The Internet and smartphone are just the latest in a 250 year long cycle of disruption that has continuously changed the way we live, the way we work and the way we interact. The coming Augmented Age, however, promises a level of disruption, behavioral shifts and changes that are unparalleled. While consumers today are camping outside of an Apple store waiting to be one of the first to score a new Apple Watch or iPhone, the next generation of wearables will be able to predict if we're likely to have a heart attack and recommend a course of action. We watch news of Google's self-driving cars, but don't likely realize this means progressive cities will have to ban human drives in the next decade because us humans are too risky. Following on from the Industrial or Machine Age, the Space Age and the Digital Age, the Augmented Age will be based on four key disruptive themes - Artificial Intelligence, Experience Design, Smart Infrastructure, and HealthTech.

Years ago when I first developed a website that allows for an easy access and reviews of scientific literature one of my long-term goals was to create recommendation system that would actually be useful for the users of this vast literature. Since my primary background is in Physics and not computer science or one of the related fields, this goal had only remained a dream. The technical difficulties seemed overwhelming and daunting, and I did not have either the time or the resources to invest in all the necessary tools and learning. However, it seems that in the intervening years there has been a great progress in developing a set of "consumer" level tools that are relatively easy to implement. "Practical Machine Learning" is a short e-book intended to introduce the readers to some of the latest machine learning tools and packages.

Every decade or so, a new era of computing comes along that shapes everything we do. Much of the 90s was about client-server and Windows PCs. By the aughts, the Web had taken over and every advertisement carried a URL. Then came the iPhone, and we're in the midst of a decade defined by people tapping myopically into tiny screens. So what comes next, when mobile gives way to something else?

To support public health reporting, the use of computers and machine learning can better help with access to unstructured clinical data--including in cancer case detection, according to a recent study. Often, the unstructured free text data made available by electronic health records is obtained by means that are "resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches," according to the authors of the study, published in the Journal of Biomedical Informatics. The researchers, from the Regenstrief Institute and Indiana University-Purdue University in Indianapolis, used about 7,000 pathology reports from the Indiana health information exchange to attempt to detect cancer cases using already available algorithms and open source machine learning tools. "We think that its no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not," Shaun Grannis, M.D., interim director of the Regenstrief Center of Biomedical Informatics, said in an announcement. "We have come to the point in time that technology can handle this. A human's time is better spent helping other humans by providing them with better clinical care."

This seminar is part of the Oxford Martin School Hilary Term seminar series: Blurring the lines: the changing dynamics between man and machine Speakers: Dr Carl Frey, James Martin Fellow, Oxford Martin Programme on the Impacts of Future Technology Dr Michael Osborne, University Lecturer in Machine Learning, University of Oxford Will you one day lose your job to a robot, or even an algorithm? Dr Carl Frey and Dr Michael Osborne's recent working paper, 'The Future of Employment: How susceptible are jobs to computerisation?', found that nearly half of US jobs could be susceptible to computerisation over the next two decades. So as technology races ahead, will low-skilled workers need to retrain in order to remain part of the workforce?

Adequate evaluation of an information retrieval system to estimate future performance is a crucial task. Area under the ROC curve (AUC) is widely used to evaluate the generalization of a retrieval system. However, the objective function optimized in many retrieval systems is the error rate and not the AUC value. This paper provides an efficient and effective non-linear approach to optimize AUC using additive regression trees, with a special emphasis on the use of multi-class AUC (MAUC) because multiple relevance levels are widely used in many ranking applications. Compared to a conventional linear approach, the performance of the non-linear approach is comparable on binary-relevance benchmark datasets and is better on multi-relevance benchmark datasets.

The new Google logo is displayed at the Google headquarters on September 2, 2015 in Mountain View, California. Technology investors who bought shares of Alphabet last year on optimism that new CFO Ruth Porat would increase fiscal discipline were disappointed Friday. They pushed the Google parent's GOOGL shares down 5% to 737.77 on heavy trading volume, a day after learning that the company's investments in what it calls Other bets remain a drag on short-term profits. The category posted a first-quarter operating loss of 802 million, while generating sales of just 166 million, just a tiny fraction of overall revenue of 20 billion. Alphabet's money-losing moonshots take shine off Google's ad business Other bets include expensive, speculative projects such as building a self-driving car or bringing fiber-optic Internet cables into neighborhoods.

This is a great project undertaken by Jordi Warmenhoven to implement the concepts from the book An Introduction to Statistical Learning with Applications in R by James, Witten, Hastie, Tibshirani (2013) in Python (the book has practical exercises in R, as you may have guessed). The book is freely available in as a PDF, which makes this repo even more attractive to those looking to learn.

Job titles for data scientists, including details about the simple but powerful classifier used to categorize these job titles. This analysis provides a break down per job category, and granular reports that you can download for free (job titles broken down per company, category and level), as well as NLP (natural language processing) source code. It is based on analyzing connections from multiple LinkedIn profiles - totaling more than 10,000 professionals. The first study was published in June 2013. The table below shows the top job titles in the business analytics category.

The organizations that will prevail in the current transformation are those whose employees can learn fastest and make the best decisions. At all ages, learning readiness is influenced by technological familiarity and fluency. Our hope for solving the seemingly intractable global problems includes an optimistic outlook on the partnership between artificial intelligence and human intelligence – person by person. You'll also hear from leading executives at Konica Minolta, Cigna, Cisco, Fujitsu and Xerox. A.I. Expert Neil Jacobstein and VR Expert Andrew Wasserman will speak on the importance of these technologies in this new frontier.