FILE PHOTO: The Google logo is pictured atop an office building in Irvine, California, U.S., August 7, 2017. SAN FRANCISCO (Reuters) - Search engine Google said on Thursday it had seen no evidence on its advertising platforms of the kind of suspected Russian propaganda campaign that Facebook Inc says appeared on its network before and after last year's U.S. presidential election. "We're always monitoring for abuse or violations of our policies and we've seen no evidence this type of ad campaign was run on our platforms," Google, a unit of Alphabet Inc and the owner of YouTube, said in a statement in response to questions. Not all U.S. presidents are missed once they leave the White House. A new study shows women in their 40s and 50s aren't getting enough sleep.
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Over two days of testimony before Congress earlier this month, Facebook founder and CEO Mark Zuckerberg dodged a litany of questions from lawmakers about how the data of 87 million Americans ended up in the hands of voter profiling firm Cambridge Analytica. The spectacle put a spotlight on the company's murky data-collection and sharing practices, and sparked a much-needed discussion about if and how to hold companies accountable for their handling of user data. However much deserved, Facebook has, so far, born the brunt of public scrutiny for what has unfortunately become standard practice for web platforms and services. As the Ranking Digital Rights 2018 Corporate Accountability Index--an annual ranking of the some of the world's most powerful internet, mobile, and telecommunications companies that was released this week--shows, companies across the board lack transparency about what user data they collect and share, and tell us alarmingly little about their data-sharing agreements with advertisers or other third parties.
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at https://physics.bu.edu/~pankajm/MLnotebooks.html )
Computer hardware maker Super Micro Computer told customers on Tuesday that an outside investigations firm had found no evidence of any malicious hardware in its current or older-model motherboards. In a letter to customers, the San Jose, California, company said it was not surprised by the result of the review it commissioned in October after a Bloomberg article reported that spies for the Chinese government had tainted Super Micro equipment to eavesdrop on its clients. Super Micro had denied the allegations made in the report. Chinese motherboard manufacturer Super Micro Computer said in a letter to customers that it's conducting a review of its hardware in light of a controversial hacking report A person familiar with the analysis told Reuters it had been conducted by global firm Nardello & Co and that customers could ask for more detail on that company's findings. Nardello tested samples of motherboards in current production and versions that were sold to Apple Inc and Amazon.com
Software engineers often use Q&A forums like Stack Overflow and MSDN to ask and answer technical questions. Through a survey study and web browser log analysis, we find that both askers and answerers of technical forum questions typically conduct extensive online research before composing their posts. The inclusion of links to these research materials is beneficial to the forum participants, though post authors do not always include such citations. Based on these findings, we developed CiteHistory, a browser plugin that simplifies the process of including relevant search queries and URLs as bibliographic supplements to forum posts, and supports information re-finding for post authors. We discuss the results of a two-week deployment of CiteHistory with professional software engineers, which demonstrated that CiteHistory increased reference inclusion in posts, and offered auxiliary benefits as a personal research tracker.