America's biggest tech companies are remaking the internet through artificial intelligence. And more than ever, these companies are looking north to Canada for the ideas that will advance AI itself. This morning, Google announced it's starting an AI lab in Toronto. At the same time, it's helping to fund a public-private partnership with the University of Toronto to develop and commercialize AI talent and ideas. In November, the company made a similar move in Montreal--a city that has also attracted Microsoft's attention.

We must recognize that our cyberdefense technologies are not working and will not work. Cases in point: Our most sensitive cyberoffense technologies have been hacked; power companies admit they would have great difficulty stopping a cyberattack and are being asked to be prepared to operate at much less than full capacity under a cyberattack; 70 percent of oil and gas companies have been attacked -- and the threat is growing. The cybersecurity industry is in chaos and needs to move toward new technologies -- cyberdefense technologies that are beginning to leverage analytics, machine learning and artificial intelligence (AI). Hackers are taking advantage of the same technologies, so the cyberdefense industry needs to jump on board. Let's quit playing catch-up and instead take a proactive approach to cybersecurity.

AI applications have been deployed and used for industrial, government, and consumer purposes for many years. The experiences have been documented in IAAI conference proceedings since 1989. Over the years, the breadth of applications has expanded many times over and AI systems have become more commonplace. Indeed, AI has recently become a focal point in the industrial and consumer consciousness. This article focuses on changes in the world of computing over the last three decades that made building AI applications more feasible. We then examine lessons learned during this time and distill these lessons into succinct advice for future application builders.

This issue features expanded versions of articles selected from the 2016 AAAI Conference on Innovative Applications of Artificial Intelligence held in Phoenix, Arizona. We present a selection of three articles that describe deployed applications, two articles that discuss work on emerging applications, and an article based on the 2016 Robert S. Engelmore Memorial Lecture.

How to Cut Cake Fairly.

Foodborne illness afflicts 48 million people annually in the U.S. alone. Over 128,000 are hospitalized and 3,000 die from the infection. While preventable with proper food safety practices, the traditional restaurant inspection process has limited impact given the predictability and low frequency of inspections, and the dynamic nature of the kitchen environment. Despite this reality, the inspection process has remained largely unchanged for decades. CDC has even identified food safety as one of seven ”winnable battles”; however, progress to date has been limited. In this work, we demonstrate significant improvements in food safety by marrying AI and the standard inspection process. We apply machine learning to Twitter data, develop a system that automatically detects venues likely to pose a public health hazard, and demonstrate its efficacy in the Las Vegas metropolitan area in a double-blind experiment conducted over three months in collaboration with Nevada’s health department. By contrast, previous research in this domain has been limited to indirect correlative validation using only aggregate statistics. We show that adaptive inspection process is 64 percent more effective at identifying problematic venues than the current state of the art. If fully deployed, our approach could prevent over 9,000 cases of foodborne illness and 557 hospitalizations annually in Las Vegas alone. Additionally, adaptive inspections result in unexpected benefits, including the identification of venues lacking permits, contagious kitchen staff, and fewer customer complaints filed with the Las Vegas health department.

Toxicity analysis and prediction are of paramount importance to human health and environmental protection. Existing computational methods are built from a wide variety of descriptors and regressors, which makes their performance analysis difficult. For example, deep neural network (DNN), a successful approach in many occasions, acts like a black box and offers little conceptual elegance or physical understanding. The present work constructs a common set of microscopic descriptors based on established physical models for charges, surface areas and free energies to assess the performance of multi-task convolutional neural network (MT-CNN) architectures and a few other approaches, including random forest (RF) and gradient boosting decision tree (GBDT), on an equal footing. Comparison is also given to convolutional neural network (CNN) and non-convolutional deep neural network (DNN) algorithms. Four benchmark toxicity data sets (i.e., endpoints) are used to evaluate various approaches. Extensive numerical studies indicate that the present MT-CNN architecture is able to outperform the state-of-the-art methods.

Many applied settings in empirical economics involve simultaneous estimation of a large number of parameters. In particular, applied economists are often interested in estimating the effects of many-valued treatments (like teacher effects or location effects), treatment effects for many groups, and prediction models with many regressors. In these settings, machine learning methods that combine regularized estimation and data-driven choices of regularization parameters are useful to avoid over-fitting. In this article, we analyze the performance of a class of machine learning estimators that includes ridge, lasso and pretest in contexts that require simultaneous estimation of many parameters. Our analysis aims to provide guidance to applied researchers on (i) the choice between regularized estimators in practice and (ii) data-driven selection of regularization parameters. To address (i), we characterize the risk (mean squared error) of regularized estimators and derive their relative performance as a function of simple features of the data generating process. To address (ii), we show that data-driven choices of regularization parameters, based on Stein's unbiased risk estimate or on cross-validation, yield estimators with risk uniformly close to the risk attained under the optimal (unfeasible) choice of regularization parameters. We use data from recent examples in the empirical economics literature to illustrate the practical applicability of our results.

When you examine the lives of history's most creative figures, you are immediately confronted with a paradox: They organize their lives around their work, but not their days. Figures as different as Charles Dickens, Henri Poincaré, and Ingmar Bergman, working in disparate fields in different times, all shared a passion for their work, a terrific ambition to succeed, and an almost superhuman capacity to focus. Yet when you look closely at their daily lives, they only spent a few hours a day doing what we would recognize as their most important work. The rest of the time, they were hiking mountains, taking naps, going on walks with friends, or just sitting and thinking. Their creativity and productivity, in other words, were not the result of endless hours of toil. Their towering creative achievements result from modest "working" hours. How did they manage to be so accomplished? Can a generation raised to believe that 80-hour workweeks are necessary for success learn something from the lives of the people who laid the foundations of chaos theory and topology or wrote Great Expectations? If some of history's greatest figures didn't put in immensely long hours, maybe the key to unlocking the secret of their creativity lies in understanding not just how they labored but how they rested, and how the two relate. Let's start by looking at the lives of two figures. They were both very accomplished in their fields.

America's biggest tech companies are remaking the internet through artificial intelligence. And more than ever, these companies are looking north to Canada for the ideas that will advance AI itself. This morning, Google announced it's starting an AI lab in Toronto. At the same time it's helping to fund a public-private partnership with the University of Toronto to develop and commercialize AI talent and ideas. In November, the company made a similar move in Montreal--a city that has also attracted Microsoft's attention.