Up until very recently, computers needed a complicated and extremely precise set of instructions in order to accomplish even the simplest of tasks. Who among us remembers programming via punch cards? Or DOS? Computer programming languages have evolved over the years, but the biggest step has been moving towards the elimination of complicated programming. In other words, teaching computers to learn for themselves, dubbed machine learning.

In this post, I'll introduce you to the Julia programming language and a couple long-term projects of mine: Plots for easily building complex data visualizations, and JuliaML for machine learning and AI. Easily create strongly-typed custom data manipulators. "User recipes" and "type recipes" can be defined on custom types to enable them to be "plotted" just like anything else. We believe that Julia has the potential to change the way researchers approach science, enabling algorithm designers to truly "think outside the box" (because of the difficulty of implementing non-conventional approaches in other languages).

Some members of our team (including me) live by just 2 passions in life – Data Science & Movies! For us, slicing and dicing movies over Monday morning coffee is part of warming up ritual. So, we decided to do a poll among ourselves on the best movies related to data science and machine learning. We also thought that we would release the outcome of the results in form of an infographic. Needless to say there were heated debates and a few disappointed faces in our office! But I think the list we got to is a fairly representative of the thoughts of the group.

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October 7, 2016, 7:59 AM IBM's artificial intelligence technology, Watson, amazed the world five years ago when it won "Jeopardy." After that, Watson went on to help improve industries like education and transportation. Now Watson is discovering new insights for cancer patients, as IBM has enlisted 20 top cancer institutes to teach Watson.

Healthcare is a numbers game and the figures are running wild. Every indicator shows the creaking NHS cannot cope with rising demand and dwindling resources. But the relentless gloom over hospital waiting lists, budgetary shortfalls and demographic time bombs is being challenged by a fresh approach that could revolutionise personal and national health. A wave of innovation driven by artificial intelligence (AI) is being hailed as both a saviour of traditional healthcare and the dawn of a new era in the public's engagement with their own health. "Healthcare is one of the highest cost areas for all modern economies, which makes it ripe for AI as providers look for efficiency to care for patients," says Dan Housman, chief technology officer at ConvergeHEALTH by Deloitte. "Healthcare is complex as an industry and is generating vast volumes of data from imaging, genomics, sensors, daily care and scientific research.

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Shelcy Joseph is recent graduate from the City University of New York and when she describes what she learned from her recent job hunt, she says she found out that it's hard to get hiring manager's attention, but that "once they like you, they'll give you lots of attention." What's new is who–or what–determines which candidate has high potential. Increasingly, it is software built on machine learning and early-stage artificial intelligence technology that determines the desirability of a candidate. That's why, for the first time, we've shifted the focus of one of the largest career management MOOCs from the relationship-centered job search to the data-optimized job search. Machine Learning-driven Software will Probably Make Make Your Next Career Match In early 2016, LinkedIn (now owned by Microsoft) acquired Connectifier, a machine learning platform for recruiters, for around 100m.

He's a lightning-fast learner, he speaks eight languages and he's considered an expert in multiple fields. He's got an exemplary work ethic, is a speed reader and finds insights no one else can. On a personal note, he's a mean chef and even offers good dating advice. Named after IBM's first CEO, Watson was born back in 2007 as part of an effort by IBM Research to develop a question-answering system that could compete on the American quiz show "Jeopardy." Since trouncing its human opponents on the show in 2011, it has expanded considerably.