Last week, Netflix users raised concerns that the company was targeting black users by race in the way it promoted films--highlighting black characters who sometimes had only minor roles in a movie. The debate began after Stacia L. Brown, creator of the podcast Charm City, tweeted a screenshot of the promotion she was shown for Like Father, featuring two black characters, Leonard Ouzts and Blaire Brooks, who had "20 lines between them, tops," rather than the movie's famous white stars, Kristen Bell and Kelsey Grammer. Brown, who is black, posted a handful of other examples where Netflix highlighted black actors, presumably to entice her to watch, even though the films' casts were predominantly white. In response, Netflix issued a carefully worded statement emphasizing that the company does not track sensitive demographic data about its users. "Reports that we look at demographics when personalizing artwork are untrue," the company said in a statement.

Harmonic analysis is an important step towards creating high-level representations of tonal music. High-level structural relationships form an essential component of music analysis, whose aim is to achieve a deep understanding of how music works. At its most basic level, harmonic analysis of music in symbolic form requires the partitioning of a musical input into segments along the time dimension, such that the notes in each segment correspond to a musical chord. This chord recognition task can often be time consuming and cognitively demanding, hence the utility of computer-based implementations. Reflecting historical trends in artificial intelligence, automatic approaches to harmonic analysis have evolved from purely grammar-based and rule-based systems (Wino-grad, 1968; Maxwell, 1992), to systems employing weighted rules and optimization algorithms (T emper-ley and Sleator, 1999; Pardo and Birmingham, 2002; Scholz and Ramalho, 2008; Rocher et al., 2009), to data driven approaches based on supervised machine learning (ML) (Raphael and Stoddard, 2003; Radicioni and Esposito, 2010).

We present a statistical-modelling method for piano reduction, i.e. converting an ensemble score into piano scores, that can control performance difficulty. While previous studies have focused on describing the condition for playable piano scores, it depends on player's skill and can change continuously with the tempo. We thus computationally quantify performance difficulty as well as musical fidelity to the original score, and formulate the problem as optimization of musical fidelity under constraints on difficulty values. First, performance difficulty measures are developed by means of probabilistic generative models for piano scores and the relation to the rate of performance errors is studied. Second, to describe musical fidelity, we construct a probabilistic model integrating a prior piano-score model and a model representing how ensemble scores are likely to be edited. An iterative optimization algorithm for piano reduction is developed based on statistical inference of the model. We confirm the effect of the iterative procedure; we find that subjective difficulty and musical fidelity monotonically increase with controlled difficulty values; and we show that incorporating sequential dependence of pitches and fingering motion in the piano-score model improves the quality of reduction scores in high-difficulty cases.

It's become increasingly important for women to stop worrying about being perfect and find the courage to take action to advance their careers, according to Nell Merlino, creator of Take Our Daughters to Work Day and founder and president of Count Me In for Women's Economic Independence. "None of us can do everything," but "we are all best at something," so we all need to figure out what we're best at and then "lay claim to" that expertise and make sure other people know that also, she said during the keynote "Courage versus Perfection" at the Oct. 4 SoCal Women's Leadership Group annual meeting at the Skirball Cultural Center in Los Angeles. The event was co-located with the Hollywood Innovation & Technology Summit (HITS) Fall event.

The site originally started as 2 regional job sites in Switzerland and the UK this year and now got merged into ai-jobs.net The focus lies on a simple (mobile-friendly) site with no fluff and simple but effective job ads, an easy (paid) submission process and a simple job alert feature. The search allows for filtering by region so job seekers can add some regional flavor to the result list. Hope you enjoy it - happy hiring!

Future AI may be super powerful but, as Dr. Joanna Bryson of the University of Bath relates, that still won't make it a person. The desire to bestow human life on inanimate material has been a component of our collective imagination since at least the days of Ovid. In his work Metamorphoses he relates the tale of Pygmalion, who sculpted Galatea out of ivory and besought her animation at the hands of Aphrodite. Two thousand years later, we still see that narrative trope playing itself out in stories such as Alex Garland's Oscar-winning film Ex Machina, where an AI developer creates an autonomous female android named Ava as the key component of a Turing Test. From marriage to murder, the finales of these and other similar stories range from wish fulfillment to cautionary tale, but the psychological underpinnings remain the same: the aspiration to take something intrinsically non-human (such as ivory or silicon) and humanize it.

Wang Qishan and Benjamin Netanyahu toured a number of exhibits by high-tech Israeli start-ups focused on self-driving cars, health care and agriculture. They were scheduled to attend an evening gala event and the opening of an Israeli innovation center on Thursday.

The Amazon Fire TV Stick has just been treated to an upgrade, pushing the best cheap streaming device on the market even further ahead of the competition. It was launched in the US some time ago, but has only just come to the UK, and brings with it a number of improvements that make it well worth buying, even if you already own an Amazon Fire TV Stick. Its headline feature is support for Alexa, Amazon's excellent voice assistant. This doesn't just enable users to track down TV shows, films and apps much, much faster than ever just by speaking to the voice-controlled remote, but also to accurately control playback without having to fiddle with any buttons. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

The conversation about unconscious bias in artificial intelligence often focuses on algorithms that unintentionally cause disproportionate harm to entire swaths of society--those that wrongly predict black defendants will commit future crimes, for example, or facial-recognition technologies developed mainly by using photos of white men that do a poor job of identifying women and people with darker skin. But the problem could run much deeper than that. Society should be on guard for another twist: the possibility that nefarious actors could seek to attack artificial intelligence systems by deliberately introducing bias into them, smuggled inside the data that helps those systems learn. This could introduce a worrisome new dimension to cyberattacks, disinformation campaigns or the proliferation of fake news. According to a U.S. government study on big data and privacy (PDF), biased algorithms could make it easier to mask discriminatory lending, hiring or other unsavory business practices.

I have been really interested and felt an attraction towards ML. I pursue CSE since two years and have been coding for a year and making games for nearly 10 months. Meanwhile I saw some videos about the power of machine learning and the future of it. Recently I thought about getting started with it and looked up some few articles, papers and related things. What is a good way to get started with Machine learning?