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Children as young as four can learn to program robots, according to an expert at Queensland University of Technology. Queensland University of Technology's (QUT) Christina Chalmers, who specializes in the teaching and application of robotics in classrooms, says robotic coding is a growth area in a range of industries, a trend that increases the demands on educators to promote student education in robotics. "Preliminary findings from a current study have shown even pre-school students have gone beyond simply playing games with a NAO robot," Chalmers says. Coding and robotics were implemented into Queensland's state primary schools this year. "Research tells us that if kids don't form positive attitudes towards science, maths, and technology early in life they can find it difficult to engage later on," she says, adding robotics provides an engaging way for both students and teachers to work together.

Deep learning appeared after a long gestation, or all of a sudden. You can take your pick, depending on where you were when mainstream media discovered a collection of statistical and artificial intelligence techniques that seemed to promise a new era of automated predictive analytics. The vote here is for a long gestation, although it's fair to say there is some suddenness about the way deep learning software is pushing a new class of analytics in which applications repeatedly churn through large sets of data, learning to predict likely outcomes as they go. A lengthy birthing process seems in play because, really, deep learning is an updated take on the machine learning process, which in turn was a new take on neural networks, an early form of artificial intelligence in which simulations mimic the human brain's neuron activity by weighting outputs and building connected sets of meaning. What marks deep learning software is use of multiple processing layers.

A computer's artificial intelligence system has been taught to understand Blade Runner well enough for a Goldsmiths, University of London student to remake the entire film based on the AI system's interpretation. Terence Broad has gone on to remake other films based on the AI's understanding of Blade Runner. Over the last couple of years, scientists working in the machine-learning world have developed artificial neural networks (inspired by animals' brains) that can learn to generate new images based on information they learn from real images. The generated images have become so natural that computers and the human eye can't tell real and artificial apart. But most models create images that are variations of a similar thing – such as a face or picture of a bedroom – taken from the same angle.

With all this talk about chatbots from Facebook and Microsoft, teaching artificial intelligence to be smarter has become a central topic of the tech world. But what about what AI can teach us? Ashok Goel, a computer science professor at Georgia Tech, put that question to the test when he added "Jill Watson" – a chatbot powered by IBM's Watson technology – to his list of of teaching assistants for an online course. The chatbot was so good at answering questions that students did not notice their TA was made of silicon until after they'd turned in their finals. Jill came to be after Goel decided he and his teaching assistants were being spread thin.

Having made the improbable jump from the game console to the supercomputer, GPUs are now invading the datacenter. This movement is led by Google, Facebook, Amazon, Microsoft, Tesla, Baidu and others who have quietly but rapidly shifted their hardware philosophy over the past twelve months. Each of these companies have significantly upgraded their investment in GPU hardware and in doing so have put legacy CPU infrastructure on notice. The driver of this change has been deep learning and machine intelligence, but the movement continues to downstream into more and more enterprise-grade applications – led in part by the explosion of data. Behind this shift is an evolving perspective of how computing should operate-- one that has a particular emphasis on massive quantities of data, machine learning, mathematics, analytics and visualization.

Last week ago I posted an article called, What Do Predictive Analytics Consultants Do? Part 1, describing the general types of activities that we engage in. In the present article, I want to talk about the skills and tools that one should have to perform Predictive Analytics. Although this is not strictly a "What we do" article, knowing the skills we possess and the tools we use will provide some insight into what we do, without talking about some algorithm that you may have never heard of. I am always at a loss in describing the skills of analytics, for there are many. I just completed a new book about analytics (available for FREE--see notes) that has a different approach than Predictive Analytics using R (also available for FREE), though I am using material from three chapters.

New ideas about artificial intelligence and cognitive computing systems in education have been advanced this year by major computing and educational businesses, including Pearson and IBM. Pearson's promotion of AI reflects its growing interests in data analytics and other digital methods while IBM is seeking to extend its existing R&D on cognitive computing into the education sector. AI has been the subject of serious debate recently. High profile figures including Stephen Hawking, Bill Gates and Elon Musk have voiced concern about the threats it poses, while awareness about cognitive computing has been fueled by widespread media coverage of Google's AlphaGo system. Commenting on these recent events, the philosopher Luciano Floridi has noted that contemporary AI and cognitive computing, however, cannot be characterized as some kind of "ultraintelligence."

Having made the improbable jump from the game console to the supercomputer, GPUs are now invading the datacenter. This movement is led by Google, Facebook, Amazon, Microsoft, Tesla, Baidu and others who have quietly but rapidly shifted their hardware philosophy over the past twelve months. Each of these companies have significantly upgraded their investment in GPU hardware and in doing so have put legacy CPU infrastructure on notice. The driver of this change has been deep learning and machine intelligence, but the movement continues to downstream into more and more enterprise-grade applications – led in part by the explosion of data. Behind this shift is an evolving perspective of how computing should operate-- one that has a particular emphasis on massive quantities of data, machine learning, mathematics, analytics and visualization.

Nervana Systems, one of a handful startups focusing on a type of artificial intelligence called deep learning, today is announcing that it has released its Neon deep learning software under an Apache open-source license, allowing anyone to try it out for free. The startup is pointing to benchmarks a Facebook researcher recently conducted suggesting that the Nervana software outperforms other publicly available deep learning tools, including Nvidia's cuDNN and Facebook's own Torch7 libraries. "We really want to get the tools out there to make it easy for people to apply deep learning to the problem," Naveen Rao, chief executive and a cofounder of Nervana, told VentureBeat in an interview. "Keeping a closed environment makes it kind of hard for people to try things out and have an idea even for what people can do. If they want the fastest, they'll come to us." The technology ought to catch the attention of companies that have been working with deep learning systems, including Google and Microsoft.