Artificial intelligence (AI) can detect signs of existing or emerging A-fib in ECGs that exhibit normal sinus rhythm, Mayo Clinic researchers have found. Their retrospective analysis, published online yesterday in the Lancet, reports a high degree of accuracy with only one ECG, and this accuracy increases when AI is applied to multiple ECGs from the same patient. "A very common clinical scenario is that someone comes to the hospital with an ischemic stroke, and we want to know whether they have atrial fibrillation," senior author Paul A. Friedman, MD (Mayo Clinic, Rochester, MN), noted to TCTMD. "We have done previous work using neural networks, machine learning, that found that it was extremely powerful in detecting subtle patterns [in ECG tracings], and we wondered: If someone had atrial fibrillation yesterday, is there any way that it might leave a trace of a finding on an ECG today that's too subtle for a human to read but a computer could pick up?" To find out, the investigators, led by Zachi I. Attia, MSc, and Peter A. Noseworthy, MD, drew upon records in the Mayo Clinic Digital Data Vault.

-- Model free reinforcement learning suffers from the high sampling complexity inherent to robotic manipulation or locomotion tasks. Most successful approaches typically use random sampling strategies which leads to slow policy convergence. In this paper we present a novel approach for efficient exploration that leverages previously learned tasks. We exploit the fact that the same system is used across many tasks and build a generative model for exploration based on data from previously solved tasks to improve learning new tasks. The approach also enables continuous learning of improved exploration strategies as novel tasks are learned. Extensive simulations on a robot manipulator performing a variety of motion and contact interaction tasks demonstrate the capabilities of the approach. In particular, our experiments suggest that the exploration strategy can more than double learning speed, especially when rewards are sparse. Moreover, the algorithm is robust to task variations and parameter tuning, making it beneficial for complex robotic problems. I. INTRODUCTION Deep reinforcement learning has attracted a lot of attention for robotic applications where full robot models can be difficult to identify, especially for contact dynamics, and lead to computationally challenging planning and control problems.

Sudden shrieks of radio waves from deep space keep slamming into radio telescopes on Earth, spattering those instruments' detectors with confusing data. And now, astronomers are using artificial intelligence to pinpoint the source of the shrieks, in the hope of explaining what's sending them to Earth from -- researchers suspect -- billions of light-years across space. Usually, these weird, unexplained signals are detected only after the fact, when astronomers notice out-of-place spikes in their data -- sometimes years after the incident. The signals have complex, mysterious structures, patterns of peaks and valleys in radio waves that play out in just milliseconds. That's not the sort of signal astronomers expect to come from a simple explosion, or any other one of the standard events known to scatter spikes of electromagnetic energy across space. Ever since the first one was uncovered in 2007, using data recorded in 2001, there's been an ongoing effort to pin down their source.

What is it exactly that makes humans so smart? In his seminal 1950 paper, "Computer Machinery and Intelligence," Alan Turing argued human intelligence was the result of complex symbolic reasoning. Philosopher Marvin Minsky, cofounder of the artificial intelligence lab at the Massachusetts Institute of Technology, also maintained that reasoning--the ability to think in a multiplicity of ways that are hierarchical--was what made humans human. Patrick Henry Winston begged to differ. "I think Turing and Minsky were wrong," he told me in 2017. "We forgive them because they were smart and mathematicians, but like most mathematicians, they thought reasoning is the key, not the byproduct." Winston, a professor of computer science at MIT, and a former director of its AI lab, was convinced the key to human intelligence was storytelling. "My belief is the distinguishing characteristic of humanity is this keystone ability to have descriptions with which we construct stories. I think stories are what make us different from chimpanzees and Neanderthals. And if story-understanding is really where it's at, we can't understand our intelligence until we understand that aspect of it."

We live in a connected world and generate a vast amount of connected data. Social networks, financial transaction systems, biological networks, transportation systems, and a telecommunication nexus are all examples. The paper citation network displayed in Figure 1 is another example of connected data. The nodes represent research papers, while the edges illustrate citations between papers, with the various colour indicative of a report's subject, with seven colours coding seven topics. Representing connected data is possible using a graph data structure regularly used in Computer Science.

Scientific studies do not find any links between video games and gun violence. The claim that they do has been repeatedly tested, studied and debunked. Yet on Monday, US president Donald Trump insisted that "gruesome and grisly video games" were causative in the gun massacre deaths of 22 people in El Paso and another 9 in Dayton (not Toledo) Ohio. Why scapegoat video games and demonise the people who play them? It's established that science, expertise, evidence and the truth are not dominant themes of the Trump presidency, and with increasing numbers of people bleeding to death in US streets, he has to find someone – something – anything!

The recent proliferation of publicly available graph-structured data has sparked an interest in machine learning algorithms for graph data. Since most traditional machine learning algorithms assume data to be tabular, embedding algorithms for mapping graph data to real-valued vector spaces has become an active area of research. Existing graph embedding approaches are based purely on structural information and ignore any semantic information from the underlying domain. In this paper, we demonstrate that semantic information can play a useful role in computing graph embeddings. Specifically, we present a framework for devising embedding strategies aware of domain-specific interpretations of graph nodes and edges, and use knowledge of downstream machine learning tasks to identify relevant graph substructures. Using two real-life domains, we show that our framework yields embeddings that are simple to implement and yet achieve equal or greater accuracy in machine learning tasks compared to domain independent approaches.

Variational Bayes (VB) has become a versatile tool for Bayesian inference in statistics. Nonetheless, the development of the existing VB algorithms is so far generally restricted to the case where the variational parameter space is Euclidean, which hinders the potential broad application of VB methods. This paper extends the scope of VB to the case where the variational parameter space is a Riemannian manifold. We develop, for the first time in the literature, an efficient manifold-based VB algorithm that exploits both the geometric structure of the constraint parameter space and the information geometry of the manifold of VB approximating probability distributions. Our algorithm is provably convergent and achieves a convergence rate of order $\mathcal O(1/\sqrt{T})$ and $\mathcal O(1/T^{2-2\epsilon})$ for a non-convex evidence lower bound function and a strongly retraction-convex evidence lower bound function, respectively. We develop in particular two manifold VB algorithms, Manifold Gaussian VB and Manifold Neural Net VB, and demonstrate through numerical experiments that the proposed algorithms are stable, less sensitive to initialization and compares favourably to existing VB methods.

A PhD student in Australia has developed an automated system to detect, in real time, mysterious radio pulses emanating from the deep universe. The fleeting signals known as fast radio bursts (FRBs) have baffled scientists since they were first discovered in 2007 by a team poring through archival data. Since then, there have been numerous other instances of their detection – though what exactly causes them remains a mystery. The latest breakthrough could be a huge leap forward for scientists' ability to understand the nature of fast radio bursts, allowing them to be captured in detail the moment they reach Earth. A PhD student in Australia has developed an automated system to detect, in real-time, mysterious radio pulses emanating from the deep universe.

Harry Butler Institute, Murdoch University, are helping to protect Australia's biosecurity. In a first of its kind application in Western Australia, AI technology in the field - using IBM Power-9 based hardware and PowerAI Vision technology - is providing scientists with real-time profile of biosecurity threats within seconds, helping them to identify invasive species, even when distinguishing features may not be visible to the human eye.