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?

Artificial intelligence may not be human, but that doesn't make it exempt from the kind of bias almost every person displays. That's because we've been building prejudice into our AI, which learns both the good and the bad from human creators. It's a problem, scientists say, with a hidden benefit: By trying to understand how machines pick up human bias, we might in turn be able to learn how we acquire those biases ourselves. In 2017, Joanna Bryson, a computer scientist and AI specialist at the University of Bath, fed around 840 billion words--from tweets, the US Declaration of Independence, Reddit threads, and many other sources--into a purely statistical machine-learning model to see whether it would form biases based on the implicit linguistic patterns it found. Next, she told the machine to create related clusters of words.

Montreal, October 22 2018 -- The Board of Trustees of the Neural Information Processing Systems Foundation has decided not to change the name of their main conference. The Board has been engaged in ongoing discussions concerning the name of the Neural Information Processing Systems, or NIPS, conference. The current acronym, NIPS, has undesired connotations. The Name-of-NIPS Action Team was formed, in order to better understand the prevailing attitudes about the name. The team conducted polls of the NIPS community requesting submissions of alternative names, rating the existing and alternative names, and soliciting additional comments.