Hospitals in Paris are trialling Big Data and machine learning systems designed to forecast admission rates – leading to more efficient deployment of resources and better patient outcomes. It's just one more way in which cutting-edge data science is being applied to real-world problems in healthcare, along with creating personalized medicines, fighting cancer and streamlining pharmaceutical trials.

Artificial intelligence (AI) has spread its wings: from Tesla's self-navigating cars, to Google's DeepMind defeating the world champion at Go (a board game more complicated than chess), to ibm's Watson diagnosing diseases. Software is moving beyond executing black-and-white instructions to making complex and often subjective decisions. In 2017 society will begin to trust machines in much the same way that we trust people. AI introduces a kind of unpredictability not found in traditional software. In solving complex tasks, it is too difficult to build rules to cover every eventuality. Instead, AI systems learn from experience.

BENGALURU: Mediatech startup Dailyhunt, InShorts and ScoopWhoop -through its latest acquisition Touchfone -amongst others are increasingly leveraging machine learning capabilities to improve personalisation and interaction for the end user. From InShorts introducing quizzes to Dailyhunt's personalised news feed, they aim to increase end users' monthly time spent on the app, ultimately targetting higher ad revenue potential. InShorts recently integrated a quiz into its app, which is in its beta phase and has been introduced to 10,000 users. While the user swipes through news content, the quiz emerges as an option. The user can play with a random opponent or share an invite with a friend through WhatsApp.

An increasing number of brands and companies are turning to chatbots as a means of enhancing user experience and reaching customers in new ways. With many such chatbots popping up on the widely used platform of Facebook, the spread of chatbots is gaining considerable speed. Aside from being an obvious step forward for technological advancement, chatbots are surprisingly useful for and centered around enhancing user experience-more so than you might think. Chatbots are a simpler alternative to building an app. Shifts in browsing behavior have made mobile-first accessibility of paramount importance for brands and businesses of all disciplines.

Mark Zuckerberg has just built a digital home assistant (Jarvis). This achievement took over social networks, and every internaut wanted his own Jarvis. This reflects quite well where the world is headed. In a few years' time, robots and artificial intelligence (AI) will undoubtedly be part of people's daily lives. Research about machine learning and neuronal networks are "hot" topics, and intelligent algorithms are improved continuously.

Apple has stayed true to its promise and published its first academic paper on artificial intelligence. The world's most valuable company has traditionally kept its AI research private but earlier this month Ruslan Salakhutdinov, director of AI research at Apple, made a pledge to start being more open. The new Apple paper -- published December 22 and titled "Learning from simulated and unsupervised images through adversarial training" -- gives an insight into some of the techniques that Apple is using to develop AI. In the study, which was published through the Cornell University Library, Apple researchers explain a technique that can be used to improve how an algorithm learns to "see" what is in an image. The paper's six authors state that using synthetic images (such as those seen in a video game), as opposed to real-world images, can be more efficient when it comes to training AI models known as neural networks, which are designed to think in the same way as the human brain. Because synthetic image data is already labelled and annotated while real-world images aren't.

Alexa and Siri become new retail customers: Home-based digital assistants like Amazon's Alexa and Apple's Siri will proliferate and expand into eCommerce, enabling consumers to'shop by voice'. These assistants are becoming pervasive as the ecosystems expand outside the home; Amazon is already integrating Alexa into Ford, BMW, and Hyundai autos and Apple is a growing force in cars. IoT hits a tipping point: The Internet of Things (IoT) has been talked about a lot for both in-home and in-store applications. Omnipresent omni-channel: Platforms are being put in place that provide consistent relevant content across every digital touchpoint while ensuring a cohesive and comprehensive user experience across devices and channels both inside and outside the store. Relevancy required: Strategic hyper-personalization goes from a'nice to have' to a necessity to keep customers (especially millennials and younger shoppers) engaged and to make the most efficient use of marketing budgets.

Staples Business Advantage, the B2B eCommerce unit of the office supplies company, is ramping up its attention to machine learning in an effort to boost performance. Reports this week said Staples is tapping into machine learning to more quickly engage with potential business buyers and linking sales representatives to those prospects. Staples Business Advantage is exploring machine learning and artificial intelligence to aggregate information about corporate buyers' preferences and predict their shopping needs. Zubair Murtaza, vice president of eCommerce product management and customer experience, told B2B E-Commerce World that Staples already has these technologies in place and is developing these use cases. "We have things that are very machine learning-intensive, such as self-service on the website, triggers for savings and tools for reps powered by the same algorithms used on the website," the executive said.

The process used to create a major motion picture hasn't changed much in a century. Someone has an idea that they turn into a screenplay, which is then edited, developed and handed to a director, who brings it to life. The only difference now is that there's plenty more focus-grouping, audience analysis and number crunching to ensure each film is a hit. Except that doesn't really work, since 2016 alone has seen scores of movies unceremoniously crash and burn. But maybe that will change with ScriptBook, an algorithm that its creators say can spot most turkeys before they've even been made.

Random Forest) • Used to ensemble a diverse group of strong learners • Involves training a second-level machine learning algorithm called a "metalearner" to learn the optimal combination of the base learners 5. History of Stacking • Leo Breiman, "Stacked Regressions" (1996) • Modified algorithm to use CV to generate level-one data • Blended Neural Networks and GLMs (separately) Stacked Generalization Stacked Regressions Super Learning • David H. Wolpert, "Stacked Generalization" (1992) • First formulation of stacking via a metalearner • Blended Neural Networks • Mark van der Laan et al., "Super Learner" (2007) • Provided the theory to prove that the Super Learner is the asymptotically optimal combination • First R implementation in 2010 6.