Sorry if not okay to post this. This is an automatic summary, original reduced by 80%. In an effort to predict these cases, many doctors use guidelines similar to those of the American College of Cardiology/American Heart Association. In the new study, Weng and his colleagues compared use of the ACC/AHA guidelines with four machine-learning algorithms: random forest, logistic regression, gradient boosting, and neural networks. Using record data available in 2005, they predicted which patients would have their first cardiovascular event over the next 10 years, and checked the guesses against the 2015 records.

The task of next POI recommendation has been studied extensively in recent years. However, developing an unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to handle cold-start and endow the system with interpretability are also difficult topics. Inspired by the recent success of neural networks in many areas, in this paper, we present a simple but effective neural network framework for next POI recommendation, named NEXT. NEXT is an unified framework to learn the hidden intent regarding user's next move, by incorporating different factors in an unified manner. Specifically, in NEXT, we incorporate meta-data information and two kinds of temporal contexts (i.e., time interval and visit time). To leverage sequential relations and geographical influence, we propose to adopt DeepWalk, a network representation learning technique, to encode such knowledge. We evaluate the effectiveness of NEXT against state-of-the-art alternatives and neural networks based solutions. Experimental results over three publicly available datasets demonstrate that NEXT significantly outperforms baselines in real-time next POI recommendation. Further experiments demonstrate the superiority of NEXT in handling cold-start. More importantly, we show that NEXT provides meaningful explanation of the dimensions in hidden intent space.

Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems. The Markov Chain Monte Carlo (MCMC) algorithms are a well-known class of MC methods which generate a Markov chain with the desired invariant distribution. In this document, we focus on the Metropolis-Hastings (MH) sampler, which can be considered as the atom of the MCMC techniques, introducing the basic notions and different properties. We describe in details all the elements involved in the MH algorithm and the most relevant variants. Several improvements and recent extensions proposed in the literature are also briefly discussed, providing a quick but exhaustive overview of the current Metropolis-based sampling's world.

The preceding three decades have seen the emergence, rise, and proliferation of machine learning (ML). From half-recognised beginnings in perceptrons, neural nets, and decision trees, algorithms that extract correlations (that is, patterns) from a set of data points have broken free from their origin in computational cognition to embrace all forms of problem solving, from voice recognition to medical diagnosis to automated scientific research and driverless cars, and it is now widely opined that the real industrial revolution lies less in mobile phone and similar than in the maturation and universal application of ML. Among the consequences just might be the triumph of anti-realism over realism.

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications.

Artificial intelligence may help prevent heart failure. Doctors have lots of tools for predicting a patient's health. But--as even they will tell you--they're no match for the complexity of the human body. Heart attacks in particular are hard to anticipate. Now, scientists have shown that computers capable of teaching themselves can perform even better than standard medical guidelines, significantly increasing prediction rates.

Artificial intelligence picks up racial and gender biases when learning language from text, researchers say. Without any supervision, a machine learning algorithm learns to associate female names more with family words than career words, and black names as being more unpleasant than white names. For a study published today in Science, researchers tested the bias of a common AI model, and then matched the results against a well-known psychological test that measures bias in humans. The team replicated in the algorithm all the psychological biases they tested, according to study co-author Aylin Caliskan, a post-doc at Princeton University. Because machine learning algorithms are so common, influencing everything from translation to scanning names on resumes, this research shows that the biases are pervasive, too.

A cute, robotic language tutor called Musio, has made it from crowdfunding campaign to full-fledged product with a debut in stores this week in Japan. Priced at JPY 98,000 (about US $900), Musio is now sold online through SoftBank's marketplace and Amazon Japan, and through a handful of brick-and-mortars stores. Musio's parent company, AI venture AKA Study, is the latest startup from Raymond Jung, a co-founder of the massively successful test-prep venture, Hackers Education Group, in South Korea. AKA employs about 50 full-time today, with most in Seoul, and a small office in Santa Monica, Calif. Technically, the company is headquartered in the U.S., but it's not making any consumer electronics for the American market yet.

In late February, the UN-Secretary General held a press conference, highlighting the risk of starvation in East Africa and the necessity to raise funds to address the emergency situations in Somalia and South Sudan. Drought has been back in these countries and their neighbours since 2016, leading to a huge current food crisis. While governments are trying to handle the situation, how could technology innovations help prevent starvation and improve agriculture management in the future? We met with Laurent Rivière, a French 30 years-old entrepreneur, who shared with us his view on the subject with a combination of engineer pragmatism and changemaker idealism . Founder and CEO at Sunbirds for two years, he explained to us how his "bird of the sun," his solar drone, is addressing the agriculture challenges of the 21st century.

Maajid Nawaz highlights the scary reality of artificial intelligence - also known as AI - today. Maajid Nawaz highlights the scary reality of artificial intelligence - also known as AI - today. Standing in for James O'Brien, Maajid Nawaz tackled the subject of artificial intelligence after a new study found that the technology is becoming racist and sexist. Here Maajid Nawaz highlights just some of the prejudices AI has already picked up - and it's not pretty. Maajid said: "An algorithm declared that 18 of the 23 most captivating women in the world were white. "We're going to carry on.