A federal probe into Facebook's sharing of user data with Cambridge Analytica now involves the FBI, the Securities and Exchange Commission and the Justice Department, the Washington Post reported. Representatives from these agencies have joined the Federal Trade Commission in the inquiry, the newspaper reported, citing five unnamed people familiar with the matter. Those people spoke on condition of anonymity because the probes are not complete. The probe reportedly centers on what Facebook knew in 2015, when it learned that the political data-mining firm Cambridge Analytica had improperly accessed the personal data of tens of millions of Facebook users. Facebook didn't disclose the incident with the political firm, which later worked for the Trump campaign and other Republican candidates, until this March.

ThetaRay, a big data analytics company based in Hod HaSharon, Israel, today announced that it raised more than $30 million in a funding round led by Jerusalem Venture Partners (JVP), GE, Bank Hapoalim, OurCrowd, SVB Investments, and others. That puts its fundraising total to date at about $60 million. "In this era when criminal activity and money laundering are increasing and becoming more sophisticated and also regulation is on the rise, there is a greater demand for our solutions," Mark Gazit, CEO of ThetaRay, said in a statement. "As the amount of digital information grows, you just can't protect it without artificial intelligence systems. ThetaRay offers the most advanced and mature solutions to detect threats before they happen."

EVEN as all eyes have been trained on Singapore for the historic Trump-Kim summit, there is a rather more mundane, yet no less critical, reason for the world to look more closely at this city-state.

Deputy Defense Secretary Patrick Shanahan last Wednesday issued a memorandum that officially establishes the Defense Department's (DoD) new Joint Artificial Intelligence Center (JAIC), confirming statements made by Secretary James Mattis and other DoD officials in April that a center to house DoD's roughly 600 AI projects was forthcoming. The JAIC will be established and guided by DoD CIO Dana Deasy "with the overarching goal of accelerating the delivery of AI-enabled capabilities, scaling the Department-wide impact of AI, and synchronizing DoD AI activities to expand Joint Force advantages," Shanahan said. The director of the JAIC will report directly to Deasy. The goal for the JAIC is to enable DoD's various components "to swiftly introduce new capabilities and effectively experiment with new operational concepts in support of DoD's warfighting missions and business functions," Shannon said. The first step in the establishment of the JAIC tasks Deasy with reporting back within 30 days with an initial list of national mission initiatives – "large-scale efforts to apply AI to a cluster of closely related, urgent, joint challenges" – to be launched within the next 90 days, proposed resource allocations for fiscal years 2018 and 2019, and personnel requirements.

Automated platforms which support users in finding a mutually beneficial match, such as online dating and job recruitment sites, are becoming increasingly popular. These platforms often include recommender systems that assist users in finding a suitable match. While recommender systems which provide explanations for their recommendations have shown many benefits, explanation methods have yet to be adapted and tested in recommending suitable matches. In this paper, we introduce and extensively evaluate the use of "reciprocal explanations" -- explanations which provide reasoning as to why both parties are expected to benefit from the match. Through an extensive empirical evaluation, in both simulated and real-world dating platforms with 287 human participants, we find that when the acceptance of a recommendation involves a significant cost (e.g., monetary or emotional), reciprocal explanations outperform standard explanation methods which consider the recommendation receiver alone. However, contrary to what one may expect, when the cost of accepting a recommendation is negligible, reciprocal explanations are shown to be less effective than the traditional explanation methods.

Predicting how proteins interact with one another - that is, which surfaces of one protein bind to which surfaces of another protein - is a central problem in biology. Here we present Siamese Atomic Surfacelet Network (SASNet), the first end-to-end learning method for protein interface prediction. Despite using only spatial coordinates and identities of atoms as inputs, SASNet outperforms state-of-the-art methods that rely on complex, hand-selected features. These results are particularly striking because we train the method entirely on a significantly biased data set that does not account for the fact that proteins deform when binding to one another. Nonetheless, our network maintains high performance, without retraining, when tested on real cases in which proteins do deform. This suggests that it has learned fundamental properties of protein structure and dynamics, which has important implications for a variety of key problems related to biomolecular structure.

An adversarial machine learning approach is introduced to launch jamming attacks on wireless communications and a defense strategy is provided. A cognitive transmitter uses a pre-trained classifier to predict current channel status based on recent sensing results and decides whether to transmit or not, whereas a jammer collects channel status and ACKs to build a deep learning classifier that reliably predicts whether there will be a successful transmission next and effectively jams these transmissions. This jamming approach is shown to reduce the performance of the transmitter much more severely compared with randomized or sensing-based jamming. Next, a generative adversarial network (GAN) is developed for the jammer to reduce the time to collect the training dataset by augmenting it with synthetic samples. Then, a defense scheme is introduced for the transmitter that prevents the jammer from building a reliable classifier by deliberately taking a small number of wrong actions (in form of a causative attack launched against the jammer) when it accesses the spectrum. The transmitter systematically selects when to take wrong actions and adapts the level of defense to machine learning-based or conventional jamming behavior in order to mislead the jammer into making prediction errors and consequently increase its throughput.

SpaceX's CRS-15 Dragon cargo resupply ship has been attached to the International Space Station. the spacecraft rendezvoused with the orbiting outpost in the early-morning hours of July 2, 2018, and is expected to remain berthed for about a month. Capture took place at 6:54 a.m. EDT (10:54 GMT) by the 57.7-foot (17.6-meter) Canadian-built robotic Canadarm2, which was under the control of Expedition 56 NASA astronauts Ricky Arnold and Drew Feustel at the robotics work station in the station's cupola window. The vehicle was grappled while the station was flying 256 miles (412 kilometers) over Quebec City. "Looking forward to some really exciting weeks ahead as we unload the science and get started on some great experiments," Arnold said.

When U.S. Special Forces entered Afghanistan in 2001, Facebook didn't exist, the iPhone had yet to be invented, and "A.I." often referred to an NBA star. Seventeen years later, American special operations forces continue to ride horseback in rural Afghanistan, but information technology has advanced rapidly. Recent breakthroughs in robotics and artificial intelligence (AI) have captured the popular imagination and prompted sober talk of an impending AI revolution. Yet surprisingly little of that talk has touched on the small wars and insurgencies that have dominated U.S. foreign policy in the 21st century. The definitive work on emerging technology and insurgency has yet to be written, but two recent books offer suggestions for how the era of big data and AI will affect the United States' modern conflicts.

Chinese researchers are working on a new handheld laser weapon capable of burning skin and clothing from up to half a mile away, according to a report. The high-powered laser rifle will be used by anti-terrorism squads in the Chinese Armed Police, the South China Morning Post reports, with prototypes of the device already being tested at the Xian Institute of Optics and Precision Mechanics at the Chinese Academy of Sciences in Shaanxi province. The ZKZM assault rifle causes "instant carbonisation" of human tissue, according to the researchers behind it, and will "burn through clothes in a split second. " The unnamed researcher from the Chinese Academy of Sciences added: "If the fabric is flammable, the whole person will be set on fire." While the gun is not powerful enough to kill someone, the researcher said: "The pain will be beyond endurance."