Ever since Deep Thought became the first programme ever to defeat a chess grandmaster in 1988, mankind has been seriously contemplating its supremacy over machines in terms of complex thinking -- not just raw computing power. After AlphaGo trounced go-master Lee Se-dol last year, the consolation prize for humans appeared to be their artistic sense. But now, as more engineers look to teach artificial intelligence how to be more creative to the extent of writing their own music, that artistic edge is being challenged. A recent project by the state-run Korea Creative Content Agency and S.M. Entertainment invited six teams of local researchers and start-up firms to use AI in creating music. The use of technology -- such as voice processing tools like auto-tune -- is already common in today's music business, but the project aims to put the computer in the driver's seat of the creative process.

There's a tendency for classifier NNs to be overconfident about outliers -- generally they extrapolate to arbitrary values outside of the training data distribution. This is only exacerbated by the use of ReLUs and the like, which grow indefinitely and generally tend to P(x c)-- 1 for some category when x moves away from the training set. In my application, I would like the classifier to give a flat distribution for inputs outside of the training set; are there any tried and tested ways of doing that?

With 60 percent of businesses across the globe expected to use A.I. by 2018, South African companies are championing how machine learning can manage business operations Artificial intelligence, or A.I., is already used by 39 percent of businesses around the globe in some form, according to Sage's latest analysis. Be it to scan and organise vast amounts of data quickly to free up an employee for other tasks, or to help a customer answer common questions on a website, A.I. uses programmed requirements and machine learning to reduce the time taken to complete mundane tasks, improving business efficiency and operating costs. Yet, despite CNBC reporting that smart technology boosts productivity, many companies are still to factor A.I. into their business model. Fortunately, South African companies are leading the way by adopting machine learning programmes to increase efficiency in numerous areas of their businesses. With the internet opening new opportunities for employers to hire highly skilled workers from all over the world, as well as the ability to post job postings to websites and social media, businesses are receiving more résumés and CVs than ever before.

RICK ASTLEY is rightly famous. His 1987 single, "Never Gonna Give You Up", has been played more than 330m times on YouTube. But in February last year Mr Astley (pictured) indulged in a rather odd experiment. The singer, looking remarkably similar to his late-'80s self, covered his own hit song, but sang the whole thing in order of pitch. The song, a video of which appears on YouTube, proceeds in a rather unconventional manner.

AI is increasingly finding its way into music videos, and not necessarily in obvious ways. Intel has revealed that the promo clip for Chinese pop star Chris Lee's "Rainy Day, But We Are Together" is the first music video to lean on its AI technology. The producers created dramatic special effects on the songstress' face (such as trickles of water and twinkling stars) by training a machine learning system to instantly reconstruct a face in 3D and track its movements in real time, including facial expressions. Instead of asking Lee (aka Li Yuchun) to wear tracking markers or the camera crew to shoot a specific way, the creative team could focus on capturing scenes that lined up with their artistic goals.

This past summer proved to be the official tipping point for voice-first shopping for many consumers. With Amazon's Echo Dot ranking as the "best-selling product from any manufacturer in any category across Amazon globally" during Prime Day 2017 and Google Home pairing up with Wal-Mart and The Home Depot, the era of AI-assisted selling officially had its breakthrough during the first half of 2017. Additionally, according to a recent Gartner study, sales of voice-activated speakers with artificial intelligence capabilities will reach $3.52 billion by 2021, signaling that adoption of voice-enabled speakers will only continue over the next few years. Though e-commerce continues to gain ground on in-store purchasing, we are collectively a group of consumers who often use our voice throughout our purchasing journeys. Whether it is asking for a different size or color, checking if our product is in stock or simply expressing how we want to pay, we are used to these interactions.

CDB Technologies, LLC is pleased to announce that EveryPig (www.everypig.co), This new development will allow the farmers and veterinarians using the app to leverage data collection and machine learning models to identify illnesses and recommend potential remedies for the pigs under care. Over time, this technology will bring an unprecedented degree of insight to the pork industry regarding the care and health of pigs. Because the actual people who physically visit the pigs every day are the ones reporting, EveryPig's founder believes that the platform will be an ideal tool to provide fast recognition of domestic and foreign disease outbreaks, which could affect the multibillion-dollar pork export market. In the one year it has been in use, EveryPig has already grown to have 500 active users including many pork production and swine veterinary management companies representing over 1,000,000 pigs under care, without a focus on sales.

I've nearly finished the Coursera John Hopkins course on data science with R. It was pretty decent, some sections were definitely better than others. I know want to move on to a python data science course. I have used both Python and R for data science extensively, though I have probably used R more. I've been using Python for a couple years now along with many of the data analysis libraries such as Numpy, Pandas, Matplotlib, seaborn and their R equivalents. I was wondering if anyone knew what the best python data science course is?

Welcome to the weekly IoT round-up! This week, we get a sneak peek at the new Star Wars VR experience (coming soon to Disney Parks), and there's good news for autonomous vehicles as California DMV rules to allow testing of driverless vehicles. IBM's thought leadership program'Science and Star Wars' explores how close real world science has come to Star Wars tech. Last week, it was time for droids to have a moment in the spotlight. In Episode 2: Droids, we met R2 Builders Club members Mike Senna and Michael McMaster, who introduced us to their very own R3-A3 droid, built with a Raspberry Pi and imbued with a personality courtesy of IBM Watson.

"I wake up in a pool of blood." "I was trapped in this hospital bed." "I think I'm being stalked." In horror stories, phrases like these conjure up scenes that can make your blood run cold or cause your heart to beat a little faster. But the author of these words has no heartbeat, nor any blood to chill. Meet "Shelley," a neural network raised on a diet of horror fiction.