Say, you're developing a new product. One thing you'll want to know is how important various features of a product or service of that type are to consumers. We often try to get at this by asking respondents directly in focus groups or quantitative surveys, but this may mislead us because many people have difficulty answering questions such as these. In surveys, for example, many will claim that just about everything about a product is important. Instead, what conjoint does is force respondents to make trade-offs.

Artificial Intelligence (AI) is considered to be the next stupendous technological development, alike past developments such as the revolution of industries, the computer era, and the emergence of smartphone technology. Advances in image and voice recognition are driving the growth of the artificial intelligence market as improved image recognition technology is critical to offer enhanced drones, self-driving cars, and robotics. The AI market can be categorized based on solutions, technologies, end use, and geography. The two major factors enabling market growth are emerging AI technologies and growth in big data espousal. The growing prominence of artificial intelligence is enabling new players to venture into the market by offering niche application-specific solutions.

This week on "Off the Shelf", Brian Abbe, senior vice president, Troy Abbott and Eric Billies, principals from Booz Allen Hamilton discuss the government's growing use of unmanned systems. Beyond drones, the Booz Allen Hamilton team discuss emerging trends in robotics, marine systems, autonomous munitions and Artificial Intelligence (AI). The Booz Allen Hamilton team also identify and discuss key challenges in the market including cyber, and the technological and operation hurdles moving from "unmanned systems" to autonomous systems. Finally, the team provides their insights on current, rapid pace of technological innovation and what it means for the federal government. Sponsored Survey: Agency managers view cost, security and analytics as criteria for adopting new data storage technologies.

We study a variant of the stochastic multi-armed bandit (MAB) problem in which the rewards are corrupted. In this framework, motivated by privacy preservation in online recommender systems, the goal is to maximize the sum of the (unobserved) rewards, based on the observation of transformation of these rewards through a stochastic corruption process with known parameters. We provide a lower bound on the expected regret of any bandit algorithm in this corrupted setting. We devise a frequentist algorithm, KLUCB-CF, and a Bayesian algorithm, TS-CF and give upper bounds on their regret. We also provide the appropriate corruption parameters to guarantee a desired level of local privacy and analyze how this impacts the regret. Finally, we present some experimental results that confirm our analysis.

Canada has produced several big breakthroughs in artificial intelligence in recent years, and its government is keen to establish the country as a global epicenter of AI. The country's prime minister, Justin Trudeau, also hopes that the technology will learn Canadian values as it grows up. Speaking at a major AI event in Toronto today, Trudeau demonstrated an impressive enthusiasm for AI and machine learning, at one point even taking a stab at describing the concept of deep reinforcement learning, an approach that lets computers learn to do complex things that can't be programmed manually (see "10 Breakthrough Technologies 2017: Reinforcement Learning"). Both deep reinforcement learning and deep neural networks, which the method exploits, were pioneered by researchers working at Canadian universities. The country's government is now investing in big efforts to spur more AI research.

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.

Machine learning and artificial intelligence (AI) are changing the world around us faster than ever before. We're closer to having cars that drive themselves, natural language processing and computers that play chess with Grand Masters. As widespread as AI and machine learning have been, they have yet to impact corporate IT in any significant way. Recently, the IT service management firm ServiceNow conducted a survey of over 500 CIOs in 11 countries and 25 industries about the state of AI in corporate IT -- The Global CIO Point of View (pdf). To gain an understanding of the data collected and what it means, I recently talked with Dave Wright, chief innovation officer of ServiceNow.

A survey of 500 CIOs from around the world by ServiceNow finds that machine learning has arrived in the enterprise, making material contributions to everyday work. To realize its full value, technology leaders must find skilled talent to work side-by-side with machines, in addition to redesigning their organizations and processes. Go from Pro to Superhero! Our Amazing, Incredible, Invincible PR Strategy Checklist is jam-packed with wisdom and resources to keep you saving the day! For The Global CIO Point of View, ServiceNow surveyed CIOs in 11 countries across 25 industries to uncover the competitive benefits of adopting machine learning and hear how those leaders are driving results.

These and many other insights are from the recently published study, Global CIO Point of View. The entire report is downloadable here (PDF, 24 pp., no opt-in). ServiceNow and Oxford Economics collaborated on this survey of 500 CIOs in 11 countries on three continents, spanning 25 industries. In addition to the CIO interviews, leading experts in machine learning and its impact on enterprise performance contributed to the study. For additional details on the methodology, please see page 4 of the study and an online description of the CIO Survey Methodology here. Digital transformation is a cornerstone of machine learning adoption.

These and many other insights are from the recently published study, Global CIO Point of View. The entire report is downloadable here (PDF, 24 pp., no opt-in). ServiceNow and Oxford Economics collaborated on this survey of 500 CIOs in 11 countries on three continents, spanning 25 industries. In addition to the CIO interviews, leading experts in machine learning and its impact on enterprise performance contributed to the study. For additional details on the methodology, please see page 4 of the study and an online description of the CIO Survey Methodology here. Digital transformation is a cornerstone of machine learning adoption.