Let a thousand virtual worlds rain down from the clouds. That's the call from Google as it gets behind a tiny British startup called Improbable. Founded by two Cambridge graduates and backed by $20 million in funding from the venture capitalists at Andreessen Horowitz, Improbable offers a new way of building virtual worlds, including not just immersive games à la Second Life or World of Warcraft, but also vast digital simulations of real cities, economies, and biological systems. The idea is that these virtual worlds can run in a holistic way across a practically infinite network of computers, so that they can expand to unprecedented sizes and reach new levels of complexity. So far, the startup has shared its technology with just a handful of coders and companies.

Artificial Intelligence (AI) is being used across the financial services industry, becoming more popular in customer-facing digital channels. While the technology is improving, and offering a clear way to improve on customer service offerings while cutting costs, many of the early case studies – featuring chatbots and virtual assistants – don't appear to make the best use of machine learning, or really help banking customers in a meaningful way. So are we entering an age of reliance on artificial, as opposed to actual, intelligence? Or is this just a new – but perhaps more efficient – way to present the same information to customers? Will banks or FinTechs be the first to capture new customers based on a compelling combination of technology and data access?

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I'm fairly new to data mining myself, but I've been reading a book called "Data Mining: Practical Machine Learning Tools and Techniques" by Witten and Frank. The book is a little light on theory, but it has a pretty good overview of many different techniques for data mining, and it has enough detail that you can start applying some of the techniques on your data immediately. I'm using the book in conjunction with a course on decision trees that I'm taking at statistics.com, which I would recommend. "Elements of Statistical Learning" seems to be the closest thing to an authoritative text on data mining that I've found, and it has more theory than the book I'm using for my class, so that may be of use to you. I'm planning on studying it more after I've finished taking my class.

Unfortunately, the new training platform created by OpenAI, a San Francisco-based nonprofit, is only available to AI -- so if you're human, you're out of luck. The new'college' is, in actuality, a training platform called Universe, whereby AI can interact with games, web browsers, protein folding software, and "transfer learning," which allows them to take what they've learned in one application and apply it to another. The AI engages via Virtual Network Computing, or VNC, which involves them sending simulated mouse and keyboard strokes. The Universe digital suite's home is in the OpenAI artificial intelligence learning center in San Francisco, where developers will begin "measuring and training AI agents." OpenAI is the non-profit brainchild of entrepreneurs Elon Musk and Peter Thiel, who have made no secret of their ambitions to greatly accelerate the research and development of transhumanist technologies.

This blog was originally published on our Text Analysis blog, the blog post set out to analyze and visualize 4 million tweets collected during Superbowl XLIX. Not surprisingly, Superbowl XLIX generated a huge amount of chatter on social networks with Twitter Estimating that over 28.4 million posts made with terms relating to the Superbowl. At AYLIEN, we collected just under 4 million Tweets from Hashtags, Handles and Keywords we were monitoring. To keep our sample clean, we removed any reTweets and spam from the Tweets collected and only worked with those Tweets that were written in English. We were left with about 3.5 million Tweets to play with.

British consumers are expected to spend £280 each on gifts over the weeks leading up to Christmas. More than half of these purchases will take place online. Almost a third of people will rely on online reviews to make their buying decisions, although recommendations from friends and family are still the main source of persuasion. Online shopping is estimated to rise by 24% by the end of this year. However, as consumers are looking for more sensory and immersive shopping experiences, the pressure is on for online retailers to find new ways to excite customers and keep them satisfied – and artificial intelligence (AI) is the new technology they will use.

Whether we like it or not, artificial intelligence is slowly but surely automating the processes we take for granted will be done by humans and making it more efficient, cost and time-wise. Big data, machine learning, natural language processing – all fancy terms used to explain the advent of machines learning to think and act and intuit like humans. Almost all tech industries will be, and are being, impacted by AI – and none more so than the B2C vertical of ecommerce. This infographic explains how ecommerce will be impacted by the growth of AI through chatbots, automation and other processes – all to enrich the customer buying experience.

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. At four of the hospitals which make up the Assistance Publique-Hôpitaux de Paris (AP-HP), data from internal and external sources – including 10 years' worth of hospital admissions records has been crunched to come up with day and hour-level predictions of the number of patients expected through the doors. The core of the analytics work involves using time series analysis techniques – looking for ways in which patterns in the data can be used to predict the admission rates at different times. Machine learning is employed to determine which algorithms provide the best indicator of future trends, when they are fed data from the past.