Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexperimental) studies of peer effects. Randomized field experiments that identify peer effects, however, are often expensive or infeasible. Thus, many studies of peer effects use observational data, and prior evaluations of causal inference methods for adjusting observational data to estimate peer effects have lacked an experimental "gold standard" for comparison. Here we show, in the context of information and media diffusion on Facebook, that high-dimensional adjustment of a nonexperimental control group (677 million observations) using propensity score models produces estimates of peer effects statistically indistinguishable from those from using a large randomized experiment (220 million observations). Naive observational estimators overstate peer effects by 320% and commonly used variables (e.g., demographics) offer little bias reduction, but adjusting for a measure of prior behaviors closely related to the focal behavior reduces bias by 91%. High-dimensional models adjusting for over 3,700 past behaviors provide additional bias reduction, such that the full model reduces bias by over 97%. This experimental evaluation demonstrates that detailed records of individuals' past behavior can improve studies of social influence, information diffusion, and imitation; these results are encouraging for the credibility of some studies but also cautionary for studies of rare or new behaviors. More generally, these results show how large, high-dimensional data sets and statistical learning techniques can be used to improve causal inference in the behavioral sciences.

There is general consensus that learning representations is useful for a variety of reasons, e.g. efficient use of labeled data (semi-supervised learning), transfer learning and understanding hidden structure of data. Popular techniques for representation learning include clustering, manifold learning, kernel-learning, autoencoders, Boltzmann machines, etc. To study the relative merits of these techniques, it's essential to formalize the definition and goals of representation learning, so that they are all become instances of the same definition. This paper introduces such a formal framework that also formalizes the utility of learning the representation. It is related to previous Bayesian notions, but with some new twists. We show the usefulness of our framework by exhibiting simple and natural settings -- linear mixture models and loglinear models, where the power of representation learning can be formally shown. In these examples, representation learning can be performed provably and efficiently under plausible assumptions (despite being NP-hard), and furthermore: (i) it greatly reduces the need for labeled data (semi-supervised learning) and (ii) it allows solving classification tasks when simpler approaches like nearest neighbors require too much data (iii) it is more powerful than manifold learning methods.

The cars are equipped with GM's second-generation self-driving software and equipment. They will join 50 self-driving Bolts that are already being tested in San Francisco; Scottsdale, Arizona; and the Detroit area.

Universal Pictures built its legacy with horror movies featuring Dracula, Frankenstein and the Wolf Man during the heydays of Boris Karloff and Bela Lugosi in the 1930s and '40s. More recently, the studio has made a well-publicized bet that it can create a series of successful films by bringing those creatures back from the dead. But its long-gestating plan to transform old-school monsters into modern-day blockbusters hit a snag last weekend, as the big-budget Tom Cruise movie "The Mummy" flopped at the domestic box office. The weak opening underscores the challenges facing studios as they seek to revive old franchises for contemporary audiences that have more options than going to the multiplex. "This is a brand they're trying to create, and it's a horrible start," said Jeff Bock, a box-office analyst for Exhibitor Relations. "There is a learning curve, and that's what Universal will probably write this off as." Studios have always relied on sequels and reboots to capitalize on the popularity of well-known material.

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Honest question, and I'm really not trying to be adversarial, but what has Francois actually done that would merit him writing a book on DL? Keras is good for the community because it's accessible, even people who don't like it for research generally agree on that, and he has a high kaggle score, and a paper or two that look like a promising start to a research career (though Xception is IMO incremental it's still a decent paper). To me, this honestly seems like him riding the popularity of Keras for a moneygrab, practically on par with that PyImageSearch dude. Counterpoint: the Goodfellow DL book is a regular ole textbook; Maybe the point of this is that it abstracts most of the details and gives a higher level overview that's targeted at laymen? Briefly browsing the table of contents, it looks like a list of "topics that have recently been popular and that you might like to play with or build a neat applet with." Counter counterpoint: To me that doesn't merit wasting ink and paper, especially when there are so many solid resources and tutorials people have put out on the internet.

As artificial intelligence (AI) is applied to the creative arts, the first samples of computer-created music are emerging. In mid-2016, Google announced its AI Project Magenta to create music and art. Then, last May at Techstars Music 2017 in Los Angeles, cutting-edge music startup Amper presented samples of its AI-created compositions. This begs the question: Will computers eventually create quality music compositions? Whenever pioneering technologies emerge, my advice to students in digital innovation courses is not to wonder whether or not the technology will disrupt an industry, but the extent to which it will.

NEW YORK, May 16, 2017 – The Tax & Accounting division of Thomson Reuters, the world's leading source of trusted answers for businesses and professionals, today announced that it will be collaborating with MindBridge Analytics Inc. to provide audit firms with unparalleled data analytics capabilities. The relationship will fully leverage MindBridge Artificial Intelligence (Ai) Auditor to further expand the capabilities of the market-leading Thomson Reuters Tax & Accounting Audit Suite. "The machine learning capabilities of MindBridge Ai Auditor are unique in the audit field and go well beyond the capabilities of any data analytics technology we've seen for the full audit profession," said Salim Sunderji, Managing Director, Checkpoint, with the Thomson Reuters Tax & Accounting business. "We look forward to working with both MindBridge and our audit customers to deliver game-changing improvements in the audit process through the use of integrated, cloud-based data analytics, and to deliver integrated audit solutions that bring the profession into a new era of productivity and effectiveness." MindBridge's Ai Auditor acquires and analyzes financial data to pinpoint unusual activity using a combination of machine learning, data science and artificial intelligence technologies.

When the Netflix series House of Cards premiered in 2013, it quickly became the most downloaded content in the company's history – a statistic that came as no surprise to Netflix executives. They had previously examined a vast pool of Netflix data on subscribers' viewing habits and determined that the show was likely to become a hit even before they purchased it. The wisdom behind Netflix's sure-fire choice came from machine learning, which, loosely defined, is the ability of computers to learn on their own (without being programmed) by using algorithms that churn through large quantities of data. Machine learning's talents aren't limited to picking the next TV blockbuster, either. Consider some of the more down-to-earth uses that we already take for granted today. Have you noticed how spam e-mails have almost disappeared from your inbox?

For a practical guide to integrate and test machine learning algorithms, check out Matthew Kirk's Thoughtful Machine Learning with Python. We truly live in an exceptional point in history. The ability to ask your TV to queue up the next episode of Game of Thrones, or to have it "learn" what you like to watch, and then suggest new options, is staggering. For years, companies have latched on to the trend of utilizing machine learning algorithms for great effect, whether it's trading on Wall Street or recognizing cat images. But there's a catch: there are many problems associated with shipping machine learning code.