A panel of academic and industrial thinkers has looked ahead to 2030 to forecast how advances in artificial intelligence (AI) might affect life in a typical North American city – in areas as diverse as transportation, health care and education – and to spur discussion about how to ensure the safe, fair and beneficial development of these rapidly emerging technologies. Titled "Artificial Intelligence and Life in 2030," this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines. "We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," said Peter Stone, a computer scientist at the University of Texas at Austin and chair of the 17-member panel of international experts. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared." The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford.

For the 1964 World Fair, science fiction author Isaac Asimov wrote an article for the New York Times, envisioning what the exhibits at the event would look like in fifty years' time. Asimov's predictions were scrutinized and used in numerous think pieces and tech forecasts of 2014, the year that marked the passing of the five decades since the article's publish date. Since a large body of Asimov's work concerned itself with human relationship with artificial intelligence, much attention was focused on the following quote: "If machines are that smart today, what may not be in the works 50 years hence? It will be such computers, much miniaturized, that will serve as the "brains" of robots." Most writers summarized that, while the closest we have to an android housekeeper is a Roomba, Asimov was right to draw the parallel between brains and computers.

John R. Hauser is the Kirin Professor of Marketing at M.I.T.'s Sloan School of Management where he teaches new product development, marketing management, and statistical and research methodology. He has served MIT as Head of the MIT Marketing Group, Head of the Management Science Area, Research Director of the Center for Innovation in Product Development, and co-Director of the International Center for Research on the Management of Technology.He is the co-author of two textbooks, Design and Marketing of New Products and Essentials of New Product Management, and a former editor of Marketing Science (now on the advisory board).I think it wouldn't be smart to start this interview with something as dull and complex as a definition. Or am I the only one that likes to read light weight and short articles? Let's just get it over with. "Website morphing matches the look and feel of a website to each customer so that, over a series of customers, revenue or profit are maximized."

Suppose your learning algorithm has the following form: 1. learn a transformation of the always-present attributes, 2. learn a prediction rule as a function of the transformed features. Alternatively, if you're doing variable selection, you can think of choosing the relevant variables as the transformation). You can use additional information that is only present in the training data to assist with the first step. For example, if a feature transformation is useful for predicting not only whether the email was opened or not, but also the other training-only features, then it is likely more meaningful than a transformation that is only useful for predicting whether the email was opened. One way you might exploit this intuition algorithmically is to just apply your algorithm to learn a predictor for all of the features that you don't have in the testing data (i.e., a single vector-valued predictor that predicts if the email was opened or not, together with all of the other training-only attributes).

Are you putting much time and effort in your online marketing and is it just not working? This blog will first list 7 reasons why you may not succeed in online marketing. After that, this blog will present 7 developments in technologies that are worth considering when designing your plan of online marketing success. Is your online marketing just not working? Ryan Shelley says there are two main issues when it comes to succeeding in online marketing.

In a world where personal information is ubiquitous and accessible, shouldn't you have the right to be forgotten? How should we deal with traces of our online selves? These are just two of many questions and issues explored in Sheila Jasanoff's new book, The Ethics of Invention, which published this week. Jasanoff, a professor of science and technology studies at the Harvard Kennedy School of Government, explores ethical issues that have been created by technological advances--from how we should deal with large-scale disasters such as Bhopal or Chernobyl to the more hidden conundrums of data collection, privacy, and our relationship with tech giants like Facebook and Google. Jasanoff believes we don't sufficiently acknowledge how much power we've handed over to technology, which, she writes, "rules us as much as laws do."

The trader was in deep trouble. A millennial who had only recently been allowed to set foot on a Wall Street floor, he made bad bets, and in a panic to recoup his losses, he'd blown through risk limits, losing 4.9 million in a single afternoon. The trader was taking part in a simulation run by Andrew Lo, an MIT finance professor. The goal: find out if top performers can be identified based on how they respond to market volatility. Lo had been invited into the New York-based global investment bank--he wouldn't say which one--after giving a talk to its executives.

When you create a new solution, in a new space, people naturally have questions. They want you to define the terms you're using, explain how you compare to solutions they're more familiar with, give examples of how the solution works, and so on. We've been cheerily fielding these kinds of queries over the phone, online, and at events in our quest to spread the TDaaS word. It's very fun, but uh, not very efficient--we realized we needed to outline all these answers in one skimmable place. So here we go, answers to the most frequently asked questions of Spare5.

The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics. We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs. Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.

Ritika Gunnar is vice president of offering management, data and analytics at IBM. She has also served as a software engineer and as vice president for information integration and governance in IBM's platform analytics group. In this exclusive interview with SearchCloudApplications, she discusses the evolution of the data science industry and the skills that developers must possess to flourish in a data-driven world. How can you speed deployment and boost ROI? It's not easier said than done. Learn the latest techniques allowing companies to eliminate barriers between development, testing and deployment.