Fresh off a stunning victory in a nearly-impossible-to-master board game, Google's DeepMind artificial intelligence project is bringing its brainpower to the humble eye scan. Google, which acquired British startup DeepMind in 2014, is partnering with the UK's state-run National Health Service. The mission: to create a system whereby sight-threatening conditions can be diagnosed from just a single scan of the eye, the company said Tuesday. Together, these diseases affect more than 100 million people worldwide, according to Google. Google will investigate how DeepMind's technology can be taught to analyze scans for the two diseases --the diagnoses of which have been time-consuming efforts for eye doctors due to their complexity.

Google's DeepMind division has announced a partnership with the NHS's Moorfields Eye Hospital to apply machine learning to spot common eye diseases earlier. The five-year research project will draw on one million anonymous eye scans which are held on Moorfields' patient database, with the aim to speed up the complex and time-consuming process of analysing eye scans. The hope is that this will allow diagnoses of common causes of sight loss, like diabetic retinopathy and age-related macular degeneration, to be spotted more rapidly and hence be treated more effectively. For example, Google says that up to 98 percent of sight loss resulting from diabetes can be prevented by early detection and treatment. Mobile app called "Streams" provides medical staff with latest clinical information.

Farber goes on, "Reflecting Intel's very strong commitment to open source, the CPU optimized MKL-DNN library for machine learning has been open sourced. Rounding out a cornucopia of machine learning technology announcements, the company has created a single portal for all their machine learning efforts at http://intel.com/machinelearning. Through this portal, Intel hopes to train 100,000 developers in the benefits of their machine learning technology. They are backing this up by giving early access to their machine learning technology to top research academics. Interest in machine learning is accelerating as commercial and scientific organizations are realizing the tremendous impact it can have across a wide range of markets ranging from Internet search, to social media, to real-time robotics, self-driving vehicles, drones and more."

The Mogees Play is the latest product from London-based startup Mogees. Based on the same contact microphone and machine-learning technology first seem in the company's original product, the Mogees Pro, it promises to turn any surface into a music and gaming input device, bridging the physical and digital worlds in new and delightfully creative ways. Once again, Mogees is launching a Kickstarter campaign to brings it wares to market, but unlike many crowdfunding campaigns, which I tend to be very hesitant to cover, the startup has form in shipping product and has to date sold thousands of Mogees Pros. The Mogees Play hopes to build on that legacy with a more mass market device that fulfils founder Bruno Zamborlin's mission to introduce non-musicians to the technology and encourage everybody to begin making music and exploring their creativity right out of the box. The Mogees Play will ship with three iOS apps: Mogees Pulse, a rhythm game, which is a little reminiscent of Guitar Hero (and has the backing of Guitar Hero founder Charles Huang); Mogees Jam, a recording studio in your pocket that enables you to build rhythms, melodies and loops using the acoustic properties of any object a Mogees Play is attached to; and Mogees Keys, which is a'smart' keyboard to trigger melodies, arpeggios and chords using the Mogees Play.

Sure, you can use WhizzML to fill in missing values or to do some basic data cleaning, but what if you want to go crazy? WhizzML is a fully-fledged programming language, after all. We can go as far down the rabbit hole as we want. As we've mentioned before, one of the great things about writing programs in WhizzML is access to highly-scalable, library-free machine learning. To put in another way, cloud-based machine learning operations (learn an ensemble, create a dataset, etc.) are primitives built into the language.

Natural Language Processing seems to have become somewhat of a commodity in recent years. More than a few companies have sprung up that offer basic NLP capabilities through a cloud API. If you'd like to know whether a text carries a positive or negative message, or what people or companies it mentions, you can just send it to one of these black boxes, and receive the answer in less than a second. Superficially, all these NLP APIs look more or less the same. Textrazor, AlchemyAPI, Aylien, MeaningCloud and Lexalytics all offer similar services (named entity recognition, sentiment analysis, keyword extraction, topic identification, etc.), and do so through similar interfaces.

Our planet is turning into an electronically controlled ball of energy. There is a lot of hype over Internet of Things (IoT) in today's technology oriented world. Let's just step back in time, say about a decade ago, when Internet of Things was as anonymous as the iPhone. And now all of a sudden it is all over the place and news. It was done in 1999 by Kevin Ashton. As per him it is an Internet system which connects to the world via sensors and storage of data.

Coding will join this list in time, however, where it differs wildly from the afore mentioned examples is it is unlikely to be lovingly preserved for future generations to admire, fiddle with or better still, reactivate. Its essence will not be reified for one specific reason – it can't be touched and humans value tactility. We touch immediately, both inside and outside the womb. Today, we find ourselves at a pivotal moment in our existence and about to experience an exponential period of rapid technological growth the likes of which is quite probably beyond our comprehension and at a base level, will have serious implications for coding. We rather arrogantly think that because we have a good grasp of our own technological advancement so far, we can somehow predict the mass cultural and behavioural shift about to happen as we question our own skills in the world. Us techies hold on to the notion that we are the masters of code, and we will be forever commanding line by line, the computers to do our bidding.

Bob Dylan walks into a room and speaks with Watson, IBM's artificial intelligence program (and Jeopardy quiz champion). We learn that the computer can not only seemingly have a conversation with a person, but can analyze Dylan's complex and frequently ambiguous lyrics. This commercial is part of IBM's Cognitive Business ad campaign that claims artificial intelligence (AI) systems such as Watson can make "virtually everything--every object, product, service, and process--…cognitive." AI technologies can not only sort data but, in a sense, think about what the data means and also act upon it to achieve best outcomes. As Charles McLellan points out, "AI has often been popularly envisaged in super-smart humanoid robot form. In fact, it's more commonly implemented as behind-the-scenes algorithms that can process'big' data to accomplish a range of relatively mundane tasks far more efficiently than humans can." AI is taking off as a result of raw computing processing power enabled by highly affordable and ever-shrinking integrated circuit chip sizes used in networks that can analyze vast amounts of data in parallel.

Here's a question IBM supercomputer Watson never got to ask on "Jeopardy": What is a cure for cancer? Watson, best known for beating champions Ken Jennings and Brad Rutter on the game show, will get its chance as it aids scientists using analysis of gene structures to figure out the cause of and potential cures for different strains of cancer. IBM unveiled Watson for Genomics, specifically designed for this task, at the National Cancer Moonshot Summit on Wednesday. The addition of Watson's brain power, which can understand questions in natural language and not just the computer language of ones and zeros, could significantly accelerate cancer treatment. Typically, finding the appropriate treatment for a specific patient means sequencing his or her genome -- the complete DNA structure packed into a single cell -- finding mutations and then getting a team of seasoned doctors in a room to decide the best options.