Are you concerned about the continued development of smart gadgets increasing their ability to listen, think, speak and feel more human than humans? When we think about science fiction movies like The Terminator, iRobot, Maximum Overdrive, RoboCop, 2001: A Space Odessey, The Machine, Transcendence, Project Almanac, Robot and Frank, and The Matrix Revolutions, many people worry these smart gadgets will overpower the world. Voice Command Devices (VCD) are evidence of how science and technology leaped into an incredible world of artificial intelligence (AI). Voice-activated smart devices such as Alexa, Siri, and Google are programmed to interact, play music, provide the weather, play audiobooks, keep up with your calendar appointments and provide other real-time information requested by the user. One funny, but kind of freaky occurrence has been reports made by individuals who were on the telephone with someone who owns one of these VCD's and heard those devices suddenly start to talk on their own.

As Matt Asay observed last week, AI appears to be reaching "peak ludicrous mode," with almost every software vendor laying claim to today's most hyped technology. Hang on -- see what I did there? I used "AI" and "machine learning" interchangeably, which should get me busted by the artificial thought police. The first thing you need to know about AI (and machine learning) is that it's full of confusing, overlapping terminology, not to mention algorithms with functions that are opaque to all but a select few. This combination of hype and nearly impenetrable nomenclature can get pretty irritating.

Dr Hensman (above), who was most recently a lecturer at the University of Lancaster, has more than 35 published academic journal articles to his name, based on his industry-defining research on Gaussian processes. This approach moves away from the use of limited decision trees for AI, which are based on'if X then Y principles', to focusing on the development of self-learning, autonomous agents. These can be applied to a whole variety of problems from autonomous vehicle control, to non-player characters in video games, to smart city simulations. Dr Hensman will be encouraged to continue his academic research while at PROWLER.io – and his findings will support the development of world-leading AI technology. "Until now I've had no interest in departing from full-time academia," said Dr Hensman.

In some cases, the AI designation might be warranted, even if with some aspiration. Autonomous vehicles, for example, don't quite measure up to R2D2 (or Hal), but they do deploy a combination of sensors, data, and computation to perform the complex work of driving.

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox. February is only just gone, and already 2017 is shaping up to be a year full of big ideas and ambitions. The Future of Life Institute, for example, just published the Asilomar AI principles: 23 guidelines to ensure AI developments are beneficial to humanity. They are calling for shared responsibility and caution against an AI arms race.

The creator of a chatbot which overturned more than 160,000 parking fines and helped vulnerable people apply for emergency housing is now turning the bot to helping refugees claim asylum. The original DoNotPay, created by Stanford student Joshua Browder, describes itself as "the world's first robot lawyer", giving free legal aid to users through a simple-to-use chat interface. The chatbot, using Facebook Messenger, can now help refugees fill in an immigration application in the US and Canada. For those in the UK, it helps them apply for asylum support. The London-born developer worked with lawyers in each country, as well as speaking to asylum seekers whose applications have been successful.

Machine learning and deep learning projects are gaining more and more importance in most enterprises. The complete process includes data preparation, building an analytic model and deploying it to production. This is an insights-action-loop which improves the analytic models continuously. Forrester calls the complete process and the platform behind it the Insights Platform. A key task when you want to build an appropriate analytic model using machine learning or deep learning techniques, is the integration and preparation of data sets from various sources like files, databases, big data storage, sensors or social networks. This step can take up to 80 percent of the whole analytics project. This article compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing, streaming ingestion and data wrangling.

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.