It's official, Microsoft has killed off Internet Explorer, its new browser will be Microsoft Edge although you'll be forgiven for thinking nothing has changed if you're looking at the logo. The browser, formally known as Project Spartan after the protagonist of Microsoft's Halo game series, is completely new according to the company. It announced the new name as part of its Windows 10 presentation at its Build developers conference in San Francisco. Edge is meant to give Microsoft a clean break from the negative connotations attached to the Internet Explorer brand by millions of people forced to use old, decrepit versions on work computers. It will live on for many of Microsoft's customers as the centrepiece of much software as a services packages, often required by administrative applications and point of sale.

Apple is refusing to explain how and why the Russian language version of its voice-controlled virtual assistant, Siri, provided homophobic answers to queries relating to gay or lesbian topics. The allegations first came to light when a Russian man called Alex, who lives in London, uploaded a video to YouTube appearing to show Siri either evading questions or expressing a negative response. Alex asked questions such as: "Are there any gay bars around me?", "tell me about gay marriage?" "I would have turned red if I could", "you are so rude!" and "I will pretend I haven't heard". These responses seem to suggest that the word gay () in Russian has been programmed as profanity. Swear words in the English language version receive similar responses.

A YouTube video sent to Apple blog 9to5Mac lets you hear the difference between the old Siri and the new one. In the video, a more lifelike Siri says certain words much more naturally, as if they were recorded by an actual person. The digital assistant's cadence has also improved, as the pitch of Siri's voice drops at the end of clauses just like a natural English speaker's would. Siri's voice has undergone periodic tweaks and fixes, but none so major as when Apple (AAPL, Tech30) released iOS 7 in 2013. In that update, Siri got an entirely new sound, switching from actress Susan Bennett's voice to two other unnamed actors (one male and one female).

We address the collective matrix completion problem of jointly recovering a collection of matrices with shared structure from partial (and potentially noisy) observations. To ensure well--posedness of the problem, we impose a joint low rank structure, wherein each component matrix is low rank and the latent space of the low rank factors corresponding to each entity is shared across the entire collection. We first develop a rigorous algebra for representing and manipulating collective--matrix structure, and identify sufficient conditions for consistent estimation of collective matrices. We then propose a tractable convex estimator for solving the collective matrix completion problem, and provide the first non--trivial theoretical guarantees for consistency of collective matrix completion. We show that under reasonable assumptions stated in Section 3.1, with high probability, the proposed estimator exactly recovers the true matrices whenever sample complexity requirements dictated by Theorem 1 are met. The sample complexity requirement derived in the paper are optimum up to logarithmic factors, and significantly improve upon the requirements obtained by trivial extensions of standard matrix completion. Finally, we propose a scalable approximate algorithm to solve the proposed convex program, and corroborate our results through simulated experiments.

We consider the classic problem of establishing a statistical ranking of a set of n items given a set of inconsistent and incomplete pairwise comparisons between such items. Instantiations of this problem occur in numerous applications in data analysis (e.g., ranking teams in sports data), computer vision, and machine learning. We formulate the above problem of ranking with incomplete noisy information as an instance of the group synchronization problem over the group SO(2) of planar rotations, whose usefulness has been demonstrated in numerous applications in recent years. Its least squares solution can be approximated by either a spectral or a semidefinite programming (SDP) relaxation, followed by a rounding procedure. We perform extensive numerical simulations on both synthetic and real-world data sets, showing that our proposed method compares favorably to other algorithms from the recent literature. Existing theoretical guarantees on the group synchronization problem imply lower bounds on the largest amount of noise permissible in the ranking data while still achieving exact recovery. We propose a similar synchronization-based algorithm for the rank-aggregation problem, which integrates in a globally consistent ranking pairwise comparisons given by different rating systems on the same set of items. We also discuss the problem of semi-supervised ranking when there is available information on the ground truth rank of a subset of players, and propose an algorithm based on SDP which recovers the ranks of the remaining players. Finally, synchronization-based ranking, combined with a spectral technique for the densest subgraph problem, allows one to extract locally-consistent partial rankings, in other words, to identify the rank of a small subset of players whose pairwise comparisons are less noisy than the rest of the data, which other methods are not able to identify.

Speech to personal assistants (e.g., reminders, calendar entries, messaging, voice search) is often uttered under cognitive load, causing nonfinal pausing that can result in premature recognition cut-offs. Prior research suggests that prepausal features can discriminate final from nonfinal pauses, but it does not reveal how speakers would behave if given longer to pause. To this end, we collected and compared two elicitation corpora differing in naturalness and task complexity. The Template Corpus (4409 nonfinal pauses) uses keyword-based prompts; the Freeform Corpus (8061 nonfinal pauses) elicits open-ended speech. While nonfinal pauses are longer and twice as frequent in the Freeform data, prepausal feature modelling is roughly equally effective in both corpora. At a response latency of 100 ms, prepausal features modelled by an SVM reduced cut-off rates from 100% to 20% for both corpora. Results have implications for enhancing turn-taking efficiency and naturalness in personal-assistant technology.

Microsoft is reportedly developing a version of its year-old digital assistant, named Cortana, for iOS and Android. The move seems to indicate Microsoft is trying new ways to get its products onto the two dominant mobile operating systems across the US. For mobile users, the question will be: how does Cortana stack up next to Siri? The Cortana project came out of a branch of Microsoft developing artificial intelligence, dubbed "Einstein." Cortana, which is named after a central character in the massively popular Microsoft-owned video game series Halo, has been installed on Microsoft phones for the past year.

Apple personal virtual assistant, Siri, might soon be competing for your time with Cortana, its counterpart from Microsoft. Cortana will be coming to iOS and Android at some point after Windows 10 rolls out with an updated version of Microsoft's virtual assistant software, Reuters reported Friday, citing people who claim to have knowledge of the software giant's plans. It would be a standalone app, available in the Google Play marketplace and Apple App Store, and work just as it already does on Windows Phone, according to the report. Microsoft also is working toward a more advanced version of Cortana, drawing from a research project called Einstein. "This kind of technology, which can read and understand email, will play a central role in the next rollout of Cortana, which we are working on now for the fall time frame," Eric Horvitz, Microsoft Research managing director, told Reuters in an interview. The company has already incorporated Cortana into its Windows 10 operating system, which will be coming to PCs in the latter part of this year.

Investors in Sirius XM Holdings Inc (NASD: SIRI) saw new options become available today, for the May 1st expiration. At Stock Options Channel, our YieldBoost formula has looked up and down the SIRI options chain for the new May 1st contracts and identified the following call contract of particular interest. The call contract at the $4.00 strike price has a current bid of 6 cents. If an investor was to purchase shares of SIRI stock at the current price level of $3.90/share, and then sell-to-open that call contract as a "covered call," they are committing to sell the stock at $4.00. Considering the call seller will also collect the premium, that would drive a total return (excluding dividends, if any) of 4.10% if the stock gets called away at the May 1st expiration (before broker commissions).

Remember those charming smartphone ads a few years ago where people held conversations with Siri, Apple iOS' enchanting virtual assistant? The concept was innovative for operating the phone hands-free but not so helpful in searching the web on your behalf. Oftentimes, Siri comes up with entirely wrong answers, too many options or services only tangentially relevant to your inquiry. Asking for a modestly-priced tailor or an upscale sushi bar known for uni is too ambitious. She hears us, but she's not listening.