A true genius, Alan Turing was played brilliantly by Benedict Cumberbatch inThe Imitation Game -- the movie about his life and role in ending WWII -- which introduced him to a whole new generation of admirers. It wasTuring who predicted machine learning would play a big role in modern computing in his article the "Turing Test," way back in 1950. Indeed, Turing was way ahead of his time, which was a major theme in the movie, but now the world has caught up. The major advancements in readily accessible computing power, the quantity of data available, and algorithms that truly make machine learning possible are driving our ability to process data, analyze it, and act on it in ways that would make Mr. Turing proud. These advances have completely changed the machine learning game: The fundamental concept remains the same, but now it's far more sophisticated, efficient, and easily deployable. Beyond the big headline-grabbing examples of how machine learning will impact our lives -- such as through driverless cars -- it has exciting potential to put an end to the bland and sometimes ineffective customer experiences that many retailers are delivering to their customers.

It does look like a cheat sheet with everything you need to know in terms of formulas used in standard machine learning algorithms. It would be helpful if someone could add some meat to this document, in particular, adding some context and explanations for each formula. Many of these formulas are nicely illustrated here (scroll to the bottom of the page after clicking on the link.)

To draw a sketch on your mind only imagination in sufficient but here we are talking about robotics. For a robotics project it is important to know some computer language by which you can program your bot. Initially when I have started working on robotic model for some robotic competitions, I was totally unaware of these languages. I have started my work with relay logic. It was a most basic thing in electronic and as an electronic engineer I was expert with relay logic. It was the time of year 2000 when internet connection was not so cheap as compare to now.

Earlier in the series, we discussed Microsoft's recent acquisition of Simplygon in the AR space. To move ahead in the AI space, Microsoft (MSFT) announced that it plans to acquire Maluuba, an AI (artificial intelligence) startup. AI is an umbrella term that encompasses natural language processing, machine learning, and robotics. AI enables sensing, prediction, analysis, and solutions for various IT (information technology) issues. Research and algorithm development for machine learning is Maluuba's strength.

In the near future, artificial intelligence (AI) would be in major demand by the consumers to make interactions as seamless as possible. Many of us as an end user are unaware of the fact that Google uses AI to improve their search. It provides us with the correct results most of the times. Behind the scenes of any AI-powered systems, there is a long and complex computational process involved with the trained data set for the algorithm to perform so that the user gets an overwhelming experience. This experience is so fast and seamless that the user thinks everything is happening magically.

Through the cooperative research and development agreement, scientists at the IU School of Informatics and Computing and at the Naval Surface Warfare Center, Crane Division will work together to transform existing military sensor technology through machine learning and artificial intelligence. The agreement was signed at the base, which is near Crane, Indiana. The lead researchers on the project are Sriraam Natarajan, associate professor in the IU School of Informatics and Computing at IU Bloomington, and Robert Cruise, chief scientist for the Special Warfare and Expeditionary Systems Department at NSWC Crane. The agreement is part of a larger effort at the IU School of Informatics and Computing to foster partnerships with NSWC Crane, a major economic driver in southern Indiana and one of the largest naval bases in the country. "Artificial intelligence, machine learning and human-computer interaction are three areas of interest to the researchers at Crane, and also areas of great strength at our school," said Martina Barnas, assistant dean for research and director of research collaborations at the IU School of Informatics and Computing.

Apple Inc. is set to join the Partnership on AI, an artificial intelligence research group that includes Amazon.com Apple's admission into the group could be announced as soon as this week, according to people familiar with the situation. Representatives at Apple and the Partnership on AI declined to comment. When the nonprofit organization was announced in September, it anticipated gaining additional members. Apple, Twitter Inc., Intel Corp. and China's Baidu Inc. were among noticeable absentees at the time.

At Continental, we're working to make mobility clean, intelligent and safe. Continental Digital Services France (CDSF) is a new entity dedicated to take up this innovation challenge in a unique way, achieving a fusion between on board and collective intelligence in our collaborative platform. With one of our Lighthouse project – eHorizon – we want to provide connected and autonomous vehicles with the ability to "see" beyond the reach of its sensors (and obstacles, corners, etc). By "crowdsensing" data from all vehicles, our platform can generate smarter insights and enable both on and off board systems to make better decisions. We're also working on imagining, developing and deploying new mobility services that will seamlessly be integrated in people's overall experience with digital services in their personal, social or professional lives, especially in smart cities.

Ever since 2007 with the explosion in the use of parallel hardware, the field of machine learning has become more exciting and more promising. It seems that the dream of true AI is finally just around the corner. Certainly, there are many companies that are starting to rely heavily on AI for their products. These include companies in search like Facebook, Google, as well as retailers and multimedia companies like Amazon and Netflix. But more recently many others in the health-care and cyber security industries are also interested in what AI and machine learning can do for them. Some of these technologies such as Tensorflow (which came about around 2015) are new and not widely understood.

Reading Data Science – Machine Learning vs Rules Based Systems by Karthik Guruswamy Data Scientist at Teradata got me thinking about how machine learning is replacing rule-based systems in how we manage and use enterprise content, knowledge, and ideas. These types of rules are extremely easy to understand and easy to code. But, Karthik points out, things quickly get out of hand: "When a system gets operationalized, one starts with 100 scenarios with 100 rules to handle it. As time goes by we encounter more and more exceptions and start making more rules to keep exceptions under control. Think US Tax Code – things get unwieldy and cumbersome over time."