We investigate the association between musical chords and lyrics by analyzing a large dataset of user-contributed guitar tablatures. Motivated by the idea that the emotional content of chords is reflected in the words used in corresponding lyrics, we analyze associations between lyrics and chord categories. We also examine the usage patterns of chords and lyrics in different musical genres, historical eras, and geographical regions. Our overall results confirms a previously known association between Major chords and positive valence. We also report a wide variation in this association across regions, genres, and eras. Our results suggest possible existence of different emotional associations for other types of chords.

Learning attribute applicability of products in the Amazon catalog (e.g., predicting that a shoe should have a value for size, but not for battery-type) at scale is a challenge. The need for an interpretable model is contingent on (1) the lack of ground truth training data, (2) the need to utilise prior information about the underlying latent space and (3) the ability to understand the quality of predictions on new, unseen data. To this end, we develop the MaxMachine, a probabilistic latent variable model that learns distributed binary representations, associated to sets of features that are likely to cooccur in the data. Layers of MaxMachines can be stacked such that higher layers encode more abstract information. Any set of variables can be clamped to encode prior information. We develop fast sampling based posterior inference. Preliminary results show that the model improves over the baseline in 17 out of 19 product groups and provides qualitatively reasonable predictions.

A French company named NAVYA has settled on Perth as one of three cities around the world for testing its driverless cars, named Autonoms. The trial is scheduled to commence in April next year and will be supported by the WA government and the RAC WA. Currently, both the government and the motorists' association are assessing various locations that would be suitable for closed-environment testing. "I am very proud that Western Australia is leading the way with Perth being one of only three cities worldwide trialling these vehicles," says WA's minister for transport, Rita Saffioti. "We will work closely with RAC and NAVYA to ensure the trial is a success, with the safety of the public being of the highest priority," Ms Saffioti was quoted saying in a press release.

Politicians in New Zealand might want to watch their backs, as they could soon face stiff competition in the form of a virtual bot. 'Sam' is the world's first virtual politician that users can interact with through Facebook Messenger. The AI chatbot is'representing' New Zealand's constituents, and claims to consider everyone's position, without bias, when making decisions. And it may not be long before we see the AI bot in action, as Sam's creator claims that it will be ready to run for office in 2020. 'Sam' is the world's first virtual politician that users can interact with through Facebook Messenger.

A second wave of automation in banking will increase capacity and free employees to focus on higher-value projects. To capture the opportunity, banks must take a strategic, rather than tactical, approach. Automation is the focus of intense interest in the global banking industry. Many banks are rushing to deploy the latest automation technologies in the hope of delivering the next wave of productivity, cost savings, and improvement in customer experiences. While the results have been mixed thus far, McKinsey expects that early growing pains will ultimately give way to a transformation of banking, with outsized gains for the institutions that master the new capabilities.

As long as this world is moving ahead, the visualization of AI is turning out into a reality at quite a relentless pace. At present, the election candidates are all humans and there is absolutely no involvement of AI in any elections of the world. But, to turn the tables around, New Zealand is all set to launch their first AI politician-Sam in the upcoming elections of 2020. The Veteran politician, called Sam, was created by a 49-year old Entrepreneur- Nick Gerritsen in New Zealand. Under constant learning and with an intention to create something new in this ever running world, scientists developed the world's first AI politician that can answer to any persons queries, policies around housing, local issues education, migration and much more.

Amazon just dropped its umpteenth Alexa skill, this time for Destiny 2 fans. Already in the tens of thousands, the digital assistant's tricks span shopping, news, smart home controls, pop trivia, kiddie pastimes, and now video games. But while a growing number of regions have access to Amazon's Echo family of smart speakers (including recent additions India and Japan), they're still missing in some spots. Now, it seems Alexa's global expansion is picking up speed, as the digital helper is (officially) heading Down Under. Amazon has announced that Alexa and Alexa-enabled devices will land in Australia and New Zealand in early 2018.

The presence of noisy instances in mobile phone data is a fundamental issue for classifying user phone call behavior (i.e., accept, reject, missed and outgoing), with many potential negative consequences. The classification accuracy may decrease and the complexity of the classifiers may increase due to the number of redundant training samples. To detect such noisy instances from a training dataset, researchers use naive Bayes classifier (NBC) as it identifies misclassified instances by taking into account independence assumption and conditional probabilities of the attributes. However, some of these misclassified instances might indicate usages behavioral patterns of individual mobile phone users. Existing naive Bayes classifier based noise detection techniques have not considered this issue and, thus, are lacking in classification accuracy. In this paper, we propose an improved noise detection technique based on naive Bayes classifier for effectively classifying users' phone call behaviors. In order to improve the classification accuracy, we effectively identify noisy instances from the training dataset by analyzing the behavioral patterns of individuals. We dynamically determine a noise threshold according to individual's unique behavioral patterns by using both the naive Bayes classifier and Laplace estimator. We use this noise threshold to identify noisy instances. To measure the effectiveness of our technique in classifying user phone call behavior, we employ the most popular classification algorithm (e.g., decision tree). Experimental results on the real phone call log dataset show that our proposed technique more accurately identifies the noisy instances from the training datasets that leads to better classification accuracy.

A 95-year-old woman dubbed Western Australia's "real life tomb raider" and "Indiana Joan" in a profile earlier this month is now under investigation for how she acquired a trove of ancient artifacts. The woman, Joan Howard, was married to a senior diplomat with the United Nations in the Middle East. In the 1960s and 70s, she volunteered with American and British archaeologists, according to the West Australian. Given her diplomatic status, she traveled to several countries including Syria, Egypt, Lebanon, Jordan, Palestine and Israel looking for ancient artifacts, which she brought home to Perth, Australia. "It was all good fun. But as it turned out, very, very rewarding," Howard told the West Australian.

Companies that are able to adapt to a world where innovation is increasingly driven by machine learning, or artificial intelligence more broadly, are the ones that will come out the other end of the tunnel and thrive, according to Oliver Rees, GM of Torque Data at Virgin Australia. Rees, whose data analytics consultancy firm Torque Data was acquired by Virgin Australia in 2015, told ZDNet that one of its tasks has been "reengineering [Virgin's] analytical capability", ensuring the airline is well-prepared to embrace the opportunities that are offered by machine learning. While not new to machine learning, Virgin Australia has been seeking better methods of developing, applying, and assessing machine learning algorithms, recently turning to Massachusetts-based company DataRobot, which operates on the belief that automated machine learning will not only increase productivity for data scientists, but also open up the world of data science. Rees told ZDNet that Torque, as the data analytics arm of Virgin, has been investigating ways to improve customer experience for members of Virgin's Velocity Frequent Flyer loyalty program. "We want people within our program to be able to redeem points for great experiences, and to do that, we want to be able to better predict when is the best time for particular people to redeem points and what should they be redeeming them against," Rees said.