Chinese internet giant Alibaba has built artificial intelligence that it hopes will be able to correctly predict the outcome of reality TV talent show I'm a Singer. According to Tech in Asia, Alibaba's technology uses performance information such as "voice pitch and energy," and maps that against factors such as song choice and real-time audience response. The results will be shown online, pitching the technology, named'Ai', against the judges as the show is aired. The experiment is being held as a "proof-of-concept" for the technology, with Alibaba suggesting that it'll be used for purposes closer to its core business of online retail in the future. It's not the only internet company to be testing new technology on reality TV shows in Asia.

An interesting paper from Clickz, by Martin Talks, a digital innovation and transformation expert, written for C-suite executives, marketers, and advertising teams, presents ways in which artificial intelligence (AI) can be used in marketing. Talks says that when we think of artificial intelligence we often conjure up images of Terminator-like figures attempting to take control of our world. But it's crucial that marketers do not dismiss AI as only for the movies, but rather embracing AI technologies to research markets, deliver ads and make creative assets. Marketers need not fear that AI is purely for the big companies or research organizations. Through collaborative interfaces, AI is now relevant, applicable and achievable for organizations of all sizes.

Of course, some of this is because Facebook was wise enough to seed its SDK to developers months in advance. But it's also because the company laid out its groundwork for bots in Messenger as early as last year. In 2015, Facebook unveiled its Messenger for Businesses program, which would allow consumers like you and me to talk with businesses on Messenger. You could chat with Hyatt to ask for more towels in your room or with Sprint to find out why your network was slow. Of course, you'd be speaking to a customer-support agent rather than a bot, but it was a starting point. On top of that, Facebook has been working with partners like Uber, Lyft and KLM to try out an early version of a bot system where you could request a car or book a flight through Messenger.

Intel is in transition right now: An executive shakeup this month laid the path for new boss Venkata Renduchintala to put his imprint on the company's PC, Internet of Things and software operations. So no wonder the vibe at this week's Intel Developer Forum in Shenzhen was mellow. Intel kept the show a low-key affair, choosing not to bring it to the attention of a worldwide audience, unlike previous years. But IDF Shenzhen remains an important event on Intel's calendar. China is a huge market, and it's also a place where the chip maker encourages small hardware shops in the alleys of Shenzhen to experiment with PC, mobile and now, IoT ideas.

Synxi, a ManyWorlds brand, discovers content and expertise across your organization most relevant to you. Delivered as apps for collaborative platforms including Microsoft SharePoint, Yammer and Tibbr, Synxi uses patented machine learning and behavioral inferencing technologies to anticipate and adapt to your context and needs.

Of course, the effectiveness of a machine learning fraud solution boils down to how well it works at doing what it promises to do: predict fraud. But when talking about accuracy, don't forget to think about the flip side of stopping bad users – namely, not stopping good users. How well does the tool do at recognizing your good users? So, how do you actually measure the accuracy of a new tool you're not already using? Some platforms may give you the option of trying them out for free, without the commitment of a long-term contract.

Cloud-based athenahealth is expanding its portfolio to include machine learning and artificial intelligence with its acquisition of analytics startup Arsenal Health. Arsenal's Smart Scheduling tool has already been effective with athenahealth's providers, officials said. The acquisition, terms of which were not disclosed, will move Arsenal from a third-party vendor to a native capability available for all athenahealth's customers through its athenaCoordinator network. In the future, athenahealth's officials say they hope the acquisition will accelerate the company's analytics and AI capabilities, broadening insights and enhancing offerings for its 74 million patient records. "The prospect of building on Arsenal Health's technology and combining it with our own valuable data to positively impact care and expand the power of our network is extremely compelling," said Robinson.

Pop quiz: is the following statement true or false? Canada is the birthplace of a transformative technology set to disrupt countless industries and potentially lead the next wave of global economic growth. Most Canadians aren't aware of it, but artificial intelligence (more specifically its subset, deep learning) -- the inspiration for scores of dystopian science-fiction movies -- is a made-in-Canada technology that will become profoundly important over the next few years. Deep learning was the name given to a group of complex mathematical models that came out of the University of Toronto in 2006. In a nutshell, the technology mimics the neural networks of a human brain, giving machines the capacity to learn on their own and discover previously undetectable patterns within massive data sets.

As Machine Learning tools become mainstream, and ever-growing choice of these is available to data scientists and analysts, the need to assess those best suited becomes challenging. In this study, 20 Machine Learning models were benchmarked for their accuracy and speed performance on a multi-core hardware, when applied to 2 multinomial datasets differing broadly in size and complexity. It was observed that BAG-CART, RF and BOOST-C50 top the list at more than 99% accuracy while NNET, PART, GBM, SVM and C45 exceeded 95% accuracy on the small Car Evaluation dataset. On the larger and more complex Nursery dataset, we observed BAG-CART, BOOST-C50, PART, SVM and RF exceeded 99% accuracy, while JRIP, NNET, H2O, C45, and KNN exceeded 95% accuracy. However, overwhelming dependencies on Speed (determined on an average of 5-runs) were observed on a multicore hardware, with only CART, MDA and GBM as contenders for the Car Evaluation dataset.

Digital engagement is once again shifting, as we can see from the main discussions at Facebook's F8 conference this week about the new release of Messenger and its smart chatbots, or when we look at what's happening with popular team messaging services like Slack, which is being "overrun by friendly, wonderful bots." While bots seem like a minor improvement to digital user experience, some believe -- including myself -- that a combination of today's latest technologies will transform this what's-old-is-new-again technology into a major new force in contemporary digital experience and social engagement. Over the last couple of years, conversing in everyday language with our digital devices has become relatively commonplace with the advent of widely used digital concierge services like Siri, Google Now, and Amazon Echo. Known more formally as'conversational user experiences (UXs)', this dialogue-based interaction model actually has quite a long history going way back to command-line programs like Eliza and Zork (both of which yours truly spent far too much time with when younger), the first commercial expert systems in the 1980s, IRC bots, and other early examples. While there's always been an assumption that bots had a bit code behind them with a little situated intelligence -- from performing simple services like scheduling reminders via IM all the way up to the first textual AI-based systems such as MYCIN for helping doctors diagnose infections -- most conversational interfaces tend to be relatively simple affairs with a little bit of basic natural language processing connected to a decision tree.