Customer Engagement approaches, and the technology used to enable them, have evolved immensely over the last 25 years. Two distinct eras define this period, as well as a major technological shift to real-time systems with AI feedback loops. The BI Guesswork Era During the advent of the Business Intelligence (BI), Marketing Technology and Campaign Management era (circa 1990), marketers had limited predictive powers. In many cases, when it came to what individuals really needed, they resorted to guesswork. They channeled their energy to perfect efficiencies in targeting and automation.

Machine learning is taking on Hollywood's gender bias. Technology that automatically detects how often men and women appear on screen reveals that in recent popular films, men have had almost twice as much screen time as women. The software uses algorithms for face and voice recognition that have been trained on annotated video to identify whether a character is male or female, and can measure how long they are on screen to a fraction of a second. It was developed by Shri Narayanan at the University of Southern California, Los Angeles, in partnership with the Geena Davis Institute on Gender in Media and Google.org, the search engine's charitable arm. In an analysis of the 100 highest-grossing live-action films from each of the past three years, the software found that women appear on average for just 36 per cent of the total time that characters are on screen.

Now they look to the sea. Drones capable of underwater operation are little more than novelties at the moment, but the technology could advance marine research, revolutionize undersea cinematography, and let anyone explore places even experienced divers cannot reach. A handful of companies are leading this leap into the water. PowerVision, a Chinese robotics firm with 10 offices around the world, introduced its PowerRay underwater drone earlier this year. OpenROV of Berkeley, California, staged a successful crowdfunding campaign for the Trident, its remotely operated vehicle. These machines offer only limited practicality, but point the way to a future where consumer drones do all sorts of things beneath the waves.

In 2011, IBM sent its supercomputer Watson onto the popular American TV quiz show Jeopardy where it succeeded in matching wits with and beating two of the TV show's most successful players. That was over five years ago, but if you ask members of the public to describe IBM Watson, those in the know will say that it's a huge great black mainframe computer that's incredibly smart. Yet according to IBM, that's where you'd be wrong – the computing giant is adamant that the future of artificial intelligence will not be one big scary digital brain, and technology is definitely not going to kill us off one day. "When IBM Watson first came out, we used to think about it as a giant brain in a jar, but it's not that," John Cohn, an IBM Fellow in the IBM Watson Internet of Things (IoT) division tells IBTimes UK while showing us around IBM's new global IoT headquarters in Munich, Germany. "It's a bunch of tools that you can use to compose systems that interact naturally with humans, learns from their situation, adapting and then applying that knowledge. It's not an easy philosophy to apply, but it can bring benefits to so many different industries."

This paper is a tutorial on Formal Concept Analysis (FCA) and its applications. FCA is an applied branch of Lattice Theory, a mathematical discipline which enables formalisation of concepts as basic units of human thinking and analysing data in the object-attribute form. Originated in early 80s, during the last three decades, it became a popular human-centred tool for knowledge representation and data analysis with numerous applications. Since the tutorial was specially prepared for RuS-SIR 2014, the covered FCA topics include Information Retrieval with a focus on visualisation aspects, Machine Learning, Data Mining and Knowledge Discovery, Text Mining and several others.

Do you think you can tell a piece of music composed by artificial intelligence (AI) from one created by a human composer? Before you read any further, let's find out. The following audio consists of two fragments, one written by AI, the other by a human. We're inviting 250 to exhibit at TNW Conference and pitch on stage! If you didn't get it right the first time, no worries--we'll have a couple more mini-quizzes like this below.

Sonos has unveiled its Playbase – a huge, smart speaker that TVs can sit on. The new speaker is similar to its existing Playbar, which is a long flat set of speakers that sits under the TV and throws out wide and loud sound for films or music. But it is built into a body that can have a television sit on top of it – which Sonos says is perfect for people who don't have the space or the setup for a soundbar. The new speaker will go on sale in April in black and white and cost £699. Customers can sign up to be notified when the new speaker goes on sale. Like the rest of Sonos' speakers, the Playbase can connect to the internet and pull down music from a wide variety of streaming services.

You didn't have to be following the coverage from this year's CES too closely to notice one name repeatedly cropping up, across what felt like every announcement at the show: Alexa. Alexa is an artificial intelligence, a virtual personal assistant developed by Amazon. And right now, it looks likely to be remembered as AI's first major foray into the mainstream. Developed by Amazon's Lab126 R&D division, and building on the eCommerce giant's acquisition of Cambridge AI startup Evi in 2012, Alexa made its debut – there's a temptation to say'her debut', given the gendered name and voice, but let's stay neutral for the moment – back in November 2014. It was first sold as part of the Amazon Echo, a smart speaker that enabled users to ask questions, select and listen to music and, of course, order shopping using just their voice. It wasn't until last year, however, that Alexa made a real splash.

Looks like even Google's voice-activated speaker Google Home is not safe from fake news. It was found out recently that Amazon Echo's rival is vulnerable to hoaxes, propaganda and disinformation published online. Apparently, when SearchEngineLand editor Danny Sullivan discovered that Google serves up fake news as answers to questions asked by users on Google's search bar, it led to the discovery that Google Home could also respond to user queries with data from fake news sites. Re/code says the reason why Google Home also uses bogus search results to answer to inquiries is due to Featured Snippets, a feature of Google Search that generates algorithmic matches to search queries from third-party sites. A Google spokesperson has since confirmed the problem with Featured Snippets in an email to Re/code.

Summary: There are five basic styles of recommenders differentiated mostly by their core algorithms. You need to understand what's going on inside the box in order to know if you're truly optimizing this critical tool. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In this article we'll cover the five basic types of recommenders and their strengths and weaknesses. Given that Recommenders add 10% to 25% of incremental income to your ecommerce business you need to know exactly how these are working. Optimization will involve fine tuning as well as potentially combining different models.