Most of us are familiar with such blockbuster movie concepts perhaps best expressed by the early Terminator films which revolve around the notion of artificial intelligence (AI) "taking over" and ultimately wiping out humanity. While such movies are complete fiction, the dangers of AI are very real. And, if we listen to people such as Elon Musk and even Julian Assange, that danger is more urgent than even the most paranoid of us suspect. It is perhaps interesting that many who are critical of such claims of the need to regulate the spread of technologies around the planet usually have connections of one kind or another to the very big business that makes untold millions off the back of the sale, implementation and ultimate rolling out of such intelligence-based technologies. This tells us, does it not, that the collective interests of humanity is perhaps not at the top of the list of such big business corporations.

And humans need health screenings, especially for the heart. That's because heart disease is the leading cause of death worldwide. With deep learning, heart disease diagnosis is becoming easier and more accessible -- which in turn can improve treatment and patient outcomes. Echocardiograms -- ultrasound tests that generate images of the heart -- are used to detect and manage heart disease cases. An echo, as it's commonly called, is also used as an assessment tool for specific populations, such as chemotherapy patients, because of their increased risk of heart failure.

On a recent weeknight, Dahlia and Adam Brown came home to their spacious Colonial on a quiet cul-de-sac in Marietta, Ga. The Browns both work demanding jobs and have two young sons. They bought the house in June using Knock, a company that's trying to revolutionize the real-estate industry with a "home trade-in platform" making it easier to buy and sell at once. That solution was ideal for the Browns, who are just as busy as most couples but more introverted, making the idea of prospective buyers tramping through their private space seem excruciating. Across town, Martha Seay was overseeing movers in a rambling brown ranch-style house nestled among tall hickory trees. The day before, she had closed on the sale of the house, where she and her husband had raised their family, to the real-estate company Zillow.

In the Bayesian analysis of signals immersed in noise [1], we seek a representation for the posterior probability of one or more parameters that govern the shape of the signals. Unless the parameter-to-signal map (the forward model) is very simple, the analysis (or inverse solution) comes at significant computational cost, as it requires the stochastic exploration of the likelihood surface at a large number of locations in parameter space. Such is the case, for instance, of parameter estimation for gravitational-wave sources such as the compact binaries detected by LIGO-Virgo [2, 3]; here each likelihood evaluation requires that we generate the gravitational waveform corresponding to a set of source parameters, and compute its noise-weighted correlation with detector data [4]. Waveform generation is usually the costlier operation, so gravitational-wave analysts often utilize faster, less accurate waveform models [5, 6], or accelerated surrogates of slower, more accurate models [7]. Extending the analysis from the data we have to the data we might measure (i.e., characterizing the parameter-estimation prospects of future experiments) compounds the expense, since we need to explore posteriors for many noise realizations, and across the domain of possible source parameters. For concreteness, we price the evaluation of a single Bayesian posterior at null 10 6 times the cost of generating a waveform, and the characterization of parameter-estimation prospects at null 10 6 times the cost of a posterior. With current computational resources, this means that (for instance) accurate component-mass estimates only become available hours or days after the detection of a binary black-hole coalescence [8, 9], while any extensive study of parameter-estimation prospects must rely on less reliable techniques such as the Fisher-matrix approximation [10]. In this Letter, we show how one-or two-dimensional marginalized Bayesian posteriors may be produced using deep neural networks [11] trained on large ensembles of signal noise data streams.

Palo Alto Research Center, Mail Stop: 3333 Coyote Hill Road, Palo Alto, CA 94034 USA Abstract Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA. 1. Introduction Transportation is one of the largest consumers of energy in the ...

Graphs evolving over time are a natural way to represent data in many domains, such as social networks, bioinformatics, physics and finance. Machine learning methods for graphs, which leverage such data for various prediction tasks, have seen a recent surge of interest and capability. In practice, ground truth edges between nodes in these graphs can be unknown or suboptimal, which hurts the quality of features propagated through the network. Building on recent progress in modeling temporal graphs and learning latent graphs, we extend two methods, Dynamic Representation (DyRep) and Neural Relational Inference (NRI), for the task of dynamic link prediction. We explore the effect of learning temporal attention edges using NRI without requiring the ground truth graph. In experiments on the Social Evolution dataset, we show semantic interpretability of learned attention, often outperforming the baseline DyRep model that uses a ground truth graph to compute attention. In addition, we consider functions acting on pairs of nodes, which are used to predict link or edge representations. We demonstrate that in all cases, our bilinear transformation is superior to feature concatenation, typically employed in prior work. Source code is available at https://github.com/uoguelph-mlrg/LDG.

We focus on the following natural question: is it possible to influence the outcome of a voting process through the strategic provision of information to voters who update their beliefs rationally? We investigate whether it is computationally tractable to design a signaling scheme maximizing the probability with which the sender's preferred candidate is elected. We focus on the model recently introduced by Arieli and Babichenko (2019) (i.e., without inter-agent externalities), and consider, as explanatory examples, $k$-voting rule and plurality voting. There is a sharp contrast between the case in which private signals are allowed and the more restrictive setting in which only public signals are allowed. In the former, we show that an optimal signaling scheme can be computed efficiently both under a $k$-voting rule and plurality voting. In establishing these results, we provide two general (i.e., applicable to settings beyond voting) contributions. Specifically, we extend a well known result by Dughmi and Xu (2017) to more general settings, and prove that, when the sender's utility function is anonymous, computing an optimal signaling scheme is fixed parameter tractable w.r.t. the number of receivers' actions. In the public signaling case, we show that the sender's optimal expected return cannot be approximated to within any factor under a $k$-voting rule. This negative result easily extends to plurality voting and problems where utility functions are anonymous.

Brad Smith, Microsoft's president, last week told the Guardian that tech companies should stop behaving as though everything that is not illegal is acceptable. Mr Smith made a good argument that technology may be considered morally neutral but technologists can't be. He is correct that software engineers ought to take much more seriously the moral consequences of their work. This argument operates on two levels: conscious and unconscious. It is easy to see the ethical issue in Microsoft's sale of facial recognition technology to US Immigration and Customs Enforcement while the Trump administration was separating children from parents at the US's southern border.

Brad Smith, Microsoft's president, last week told the Guardian that tech companies should stop behaving as though everything that is not illegal is acceptable. Mr Smith made a good argument that technology may be considered morally neutral but technologists can't be. He is correct that software engineers ought to take much more seriously the moral consequences of their work. This argument operates on two levels: conscious and unconscious. It is easy to see the ethical issue in Microsoft's sale of facial recognition technology to US Immigration and Customs Enforcement while the Trump administration was separating children from parents at the US's southern border.

Over the last decade, the cybersecurity environment has undergone significant changes. But while the Internet may be vast, it isn't necessarily becoming safer. Major data leaks from high profile companies like Equifax set a clear message that even big players are vulnerable to security breaches – and companies in industries including insurance are turning to artificial intelligence (AI) to secure their sensitive data. It is imperative that companies across all industries – especially when dealing with the personal data of consumers – work to avoid data breaches or liabilities when it comes to user information. Let's delve into the top data security considerations for insurance companies and explore how AI can help protect enterprises of all sizes.