Michael Bennett Cohn was head of digital product and revenue operations at Condé Nast, where he created the company's first dynamic system for digital audience cross-pollination. At a traditional boutique ad agency, he founded and ran the digital media buying team, during which time he planned and executed the digital ad campaign that launched the first Amazon Kindle. At Federated Media, where he was the first head of east coast operations, he developed and managed conversational marketing campaigns for top clients including Dell, American Express, and Kraft. He also has a master's degree in cinema-television from the University of Southern California.

Advancements in AI, Big Data, the Internet of Things and automation have many industries worried that these systems will push them out, absorb their work, make humans redundant, or accelerate the speed of business too fast for them to adapt. From formal models of legal reasoning to automated information extraction from legal databases and texts, the interaction of artificial intelligence and law will disrupt the contemporary status of legal practice. With the latest'wonder automation' in the form of JP Morgan's COIN, building upon the adoption of AI systems by firms such as Clifford Chance, and development projects like Denton's NextLaw Labs leading the way, are lawyers set to be replaced? AI will spell the end of lawyers. However, the age of automation and digitisation gives birth to an even more beautiful legal specialist: the cyber-lawyer – an augmented specialist, combining the processing power of AI with powerful searches of legal indexes in mere seconds through Big Data, produced through a human interface.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us.

A group of schoolchildren who won a robotics competition were subjected to a barrage of racist abuse from some rival pupils and their parents who shouted: "Go back to Mexico". It was the first time that pupils from Pleasant Run Elementary School had entered the robotics challenge. Their victory over the youngsters from other Indianapolis schools, put them a step closer to the state championship. Yet as the children, aged nine and ten, left the event and walked out to the parking area, some of the children they had just beaten, along with their parents, unleashed racist comments. Kids on winning robotics team told, 'Go back to Mexico' https://t.co/iGmm9yOQsF

Summary: Looking beyond today's commercial applications of AI, where and how far will we progress toward an Artificial Intelligence with truly human-like reasoning and capability? This is about the pursuit of Artificial General Intelligence (AGI). There is no question that we're making a lot of progress in artificial intelligence (AI). So much so that we are rapidly approaching or have already arrived at a plateau in development where more effort is being put into commercializing existing AI capabilities than in improving it. As far back as November 2014 Kevin Kelly, cofounder of Wired magazine and prolific futurist observed "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Well Kevin, you're right.

Education, the ability to pass on knowledge, is one of the most ancient practices that sets humans apart from all other species on earth. It is through education that, instead of rediscovering and mastering the laws that govern the world we live in, new generations are able to pick up where their predecessors left off and enhance the knowledge and skills we possess. Now, the learning and teaching process is undergoing an unprecedented transformation thanks to ed-tech, a conglomerate of technologies that is redefining classrooms, schools, universities and the entire education process. At the forefront of those technologies is Artificial Intelligence, the often mystic and misunderstood science that is taking the world by storm and is helping (or replacing) humans at performing complicated tasks in various industries. Here's how AI is changing education for the better.

There have been tremendous advances made in making machine learning more accessible over the past few years. Online courses have emerged, well-written textbooks have gathered cutting edge research into an easier to digest format and countless frameworks have emerged to abstract the low level messiness associated with building machine learning systems. In some cases these advancements have made it possible to drop an existing model into your application with a basic understanding of how the algorithm works and a few lines of code. However, machine learning remains a relatively'hard' problem. There is no doubt the science of advancing machine learning algorithms through research is difficult.

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

In this paper, we study the problem of designing objective functions for machine learning problems defined on finite \emph{sets}. In contrast to traditional objective functions defined for machine learning problems operating on finite dimensional vectors, the new objective functions we propose are operating on finite sets and are invariant to permutations. Such problems are widespread, ranging from estimation of population statistics \citep{poczos13aistats}, via anomaly detection in piezometer data of embankment dams \citep{Jung15Exploration}, to cosmology \citep{Ntampaka16Dynamical,Ravanbakhsh16ICML1}. Our main theorem characterizes the permutation invariant objective functions and provides a family of functions to which any permutation invariant objective function must belong. This family of functions has a special structure which enables us to design a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks. We demonstrate the applicability of our method on population statistic estimation, point cloud classification, set expansion, and image tagging.

We introduce the value iteration network (VIN): a fully differentiable neural network with a `planning module' embedded within. VINs can learn to plan, and are suitable for predicting outcomes that involve planning-based reasoning, such as policies for reinforcement learning. Key to our approach is a novel differentiable approximation of the value-iteration algorithm, which can be represented as a convolutional neural network, and trained end-to-end using standard backpropagation. We evaluate VIN based policies on discrete and continuous path-planning domains, and on a natural-language based search task. We show that by learning an explicit planning computation, VIN policies generalize better to new, unseen domains.