In December 2015, the revered French fashion house Louis Vuitton made a surprise announcement about the advertising campaign for its forthcoming spring-summer collection. The new range of clothes and accessories would be modelled on screen and in the pages of glossy magazines not by a famous actor or popstar but by a video game character: the pink-haired warrior Lightning from Final Fantasy XIII. Nicolas Ghesquière, the brand's creative director told the press he considered Lightning to be the "perfect avatar for a global heroic woman". The fictional character even carried out interviews to promote the partnership. It was not the first time a fashion brand had collaborated with a major video game. Previously, H&M, Moschino and Diesel had made digital clothes for The Sims.

From Apple to Coca-Cola, many of the world's biggest brands have logos that are instantly recognised by people around the world. But what if these logos had been designed by famous artists? Tech company Gnatta has used artificial intelligence (AI) to reimagine a number of logos in the styles of artists such as Banksy, Picasso and Monet. The system pieces together a completely new logo based on the artist's previous creations - can you guess what famous logos they are? Using AI communication tech company Gnatta has reimagined a number of company's logos from Nike to Toblerone in the styles of artists such as Banksy, Picasso and Monet - can you guess what brands these are based on?

Deep learning is a subset of artificial intelligence, in particular, the field of machine learning. Deep learning uses a multi-layered artificial neural network to carry out a range of tasks, from fraud detection to speech recognition or language translation. Deep learning differs from traditional machine learning systems in that it is capable of self-learning and improving as it analyses large data sets. A highly flexible system it has a number of applications in business. In this article, we explain exactly what deep learning is and explore the ways that it is already transforming businesses. Deep learning is a function of artificial intelligence. It is designed to replicate the way that the human brain processes data. It also re-creates the patterns found in the brain's decision-making process. Sometimes called deep neural networking or neural learning, it is part of the wider field of machine learning. It is powered by networks that can carry out unsupervised learning. This process uses algorithms to analyse raw data, extracting information and presenting it in a structured, useful model. Often it is also used to process unstructured or unlabeled data.

British fashion brand Burberry is one of the most recognized luxury clothes labels in the world. Starting in 2006, the company aimed to reinvent itself as an "end to end" digital enterprise. Its strategy was to use Big Data and Artificial Intelligence (AI) to boost sales and customer satisfaction. It does this by asking customers to voluntarily share data through a number of loyalty and reward programs. This information is used to offer personalized recommendations, online and in store.

Trunk Club, an apparel subscription service owned by Nordstrom, has increased Pinterest engagement by more than 100 percent in recent months by embedding artificial intelligence into its digital-images marketing. AI-image search is central to its ambitions of building a data-science model that drives highly relevant product offerings to consumers across the social web. More broadly, the effort speaks to how AI search is quickly becoming the retail sector's next big digital shopping experience. "We are trying to understand how one pair of jeans plays out against another pair that was released in another season," explained Justin Hughes, vp of product development and design at Trunk Club. "We want to get really granular and understand what really works."

British fashion brand Burberry is one of the most recognized luxury clothes labels in the world. Starting in 2006, the company aimed to reinvent itself as an "end to end" digital enterprise. Its strategy was to use Big Data and Artificial Intelligence (AI) to boost sales and customer satisfaction. It does this by asking customers to voluntarily share data through a number of loyalty and reward programs. This information is used to offer personalized recommendations, online and in store.

Technology is playing an ever-important role in the shopping side of the holiday season. Logistics aside, which is of course critical at this time of year, tech is also proving increasingly key from an experiential and a customer service perspective both online and offline. Leading that charge for 2016 are virtual reality (VR) and artificial intelligence (AI). Google has employed the former this year, for instance, to allow consumers to'walk' along Fifth Avenue in New York to experience all the holiday window displays. Window Wonderland, as the initiative is called, is a VR experience that lets users view 18 different retailers including Bloomingdale's, Barneys New York, Saks Fifth Avenue, Tiffany & Co, Burberry and more.

Sentiment analysis is already an important component of many brands' social media strategies, but it can often be limited to basic interpretations of whether a conversation is positive, negative or neutral. At the Cannes Lions international advertising festival in June, data visualization technology provider Buzz Radar conducted an experiment that took sentiment analysis further, diving deeper into different types of emotional nuances. Patrick Charlton, director and co-founder of Buzz Radar, spoke to eMarketer's Maria Minsker just before the festival about what the company hoped to learn from the project. Patrick Charlton: Burberry has used our Command Center platform to look at conversations on social media surrounding their campaigns. We pull in every single mention of Burberry from conversations about London Fashion Week, for example, and analyze the sentiment.

Machine learning algorithms can warn investors when a particular stock is going to fall by predicting likely instances of insider trading (when information that's not in the public domain is capitalised upon by people in the know). This can be done by analysing previous occasions when company insiders did apparently well-timed trades in their own stocks, and recognising these patterns. This might seem deceptively simple, but it isn't, explains Tom Doris, CEO of OTAS Technologies, a London-based market analytics and machine learning trading system. While company executives are required to file details of transactions in their company's stock, most insider trades are few and far between: a needle in a haystack. Doris told IBTimes UK: "We look at all of the insiders, the directors of companies, and we see all of their historical transactions in their own stock. If you are the chief financial officer of Vodafone, any time you buy or sell Vodafone stock, you're obliged under regulation to file details of those transactions. So that would include the amount that you bought or sold, when you did it and what price you got and the reason, if any, for the transaction. There is an enormous database of all of these transactions for all of the world's listed stocks and we go and we basically back-test all of the insiders and we find the ones that are apparently good at timing their own stocks."