From robotics to virtual reality technologies to 3-D printers, advanced, cutting-edge technology was on display at this year's Educational IT Solutions Expo, which kicked off Wednesday in Tokyo's Koto Ward. The seventh such expo, this year's event featured a special section to showcase "the learning of the future" for the first time. Some 50 companies are using the opportunity to display what they believe the future of education will look like with advancements in new information technologies. The highlight of the expo is programming education, as the education ministry is working to incorporate computer programming into the curriculum of all public elementary schools by 2020. Lego Japan Ltd., which has been involved in programming education for 15 years, showcased the WeDo 2.0 robotics kit for elementary school students.

The trippy swirling patterns seen in the heads of sunflowers may have finally given up their mathematical secret. Researchers and citizen scientists have analysed the golden coloured flowers as part of a project celebrating the work of Alan Turing, the code-cracking computer scientist and forefather of artificial intelligence. Led by the Museum of Science and Industry in Manchester and the Manchester Science Festival, the project set out to finish the work which Turing started before his death in 1954. Researchers enlisted the help of gardeners and citizen scientists all over the UK and beyond to analyse sunflower heads, testing how closely they followed the Fibonacci rule. Long known to mathematicians and fans of the psychedelic, the Fibonacci sequence is a series of numbers which, when plotted, predict the ratio of concentric swirls seen throughout nature – from intricate shells, to the petals and seed heads of some flowers, including sunflowers.

For consumer product companies to leverage sales and increase overall growth, professional development is a top priority for the VP of sales. But it can be challenging to determine where to allocate resources and training budgets. The VP of sales needs to pay careful attention to return on investment (ROI) and ensure that educational spending is improving sales team performance. Here are three data-driven approaches that executives can use to tackle team education and enablement challenges for consumer product sales. At a CPG company, the VP of sales is responsible for developing and driving go-to-market strategies while also ensuring a unified value proposition.

Imagine discovering someone you thought was human is, in fact, a robot. It sounds like the stuff of science fiction. But that's what happened to a class full of Georgia Tech students recently, when they learned that "Jill," their teaching assistant, was actually a piece of software. CBC Radio technology columnist Dan Misener explains what happened. The story starts with a computer science professor named Ashok Goel, who teaches at the Georgia Institute of Technology.

Excited to talk about Machine Learning, Data Science, and Artificial Intelligence? Ready to discover the key influencers you need to follow to stay current on all the latest happenings in these fields? We've got the resources you need. You see, at Udacity, we talk a great deal about Machine Learning, Data Science, and Artificial Intelligence. We talk to each other, we talk to our students, and we talk to the world at large.

The University of Oxford and an independent alternative investment manager, Man AHL, will expand its centre for machine learning into quantitative finance, which will become part of the university's engineering science department from 1 August, 2016. Man AHL said the "world-leading academic institute for quantitative finance research", The Oxford-Man Institute (OMI), would become a hub where researchers "focused on machine learning techniques", and could share and leverage data analytics expertise and knowledge. Man AHL's chief scientist and academic liaison, Dr Anthony Ledford said Man AHL had actively been researching machine learning techniques and applying them in client trading programs for several years. But the partnership with OMI directly connected them to "cutting-edge quantitative finance research" and world-leading academics in the field, he said. The hub's existing researchers would be joined by a team of "20 leading machine learning researchers", from Oxford University's department of engineering science's machine learning group, said Man AHL. They would relocate to Eagle House, in Oxford in the United Kingdom.

This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1) opacity as intentional corporate or state secrecy, (2) opacity as technical illiteracy, and (3) an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented), and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm. This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These are just some examples of mechanisms of classification that the personal and trace data we generate is subject to every day in network-connected, advanced capitalist societies. These mechanisms of classification all frequently rely on computational algorithms, and lately on machine learning algorithms to do this work. Opacity seems to be at the very heart of new concerns about'algorithms' among legal scholars and social scientists. The algorithms in question operate on data. Using this data as input, they produce an output; specifically, a classification (i.e. They are opaque in the sense that if one is a recipient of the output of the algorithm (the classification decision), rarely does one have any concrete sense of how or why a particular classification has been arrived at from inputs.

On the Internet, "nobody knows you're a dog," as the old meme goes, and today, the same can increasingly be said of robots. There are already scheduling robots that are virtually indistinguishable from humans, and recently students at the Georgia Institute of Technology learned that "Jill Watson" -- a teaching assistant they had relied upon all semester -- was in fact artificially intelligent. "The world is full of online classes, and they're plagued with low retention rates," said Ashok Goel, a Georgia Tech professor who teaches a class entitled Knowledge-Based Artificial Intelligence. "One of the main reasons many students drop out is because they don't receive enough teaching support. We created Jill as a way to provide faster answers and feedback."

On the Internet, "nobody knows you're a dog," as the old meme goes, and today, the same can increasingly be said of robots. There are already scheduling robots that are virtually indistinguishable from humans, and recently students at the Georgia Institute of Technology learned that "Jill Watson" -- a teaching assistant they had relied upon all semester -- was in fact artificially intelligent. "The world is full of online classes, and they're plagued with low retention rates," said Ashok Goel, a Georgia Tech professor who teaches a class entitled Knowledge-Based Artificial Intelligence. "One of the main reasons many students drop out is because they don't receive enough teaching support. We created Jill as a way to provide faster answers and feedback."

Amazon is making a bigger leap into open-source technology with the unveiling of its machine-learning software DSSTNE. The newly released program is competing with Google's TensorFlow, which the search giant open-sourced last year. Amazon says DSSTNE (which stands for Deep Scalable Sparse Tensor Network Engine and is pronounced "Destiny") excels in situations where there isn't a lot of data to train the machine-learning system, whereas TensorFlow is geared for handling tons of data. DSSTNE is also faster than TensorFlow, with Amazon claiming up to 2.1 times the speed in low-data situations. The software comes from Amazon's need to make recommendations in its retail platform, which required the company to develop neural network programs.