It's possible that someone may be watching your screen--by listening to it. A recent study from cybersecurity analysts at the universities of Michigan, Pennsylvania and Tel Aviv found that LCD screens "leak" a frequency that can be processed by artificial intelligence to provide a hacker insight into what's on a screen. "Displays are built to show visuals, not emit sound," says Roei Schuster, a PhD candidate at Tel Aviv University and a co-author of the study with doctoral candidates Daniel Genkin, Eran Tromer and Mihir Pattani. Yet the team's study shows that's not the case. The researchers were able to collect the noise through either a built-in or nearby microphone or remotely over Google Hangouts, for example.

The subject of outer space has been making headlines of late thanks to the proposal by US President Donald Trump for a United States Space Force as a new branch of the US military. If the Space Force does materialise, it will become the sixth armed forces branch in the US, joining the Navy, Army, Marine Corps, Air Force and Coast Guard. It also underscores the burgeoning importance of space in Earthly affairs. The Space Force will focus on national security, and preserving the satellites and vehicles that are dedicated to international communications and observation. Talk of the Space Force has been exciting news to fans of space-based science fiction such as Star Wars and military buffs alike, who imagine high-tech weaponry and elite soldiers doing battle in the wide and wondrous expanse of outer space.

Like it or not, facial recognition is creeping into more public spaces. The US Secret Service has quietly started testing the technology in and around the White House grounds, including nearby parks and streets, to see if it can "biometrically confirm" the identities of volunteer Secret Service employees. The pilot program will only retain images if there's a match and won't share information with other agencies, but the ultimate goal is to spot known "subjects of interest" (read: potential threats) before there's a run-in with law enforcement. The test runs until August 30th, after which point the Secret Service will delete any facial data it collected during the period unless there's an "open law enforcement matter." Not surprisingly, the ACLU has expressed many concerns about the pilot.

This paper considers extractive summarisation in a comparative setting: given two or more document groups (e.g., separated by publication time), the goal is to select a small number of documents that are representative of each group, and also maximally distinguishable from other groups. We formulate a set of new objective functions for this problem that connect recent literature on document summarisation, interpretable machine learning, and data subset selection. In particular, by casting the problem as a binary classification amongst different groups, we derive objectives based on the notion of maximum mean discrepancy, as well as a simple yet effective gradient-based optimisation strategy. Our new formulation allows scalable evaluations of comparative summarisation as a classification task, both automatically and via crowd-sourcing. To this end, we evaluate comparative summarisation methods on a newly curated collection of controversial news topics over 13 months. We observe that gradient-based optimisation outperforms discrete and baseline approaches in 15 out of 24 different automatic evaluation settings. In crowd-sourced evaluations, summaries from gradient optimisation elicit 7% more accurate classification from human workers than discrete optimisation. Our result contrasts with recent literature on submodular data subset selection that favours discrete optimisation. We posit that our formulation of comparative summarisation will prove useful in a diverse range of use cases such as comparing content sources, authors, related topics, or distinct view points.

Large scale astronomical surveys continue to increase their depth and scale, providing new opportunities to observe large numbers of celestial objects with ever increasing precision. At the same time, the sheer scale of ongoing and future surveys pose formidable challenges to classify astronomical objects. Pioneering efforts on this front include the citizen science approach adopted by the Sloan Digital Sky Survey (SDSS). These SDSS datasets have been used recently to train neural network models to classify galaxies in the Dark Energy Survey (DES) that overlap the footprint of both surveys. While this represents a significant step to classify unlabeled images of astrophysical objects in DES, the key issue at heart still remains, i.e., the classification of unlabelled DES galaxies that have not been observed in previous surveys. To start addressing this timely and pressing matter, we demonstrate that knowledge from deep learning algorithms trained with real-object images can be transferred to classify elliptical and spiral galaxies that overlap both SDSS and DES surveys, achieving state-of-the-art accuracy 99.6%. More importantly, to initiate the characterization of unlabelled DES galaxies that have not been observed in previous surveys, we demonstrate that our neural network model can also be used for unsupervised clustering, grouping together unlabeled DES galaxies into spiral and elliptical types. We showcase the application of this novel approach by classifying over ten thousand unlabelled DES galaxies into spiral and elliptical classes. We conclude by showing that unsupervised clustering can be combined with recursive training to start creating large-scale DES galaxy catalogs in preparation for the Large Synoptic Survey Telescope era.

