Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation bias exists due to a feedback loop which causes the model to learn to only predict recommendations similar to previous ones. Imbalance in data occurs when systematic societal, historical, or other ambient bias is present in the data. In this paper, we address both biases by proposing a hybrid fairness-aware recommender system. Our model provides efficient and accurate recommendations by incorporating multiple user-user and item-item similarity measures, content, and demographic information, while addressing recommendation biases. We implement our model using a powerful and expressive probabilistic programming language called probabilistic soft logic. We experimentally evaluate our approach on a popular movie recommendation dataset, showing that our proposed model can provide more accurate and fairer recommendations, compared to a state-of-the art fair recommender system.

In this paper, we study a novel task that learns to compose music from natural language. Given the lyrics as input, we propose a melody composition model that generates lyrics-conditional melody as well as the exact alignment between the generated melody and the given lyrics simultaneously. More specifically, we develop the melody composition model based on the sequence-to-sequence framework. It consists of two neural encoders to encode the current lyrics and the context melody respectively, and a hierarchical decoder to jointly produce musical notes and the corresponding alignment. Experimental results on lyrics-melody pairs of 18,451 pop songs demonstrate the effectiveness of our proposed methods. In addition, we apply a singing voice synthesizer software to synthesize the "singing" of the lyrics and melodies for human evaluation. Results indicate that our generated melodies are more melodious and tuneful compared with the baseline method.

By Snehal Shah In the movie Avengers: Age of Ultron, the Iron Man and Bruce Banner – a genius scientist when he wasn't playing Hulk – built an artificial intelligence system named'Ultron' to help protect the earth. But Ultron – the peacekeeping programme embedded in a synthetic body – turned hostile, making it his mission to eradicate humans from the face of the earth. As earth's fate hung in the balance, the mightiest of Avengers had to come together to save the planet from complete annihilation. Does this, another Marvel Comic story turned into a sci-fi Hollywood film, have a semblance of realism? A couple of years ago a unique experimental self-driving car was released on New Jersey roads, that was not coded or programmed by engineers.

A creepy AI transfers the facial expressions of one person to another to create eerily realistic'deep fake' videos. The software accurately flips a segment of one video - such as the mouth of a character - to the style of another to create life-like fake clips. A video produced by the team transferred the mouth movements of British comedian John Oliver onto the face of US talk-show host Stephen Colbert. Researchers warned the technology could be used to create fake news clips that falsely put words into the mouths of politicians or other powerful figures. An AI transfers the facial expressions of one person to another to create eerily realistic'deep fake' videos.

The iPhone X, a dramatically redesigned model released last fall, got rid of the home button and introduced facial-recognition technology to unlock the device. It was the first mass-market smartphone to demand a $1,000 starting price. Although the iPhone X didn't fulfill analysts' lofty sales expectations, it fared well enough for Apple to up the ante with the bigger model, whose price is expected to unveil Wednesday.

Deep learning is a subfield of machine learning and it comprises several approaches to tackling the single most important goal of AI research: allowing computers to model our world well enough to exhibit something like what we humans call intelligence. On a basic conceptual level, deep learning approaches share a very basic trait. DL algorithms interpret the raw data through multiple processing layers. Each of these layers takes the output of the previous one as its input and creates a more abstract representation of it. As a result, the more data is being fed into the right algorithm, the more general are the rules and features that it's able to infer in relation to a given scenario and, therefore, the apter it gets at handling new, similar situations.

When he plays a techno show, Moritz Simon Geist doesn't reach for a laptop. Instead, he calls on his army of sonic robots--a collection of small, motorized creations that click, clank, and whirr in an intricate mechanical symphony. Geist composes robotic electronic music, a burgeoning genre of electro jams that relies on hardware, not software, to engineer electronic sounds and beats. His forthcoming EP, The Material Turn, debuts in October with four tracks made entirely from self-fashioned instruments--futuristic robo-kalimbas, a droning guitar, and salvaged hard drives turned into percussive beat machines. Watching Geist play music is a little like watching a mad scientist in a lab.

Las Vegas, HR Technology Conference & Expo #HRTech -- LEADx, Inc., the world's leading Conversational Learning (CL) platform for leadership enablement, today launched LEADx Coach Amanda, an executive coach virtual assistant powered by IBM Watson Assistant. "We believe every manager deserves a coach," said Kevin Kruse, LEADx founder and CEO. "Traditional leadership development, based on workshops and online tutorials, has long failed enterprises and managers alike. Executive coaches work well, but due to their cost they are ironically reserved for the leaders who have the most experience. But now, we've tapped the power of AI to democratize leadership development."

According to SAS, SAS Visual Data Mining and Machine Learning offers users a single platform to solve complex analytical problems. Combining data preparation, visualization, advanced analytics and model deployment, it unifies the entire machine learning process, from data access/transformation and preparation to scoring, in one environment. Running on the SAS Viya engine, SAS Visual Data Mining and Machine Learning includes the latest statistical, machine learning, deep learning and text analysis algorithms that accelerate structured and unstructured data explorations, while also supporting popular open source languages.