The University of Minnesota has been a leader in recommender systems since developing GroupLens, the first automated recommender system in 1993. Today the University continues that leadership with leading research on recommender algorithms, applications, and evaluation. The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation's most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.

There is an increasing demand for algorithms to explain their outcomes. So far, there is no method that explains the rankings produced by a ranking algorithm. To address this gap we propose LISTEN, a LISTwise ExplaiNer, to explain rankings produced by a ranking algorithm. To efficiently use LISTEN in production, we train a neural network to learn the underlying explanation space created by LISTEN; we call this model Q-LISTEN. We show that LISTEN produces faithful explanations and that Q-LISTEN is able to learn these explanations. Moreover, we show that LISTEN is safe to use in a real world environment: users of a news recommendation system do not behave significantly differently when they are exposed to explanations generated by LISTEN instead of manually generated explanations.

According to Harvard Business Review, Amazon has sold 25 million Alexa speakers, and Google Assistant is currently available on 400 million devices across the world. Envision a universe coming to a galaxy near you very shortly: You inquire Alexa, "Which commercial realtor at Peoria has the maximum satisfaction score?" Alexa leads you to a site. By virtue of your speech and business name you're sent to your salesperson that specializes in your business: pc program. You get a welcome package based on your own click history and appropriate to the character of your query.

Leading television manufacturer Skyworth has signed a strategic cooperation agreement with Chinese Internet giant Baidu. In the deal announced, Baidu will invest CN¥1.01 billion (US$159.7 million) in Skyworth's Smart TV unit Coocaa; while its AI assistant system DuerOS will be integrated into Skyworth's Super AI TV. Scott Amyx shares how AI is to better personalize content recommendations and to bring attention back to advertisements.

It came as a total surprise: the most impressive demonstration at Google's I/O conference yesterday was a phone call to book a haircut. Of course, this was a phone call with a difference. It wasn't made by a human, but by the Google Assistant, which did an uncannily good job of asking the right questions, pausing in the right places, and even throwing in the odd "mmhmm" for realism. The crowd was shocked, but the most impressive thing was that the person on the receiving end of the call didn't seem to suspect they were talking to an AI. It's a huge technological achievement for Google, but it also opens up a Pandora's box of ethical and social challenges.

A comprehensive approach to any decision making for a developing business comes from extensive insightful research. Experts offering software consulting company bring this post to talk about the latest trends of BI software that community people can follow in 2018. Business Intelligence is surely not the new term but the technological advancement and competitive edge on a global scale to make it evolve. For that, it is significant to determine scenarios acting upon the business world and the role of Business intelligence in changing years. Here is some introspection on these new evolving trends of Business Intelligence software in 2018. Making machine adaptive to intellect upon themselves on basis of experiences and feedbacks, especially when it requires analyzing is a game changer in Business Intelligence system.

In this AI based Science article, we explore How Netflix adopted an Open Source Model to improve their Entertainment Recommender Systems. First, let us discuss in brief, what Machine Learning basically means. In simple terms, Machine Learning is a technique by which a computer can "learn" from data, without using a complex set of different rules. This approach is mainly based on training a model from datasets. The better the quality of the datasets, the better the accuracy of the Machine Learning Model.

I recently overheard my 2-year-old daughter talking to Amazon's voice assistant Alexa, and two things struck me. First, she doesn't distinguish the disembodied voice from that of a regular human. Second, she barks orders at Alexa in a way that would be considered rude by any social convention. I was suddenly aware and troubled that Alexa is setting a terrible example for my daughter--that women are subservient, should accept rudeness, and belong in the home. All four of the major in-home artificial intelligence, or AI, assistants--Alexa, Apple's Siri, Google Assistant, and Microsoft's Cortana--speak by default with a female voice.

The key message from a new policy report from the University of Manchester is that future-state artificial intelligence needs to be socially responsible. This is because the current path of development of new artificial intelligence technology has been shown, through experimentation, to contain bias. For example, Joy Buolamwini has challenged inherent ethnic and female bias in many facial recognition systems. This issue of bias becomes serious when used in the business setting and by governments, such as when assessing welfare claims. Here many developed systems can be discriminatory.

At Google's annual developer conference this past week, CEO Sundar Pichai played a clip of what seemed to be a mundane human interaction: Someone calling to make an appointment with their hairdresser. The two voices negotiated the date and time, with the "assistant" providing the client's name for the reservation. Instead, Pichai said, it was Google's artificial intelligence making the call to the unwitting hair salon receptionist. The technology is called Google Duplex, a system that combines natural language processing and speech generation that allegedly allows Google to accomplish customer service tasks in a number of limited situations. Right now, the company is only testing the technology internally, focusing on booking reservations at restaurants and hair salons, as well as inquiring about holiday hours for businesses.