Machine Learning revolutionizes recruiting by applying complex mathematical calculations to analyze the behaviour of a potential employee, hence saving the time spent on recruitment. Given the right human insight, machine learning can eliminate interviewer bias as it analyzes the big data as per user defined parameters and the results are drawn objectively. Predictive models can be designed by Machine Learning algorithms classifying potential employees as per their probability of attrition, derived by past behavioural patterns within their previous workplaces. It captures sophisticated patterns to predict the position a candidate is best suited for. It helps make faster hiring decisions as the requirements for the right candidate are clearly defined, reducing the resource time and efforts, hence reducing the overall costs for the organization.

Continuing our series of articles about the different foundational aspects of artificial intelligence(AI), today I would like to focus on game theory. Games have been one of the most visible areas of progress in the AI space in the last few years. Chess, Jeopardy, GO and, very recently, Poker are some of the games that have been mastered by AI systems using break through technologies. From that viewpoint, the success of AI seems to be really tied to the progress on game theory. While games are, obviously, the most visible materialization of game theory, is far from being the only space on which those concepts are applied.

Computer researchers are betting they can take on the house after designing a new artificial intelligence program that has beat professional poker players. Researchers from University of Alberta, Czech Technical University and Charles University in Prague developed the "DeepStack" program as a way to build artificial intelligence capable of playing a complex kind of poker. Creating an AI program that can win against a human player in a no-limit poker game has long been a goal of researchers due to the complexity of the game. Michael Bowling, a professor in the Department of Computing Science in the University of Alberta, explained that computers have been able to win at "perfect" games such as chess or Go, in which all the information is available to both players, but that "imperfect" games like poker have been much harder to program for. "This game [poker] embodies situations where you find yourself not having all the information you need to make a decision," said Bowling.

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"The aim of marketing is to know and understand the customer so well the product or service fits him and sells itself." Anyone who shops online or uses a music streaming service will have experienced recommendations. Their accuracy can be surprising at first glance, but these recommendations aren't made by accident. They are based on sophisticated machine learning techniques, pattern analysis and automated decision making. Systems like these rely on a technology infrastructure that can import, analyse and interpret huge volumes of data and take appropriate action without the need for human intervention.

It's official: driverless cars have hit the race tracks. Roborace, the autonomous race car maker, had its two self-driving'DevBots' compete against each other at the Formula E Buenos Aires ePrix. The race didn't go without its own surprises: One car had to dodge a random dog that ended up on the race track, and the other ended up hitting a barrier, unable to finish the race. Roborace's self-driving car races will take place at Formula E events throughout 2017. All cars competing will be made identically.

If the robots take over, at least Elon Musk will be able to say "I told you so." The billionaire inventor loves to make the impossible possible, but he is deeply afraid of artificial intelligence (AI). On Twitter this weekend, Musk said that "we need to be super careful with AI," adding that they are "potentially more dangerous than nukes." If that weren't concerning enough, Musk followed up his statement with another tweet that read: "Hope we're not just the biological boot loader for digital superintelligence. Unfortunately, that is increasingly probable."

If you're as plugged in as we are, then you probably have something like 150 online accounts. That means you probably have 150 variations of the same half-dozen login names and passwords. And chances are, you've gotten a message from a friend in the last few months telling you that it looks like you got hacked again, after he received email spam to enlarge an embarrassingly small body part. Well, we think the days of the alphanumeric password are numbered. The age of biometric authentication is dawning.

When it comes to machine learning and data science, there are so many language options to choose from. Data scientist Jean-Francois Puget does some analysis to decide which one is best. What programming language should you learn to get a machine learning or data science job? I could provide my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data.

Last week, I compiled a list of the 5 jobs robots will take first. Today, let's have a go at the 5 jobs robots will take last. For this article only, let's define "robots" as technologies, such as machine learning algorithms running on purpose-built computer platforms, that have been trained to perform tasks that currently require humans to perform. For example, an assembly line worker performs mostly manual repetitive tasks which, depending on complexity and a cost/benefit analysis, can be automated. A CEO of a major multinational conglomerate performs mostly cognitive nonrepetitive tasks which are much harder to automate.