Great hardware needs great software. To help data scientists and developers make the most of the vast opportunities in deep learning, we're announcing today at the International Supercomputing show, ISC16, a trio of new capabilities for our deep learning software platform. The three -- NVIDIA DIGITS 4, CUDA Deep Neural Network Library (cuDNN) 5.1 and the new GPU Inference Engine (GIE) -- are powerful tools that make it even easier to create solutions on our platform. NVIDIA DIGITS 4 introduces a new object detection workflow, enabling data scientists to train deep neural networks to find faces, pedestrians, traffic signs, vehicles and other objects in a sea of images. This workflow enables advanced deep learning solutions -- such as tracking objects from satellite imagery, security and surveillance, advanced driver assistance systems and medical diagnostic screening.

There are countless internet quizzes that helps sort you into Hogwarts houses depending on your personality, but nothing comes quite close to this: an actual sorting hat powered by IBM Watson. The hat, created by IBM engineer Ryan Anderson, started off as a fun project for him and his two daughters to help expose the girls to STEM while bridging it with their interests in the Harry Potter series. His daughter helped by adding lines of established "ground truths" for each of the four houses, with Watson using deep learning to figure out more attributes and expanding known qualities of each house every time the hat is worn. With above 90 percent certainty, the hat sorted Hawking and Clinton into Ravenclaw for their wits.

An online learning program which can tell when a student is becoming bored and inattentive is one of the key developments forecast to reshape university education in Australia in the next five years, according to a new report. The 2016 NMC Technology Outlook for Australian Tertiary Education says that so-called "affective computing", which is able to use video imagery of facial expressions to discern human emotions, will soon be coupled with online learning platforms to encourage students to keep their minds on their work. The report says this is likely to be adopted by universities in the next four to five years. It forecast "online learning situations wherein a computerised tutor reacts to facial cues of boredom in a student in an effort to motivate or boost their confidence." "Software technology will literally learn to learn, interpreting and responding to learners' most nuanced gestures and emotions – whether they are feeling bored, intimidated or satisfied," says Brenda Frisk, head of learning technology at Open Universities Australia, a partner in the report.

We know we have a problem, but will AI be the solution or an added burden? "A recent survey by the Federation of Small Business (FSB) found 66% of those questioned had been a victim of cybercrime over the past two years, and only 4% had an incident response plan in place in anticipation of an attack."

On Monday, Twitter (TWTR) announced it was acquiring Magic Pony, a U.K.-based startup featuring 11 PhDs, for an undisclosed sum. The move should help improve the social networks's video content, but it's unclear whether it can really move the needle in terms of its larger challenges of attracting and retaining users. Magic Pony was working on artificial intelligence (AI)/machine learning technology that can automatically analyze and enhance photo and video content. Interestingly, its solution relies not only on analysis of content within an image itself, but also of similar images, in much the same way humans classify and group objects they have seen. Twitter, whose user Timelines tend to be chock-full of video content from its standard 30-second video upload service, its Vine 6-second video platform and increasingly, its Periscope livestreaming platform, said the purchase will expand "the machine learning capabilities in Twitter's video technology and [add] valuable talent" to its Cortex machine learning team.

Andrew Ng, Chief Scientist for Baidu Research in Silicon Valley, Stanford University associate professor, chairman and co-founder of Coursera, and machine learning heavyweight, is authoring a new book on machine learning, titled Machine Learning Yearning. This isn't your typical machine learning book, however; it focuses on the skills and strategies needed to implement machine learning systems, as opposed to acting as an overview of various classification algorithms or the current state of the art science. The goal of this book is to teach you how to make the numerous decisions needed with organizing a machine learning project. Ng also notes that the book will be "around 100 pages, and contain many easy-to-read 1-2 page chapters." However, not only does the website provide an overview of the book, it states that, if you sign up to the email list by Friday, June 24th, you will gain free access to draft chapters as they are finished.

The researcher hired by Toyota Motor Corp. to spearhead its robotics and artificial intelligence efforts says the automaker's production principles can be applied to build affordable helper robots for rapidly aging societies. Robot makers are struggling with the same scale challenges that the auto industry overcame with the "miracle" that occurred when Henry Ford developed the assembly line, according to Gill Pratt, the chief executive officer of Toyota Research Institute. Toyota's vaunted production system later showed how to make cars both more cheaply and reliably, despite mistake-prone humans' role in manufacturing, he said. "My thought is, if the Toyota production system can be applied to cars, maybe it can also be applied to robots, because they're quite similar," Pratt told reporters Friday in Tokyo. He's particularly sanguine about the prospects for devices that would help the elderly age where they live.

Twitter has made no secret of its interest in machine learning in recent years, and on Monday the company put its money where its mouth is once again by purchasing London startup Magic Pony Technology, which has focused on visual processing. "Magic Pony's technology -- based on research by the team to create algorithms that can understand the features of imagery -- will be used to enhance our strength in live [streaming] and video and opens up a whole lot of exciting creative possibilities for Twitter," Twitter cofounder and CEO Jack Dorsey wrote in a blog post announcing the news. The startup's team includes 11 Ph.Ds with expertise across computer vision, machine learning, high-performance computing and computational neuroscience, Dorsey said. They'll join Twitter's Cortex group, made up of engineers, data scientists and machine-learning researchers. The acquisition follows several related purchases by the social media giant, including Madbits in 2014 and Whetlab last year.

Gartner's latest Magic Quadrant for Advanced Analytics predicts that by 2018, more than half of all large organizations worldwide will use advanced analytics and respective algorithms to compete in their markets. Advanced, predictive analytics are about calculating trends and future possibilities, simulating options, predicting potential outcomes, and making recommendations. It reports to more sophisticated methods based on statistics, like descriptive and predictive data mining, simulations and optimizations that find trends and patterns in data, and finally machine learning.

Drowning in data is a real hazard with the Internet of Things (IoT). How should decisions be made with this flood of sensor data? A hybrid approach combining human intelligence and computing power works well. People are good at making decisions that require nuance and judgement, such as identifying hate speech in online postings. Computerized analytics is better at quickly processing large volumes of data.