Dr George Beaton is a partner in beaton and a senior fellow in Melbourne Law School, Australia. His published works include NewLaw New Rules – A Conversation About the Future of the Legal Services Industry (2013) and Remaking Law Firms: Why & How (2016). You have been a pioneer in research into NewLaw, what place does technology have in NewLaw? Is it central to its development? Just 18 months ago when I wrote Fresh thinking on the evolving BigLaw–NewLaw taxonomy little mention was made of the role of technology in NewLaw or BigLaw business model firms.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

Charles Givre is an unapologetic data geek who is passionate about helping others learn about data science and become passionate about it themselves. He has worked at Booz Allen Hamilton for the last five years as a data scientist for various government clientsand done some really neat data science work along the way, which hopefully saves U.S. taxpayers some money. Most of his work has been in developing meaningful metrics to assess how well the workforce is performing. For the last two years, Charles has been part of the management team for one of the company's largest analytic contracts. His responsibility has been to increase the amount of data science on the contract, both in terms of tasks and people.

In January 2014, Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR).

In an Artificial Intelligence future, those with high-end computing skills will not only survive, but thrive… This is according to Digital Skills Academy CEO and Founder, Paul Dunne. "No industry – from farming to fintech – is immune to the changes being wrought by the new digital economy," says Dunne. And Artificial Intelligence (AI), once the stuff of sci-fi, is already making an impact in the digital world, he notes. Gartner predicts that by 2018, 20 percent of business content will be authored by machines, more than 3 million workers globally will be supervised by a'robo-boss' and 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines. By 2020, autonomous software agents outside of human control will participate in five percent of all economic transactions and smart agents will facilitate 40 percent of mobile interactions.

Ability to drive technical development and prototyping in a fast-paced startup-like environment. Strong understanding and ability to apply advanced mathematical concepts to solve real world problems. The Machine Learning Data Scientist will come from a very strong background in mathematics, applied mathematics, statistics and computer science (at least MA/MSc level, Engineer school/PhD from university a plus). A post-graduate degree in Machine Learning, Artificial Intelligence or a related technical field is a strong plus (any ML background will be considered). Global Markets Labs is a spin-off quantitative research team which mandate is to build the next generation of Data Intelligence and language understanding products used in the Banks Global Markets. This small team works on projects using the latest techniques in Artificial Intelligence, Datamodelling and Natural Language Understanding.

Sum-Product Networks (SPN) have recently emerged as a new class of tractable probabilistic graphical models. Unlike Bayesian networks and Markov networks where inference may be exponential in the size of the network, inference in SPNs is in time linear in the size of the network. Since SPNs represent distributions over a fixed set of variables only, we propose dynamic sum product networks (DSPNs) as a generalization of SPNs for sequence data of varying length. A DSPN consists of a template network that is repeated as many times as needed to model data sequences of any length. We present a local search technique to learn the structure of the template network. In contrast to dynamic Bayesian networks for which inference is generally exponential in the number of variables per time slice, DSPNs inherit the linear inference complexity of SPNs. We demonstrate the advantages of DSPNs over DBNs and other models on several datasets of sequence data.

Learning needs to be less like memorization, and more like…Angry Birds. Half of school dropouts name boredom as the number one reason they left. The post is about why the future of education will be about flipping our current model on its head and about how key exponential technologies like AI, VR and gamification are going to drive a revolution in education. In the traditional education system, you start at an "A," and every time you get something wrong, your score gets lower and lower. You start with zero, and every time you come up with something right, your score gets higher and higher. It completely flips the way we currently learn, and it's addictively fun.

Skoolbot has been chosen winner of the first-ever international botathon, organized by VentureBeat. The bot, which helps students using Google Classroom monitor grades, homework, and communication, was made by Liam McKinley of Great Falls, Virginia. McKinley was not physically onstage, but did join a crowd of more than 150 in San Francisco through a roving video robot. Other members of Team Skoolbot include family friend Miko Borys and father John. Hugh Cameron of Melbourne, creator of a bot that quickly responds to emails, won the popular vote.

Broadly speaking, educational activities can be split into two categories: life skills and professional skills. The life skills that we all need to learn, and the way we learn them, have remained relatively consistent across the ages: how to communicate, socialize and survive. But you can argue that today's education system is skewed toward the second category, the teaching of professional skills and it's this category that will face the greatest opportunities and challenges over the next 50 years. While educators prepare students for lives of learning, it's more true to say their role is to prepare students for lifelong careers. But while that was a relatively simple task in the past, it's now much more difficult.