Opinion piece: Constitutional democracy and technology in the age of artificial intelligence Paul Nemitz Research article: Artificial intelligence policy in India: a framework for engaging the limits of data-driven decision-making Vidushi Marda Research article: Algorithms that remember: model inversion attacks and data protection law Michael Veale, Reuben Binns, Lilian Edwards Research article: Ethical governance is essential to building trust in robotics and artificial intelligence systems Alan F. T. Winfield, Marina Jirotka Research article: Apples, oranges, robots: four misunderstandings in today's debate on the legal status of AI systems Ugo Pagallo Research article: Democratizing algorithmic news recommenders: how to materialize voice in a technologically saturated media ecosystem Jaron Harambam, Natali Helberger, Joris van Hoboken