We present a new approach to estimating the interdependence of industries in an economy by applying data science solutions. By exploiting interfirm buyer--seller network data, we show that the problem of estimating the interdependence of industries is similar to the problem of uncovering the latent block structure in network science literature. To estimate the underlying structure with greater accuracy, we propose an extension of the sparse block model that incorporates node textual information and an unbounded number of industries and interactions among them. The latter task is accomplished by extending the well-known Chinese restaurant process to two dimensions. Inference is based on collapsed Gibbs sampling, and the model is evaluated on both synthetic and real-world datasets. We show that the proposed model improves in predictive accuracy and successfully provides a satisfactory solution to the motivated problem. We also discuss issues that affect the future performance of this approach.

We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have been previously employed for consistent learning of (passive) latent variable models such as hidden Markov models, POMDPs are more challenging since the learner interacts with the environment and possibly changes the future observations in the process. We devise a learning algorithm running through episodes, in each episode we employ spectral techniques to learn the POMDP parameters from a trajectory generated by a fixed policy. At the end of the episode, an optimization oracle returns the optimal memoryless planning policy which maximizes the expected reward based on the estimated POMDP model. We prove an order-optimal regret bound with respect to the optimal memoryless policy and efficient scaling with respect to the dimensionality of observation and action spaces.

In this article we introduce modern statistical machine learning and bioinformatics approaches that have been used in learning statistical relationships from big data in medicine and behavioral science that typically include clinical, genomic (and proteomic) and environmental variables. Every year, data collected from biomedical and behavioral science is getting larger and more complicated. Thus, in medicine, we also need to be aware of this trend and understand the statistical tools that are available to analyze these datasets. Many statistical analyses that are aimed to analyze such big datasets have been introduced recently. However, given many different types of clinical, genomic, and environmental data, it is rather uncommon to see statistical methods that combine knowledge resulting from those different data types. To this extent, we will introduce big data in terms of clinical data, single nucleotide polymorphism and gene expression studies and their interactions with environment. In this article, we will introduce the concept of well-known regression analyses such as linear and logistic regressions that has been widely used in clinical data analyses and modern statistical models such as Bayesian networks that has been introduced to analyze more complicated data. Also we will discuss how to represent the interaction among clinical, genomic, and environmental data in using modern statistical models. We conclude this article with a promising modern statistical method called Bayesian networks that is suitable in analyzing big data sets that consists with different type of large data from clinical, genomic, and environmental data.

In March, the Toyota Research Institute bought up Cambridge-based startup Jaybridge Robotics, and according to Tech Insider, they may be expanding with another famous Massachusetts company: Google's Boston Dynamics, the maker of the Big Dog and Atlas robots. Google has been talking about selling the Waltham-based Boston Dynamics, for a couple of months now, with companies such as Toyota Research Institute and Amazon.com Around that time, TRI, which seeks to create a car that is incapable of crashing, announced a deal to acquire Jaybridge, a 16-member software engineering company, in order to add more expertise to creating "autonomous vehicle products." Tech Insider noted that the deal hasn't been finalized, but that "the ink is nearly dry." On the other side of the alleged deal is Google, which acquired the Waltham-based company in 2013.

Rawi will present these ideas during a live webinar on May 24th at 9 AM PT / 12 PM ET. Get your questions answered in real-time during this one hour event. We create, interpret, and experience stories every day, whether we realize it or not. Our brains are constantly receiving input and stringing things together in order for us to make sense of the world. While our brains create countless stories, only the few great ones stay with us.

Machine learning algorithms are a very large part of machine learning. You have to understand how they work to make any progress in the field. In this post you will discover a 14-part machine learning algorithms mini course that you can follow to finally understand machine learning algorithms. We are going to cover a lot of ground in this course and you are going to have a great time. Machine Learning Algorithms Mini-Course Photo by Jared Tarbell, some rights reserved. Before we get started, let's make sure you are in the right place. This mini-course will take you on a guided tour of machine learning algorithms from foundations and through 10 top techniques.

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 we all learn to communicate, socialise and survive. But you can argue that today's education system is skewed towards the second category, the teaching of Professional Skills and it's this category that will face the greatest opportunities and challenges over the next fifty years. While educators prepare their students for a life of learning, it's more true to say their role is to prepare students for life-long careers. But while that was a relatively simple task in the past, it's now much more difficult.

It's the same goal that's pushed universities to make more and more courses and degree programs available over the internet, making it possible for students living on the far sides of the word to get degrees from American universities - and vice versa. But online education has a problem: Of the hordes of students that sign up for massive open online classes (MOOCs), an average of less than 7% finish. Goel thinks artificial intelligence can change that. "There are many reasons" students don't finish, he told Tech Insider. "But one reason is that these MOOCs do not provide any teaching assistants. So you can sign up for a course, say in mathematics, or computer science, or web design, or whatever. But you cannot ask anyone a question like'So how do I download this material?'

I recently caught up with Fernando Lucini, Chief Technology Officer, Big Data Hewlett-Packard Enterprise, to discuss the future of machine learning at HPE Big Data. Fernando leads HPE IDOL software portfolio and is the global business leader for the Haven OnDemand platform, as part of the Big Data Platform business group in HPE Software. HPE IDOL is the industry's leading augmented intelligence software for human information and the Haven OnDemand platform powers a new generation of applications with machine learning APIs and Services. He has a deep technical background, and has been critical in the launch of many products throughout his career, spanning enterprise search, rich media analytics, and compliance, among many others. He holds a BEng Hons in Communications and Electronic Engineering from the University of Kent, and an MBA from IE Business School, Madrid.

Artificial intelligence, said United Nations chief information technology officer Atefeh Riazi, might be the last innovation humans create. "The next innovations," said the cabinet-level diplomat during a recent interview at her office at UN headquarters in New York, "will come through artificial intelligence." From then on, said Riazi, "it will be the AI innovating. We need to think about our role as technologists and we need to think about the ramifications--positive and negative--and we need to transform ourselves as innovators." Appointed by Secretary-General Ban Ki-moon as CITO and Assistant Secretary-General of the Office of Information and Communications Technology in 2013, Riazi is also an innovator in her own right in the global security community. Riazi was born in Iran, and is a veteran of the information technology industry. She has a degree in electrical engineering from Stony Brook University in New York, spent over 20 years working in IT roles in the public and private sectors, and was the New York City Housing Authority's Chief Information Officer from 2009 to 2013. She has also served as the executive director of CIOs Without Borders, a non-profit organization dedicated to using technology for the good of society--especially to support healthcare projects in the developing world.