A former Google software engineer is sounding the alarm on killer robots. Laura Nolan resigned from Google last year when the tech giant started working with the U.S. military on drone technology, and since then, she has joined the Campaign to Stop Killer Robots, warning that autonomous robots with lethal capabilities could become a threat to humanity. Discussions concerning possibly banning autonomous weapons fell apart on August 21 during a United Nations meeting in Geneva, when Russian diplomats allegedly made a fuss over the language that was used in a document meant to begin the process of establishing a ban. "If you're a despot, how much easier is it to have a small cadre of engineers control a fleet of autonomous weapons for you than to have to keep your troops in line?" Nolan tells Inverse. "Autonomous weapons are potential weapons of mass destruction. They need to be made taboo in the same way that chemical and biological weapons are."

Sen. Jim Risch, R-Idaho, says the United States is considering a "suite of options" in response to Iran's alleged responsibility for drone attack on Saudi Arabia's oil fields. "We have a commander-in-chief and he has said he does not want war with Iran and the Saudi Arabians have said the same thing," Risch said on "The Story with Martha MacCallum Wednesday. "The unfortunate part of all of this is Iran continues to push the envelope." U.S. Secretary of State Mike Pompeo on Wednesday doubled down on accusations Iran is responsible for the weekend bombing on Saudi Arabia's oil facilities, telling reporters that the strike was "an act of war." Earlier President Trump tweeted that he had ordered Treasury Secretary Steven Mnuchin to "substantially increase sanctions" on Iran. Risch said the Iranians are "notorious for making bad judgments" and said that's what is happening with the current situation. "[Trump] doesn't want war with Iran.

The UK's success in the field of robotic surgery could be hampered if the country loses its research partnerships with Europe after Brexit, according to a new study. The study's authors said there was a "consensus" that Brexit was likely to "undermine the UK's status as a global leader in science and innovation". Robotic surgery has been touted as one of the technologies that is key to future growth in the UK, according to the Imperial College London study, and international collaboration is key to that success. Dr George Garas, lead author of the study from the department of surgery and cancer at Imperial College London, said: "There is a consensus within the scientific and healthcare communities that Brexit is likely to undermine the UK's status as a global leader in science and innovation. "We need to understand what the impact of losing the existing valuable EU links would be so as to tactically plan the UK's research and innovation strategy after Brexit." The UK currently ranks third in the world for robotic surgery innovation, behind Italy and the US. The best scenario following Brexit would be for the UK to continue its research partnerships with the EU, the study's authors suggested. If this isn't possible, the UK should look to collaborate with the US. But under this scenario the UK's research impact would ultimately suffer unless its new US partners were the top-performing ones in the field, the study found. "Our research shows that in the field of robotic surgery research, replacing EU partners with top US collaborators might maintain or even improve the UK's position," Dr Garas said. "Unfortunately, in the short term this could be difficult and costly.

Fox News Flash top headlines for Sept. 18 are here. Check out what's clicking on Foxnews.com Artificial intelligence-based solutions may not be able to save us from deceptively altered videos, known as deepfakes, according to a new report from Data and Society. In the report, authors Britt Paris and Joan Donovan put deepfakes on a long continuum of media manipulation and say that they require social and technical fixes. "The panic around deepfakes justifies quick technical solutions that don't address structural inequality," Paris told The Verge.

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. To overcome this limitation, here we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.

--The prevalence of social media has made information sharing possible across the globe. The downside, unfortunately, is the wide spread of misinformation. Methods applied in most previous rumor classifiers give an equal weight, or attention, to words in the microblog, and do not take the context beyond microblog contents into account; therefore, the accuracy becomes plateaued. In this research, we propose an ensemble neural architecture to detect rumor on Twitter . The architecture incorporates word attention and context from the author to enhance the classification performance. In particular, the word-level attention mechanism enables the architecture to put more emphasis on important words when constructing the text representation. T o derive further context, microblog posts composed by individual authors are exploited since they can reflect style and characteristics in spreading information, which are significant cues to help classify whether the shared content is rumor or legitimate news. The experiment on the real-world Twitter dataset collected from two well-known rumor tracking websites demonstrates promising results. It is indisputable that social media has significant influences on people's lives these days.

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent on extensive set of data, and being not applicable for real-time purposes are the important shortcomings of the existing studies. To address these challenges, we propose a new solution for real-time traffic accident prediction using easy-to-obtain, but sparse data. Our solution relies on a deep-neural-network model (which we have named DAP, for Deep Accident Prediction); which utilizes a variety of data attributes such as traffic events, weather data, points-of-interest, and time. DAP incorporates multiple components including a recurrent (for time-sensitive data), a fully connected (for time-insensitive data), and a trainable embedding component (to capture spatial heterogeneity). To fill the data gap, we have - through a comprehensive process of data collection, integration, and augmentation - created a large-scale publicly available database of accident information named US-Accidents. By employing the US-Accidents dataset and through an extensive set of experiments across several large cities, we have evaluated our proposal against several baselines. Our analysis and results show significant improvements to predict rare accident events. Further, we have shown the impact of traffic information, time, and points-of-interest data for real-time accident prediction.

Learning Y our Way Without Map or Compass: Panoramic T arget Driven Visual Navigation David Watkins-V alls,1, Jingxi Xu,1, Nicholas Waytowich 2 and Peter Allen 1 Abstract -- We present a robot navigation system that uses an imitation learning framework to successfully navigate in complex environments. Our framework takes a pre-built 3D scan of a real environment and trains an agent from pre-generated expert trajectories to navigate to any position given a panoramic view of the goal and the current visual input without relying on map, compass, odometry, GPS or relative position of the target at runtime. Our end-to-end trained agent uses RGB and depth (RGBD) information and can handle large environments (up to 1031 m 2) across multiple rooms (up to 40) and generalizes to unseen targets. We show that when compared to several baselines using deep reinforcement learning and RGBD SLAM, our method (1) requires fewer training examples and less training time, (2) reaches the goal location with higher accuracy, (3) produces better solutions with shorter paths for long-range navigation tasks, and (4) generalizes to unseen environments given an RGBD map of the environment. I NTRODUCTION The ability to navigate efficiently and accurately within an environment is fundamental to intelligent behavior and has been a focus of research in robotics for many years. Traditionally, robotic navigation is solved using model-based methods with an explicit focus on position inference and mapping, such as Simultaneous Localization and Mapping (SLAM) [1]. These models use path planning algorithms, such as Probabilistic Roadmaps (PRM) [2] and Rapidly Exploring Random Trees (RRT) [3], [4] to plan a collision-free path. These methods ignore the rich information from visual input and are highly sensitive to robot odometry and noise in sensor data.

You can now see what you look like through the eyes of an AI. ImageNet Roulette was trained with millions of images and uses a neural network to classify pictures of people, with some'dubious and cruel' results. The technology was developed to show the importance of choosing the correct data when training a machine learning system, as it may learn how to be bias. The AI was trained using ImageNet, which is a massive 14 million image data system created in 2009. ImageNet Roulette uses a neural network to classify pictures (such as this one of Kim Kardashian West) of people with some'dubious and cruel' results.