Saya had been teaching for seven years. Her impressive but short CV included stints in a few rural areas, overseas and as a substitute teacher. The difference is Saya is a remote controlled robot who taught her first class of 10-year olds in 2009. While we've all heard and read the stories of manual or labour-type jobs easily replaced by robots, increasingly the jobs we previously thought safe are no longer -- teachers, bankers, data analysts and the like are all at risk. But what do we really have to fear?

But the newest artificial intelligence system from Google's DeepMind division does indeed dream, metaphorically at least, about finding apples in a maze. Researchers at DeepMind wrote in a paper published online Thursday that they had achieved a leap in the speed and performance of a machine learning system. It was accomplished by, among other things, imbuing technology with attributes that function in a way similar to how animals are thought to dream. The paper explains how DeepMind's new system -- named Unsupervised Reinforcement and Auxiliary Learning agent, or Unreal -- learned to master a three-dimensional maze game called Labyrinth 10 times faster than the existing best AI software. It can now play the game at 87 per cent the performance of expert human players, the DeepMind researchers said.

With a large amount of information all over the web, it can be quite difficult to keep up, especially for enterprises that depend on data analytics for better decision making. While some companies might be able to work with some software tools, others that deal with "Big Data" always struggle with managing and filtering non-essential information. This growing concern has affected all departments of organizations from supply chain to human resource management, and also led to the emergence of unique concepts such as Enterpriseof1. Enterpriseof1 is the future of work. Data analytics, Machine Learning and Algorithms are the enablers. This convergence is touted to transform the way in which we work and pave the way for enhanced user experience and foster democratization.

Death by selfie sounds like a scene from one of the Final Destination movies but, apparently, it's actually a thing. In 2014, 15 people died while snapping a selfie, followed by 39 people in 2015, and 73 in the first eight months of 2016. So what, if anything, can be done about this escalating trend? That's what a new research project carried out by researchers in India wants to find out. "There was a news article that was circulated in my research group about a death by selfie during summer 2016," Ponnurangam Kumaraguru, an assistant professor at Indraprastha Institute of Information Technology in Delhi, told Digital Trends.

Big data and artificial intelligence will affect the world -- and already are -- in mind-boggling ways. That includes, of course, our data centers. As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement. In this complimentary guide, discover an expert breakdown of how DevOps impacts day-to-day operations management in modern IT environments. This email address is already registered.

This matrix can be used for 2-class problems where it is very easy to understand, but can easily be applied to problems with 3 or more class values, by adding more rows and columns to the confusion matrix. Let's make this explanation of creating a confusion matrix concrete with an example. Let's pretend we have a two-class classification problem of predicting whether a photograph contains a man or a woman. We have a test dataset of 10 records with expected outcomes and a set of predictions from our classification algorithm. Let's start off and calculate the classification accuracy for this set of predictions. The algorithm made 7 of the 10 predictions correct with an accuracy of 70%. First, we must calculate the number of correct predictions for each class. Now, we can calculate the number of incorrect predictions for each class, organized by the predicted value.

Although open source code in Python and R is popular because of its low cost, flexibility, and power, the time required to properly create code and ensure that it is working correctly can be frustrating. Not everyone is a programmer or wants to program! That's why the announcement by IBM in June about IBM Data Science Experience is such a game-changer! IBM Data Science Experience is a way for data scientists to collaborate and work on data science programs in the most efficient way possible. What if the collaboration could be extended to the data scientist or analyst who wants to build predictive models without code?

Few areas of technology are attracting as much interest as self-driving cars. Wall Street Journal Business Editor Jason Anders discussed the heart of that technology with Jen-Hsun Huang, chief executive of Nvidia Corp., whose chips are used to power autonomous driving and other artificial-intelligence applications. ANDERS: What can self-driving cars do today that they couldn't do a year ago? HUANG: Well, the biggest problem is that the car has to be able to perceive the environment. Reasoning, planning and learning are a big part of artificial intelligence.

We have all had the experience. You are doing some on-line shopping. You drop a few items into your shopping cart and the website gets'helpful.' Across the bottom of the screen you see a collection of other items and you are told that they might be of interest to you. And the odd thing is, they mostly are kind of interesting.

The House of Lords has passed the Investigatory Powers Bill, putting the huge spying powers on their way to becoming law within weeks. The bill – which forces internet companies to keep records on their users for up to a year, and allows the Government to force companies to hack into or break things they've sold so they can be spied on – has been fought against by privacy campaigners and technology companies including Apple and Twitter. But the Government has worked to continue to pass the bill, despite objections from those companies that the legislation is not possible to enforce and would make customers unsafe. In its facilities, JAXA develop satellites and analyse their observation data, train astronauts for utilization in the Japanese Experiment Module'Kibo' of the International Space Station (ISS) and develop launch vehicles 23/40 The robot developed by Seed Solutions sings and dances to the music during the Japan Robot Week 2016 at Tokyo Big Sight. At this biennial event, the participating companies exhibit their latest service robotic technologies and components 24/40 The robot developed by Seed Solutions sings and dances to music during the Japan Robot Week 2016 at Tokyo Big Sight 25/40 Government and industry are working together on a robot-like autopilot system that could eliminate the need for a second human pilot in the cockpit 26/40 Aurora Flight Sciences' technicians work on an Aircrew Labor In-Cockpit Automantion System (ALIAS) device in the firm's Centaur aircraft at Manassas Airport in Manassas, Va.