Meet the future face of healthcare assistance - a friendly faced robot named Pepper. Its diction is still a little odd, and his movements sometimes a bit hesitant, but the robot is all geared up to help patients at two Belgian hospitals. The humanoid assistant, who has a screen on his chest and a round head, is the first robot in the world to be used to greet people in a medical setting, its software creators said. Two of Belgium's hospitals have installed Pepper robots in their receptions for trials. The robots will help patients in Ostend and Liege. In Liege, at the Centre Hospitalier Regional La Citadelle, the robot helper will remain in the hospital's reception area.

Part II Learning data – You are the pie for a machine learning API Learning any data? In the previous blog I ended with the question "Are you moving away from data and focusing on growing up by creating some learning awareness? You yourself, are a growing business that share's intelligence or learns? This is the second blog in a series of three. Click here to read the first blog on pie eating at Start-Fest Europe.

One of the most requested features when it comes to Azure ML is and has always been the integration into PowerBI. By now we are still lacking a native connector in PowerBI which would allow us to query a published Azure ML web service directly and score our datasets. Reason enough for me to dig into this issue and create some Power Query M scripts to do this. But lets first start off with the basics of Azure ML Web Services. Every Azure ML project can be published as a Web Service with just a single click.

Drawing an accurate sketch of a person's face is an art that is hard for most people to master. But it turns out to be relatively easy for computers. Various programs exist for converting images into line drawings. That often produces a decent start, although these systems can have difficulty with shadows and high contrast. A more promising approach is to use machine-vision algorithms that rely on neural networks to extract features from an image and use these to produce a sketch.

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Product shipments of artificial intelligence (AI) systems for vehicles will grow from 7 million in 2015 to 122 million by 2025, which, according to IHS Technology, is a reflection of the automotive industry's growing appetite for AI.

The most common question asked by prospective data scientists is – "What is the best programming language for Machine Learning?" The answer to this question always results in a debate whether to choose R, Python or MATLAB for Machine Learning. Nobody can, in reality, answer the question as to whether Python or R is best language for Machine Learning. However, the programming language one should choose for machine learning directly depends on the requirements of a given data problem, the likes and preferences of the data scientist and the context of machine learning activities they want to perform. According to a survey on Kaggler's Favourite Tools, the open source R programming language turned out to be the favourite among 543 Kagglers of the 1714 Kaggler's listing their data science tools.

Modern computers are powerful data-processing machines, but they still need a tremendous amount of information to accomplish certain feats of recognition. Researchers at Harvard University are working hard to rectify this situation. Computers have grown from bulky room-size contraptions that could do simple calculations, to supercomputers capable of simulating extremely complex tectonic movements, involving 1 quadrillion floating-point operations (FLOPS) each second. To illustrate the matter even better, here's a great article that explains a 1 trillion-fold increase in computing performance through history. Then again, even though our brains can't compete with the sheer speed with which computers churn out results of even the most complex equations, when it comes to things we take for granted, like creativity, abstract thinking or pattern matching, machines fall way behind.

Tokyo, December 10, 2015 - NEC Corporation (NEC; TSE: 6701) today announced the development of a'system operations-visualization and anomaly-analysis technology' that uses artificial intelligence (AI) to automatically detect unknown cyber-attacks against social infrastructure and enterprise systems. The new technology learns (through machine learning) the normal state of OS-level operations (program start-up, file access, communications, etc.) for entire ICT systems, including PCs and servers. It then carries out real-time comparisons and analysis of current operations in the system's normal state and automatically isolates particular points that deviate from the normal state by using system operation tools and Software-Defined Networking (SDN). Further, a detailed knowledge of the system behavior makes it possible to identify the extent of damage 90% faster than the time required in conventional manual investigation. Accurate anomaly detection and quick specification of damaged areas by the new technology minimize the damage from cyber-attacks and enable recovery without stopping an entire user-system.

A new office manager has been hired on a trial basis at the Transport Systems Catapult in Milton Keyenes in an attempt to determine if artificial intelligence can do the job better than a human can. Betty the Robot will be on staff for a two-month trial period as she acclimates to the office environment and carries out tasks that are normally handled by a human counterpart. Some of the duties that Betty the Robot will be carrying out include regular patrols of the office spaces, keeping track of the number of employees that work after hours, collecting data regarding clutter, regulating the temperature in the office, measuring the noise and humidity levels, as well as ensuring doors are closed and desks are clean of clutter in the absence of workers, according to the Mirror. Betty the Robot may actually be doing more work than her human counterparts, in less time. She will not complain about the workload and she will eagerly work beyond the normal 9-5 shift without worry of being impacted by stress, sickness, or other unexpected personal issues.