Lawyers representing South Korean plaintiffs in a World War II labor court case against Japan's Nippon Steel & Sumitomo Metal Corp. have set a Dec. 24 deadline for the firm to show willingness to discuss a court verdict on compensation. If the firm fails to respond, the lawyers, who spoke after being denied a meeting with company officials for a second time on Tuesday, said they would start procedures to seize its South Korean assets. Tuesday's incident stemmed from a ruling by South Korea's Supreme Court late in October that Nippon Steel must pay 100 million won ($90,500) to each of four South Koreans for forced labor during the war. The Japanese government has denounced the verdict, saying all wartime reparations were dealt with in a 1965 treaty that normalized ties between the two nations. At the time of the ruling, Nippon Steel called it "extremely regrettable," but added that it would review the decision carefully in considering further steps.

Sitting in front of a Converus EyeDetect station, it's impossible not to think of Blade Runner. In the 1982 sci-fi classic, Harrison Ford's rumpled detective identifies artificial humans using a steam-punk Voight-Kampff device that watches their eyes while they answer surreal questions. EyeDetect's questions are less philosophical, and the penalty for failure is less fatal (Ford's character would whip out a gun and shoot). But the basic idea is the same: By capturing imperceptible changes in a participant's eyes--measuring things like pupil dilation and reaction time--the device aims to sort deceptive humanoids from genuine ones. It claims to be, in short, a next-generation lie detector. Polygraph tests are a $2 billion industry in the US and, despite their inaccuracy, are widely used to screen candidates for government jobs.

As we survey the fallout from the midterm elections, it would be easy to miss the longer-term threats to democracy that are waiting around the corner. Perhaps the most serious is political artificial intelligence in the form of automated "chatbots," which masquerade as humans and try to hijack the political process. Chatbots are software programs that are capable of conversing with human beings on social media using natural language. Increasingly, they take the form of machine learning systems that are not painstakingly "taught" vocabulary, grammar and syntax but rather "learn" to respond appropriately using probabilistic inference from large data sets, together with some human guidance. Some chatbots, like the award-winning Mitsuku, can hold passable levels of conversation.

The tantalizing prospect of predicting crime before it happens has got law enforcement agencies excited about AI. Most efforts so far have focused on forecasting where and when crime will happen, but now British police want to predict who will perpetrate it. The idea is reminiscent of sci-fi classic Minority Report, where clairvoyant "precogs" were used to predict crime before it happened and lock up the would-be criminals. The National Data Analytics Solution (NDAS) under development in the UK will instead rely on AI oracles to scour police records and statistics to find those at risk of violent crime. Machine learning will be used to analyze a variety of local and national police databases containing information like crime logs, stop and search records, custody records, and missing person reports. In particular, it's aimed at identifying those at risk of committing or becoming a victim of gun or knife crime, and those who could fall victim to modern slavery.

NASA's OSIRIS-REx spacecraft has officially arrived at the asteroid Bennu after a more than two-year-long journey. The spacecraft rendezvoused with its target around noon EST, and NASA confirmed the arrival a few minutes thereafter during a live event today. Achievement unlocked: "We have arrived!" Our @OSIRISREx mission reached asteroid Bennu, where it will spend almost a year mapping and studying to find a safe location to collect a sample. OSIRIS-REx launched in 2016 and its mission is to collect a sample from Bennu and return it back to Earth for study.