While many blue collar professions have already felt the ache wrought by automation, white collar professions aren't far behind either. Data analysts, bookkeepers and even journalists aren't safe in their cubicles, and automation has crept into those industries as well. In fact until very recently, so-called "expert" industry professionals -- think doctors, scientists and upper-level management positions -- were a few of the arenas just barely touched by automation and bots. These positions require a human who can draw on their expert knowledge of a subject and take account for the context of a situation to provide insightful results, which makes them difficult to replicate with technology. The problem, of course, is that humans are slow, and vast resources of knowledge can be lost if a human leaves a company or retires.

Whether you're reading this article on a smartphone, tablet or laptop, chances are the device in front of you contains components from at least six countries spanning three or more continents. Its sleek exterior belies the complicated and intricate set of internal parts that only a global supply chain can provide. Over the past century, finished products made in a single country have become increasingly hard to find as globalization -- weighted a term as it is -- has stretched supply chains to the ends of the Earth. Now, anything from planes, trains and automobiles to computers, cellphones and appliances can trace its hundreds of pieces to nearly as many companies around the world. And its assembly might take place in a different country still.

Claned Co-founder Vesa Perala believes that instead of attempting to retrofit technology to out-dated educational systems, EdTech start-ups should be helping to write a new rulebook. For the past 3 years, Claned has been in what he describes as semi-stealth mode, focusing on developing a robust artificial intelligence system that uses machine-learning algorithms to map out what factors most impact individual learning. That knowledge, he says, was already out there, because it's something universities routinely do. Over time, tutors build an understanding of how each student learns, yet that data is trapped in a system which simply isn't scalable. Claned set out to solve this by combining these tried-and-tested academic evaluation metrics with machine learning algorithms and Artificial Intelligence.

Think about it, how hilarious would it be if we had an assistant chatbot wired up like a stereotypical Indian Dad. They are fast becoming the talk of the tech town; and it was about time we showed off our AI capabilities. At Haptik, we've always strived to bridge the gap between artificial intelligence & human assistance; and over the last two years have built a very sophisticated chat-bot that acts as an underlying layer of assistance to our actual real army of 300 assistants. Over 30% off our tasks/chats today are completed by the bot, and we can't be excited enough about everything ahead. So while the world is talking about Chat Bots, we decided to leverage that excitement in the space and direct it towards creating something that can potentially blow your minds & ours too.

AI is the term used most commonly for Artificial Intelligence. The concept is to develop computer programs that perform tasks that are normally performed by human. The goal is to give machines (robots) ability to seem like they have human intelligence. I'm pretty sure that at this point we all have seen robots doing the grunt work in factories, intelligence driverless cars, and companies are using AL to improve their product and increase sales. Since 2007, Gartner has been predicting key strategic technology trends for the coming years – not an easy task considering the rapid change in the IT market.

Opining about the future of AI at the recent Brilliant Minds event at Symposium Stockholm, Google Executive Chairman Eric Schmidt rejected warnings from Elon Musk and Stephen Hawking about the dangers of AI, saying, "In the case of Stephen Hawking, although a brilliant man, he's not a computer scientist. Elon is also a brilliant man, though he too is a physicist, not a computer scientist." This absurd dismissal of Musk and Hawking was in response to an absurd question about "the possibility of an artificial superintelligence trying to destroy mankind in the near future." Schmidt went on to say, "It's a movie. The state of the earth currently does not support any of these scenarios."

Today another revolution is underway with potentially even further reaching consequences. Artificial Intelligence in industry, experts are predicting, will change everything about the way we produce, manufacture and deliver. Cognitive computing, machine learning, natural language processing – different terms have emerged as development of the technology has progressed in recent years.

Blizzard, the developer behind games like Hearthstone, World of Warcraft and Overwatch, has confirmed it is having issues with its Battle.net Reports of players not being able to log in to Battle.net Blizzard's customer service has since confirmed the problem. We're currently investigating an issue affecting our authentication servers, which may result in failed or slow login attempts. It is unclear if the problem arose because of a technical issue or if it was caused by a malicious attack.

Dato, a Seattle machine learning startup, will put its services in front of a large audience of potential customers through an integration with the next product release from its neighbor, Tableau Software. Tableau (NYSE: DATA), the data visualization and analytics company headquartered in Seattle's Fremont neighborhood, is currently beta-testing Tableau 10, which is due out later this summer and will include the Dato Predictive Services integration along with a host of new features. Dato product manager Roman Schindlauer says the integration will allow Tableau users to create predictive datasets within Tableau using the Python programming language, along with its hundreds of machine learning libraries and tools. That will enable "more complex scenarios" within Tableau--things like sentiment analysis, churn prediction, lead scoring and other predictive analytics that help companies put the reams of data they gather to good use, he says. "It's really the ability to make predictions about the potential future behavior of your users as a company," he says.

Putting it all together, the following example shows the equity curve representing cumulative returns of the model strategy, with all values expressed in dollars. To increase the precision of forecasted values, instead of a standard probability of 0.5 (50 percent) we choose a higher threshold value, to be more confident that the model predicts an Up day. As we can see by the chart above, the equity curve is much better than before (Sharpe is 6.5 instead of 3.5), even with fewer round turns. From this point on, we will consider all next models with a threshold higher than a standard value. We can apply our research, as we did previously with the decision tree, into a Logistic Classifier model.