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.

It is a strong indicator of today's globalized world and rapidly growing access to Internet platforms, that we have users from over 188 countries and 500 cities globally using our Text Analysis and News APIs. Our users need to be able to understand and analyze what's being said out there, about them, their products, services, or their competitors, regardless of the locality and the language used. Social media content on platforms like Twitter, Facebook and Instagram can provide unrivalled insights into customer opinion and experience to brands and organizations. A look at online review platforms such as Yelp and TripAdvisor, as well as various news outlets and blogs, reveals similar patterns regarding the variety of language used. Therefore, no matter if you are a social media analyst, or a hotel owner trying to gauge customer satisfaction, or a hedge fund analyst trying to analyze a foreign market, you need to be able to understand textual content in a multitude of languages.

"Yet, a major challenge stands between the dream assistant and the current reality. It's called the multi-agent problem, and most companies are reluctant to talk about it," Jolley writes. "The solution will ultimately determine how much of an impact assistants will have." Jolley writes, "How does an assistant, with limited knowledge of the world and a limited set of isolated agents, many of which might claim to do the same thing – choose which one to activate for every command in a way that will make users happy?" Much more in the full article – recommended – here. MacDailyNews Take: We're still in the infancy of personal assistants and artificial intelligence.

Artificial intelligence is a branch of computer science that deals with making the computers or a robot controlled by a computer, or software think intelligently just like humans. The term was first coined by John McCarthy in 1956 at the MIT, Massachusetts Institute of Technology. According to him, AI is "the science and engineering of making intelligent machines, especially intelligent computer programs". The field of AI draws upon computer science, psychology, mathematics, linguistics, neuroscience, philosophy and artificial psychology. The major goal of AI is creating expert intelligent systems which can learn, explain and advice the users and implementing human intelligence in the machines.

Now, the video game has a scientific connection. Microsoft has made Project Malmo available for programmers on GitHub via an open-source license. This platform uses the world of Minecraft to test advancements in artificial intelligence research. Before this move, the system was only used in private viewing by computer scientists. "We're trying to put out the tools that will allow people to make progress on those really, really hard research questions," Katja Hofmann said, the leader of Project Malmo and a researcher in Microsoft's research lab in Cambridge, UK.

We spoke to Paul Davies, UK CMO at Microsoft and one of our Vision 100 on what it takes to be a visionary. Davies said it takes a lot of curiosity and that visionaries need to be curious about the world around them and what makes people tick. He said that they should also have courage and the bravery to try different things. He said he admires the astronaut Tim Peake for "inspiring a young generation and telling them they really can do anything with their lives." When asked what he feels the biggest change will be for the industry in the next ten years, he said it would be artificial intelligence, with "data being pulled together in different ways to create new experiences for consumers in ways that will be invisible."

In 2015, the Chinese e-commerce market generated an estimated 562 billion in sales, with shopping named as the fastest-growing online activity among Chinese consumers. Though the rise of e-commerce is hardly a surprise at this point, the global reach and consistent growth in this sector make it one of the most significant global trends. What makes this all the more notable is that the rapid transition from the brick-and-mortar shops of old is still heavily limited by technology – specifically, the limitations of online product searches, which, especially compared to a conversation with a real world sales associate, return far too many irrelevant and unspecific results to be of reliably convenient use. Accordingly, what is left is a global phenomenon that has succeeded in profoundly disrupting the traditional shopping experience, but has yet to reach its full potential. The bright side to this story is that the key to unlocking the next wave of e-commerce disruption has arrived, this time with the advent of artificial intelligence (AI).

Using the power of analogy, a new structure-mapping engine gives computers the ability to reason like humans and even solve moral dilemmas. Northwestern University's Ken Forbus is closing the gap between humans and machines. Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. "In terms of thinking like humans, analogies are where it's at," said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern's McCormick School of Engineering.

Its frustrating when people claim algorithms are unbiased because while that may be true in some sense it ignores important problems that may arise in real world contexts where they are trained and deployed by fallible humans on imperfect data. For the most part I believe algorithms are unbiased. The main places these regulations are targeted, insurance companies, have unbiased ground truth on claims and accident rates. It's silly to ban machine learning across many industries and applications, instead of banning it in the specific places it is causing problems (which is what, exactly?) There are actually principled ways of addressing bias in data. These methods are totally broken.

During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me. One important detail is missing in this scenario – continuous flow of transactional data is not needed for model retraining.