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.

Machine Learning is a form of Artificial Intelligence (AI) which allows computers to learn by way of observation and experience, rather than rigid pre-programming. Machine Learning uses computer programs that are capable of growth and change as they process new data. Using algorithms, Machine Learning allows computers to develop habitual responses based on the repeated behaviors and actions of the person using the computer. The concept of learning repeated behaviors is important. As models are presented with new data, they adapt, learning from earlier experiences to provide reliable, consistent results and responses. While the science of Machine Learning is not new, it has been gaining a renewed popularity as it becomes a fundamental building block in AI technology, Big Data, and the evolution of virtual assistants.

Enterprise technology vendors are rushing to get on the chatbot bandwagon. Last week saw IBM Corp. and Cisco Systems Inc. add their names to the list by announcing a partnership to deliver artifical intelligence services for the latter's collaboration software. According to the companies, the development effort will focus primarily on providing "real-time advice and handling tasks". They didn't go into more detail, but offered a few examples of how the upcoming chatbot functionality might come handy. Financial advisors, for instance, could have certain investment recommendations generated automatically when talking with clients via Cisco WebEx.

Google and the U.K.'s government health service have partnered to study whether computers can be trained to spot degenerative eye problems early enough to prevent blindness. Google DeepMind, the London-based artificial intelligence unit owned by Alphabet Inc., announced a research partnership today with the National Health Service to gain access to a million anonymous eye scans. DeepMind will use the data to train its computers to identify eye defects. The aim is to give doctors a digital tool that can read an eye-scan test and recognize problems faster. Earlier detection of eye disorders related to diabetes and age-related macular degeneration could allow doctors to prevent loss of vision in many people, according to a statement by DeepMind Tuesday announcing the project with the Moorfields Eye Hospital NHS Foundation Trust.