The prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities and backgrounds. There is thus a growing need to accommodate for individual differences in e-learning systems. This paper presents an algorithm called EduRank for personalizing educational content to students that combines a collaborative filtering algorithm with voting methods. EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. These aspects include grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for each student, rather than ordering them according to the student's predicted score. The EduRank algorithm was tested on two data sets containing thousands of students and a million records. It was able to outperform the state-of-the-art ranking approaches as well as a domain expert. EduRank was used by students in a classroom activity, where a prior model was incorporated to predict the difficulty rankings of students with no prior history in the system. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance.

Contribute to a product that is redefining mobile computing. Create groundbreaking technology for large-scale systems, spoken language, big data, and artificial intelligence. And work with the people who created the intelligent assistant that helps millions of people get things done -- just by asking. In this role you'll work with a great small team to solve hard problems that improve the lives of millions customers. Knowledge of Apple's development APIs (UIKit, Cocoa Touch, Cocoa, CoreFoundation) Outstanding communication and presentation skills Description You will primarily be responsible for implementing features for the Siri user experience.

Apple contractors regularly hear confidential medical information, drug deals, and recordings of couples having sex, as part of their job providing quality control, or "grading", the company's Siri voice assistant, the Guardian has learned. Although Apple does not explicitly disclose it in its consumer-facing privacy documentation, a small proportion of Siri recordings are passed on to contractors working for the company around the world. They are tasked with grading the responses on a variety of factors, including whether the activation of the voice assistant was deliberate or accidental, whether the query was something Siri could be expected to help with and whether Siri's response was appropriate. Apple says the data "is used to help Siri and dictation … understand you better and recognise what you say". But the company does not explicitly state that that work is undertaken by humans who listen to the pseudonymised recordings.

It's official: if you're using a voice-assistant -- pretty much any voice-assistant -- someone could be listening in. The Guardian reports that Apple has joined an ever-growing list of tech companies that listens in on commands uttered through its virtual voice-assistant. Snippets of audio, reports The Guardian, are sent to contractors who are responsible for listening and grading them for accuracy, including whether or not the command was accidental or whether its assistant, Siri, was able to complete the task. As is the case with other similar programs from Google and Amazon, however, a whistleblower says the program has inadvertently swept up audio data that most might find confidential. Those include, according to an unnamed source in the report, conversations between patients and doctors, sex, criminal activity, and official business talk.

Google will give away 100,000 of its smart home device to those living with paralysis according to a blog post. The company announced that it will be sending its Google Home mini to people with physical disabilities in an effort to help improve their lives at home. According to Garrison Redd, an ambassador for the Christopher & Dana Reeve Foundation, who has been unable to walk for the past 20 years, the devices can make a huge difference for someone who relies on a wheelchair to move. 'When you're paralyzed, your home goes from being a place of comfort and security to a reminder of what you've lost,' wrote Redd in a blog post for Google. 'Light switches and thermostats are usually too high up on the wall and, if my phone falls on the floor, I may not be able to call a friend or family member if I need help.

Many of today's consumer-focused AI are fashioned after women: Siri, Alexa, Cortana, just to name a few. Female voices are considered friendlier and more helpful, but consumer AI is also in a subservient role. In reality, women are adding so much more to the field of AI than their voices. Today, though only 18 percent of C-level positions in the artificial intelligence/machine learning space are filled by women, which is not an usual figure in the tech sphere, but that does not take into account all the influential women building companies centered around AI. According to some studies, only 13.5 percent of the machine learning field and fewer than 10 percent in AI are women, and while it's important to acknowledge the gender gap, one must also acknowledge the many chief scientists and researchers making great strides in machine learning and AI.

It can't be overstated how fundamentally different this paradigm for music curation is from what you're used to. To compare it to another example from around your time, Spotify's Daily Drive playlist wove audio snippets from news talk shows with personalized music recommendations. I recall the feature was heralded as innovative for combining multiple audio formats into a single interface, but it was still fundamentally limited in how it relied on metadata around past listening activity. In contrast, the music information retrieval (MIR) techniques used in YouNite draw on real-time and forward-looking predictions around both present physiological states and desired future emotional outcomes. Hope this all makes sense?

In academic literature, recommender systems are often evaluated on the task of next-item prediction. The procedure aims to give an answer to the question: "Given the natural sequence of user-item interactions up to time t, can we predict which item the user will interact with at time t+1?". Evaluation results obtained through said methodology are then used as a proxy to predict which system will perform better in an online setting. The online setting, however, poses a subtly different question: "Given the natural sequence of user-item interactions up to time t, can we get the user to interact with a recommended item at time t+1?". From a causal perspective, the system performs an intervention, and we want to measure its effect. Next-item prediction is often used as a fall-back objective when information about interventions and their effects (shown recommendations and whether they received a click) is unavailable. When this type of data is available, however, it can provide great value for reliably estimating online recommender system performance. Through a series of simulated experiments with the RecoGym environment, we show where traditional offline evaluation schemes fall short. Additionally, we show how so-called bandit feedback can be exploited for effective offline evaluation that more accurately reflects online performance.

There is hardly a single hour that passes by when we are untouched by technology. We are constantly engaged with our mobile devices such as smartphones or tablets, except for when we are asleep. Whether it is work or going about our daily chores, technology has found its way into every aspect of our lives. Contributing to this plot, companies are gearing up their operations to adopt enterprise mobility management to enhance the productivity and efficiency of their employees. Enterprise Mobility enables the company to encourage their employees to work anytime, from anywhere with access to data using their mobile devices.

With a new feature, Tinder says it wants to make the swiping experience safer for its LGBTQ users traveling and living in certain countries. On Wednesday, the dating app introduced a new safety update dubbed "Traveler Alert" that will warn users who have identified themselves as lesbian, gay, bisexual, transgender and/or queer when they enter a country that could criminalize them for being out. The app plans to use the locations from users' devices to determine if there is a threat to the user's safety, where users can opt to have their profile hidden during their stay or make their profile public again. The caveat being that if a user decides to have their profile public, their sexual preference or gender identity will no longer be disclosed on the app until they return to a location where the user is deemed safer to disclose their identity. In the statement, Tinder says they developed the feature so that users "can take extra caution and do not unknowingly place themselves in danger for simply being themselves."