Retailers can put artificial intelligence to work in many different forms, from creating a more personalised shopping experience for the consumer, to helping adopt a data-driven approach to retail marketing. ASOS Chief Executive, Nick Beighton, predicted that by 2020, mobile sales will account for 90% of total revenue for the retailer. With this in mind, retailers have to remain vigilant to changes in trends and expectations from shoppers. As customer traffic to mobile sites increases, it's important that retailers offer a bespoke and personalised service to ensure shoppers can fluently find the products they require and thus convert to purchasing much quicker.

Are we ready for Ultron type intelligence? This is part of our special series on Artificial Intelligence (AI). If you are catching it for the first time I'd recommend that you start here for some instrumental background and here where I start building the bigger idea behind AI. In my high school years, I remember a brilliant classmate, Matthew (not quite his real name), who got the necessary points at Advanced Level to study law at a local university. I was proud to see him not long ago appearing in newspapers as a commanding Intellectual Property (IP) lawyer.

LifeJourney International has launched its Day of STEM initiative, aiming to show students what it actually means to have a career in science, technology, engineering, and mathematics (STEM), with the backing of some of the country's tech heavyweights. The program, Australia 2020, aims to push students towards a STEM-based career, but operates under the assumption that telling students to study STEM is not enough to incite interest. The online platform allows kids to explore what it is like to have a career in fields such as wireless technology, cybersecurity, drone delivery, financial services, and autonomous vehicles, with students mentored by Ian Hill, chief innovation officer at Westpac; Simone Bachmann, digital trust specialist, responsible for cyber innovation and culture at Australia Post; Gerard Tracey, wireless telecommunications expert at Telstra; Anastasia Cammaroto, CIO at BT Financial Group; Celeste Lowe, cyber risk director at Deloitte; Ita Farhat, chief of staff at AMP; Cara Walsh, digital experience expert from Queensland's RACQ; Martin Levins, consultant at Australian Council for Computers in Education; and others. The program is also backed by the likes of Australian Association of Mathematics Teachers, and the Australian Computer Society, as well as an education advisory board to ensure the content stays relevant to the Australian market. A Day in STEM is pushed out to teachers and run in the classroom, with 95,000 students already signed up to the program ahead of its September 5 launch.

Machine Learning (ML) is an exciting new subfield of computer science. With origins in pattern recognition, today's hardware and software advances have made ML a new tool for many types of organizations I order to remain competitive. With today's hardware, massive amounts of data can be fed into a system, which can then use algorithms to determine possible outcomes of a task, and store that information for further use. As the amount of data that is ingested increases, the accuracy of the outcomes can improve. Similar to simulations that can give more accurate results with faster processing, more memory and improved algorithms, so can ML applications.

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The modern world seems really fast and dynamic with a multitude of new products being launched. Marketing agencies are making fortune by monitoring the markets and delivering reports on consumers' opinions. For today, the feedback analysis is a separate area, let's say a growing industry with an array of products and services. And the prices for those services are pretty exorbitant. So, do vendors have a chance to cut down expenses? Without any doubts, there's always an opportunity to start personal volcanic activities on feedback collection and analysis.

When Apple Inc. (NASDAQ:AAPL) released Siri on the iPhone 4S back in 2011, the personal digital assistant managed to earn mixed reviews from users. On one side, Siri was seen as a revolutionary feature, as a personal digital assistant that had the potential to change the way we use devices. However, numerous users felt as if Apple had blown the hype surrounding its digital personal assistant out of proportion, as Siri regularly failed to function the way it was meant to. The worst part about Siri was that it actually managed to regress under Apple, as the software that was released by its original developers was more capable then the one released by the Cupertino based tech giant. Moreover, it was apparent that one of Apple's biggest rivals Google managed to design a superior AI system as compared to what Siri had to offer.

Whether you're blogging, publishing a video, or sharing an image, you are contributing to the 2.5 quintillion bytes of data that is made everyday! The old method of publishing tons of content isn't as effective as it used to be. Many more are publishing great content nowadays to the point that it's becoming increasingly difficult to be heard over all that digital noise. It's time to blow off that dust and apply a shiny new coat of machine learning polish to your content strategy. As a sub-set of artificial intelligence, machine learning occurs when computer algorithms are programmed to learn from the data and information it inputs.

Nonmedical use of prescription medications/drugs (NMUPD) is a serious public health threat, particularly in relation to the prescription opioid analgesics abuse epidemic. While attention to this problem has been growing, there remains an urgent need to develop novel strategies in the field of "digital epidemiology" to better identify, analyze and understand trends in NMUPD behavior. We conducted surveillance of the popular microblogging site Twitter by collecting 11 million tweets filtered for three commonly abused prescription opioid analgesic drugs Percocet (acetaminophen/oxycodone), OxyContin (oxycodone), and Oxycodone. Unsupervised machine learning was applied on the subset of tweets for each analgesic drug to discover underlying latent themes regarding risk behavior. A two-step process of obtaining themes, and filtering out unwanted tweets was carried out in three subsequent rounds of machine learning.

Almost two years ago (to the day), Salesforce plunked down 392 million for RelateIQ, a specialist in software that automates sales tasks--like picking the best time to call a sales prospect or filling out call reports--using artificial intelligence. Since that time, the business software giant has snapped up at least a half-dozen other startups specializing in machine learning, predictive analytics, and other AI technologies. The latest one was just one week ago: BeyondCore, a specialist in statistical analysis. It turns out that Salesforce crm is cobbling all of these technologies together as part of project dubbed Salesforce Einstein, which will be detailed during the company's upcoming Dreamforce conference in late September, according to an article published this week by Forbes. The grand scheme is to endow Salesforce's core applications for sales management, marketing automation, and commerce with AI software that "learns" from the data it is collecting to spot trends or identify areas that might require attention from managers.