Some modern technologies may seem miraculous, but never has that been quite so literal. Thanks to technological advances, worshipers at a church in Switzerland can now speak directly to Jesus - or at least an AI version of him. As part of an art project called'Deus in Machina' (God in a Machine) St Peter's Church in Lucerne has installed an AI-powered Jesus hologram to take confessions. Worshipers simply voice their concerns and questions to get a response from the digitally-rendered face of Jesus Christ. At least two-thirds of people who spoke to AI Jesus came out of the confessional reporting having had a'spiritual' experience.

The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.

While large volumes of unlabeled data are usually available, associated labels are often scarce. The unsupervised domain adaptation problem aims at exploiting labels from a source domain to classify data from a related, yet different, target domain. When time series are at stake, new difficulties arise as temporal shifts may appear in addition to the standard feature distribution shift. In this paper, we introduce the Match-And-Deform (MAD) approach that aims at finding correspondences between the source and target time series while allowing temporal distortions. The associated optimization problem simultaneously aligns the series thanks to an optimal transport loss and the time stamps through dynamic time warping. When embedded into a deep neural network, MAD helps learning new representations of time series that both align the domains and maximize the discriminative power of the network. Empirical studies on benchmark datasets and remote sensing data demonstrate that MAD makes meaningful sample-to-sample pairing and time shift estimation, reaching similar or better classification performance than state-of-the-art deep time series domain adaptation strategies.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A University of Michigan student is one of the world's foremost "speedcubers," a person capable of quickly solving a Rubik's Cube. He also is an accomplished violinist. Stanley Chapel says the two fields go hand in hand.

Recently, a friend sent me a Wired article entitled "The Power and Paradox of Bad Software". The short piece, written by Paul Ford, discusses the idea that the software industry might be too obsessed with creating better and better tools for itself while neglecting mundane software such as resource scheduling systems or online library catalogs. The author claims that the winners of the bad software lottery are the computational scientists that develop our climate models. Since climate change might be one of the biggest problems for the next generation, some might find it a bit worrying if one of our best tools for examining climate change was written with "bad software". In this post, I discuss the question of wether climate scientists lost the "bad software sweepstakes". I'll cover the basics of climate models, what software is commonly used in climate modeling and why, and what alternative software exists. Best I can tell, the bad software sweepstakes has been won (or lost) by climate change folks.

When Prince Harry and Meghan Markle say "I do" at their royal wedding, online viewers tuning into the Sky News stream will not have to guess the names of international celebrities and British nobility in attendance. Instead, the U.K. broadcaster will use artificial intelligence to identify famous guests as they make their grand entrances at St. George's Chapel at Windsor Castle -- displaying the invitees' names and details about how they are connected to the royal couple. Dubbed "Who's Who Live," Sky News announced the livestream service last week in partnership with Amazon.com and several data and engineering firms. As the 600 guests enter the chapel, Sky News will highlight notable attendees using Amazon Rekognition, a cloud-based technology that can recognize and compare faces in images and video using artificial intelligence. Along with identifying the wedding guests, the livestream service will also show facts about them, Sky News said, using captions and on-screen graphics through the company's app.

He is best known for his adage now referred to as Amara's Law: We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run. This is a problem I regularly encounter when trying to debate with people about whether we should fear artificial general intelligence, or AGI--the idea that we will build autonomous agents that operate much like beings in the world. Now suppose a person tells us that a particular photo shows people playing Frisbee in the park. Computers that can label images like "people playing Frisbee in a park" have no chance of answering those questions.

Julia and Chapel are both newish languages aimed at productitive scientific computing, with parallel computing capabilities baked in from the start. If you are starting a new scientific computing project and are willing to try something new, which should you choose? What are their strengths and weaknesses, and how do they compare? Here we walk through a comparison, focusing on distributed-memory parallelism of the sort one would want for HPC-style simulation. Both have strengths in largely disjoint areas. If you want matlib-like interactivity and plotting, and need only master-worker parallelism, Julia is the clear winner; if you want MPI OpenMPI type scability on rectangular distributed arrays (dense or sparse), Chapel wins handily.