… Market Insights: Bringing together business and search intelligence … simplify complex data analysis with machine learning technologies such as …

There are many examples where if you're training machine–learning models used in artificial intelligence on, for example, genetic or diagnostic data from …

Underlying financial trends, and; The output of the Trefis machine learning engine which looks at past patterns to predict near term behavior.

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Besides that, there are no rules, have fun.

Artificial Intelligence is reshaping many areas of our lives, and Library and Information Sciences are not outside this influence.

In the early 2000s, Songza implemented a manual music recommendation system for its listeners, where a team of music experts and curators would create playlists. But these recommendations were not objective, as they were dependent on the personal taste of the curators. It was an average experience for listeners, with a fair share of hits and misses, because it was impossible to make a playlist which catered to the varied tastes of a diverse set of people. The technology and the data did not exist back then to build a playlist that would be personalised to the taste of each individual listener. Along came Spotify a few years later, offering a highly personalised weekly playlist called Discover Weekly that quickly became one of their flagship offerings.

In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, …

Levine has spent years at Berkeley's robotic artificial intelligence and … Robotics often relies on a form of machine learning called reinforcement …

Now researchers are fitting the low-cost computer with artificial intelligence, high-resolution cameras and robots, to sort through rubbish and reduce …

We present a system for real-time lyrical improvisation between a human and a robot in the style of hip hop. Our system takes vocal input from a human rapper, analyzes the semantic meaning, and generates a response that is rapped back by a robot over a musical groove. Previous work with real-time interactive music systems has largely focused on instrumental output, and vocal interactions with robots have been explored, but not in a musical context. Our generative system includes custom methods for censorship, voice, rhythm, rhyming and a novel deep learning pipeline based on phoneme embeddings. The rap performances are accompanied by synchronized robotic gestures and mouth movements. Key technical challenges that were overcome in the system are developing rhymes, performing with low-latency and dataset censorship. We evaluated several aspects of the system through a survey of videos and sample text output. Analysis of comments showed that the overall perception of the system was positive. The model trained on our hip hop dataset was rated significantly higher than our metal dataset in coherence, rhyme quality, and enjoyment. Participants preferred outputs generated by a given input phrase over outputs generated from unknown keywords, indicating that the system successfully relates its output to its input.