Who are the people driving the cutting edge of artificial intelligence, or AI as it is known? Where do the best of the best AI researchers come from and work? Neural Information Processing Systems (NeurIPS), considered the top AI conference, offers some answers. Its December 2019 meeting saw a record 15,920 researchers submit 6,614 papers, with a paper acceptance rate of 21.6 percent, making it one of the largest, most popular, and most selective AI conferences on record. Two things stand out from the reading of data collated from accepted papers.

The Internet Archive has had to shut down its National Emergency Library program because of a lawsuit from four publishers. The Emergency Library is a "temporary collection of books that supports emergency remote teaching, research activities, independent scholarship, and intellectual stimulation while universities, schools, training centers, and libraries are closed," according to the Internet Archive's website. Usually, books from the Internet Archive can be checked-out via a waiting list. Musician uses algorithm to generate'every melody that's ever existed' Musician uses algorithm to generate'every melody that's ever existed' In a blog post, the Archive asked for the publishers to "call off their costly assault." It also said it would not completely end the online lending completely; instead, it would go back to controlled digital lending.

Samsung has created a special edition Galaxy S20 smartphone for K-pop fans, the Galaxy S20 Plus BTS Edition. BTS, who are also known as the Bandtan Boys, are a seven-member South Korean boy band which has become the best-selling artist in South Korean history. The band is also the fastest group since the Beatles to earn four number one albums in the US in under two years. The new aesthetic for the smartphone features a "Haze Purple" colour scheme, with the BTS logo on the back and the band's heart iconography on the camera. It will also come with custom Android theme with a live wallpaper and an "inspiring" lock screen, according to Samsung.

The EU has opened a major investigation into Apple over concerns that it uses its platforms unfairly. The EU commission will pursue antitrust investigations against the company over its App Store and Apple Pay products, which critics argue have stifled competition. The Commission said it was investigating Apple Pay over allegations the tech giant wields its control over the Pay platform to force developers into using it over others. It said a preliminary investigation had raised concerns that "Apple's terms, conditions, and other measures related to the integration of Apple Pay" may "distort competition and reduce choice and innovation". In addition, the EU Commission announced it had opened a second investigation into concerns that the firm's App Store restricts developers from informing iPhone and iPad users of alternative purchasing possibilities, instead pushing "mandatory use of Apple's own proprietary in-app purchase system".

The time for experiments is over: Artificial Intelligence (AI) solutions are ready for the enterprise. Already, the early adopters are reaping the rewards and moving from trials to large-scale adoption. The chief executives of these companies are confident that - within just two years - AI deployments will deliver around 30% of both their new revenues and cost savings. Other businesses are now following suit, eager not to miss out on the AI advantage. I'm certain that companies which delay drafting and delivering their own AI strategies are bound for obsolescence.

Have you ever wondered how self-driving cars are running on roads or how Netflix recommends the movies which you may like or how Amazon recommends you products or how Google search gives you such an accurate results or how speech recognition in your smartphone works or how the world champion was beaten at the game of Go? Machine learning is behind these innovations. In the recent times, it has been proven that machine learning and deep learning approach to solving a problem gives far better accuracy than other approaches. This has led to a Tsunami in the area of Machine Learning. Most of the domains that were considered specializations are now being merged into Machine Learning. Every domain of computing such as data analysis, software engineering, and artificial intelligence is going to be impacted by Machine Learning.

Computers have been used to analyze and create music since they were first introduced in the 1950s and 1960s. Beginning in the late 1990s, the rise of the Internet and large scale platforms for music recommendation and retrieval have made music an increasingly prevalent domain of machine learning and artificial intelligence research. While still nascent, several different approaches have been employed to tackle what may broadly be referred to as "musical intelligence." This article provides a definition of musical intelligence, introduces a taxonomy of its constituent components, and surveys the wide range of AI methods that can be, and have been, brought to bear in its pursuit, with a particular emphasis on machine learning methods.

Extracting demographic features from hidden factors is an innovative concept that provides multiple and relevant applications. The matrix factorization model generates factors which do not incorporate semantic knowledge. This paper provides a deep learningbased method: DeepUnHide, able to extract demographic information from the users and items factors in collaborative filtering recommender systems. The core of the proposed method is the gradient-based localization used in the image processing literature to highlight the representative areas of each classification class. Validation experiments make use of two public datasets and current baselines. Results show the superiority of DeepUnHide to make feature selection and demographic classification, compared to the state of art of feature selection methods. Relevant and direct applications include recommendations explanation, fairness in collaborative filtering and recommendation to groups of users. Keywords: Feature selection, collaborative filtering, demographic information, matrix factorization, gradient based localization, deep learning.