Porn has reached a golden age as people are searching for mature content like'sexy granny' and'MILF' Those are among the many search terms that saw growth on Pornhub in 2023. The adult website sifted through data from billions of visits to see what content defined the year's tastes, finding the top category was'The Golden Age.' 'MILF' became the second most searched term worldwide, while'Granny' saw a 132 percent increase from 2022 - and terms like'sexy granny ' and'hot GILF' trending. Many searches also included'DILF,' with'muscle DILF' traffic growing by 71 percent. It seems Pornhub is maturing, according to its most searched fetishes of 2023 that included'Granny' and'MILF' This year, the MILF category climbed a spot on the charts to the fifth most viewed category worldwide, Pornhub revealed. The Mature category came in seventh, seeing a 69 percent increase among viewers, and it is now the second most popular among men and fifth among women.

Additionally, a summarization of command categories and frequencies showed the most popular category was "command and control" where users adjust device settings, navigate through the results and answer yes/no style of questions. The next popular category was related to entertainment questions, followed by lifestyle and shopping. Furthermore, despite signing into a device, participants made sophisticated use of the spaces around their bodies, for example to represent and refer to people or things that were the topic of their questions. Another observation was the use of a question-mark sign at the beginning of yes or no questions, to call the attention of the device, while typically this sign more often used at the end of such questions. When it came to errors, such as the device not giving the result the users were looking for, most commonly users would simply ignore the error and proceed with a different command. A close second method was to repeat the command with the exact same wording and signing style, followed by rewording the command.

This month, we get stuck into AI and consciousness. This topic has long been much-discussed, and especially so recently with one tweet in particular sparking a debate online. Joining the discussion this time are: Tom Dietterich (Oregon State University), Stephen Hanson (Rutgers University), Sabine Hauert (University of Bristol), Holger Hoos (Leiden University), Sarit Kraus (Bar-Ilan University) and Michael Littman (Brown University). Stephen Hanson: So, the topic of consciousness has come up a lot recently in discussions on the Connectionists. This area of cognitive science was pretty much wiped out in the first five years of NeurIPS [Conference on Neural Information Processing Systems].

This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where restaurants have latent characteristics (whose distribution may depend on restaurant observables, such as star ratings, food category, and price range), each user has preferences for these latent characteristics, and these preferences are heterogeneous across users. Similarly, each item has latent characteristics that describe users' willingness to travel to the restaurant, and each user has individual-specific preferences for those latent characteristics. Thus, both users' willingness to travel and their base utility for each restaurant vary across user-restaurant pairs. We use a Bayesian approach to estimation. To make the estimation computationally feasible, we rely on variational inference to approximate the posterior distribution, as well as stochastic gradient descent as a computational approach. Our model performs better than more standard competing models such as multinomial logit and nested logit models, in part due to the personalization of the estimates. We analyze how consumers re-allocate their demand after a restaurant closes to nearby restaurants versus more distant restaurants with similar characteristics, and we compare our predictions to actual outcomes. Finally, we show how the model can be used to analyze counterfactual questions such as what type of restaurant would attract the most consumers in a given location.

Games has always been a popular category for owners of Apple's iPhone, iPad or iPod Touch. Starting Thursday, the independent creators of those games will receive some extra attention. There's one massive design flaw the iPhone 8 needs to fix (Photo: Reviewed.com) Apple launched a hub devoted to "Celebrating Indie Games," where users will find the latest games from independent developers, but also some of the all-time best indie titles for iOS devices. For example, users will see a list of "Our 25 favorite indie games" at the top.

As the Web has become integrated into daily life, understanding how individuals spend their time online impacts domains ranging from public policy to marketing. It is difficult, however, to measure even simple aspects of browsing behavior via conventional methods---including surveys and site-level analytics---due to limitations of scale and scope. In part addressing these limitations, large-scale Web panel data are a relatively novel means for investigating patterns of Internet usage. In one of the largest studies of browsing behavior to date, we pair Web histories for 250,000 anonymized individuals with user-level demographics---including age, sex, race, education, and income---to investigate three topics. First, we examine how behavior changes as individuals spend more time online, showing that the heaviest users devote nearly twice as much of their time to social media relative to typical individuals. Second, we revisit the digital divide, finding that the frequency with which individuals turn to the Web for research, news, and healthcare is strongly related to educational background, but not as closely tied to gender and ethnicity. Finally, we demonstrate that browsing histories are a strong signal for inferring user attributes, including ethnicity and household income, a result that may be leveraged to improve ad targeting.