Building 8, which was created at last year's F8, has been working on a "brain-computer interface" for several months, Ms. Dugan said. Recent job postings for Building 8 show the unit is hiring engineers for a two-year project "focused on developing advanced (brain-computer interface) technologies." Ultimately, the mind-reading technology could help people type 100 words a minute from their minds--about five times faster than we type from our smartphones, Ms. Dugan told developers at the conference in San Jose, Calif. Separately, Building 8 also is working on technology that could help people "hear" with their skin, Ms. Dugan said. Building 8 tackles Facebook's bleeding edge ideas--way beyond projects such as the augmented reality technology CEO Mark Zuckerberg announced Tuesday.
Marcolino, Leandro Soriano (University of Southern California) | Lakshminarayanan, Aravind (Indian Institute of Technology, Madras) | Yadav, Amulya (University of Southern California) | Tambe, Milind (University of Southern California)
Influence Maximization is an active topic, but it was always assumed full knowledge of the social network graph. However, the graph may actually be unknown beforehand. For example, when selecting a subset of a homeless population to attend interventions concerning health, we deal with a network that is not fully known. Hence, we introduce the novel problem of simultaneously influencing and mapping (i.e., learning) the graph. We study a class of algorithms, where we show that: (i) traditional algorithms may have arbitrarily low performance; (ii) we can effectively influence and map when the independence of objectives hypothesis holds; (iii) when it does not hold, the upper bound for the influence loss converges to 0. We run extensive experiments over four real-life social networks, where we study two alternative models, and obtain significantly better results in both than traditional approaches.
Facebook's CEO goes to Washington, Apple launches its latest limited-edition Product RED phones and we check out whether those newly discovered human organs are really just that. The first hearing starts at 2:15 PM ET.Mark Zuckerberg goes to Washington, DC For Facebook's CEO, this is the start of two days speaking to lawmakers following the Cambridge Analytica scandal. His prepared testimony is already available, so we have a few ideas about what he's going to say even before the questions start flying. Meanwhile, users who have had their data exposed should see notifications, and Cambridge Analytica has already started to push its version of the facts. During a Reddit AMA with Westworld show creators, Lisa Joy and Jonathan Nolan, Nolan made an unexpected announcement -- they'd spoil their own show instead of making fans cry or letting them down.
During the past decade, Twitter rendered the "pound sign" obsolete and made the "hashtag" part of our vernacular. The hashtag's uses range from sarcasm and trolling to awareness of social causes. The latter usage has been instrumental in the transition of movements from online to the real world. In honor of Twitter's 1oth birthday, here are the 10 most influential hashtags around social causes, ranked by the number of times they've been used since their inception. All numbers have been provided by Twitter.
Many studies have shown that social data such as tweets are a rich source of information about the real-world including, for example, insights into health trends. A key limitation when analyzing Twitter data, however, is that it depends on people self-reporting their own behaviors and observations. In this paper, we present a large-scale quantitative analysis of some of the factors that influence self-reporting bias. In our study, we compare a year of tweets about weather events to ground-truth knowledge about actual weather occurrences. For each weather event we calculate how extreme, how expected, and how big a change the event represents. We calculate the extent to which these factors can explain the daily variations in tweet rates about weather events. We find that we can build global models that take into account basic weather information, together with extremeness, expectation and change calculations to account for over 40% of the variability in tweet rates. We build location-specific (i.e., a model per each metropolitan area) models that account for an average of 70% of the variability in tweet rates.