Social media sites are becoming a key factor in politics. These platforms are easy to manipulate for the purpose of distorting information space to confuse and distract voters. Past works to identify disruptive patterns are mostly focused on analyzing the content of tweets. In this study, we jointly embed the information from both user posted content as well as a user's follower network, to detect groups of densely connected users in an unsupervised fashion. We then investigate these dense sub-blocks of users to flag anomalous behavior. In our experiments, we study the tweets related to the upcoming 2019 Canadian Elections, and observe a set of densely-connected users engaging in local politics in different provinces, and exhibiting troll-like behavior.

The Army will begin equipping combat units next year with remote-controlled robotic vehicles designed to carry ammunition, water and other heavy combat necessities for soldiers, if officials at Fort Benning, Georgia, get their way. The Army has been experimenting with the concept of robotic mules for more than a decade. But the performance of four competing prototypes of a Small Multipurpose Equipment Transport (SMET) during a recent operational test demonstration with units from the 10th Mountain and 101st Airborne divisions has made believers out of officials from Benning's Maneuver Capabilities Development and Integration Directorate (MCDID). "The operational test demonstration really showed that the capability is ready," Col. Tom Nelson, director for Robotics Requirements Division at MCDID, told reporters Tuesday. The SMET is capable of hauling 1,000 pounds of soldier gear for 60 miles within 72 hours, and will also generate three kilowatts of power to charge the growing number of tactical electronic devices soldiers carry, according to officials at MCDID, the organization that has the lead for developing and testing robotics and autonomous systems designed for Army brigade combat teams (BCTs).

Adobe, the company behind the ubiquitous photo-editing program Photoshop, just unveiled a new artificial intelligence tool capable of spotting whether images have been manipulated. The research, which sprang from a partnership with scientists from UC Berkeley and funding from DARPA, focuses on edits made with Photoshop's "liquify" tool, which can subtly reshape and touch up parts of an image, according to an Adobe blog post. While Adobe doesn't plan to release the tool to the public, reports The Verge, it's a sign that the company is taking seriously the propagation of digitally-altered, misleading media. To train the edit-detecting neural net, the Adobe scientists fed it pairs of images -- an undoctored photo of someone's face and a version that had been tweaked with the liquify tool. After enough training, the neural net could spot the edited face 99 percent of the time.

The recent rapid spread of a doctored video of Nancy Pelosi has frightened lawmakers in Washington. The video--edited to make her appear drunk--is just one of a number of examples in the last year of manipulated media making it into mainstream public discourse. In January, a different doctored video targeting President Donald Trump ended up airing on Seattle television. This week, an AI-generated video of Mark Zuckerberg was uploaded to Instagram.

Through the media, this conversation may appear to sit in a cloud of worry about speculative future-bots that will wipe out humanity. However, real inklings of how we can easily lose mastery over our AI creations are observed in practical problems related to unintended behaviors from poorly designed machine learning systems. Among these potential "AI accidents" is the case of adversarial techniques. This approach takes, for instance, a trained classifier model that performs well with identifying inputs compared to how a person would classify. Then, a new input comes along that includes subtle yet maliciously crafted data that causes the model to behave very poorly. What is troublesome is that the type of poor behavior is not a reduction in the statistical performance of the model.

Deloitte Global announced that Deloitte firms are now offering clients an artificial intelligence (AI) platform. The platform can monitor, measure, and analyze changes in tax regulation in real-time to give Deloitte clients the edge in monitoring and responding to regulatory updates across the world. The AI platform is the result of a collaboration between Deloitte firms and Signal A.I. Signal's proprietary AI technology was trained by Deloitte tax experts to understand key regulatory changes in over 100 jurisdictions from over 100 regulators, tax authorities and government bodies. "Tax professionals are being asked to do more with fewer resources--to stay ahead of more risks, draw insights from more data, track more regulations across more jurisdictions. To address these challenges, they are transforming how they operate and manage processes by implementing new technologies, such as AI, robotic process automation and natural language processing," says Conrad Young, Deloitte Global Tax & Legal, Chief Digital Officer.

Estonia, a tiny Northern European nation of fewer than 1.4 million inhabitants, has made impressive strides in digitizing, streamlining, and modernizing its government functions. Estonia famously launched its "e-residency" program that allows practically anybody -- including foreigners -- to access Estonian government services.

Marking the start of London Tech Week on 10 June 2019, Oliver Dowden, the Minister for Implementation, announced the launch of a new guide designed to help the government to embrace AI and an online marketplace (the Spark Dynamic Purchasing System or Spark DPS[1]) specifically intended to help tech start-ups and other SMEs selling innovative products and services to the public sector. The AI Guide[2], announced alongside the UK government's new Technology Innovation Strategy[3] describes how public sector bodies can best assess: There are already a number of frameworks for responsible and ethical AI including those from ITechLaw[4] and the Institute for Ethical AI & Machine Learning.[5] The new AI Guide provides for a framework which leverages the The Alan Turing Institute's (ATI) SUM Values[6]: The AI Guide was produced by the Government Digital Service, the Office for AI and the ATI. It is divided into sub-guides, a number of which will be equally useful to team members across each public sector organisation and its private sector partners working on implementing AI, including data scientists, data engineers, domain experts and delivery managers.

Lung and pancreatic cancers are very often difficult to treat, especially when the cancer has spread to other organs. Today, the five-year survival rate for lung cancer that has spread is 5%. Now a group of researchers have developed a system to use computers to increase a patient's odds. One of the biggest challenges in cancer treatment is catching it at an early stage before it has spread. "Something around 30% to 40% of cancers is missed during the early stages of screening," said Naji Khosravan, a PhD Candidate at the Center for Research in Computer Vision (CRCV), University of Central Florida.

Learning to code involves recognizing how to structure a program, and how to fill in every last detail correctly. No wonder it can be so frustrating. A new program-writing AI, SketchAdapt, offers a way out. Trained on tens of thousands of program examples, SketchAdapt learns how to compose short, high-level programs, while letting a second set of algorithms find the right sub-programs to fill in the details. Unlike similar approaches for automated program-writing, SketchAdapt knows when to switch from statistical pattern-matching to a less efficient, but more versatile, symbolic reasoning mode to fill in the gaps.