Millions of people in the last year or so have bought microphones so that Amazon.com AMZN 1.63 % could listen in on them at home. And I just got more, built into new speakers called the Tap and the Echo Dot. Some day, Amazon could be embedded in appliances, listening all over your house. Why would you want that?

I was reflecting on the demonstrations of Deep Mind against Go champion Lee Se-dol, along with Watson at CeBIT and other artificial intelligence (AI) developments. It soon becomes apparent that we are evolving rapidly to a state where data learning through data analytics will be the battleground. In fact, it's clear that the battle over data is already won by those who have data architectures fit for AI. If you don't recognise this chart, please read the series of blogs I wrote in 2014 about such things. In this chart, I outlined the structure of a bank as front, middle, and back office (or retailer, processor, manufacturer), and that this structure is being attacked by technologies.

At its F8 developer conference in San Francisco today, Facebook demonstrated its latest artificial intelligence (AI) research efforts. Not surprisingly, they are about video. Video implies a whole bunch of individual images put together. So it logically flows from Facebook's progress around object recognition and image caption generation using AI. "You can imagine us building image search on steroids," Joaquin Quiñonero Candela, Facebook's director of Applied Machine Learning, said onstage today.

Ian Burkhart prepares for a training session in Columbus, Ohio. To move muscles in Burkhart's hand, the system relies on electrodes implanted in his brain, a computer interface attached to his skull, and electrical stimulators wrapped around his forearm. Ian Burkhart prepares for a training session in Columbus, Ohio. To move muscles in Burkhart's hand, the system relies on electrodes implanted in his brain, a computer interface attached to his skull, and electrical stimulators wrapped around his forearm. Ian Burkhart, now 24, was paralyzed in 2010 after diving into a wave in shallow water.

Ian Burkhart was 19 years old when he broke his neck diving into shallow water on holiday. Since then, he has been unable to move either of his legs, or his arms below the elbow (read Ian's story here). Now, in a world first, he has regained control of one hand and his fingers using a mind-reading device. In the past few years, we have seen paralysed people walk again with the aid of exoskeletons, and by using recorded brain activity to trigger electric stimulations to the leg muscles. Others have trained paralysed people to control computer cursors and robotic limbs by thought alone.

Google has employed machine learning on its Calendar application in an effort to help its users better keep track of, and complete, long-term goals. Users simply add a personal goal -- like hitting the gym three times a week, for example -- and Google Calendar will help them find the time and stick to it. Setting a goal requires the user to answer a few questions, specifying duration and times. From there Calendar will look at the user's schedule and find the best windows to schedule time to help complete the goal. It's another example of a major technology company using machine learning -- the concept of pattern recognition and computational learning theory -- to make its users' lives easier.

A recent academic study conducted by researchers from Leiden, Ben-Gurion and Toulouse Universities examined the performance of Cortical.io's Compared to the commonly used word-list method, Semantic Folding proved to have greater predictive power. Its other advantages were speed and ease of use. "Like the human brain, our Semantic Folding engine learns a language and understands the meaning of text by making analogies. Like the brain, it is both efficient and accurate. We are thrilled to see these compelling results confirmed by an independent academic study", comments Francisco Webber, inventor and co-founder of Cortical.io.

According to OECD predictions, exceeding budgets on health spending remains an issue for OECD countries. Maintaining today's healthcare systems and funding future medical advances will be difficult without major reforms. Public expenditure on health and long-term care in OECD countries is set to increase from around 6% of Gross Domestic Product (GDP) today to almost 9% of GDP in 2030 and to 14% by 2060. This will be the harsh reality unless governments and private companies change the structure of how healthcare is funded. In countries with private health insurance, certain treatments such as cancer care are so expensive that only the privileged with good insurance plans can afford them.

While video becomes more important as a digital media type, video data often remains dark to analytics. This webinar demonstrates how IBM Bluemix OpenWhisk together with Watson services begins to unlock the value of video data. Dark Vision is an application that uploads video files or streams to the cloud, transcodes video data, extracts and passes frames through the Watson Image Recognition and the Alchemy Face Recognition services, and generates meta-data to use in categorizing the video data for searchability. In the presentation, Andreas Nauerz introduces the basics of IBM Bluemix OpenWhisk and Frederic Lavigne demonstrates how to create OpenWhisk code to accomplish Dark Vision's application workflow. Maybe you want to do that because a particular team already has developed skills in this area, maybe because they do mobile development or have done mobile development in the past, and now want to outsource computer-intensive tasks.

Cameras and audio equipment are getting better all the time, but mostly through brute force: more pixels, more sensors, and better post-processing. Mammalian eyes and ears beat them handily when it comes to efficiency and the ability to only focus on what's interesting or important. Neuromorphic engineers, who try to mimic the strengths of biological systems in manmade ones, have made big strides in recent years, especially with vision. Researchers have made machine-vision systems that only take pictures of moving objects, for example. Instead of taking many images at a steady, predetermined rate, these kinds of cameras monitor for changes in a scene and only record those.