New and emerging technologies, such as artificial intelligence (AI), machine learning and cognitive computing – underpinned by Big Data and advanced data analytics – could completely transform the shopping experience as we know it. While still in its infancy in South Africa, a handful of retailers are testing some pretty cool technologies in an attempt to improve the shopping experience and better serve their customers. World Wide Worx found that online shopping in South Africa is expected to reach just 1% of overall retail spend in 2016, constrained by challenges such as high data costs, a lack of trust in e-commerce and a large unbanked population who do not have the means to transact online. The adage that online is killing the brick-and-mortar store might be true for more developed markets, but, for now, South Africans still flock to the more than 2 000 shopping malls across the country, limiting their online activities to window shopping and price comparison. If this trend continues, then South Africans could be in for a real treat when these new technologies make it to our shores.

"Artificial intelligence company Neurala is using machine learning coupled with cameras and drones to put a stop to the poaching crisis in Africa" writes Business Editor Writing for observer.com. And while the current effort is focused on elephants and rhinos, Versace said this technology could be used to save other animals from poaching as well.We have heard of a company using AI to predict the paths that poachers might take, but that is not working directly with the animals." Neurala's AI will assist human analysts by automatically sifting through terabytes of video taken by drones in real time.

Drones have the potential to play a big role in protecting endangered species, with a number of trials being conducted to investigate how small aerial surveillance aircraft can be used to combat poaching. The latest effort involves the use of artificial intelligence software to quickly identify poachers and animals in drone footage, in an attempt to better protect elephants and rhinos. Developed by Neurala, the software will be used by the Lindbergh Foundation in its efforts to combat poaching. It's designed to keep an eye on video as it's streamed back to researchers from drones in the field and identify animals, vehicles and poachers in real time without any human input. The software can analyze regular or infrared footage, so works with video taken day or night.

Ford Motor Co. is expected to announce on Monday the replacement of President and CEO Mark Fields with board member Jim Hackett. Hackett is also head of the company's smart mobility unit, which is focused on its ride-hailing and car-sharing efforts. According to several reports, the move is due to the automaker's recent disappointing profits and stock price, which has dropped nearly 40 percent since Fields took over in 2014. Fields, 56, has worked at the company for 28 years, and previously served as CEO of then Ford-owned Mazda, and president of The Americas. Ford has not officially confirmed Field's departure, and issued a statement to Automotive News that said, "we are staying focused on our plan for creating value and profitable growth.

Several organizations are already using drones to fight poaching, but the Lindbergh Foundation is taking it one step further. The environmental non-profit has joined forces with Neurala in order to use the company's deep learning neural network AI to boost the capabilities of the drones in its Air Shepherd program. Neurala taught its technology what elephants, rhinos and poachers look like, so it can accurately pinpoint and mark them in videos. It will now put the AI to work sifting through all the footage the foundation's drones beam back in real time, including infrared footage taken at night. The AI's job is to pore over these videos and quickly identify the presence of poachers to prevent them from even reaching the animals' herds. It's the perfect addition to the Air Shepherd program that aims to use cutting edge software and drones to stop poaching in Africa.

This is the 2-part blog version of a talk I've given at DOAG Conference this week. I've also uploaded the slides (no ppt; just pretty R presentation;-)) to the articles section, but if you'd like a little text I'm encouraging you to read on. That is, if you're in the target group for this post/talk. For this post, let me assume you're a SQL girl (or guy). With SQL you're comfortable (an expert, probably), you know how to get and manipulate your data, no nesting of subselects has you scared;-).

Leading funders and researchers this week agreed that all their clinical trials for vaccines and devices would in future be publicly registered and the results published. The pledge -- made by nearly a dozen groups including the Wellcome Trust and the UK Medical Research Council -- is a boost for innovation and safety. Some estimates suggest that half of all trial findings, notably those that do not yield positive results, are not made public. This is a significant waste of research, burying information that could help to better direct future work, reduce dangers to patients and boost efficiency. In an era of big data and partnership between medical research and IT, there is considerable potential to improve number crunching and identify patterns in trial data.

A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding. While Breazeal's work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as "useless". Breazeal bucked the trend with a very different vision: "I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship." She hopes her work and influence will inspire others to create robots "not only with smarts, but with heart, too."

In this paper we present GitDOX, an open source online, schema aware XML annotation interface linked to Natural Language Processing tools, which uses the online GitHub platform as a backend for version controlled electronic corpus development. We apply this platform to the use case of transcribing and annotating Coptic manuscript data from first millennium Egypt, in a collaborative team. The architecture of the tool is meant to be generic and extensible, supporting an unbounded range of annotation schemes, while being simple to use for annotators without extensive training in computational tools or version control software.

Large retailers are starting to utilise machine learning, combined with predictive analytics, to assist them in enhancing consumer engagement and in creating more accurate demand forecasts as they expand into new sales channels. With machine learning, computers learn by mining massive amounts of big data, without human intervention, to provide unprecedented consumer demand insights. When one couples machine learning with other technologies, like facial recognition solutions, says David Cosgrave, Customer Intelligence Lead at SAS, there is the potential to shift from active engagement to automated engagement in the retail space. "A good example of how such automated engagement may change the retail experience in the future can be found in the experimental Amazon Go shop. This is a new US store, for Amazon employees, which is completely automated. Consumers simply walk in, choose their products and walk out, and the in-store technology detects who is taking what thanks to facial recognition software. This is combined with deep learning that enables the system to know what products have been taken, and the system automatically debits the user's account."