The author recently completed a wonderful course in Machine Learning taught by Prof Andrew Ng of Stanford on Coursera. Machine Learning, more popularly known as Artificial Intelligence or AI is not a new topic. AI has been in the making for over 60 years. But it has started delivering creditable results in the last few years. The success of the driverless car has captured the popular imagination but progress in the areas of image recognition, natural language processing and anomaly detection is no less impressive and has applications across sectors.

Professor Fei Fei Li invests her time researching computer sciences at the Stanford University. She is Director of the Stanford AI Lab, Researcher in AI, computer vision, machine learning and cognitive neuroscience. We particularly loved her latest appearance on this TED Talks event about how systems learn how physical objects look and wanted to share this with you. YouTube: "Fei Fei Li: How we're teaching computers to understand pictures" by TED Talks

A little less than a year ago, Sphero released the first edition of its Spark robot, a rolling ball you can control with a mobile app. Since then, the product has been adopted as a tool for teaching kids about robotics and computer programming in over 1,000 schools across the US and Canada. Today it's announcing the second edition, the Sphero Spark, which has a tougher, scratch-resistant skin. It also has a more advanced version of Bluetooth, meant to make it easier to pair the bots with multiple devices in a classroom setting. Aside from these two changes, the new Spark unit is basically identical to its predecessor in size, price, battery life, and capability.

Chief Operating Officer Rachel Cooke Rachel is responsible for business operations including overseeing client services, research events and project management. Prior to joining Brandon Hall Group, Rachel was the Chief Operating Officer Co-founder of AC Growth. Rachel has over 15 years of experience in sales, marketing, business development, and sales performance management. Prior to AC Growth, she held several senior management roles and was on the leadership team at Bersin & Associates, a pioneer analyst firm in e-learning and now industry leading HR and talent Research Company. In her Senior Director role, Rachel developed the strategy and led the commercial execution of the solution provider vertical, and grew the vertical into the company's largest market segment.

There's no doubt Stephen Hawking is a smart guy. But the world-famous theoretical physicist recently declared that women leave him stumped. "Women should remain a mystery," Hawking wrote in response to a Reddit user's question about the realm of the unknown that intrigued him most. While Hawking's remark was meant to be light-hearted, he sounded quite serious discussing the potential dangers of artificial intelligence during Reddit's online Q&A session: A superintelligent AI will be extremely good at accomplishing its goals, and if those goals aren't aligned with ours, we're in trouble. Hawking's comments might seem unrelated.

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.

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.

Some say over 60-70% time is spent in data cleaning, munging and bringing data to a suitable format such that machine learning models can be applied on that data. This post focuses on the second part, i.e., applying machine learning models, including the preprocessing steps. The pipelines discussed in this post come as a result of over a hundred machine learning competitions that I've taken part in. It must be noted that the discussion here is very general but very useful and there can also be very complicated methods which exist and are practised by professionals. Before applying the machine learning models, the data must be converted to a tabular form.

Most AI systems these days have a learning component to them, and I've touched on the ways in which systems learn a few times previously. One of the more interesting approaches aims to mimic the way humans learn. Such approaches have their roots in a theory that was first published in 1995, which suggested that learning is a two pronged approach. The first system acquires knowledge gradually based upon our exposure to new experiences. The second system then stores each of these experiences so that we can replay them and effectively integrate them.

We revisit the task of learning a Euclidean metric from data. We approach this problem from first principles and formulate it as a surprisingly simple optimization problem. Indeed, our formulation even admits a closed form solution. This solution possesses several very attractive properties: (i) an innate geometric appeal through the Riemannian geometry of positive definite matrices; (ii) ease of interpretability; and (iii) computational speed several orders of magnitude faster than the widely used LMNN and ITML methods. Furthermore, on standard benchmark datasets, our closed-form solution consistently attains higher classification accuracy.