Although 42% of respondents to a Tech Pro Research survey said their company lacks the skills necessary to implement and support AI and machine learning, and only 28% said they had firsthand experience with the technology, 50% said their companies will adopt these within the next few years. Almost as many respondents said all the implementation and support work would be done in house when the time comes, and that their company is working to address AI and machine learning in the corporate security plan. Among companies currently using, or planning to use AI or machine learning, the top uses were research and consumer analysis. Further down the list, those currently using AI or machine learning were more interested in uses like fraud detection and market analysis, while those still in the planning stages were interested in security monitoring and office automation. For more data and analysis on corporate preparedness and uses for AI and machine learning, plus information on vendor selection, download the full report: AI and machine learning in the enterprise: Uses, organizational readiness and vendor choices.

One of China's leading tech companies is building an AI lab that could soon rival those operated by the likes of Google, Facebook, Baidu, and Amazon. Tencent, based in Shenzhen, in southern China, operates a range of online and mobile services, including the hugely popular social mobile apps WeChat and QQ. The company created its AI lab in April, and it is growing rapidly. Tencent sent a delegation of AI researchers, recruiters, and business representatives to the industry's preeminent event, the Neural Information Processing Systems conference, held in Barcelona, Spain, this week. Tencent's push into AI research reflects a broader shift across China's consumer technology industry toward more fundamental research designed to spur real innovation.

When Amazon first coined the strapline "Ask Alexa" for its virtual assistant, it couldn't have predicted the X-rated nature of some of the requests. "She" may boast an encyclopaedic knowledge, but research by consumer behaviour analysts Canvas8 reveals that some users are more interested in a virtual hook-up than fact finding. And she's not the only target: the equally smooth voice of Microsoft's Cortana is getting customers just as hot under the collar apparently. From perma-smiling avatars in traditionally female support roles, to hyper-sexualised "fembots" pandering to male fantasies, the female form is everywhere in techno-world - attractive, servile and at your command. A little more conservative, but just as eager to please, is virtual personal assistant Amy Ingram, the brainchild of New York start-up X.ai.

The final argument of the pessimists is that automation is "hollowing out" the workforce by replacing the jobs of the middle-skill professions, so we will be left with a world of hedge-fund managers and their maids. There has been some disproportionate losses of middle-income jobs in America and Europe since 1980, but as the MIT economist David Autor argues, it's as much to do with competition from China as automation per se. And he thinks it is running out of steam anyway. Journalists, he says "tend to overstate the extent of machine substitution for human labor and ignore the strong complementarities between automation and labor that increase productivity, raise earnings, and augment demand for labor."

If Hollywood is to be believed, the development of super-intelligent AI will spell the end of civilization as we know it and spark an unwinnable war between man and machine. It doesn't make for nearly as exciting entertainment, but artificial intelligence also offers tremendous upside, from the potential to deliver customized education to everyone, to improving disease diagnosis and treatment and eradicating poverty. Although AI researchers are focused these beneficial outcomes, the dystopian vision portrayed in so much science fiction is also a real possibility. At the recent Singularity University (SU) New Zealand Summit we talked with Neil Jacobstein, the former president and current chair of the Artificial Intelligence and Robotics Track at SU, about how the outcomes feared by so many can be avoided.

Does every painter leave a fingerprint? Accurately distinguishing the artwork of a master from a forgery can mean a difference in millions of dollars. In the Painter by Numbers playground competition hosted by Kiri Nichol (AKA small yellow duck), Kagglers were challenged to identify whether pairs of paintings were created by the same artist. In this winner's interview, Nejc Ilenič takes us through his first place solution to this painter recognition challenge. His combination of unsupervised and supervised learning methods helped him achieve a final AUC of 0.9289.

Bottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises' data, leading to diverse company-wide strategies succeeding faster and more profitably than before. The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry. Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets.

Most media companies increasingly rely on data to inform their decision making processes on both strategic and tactical levels. Yet with the widespread adoption of the Internet of Things (IoT) and Artificial Intelligence the amount of data companies can potentially harvest is set to rocket. So what data should they be focusing on, and how should they use it to make judgements about the content they produce? Steffen Konrath is the CEO of Liquid Newsroom, a company that uses technology to power its data-driven approach to content marketing, which it claims helps companies grow their B2B prospect and client list. Here Steffen, who will be speaking at DIS 2017 on'why listening is so important to creating content strategies,' offers insight into how data will shape the future of publishing. He explains why he thinks that the value of data is higher than the value of content, why technology will change the way publishers approach real time events and what role journalists will have in media offices powered by Artificial Intelligence. Digital Innovators' Summit 2017 takes place from 19-21 March (main Summit on 20 and 21 March) in Berlin, Germany.

The major roadblock is applying the right set of tools, which can pull powerful insights from this stockpile of data. But first, a big data system requires identifying and storing of digital information (lots of!!). Using Machine learning and Artificial Intelligence algorithms, businesses can optimize and uncover new statistical patterns which form the backbone of predictive analytics. Organization with huge data can begin analytics. Before beginning data scientists should make sure that predictive analytics fulfills their business goals and is appropriate for the big data environment.

In today's digital world, real estate shopping has moved almost exclusively online. Real estate listing portals, such as Zillow or Trulia, have helped usher in the era of visual search as the future of buying or selling your home online. They've gotten so popular in fact, that many of the top real estate portals are managing over one million new images per day as sellers and realtors want to provide the best presentation of their home and users demand to see every nook and cranny before actually visiting the property. In order to keep pace with the onslaught of new property listings and unprecedented traffic from potential home buyers, real estate portals have been desperately looking for solutions to manage their visual assets – ie: photos and videos of properties uploaded by users. You have to realize that along with the benefits of user-generated content (ie: access to millions upon millions of uploaded photos for virtual property tours), real estate portals are faced with the unmanageable task of collecting, organizing and displaying that content in an efficient and easy to navigate manner. First, as a real estate portal, how will you go about sorting through all of those images?