The Terminator isn't arriving anytime soon, but concern is growing that artificial intelligence is already so pervasive in society--and getting more so all the time--that there needs to be more focus on how it's being used and potentially misused (even if by accident). Aside from futuristic killer robots, there are already real dangers ranging from faulty autonomous cars to algorithms used in hiring or recruiting that have an inadvertent bias against women or ethnic groups. The giants of artificial intelligence, especially as it affects consumers and businesses, have just joined together to form a nonprofit called the Partnership on AI, with founding members Amazon, DeepMind/Google, Facebook, IBM, and Microsoft. It's the latest effort to keep a collective eye on how AI is developed and used. OpenAI, founded in December 2015, has a similar goal of conducing research and conferences to promote responsible use of AI.

Everyone is talking about driverless cars ... After reading this book, you will be knowledgeable enough to make your own informed opinion. Driverless vehicles are poised to usher in a massive disruption of our transportation system, our urban landscapes, our economy -- and quite possibly the very fabric of society. Anyone who wants to understand what's coming must read this fascinating book. Driverless is a great read for anybody interested in technological, societal, and ethical implications of self-driving cars. The book reaches across fields and issues thoughtfully, and presents a comprehensive view of the state of the art.

"Modern Machine Learning is devoted to deriving value from data, not jamming the airlocks." The above quote from Arthur C. Clark's HAL 9000 vision is about to become a reality, thanks to Big Data enabled Machine Learning (ML). With modern computers exhibiting exceptional performance and storage devices being available within easy budgets, global businesses are increasingly indulging in Machine Learning models to tackle the petabytes of Big Data. In the recent years, Machine Learning solutions have brought Big Data to the mainstream business workflow. Big Data has signaled a new era of information, where by credible estimates, about 50-75 billion connected devices have entered the daily lives of people.

Over the summer, I spent some time in Silicon Valley, the home of technology and innovation. I took part in a program at Singularity University, a group whose mission is to help all of us understand how to utilize cutting-edge technologies to positively impact the world around us. I spend every day talking and thinking about technology and ways to make it do more for UBS Wealth Management's clients and staff. For two decades I've worked with different technology and I've seen some amazing changes – I remember not only when we didn't have emails on our phones in our pockets, but when we didn't even have emails at all! So given my experience and what I do day-in, day-out, I love that the speed at which technology is developing still astounds me.

The review you are reading was written by a human, not a machine. This fact would no doubt disappoint some of the pioneers of artificial intelligence, who would have thought that by the 21st century a computer would be able to read a book, consider it in the context of other knowledge and express some thoughtful opinions about it. On the other hand, the human who wrote this review was aided in researching and preparing it by telecommunications and computer networks, including the Internet, that owe a big part of their existence and even more of their smooth functioning to theories and concepts that arose from artificial-intelligence research. The enormous, if stealthy, influence of AI bears out many of the wonders foretold 25 years ago in Machines Who Think, Pamela McCorduck s groundbreaking survey of the history and prospects of the field. A novelist at the time (she has since gone on to write and consult widely on the intellectual impact of computing), McCorduck got to the founders of the field while they were still feeling their way into a new science.

State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions, for example Rauch-Tung-Striebel and Mayne-Fraser algorithms. Such schemes are equivalent to linear algebraic techniques that minimize a convex quadratic objective function with structure induced by the dynamic model. These classical formulations fall short in many important circumstances. For instance, smoothers obtained using quadratic penalties can fail when outliers are present in the data, and cannot track impulsive inputs and abrupt state changes. Motivated by these shortcomings, generalized Kalman smoothing formulations have been proposed in the last few years, replacing quadratic models with more suitable, often nonsmooth, convex functions. In contrast to classical models, these general estimators require use of iterated algorithms, and these have received increased attention from control, signal processing, machine learning, and optimization communities. In this survey we show that the optimization viewpoint provides the control and signal processing community great freedom in the development of novel modeling and inference frameworks for dynamical systems. We discuss general statistical models for dynamic systems, making full use of nonsmooth convex penalties and constraints, and providing links to important models in signal processing and machine learning. We also survey optimization techniques for these formulations, paying close attention to dynamic problem structure. Modeling concepts and algorithms are illustrated with numerical examples.

Internet experts and highly engaged netizens participated in answering an eight-question survey fielded by Elon University and the Pew Internet Project from late November 2013 through early January 2014. Self-driving cars, intelligent digital agents that can act for you, and robots are advancing rapidly. Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025? Describe your expectation about the degree to which robots, digital agents, and AI tools will have disrupted white collar and blue collar jobs by 2025 and the social consequences emerging from that. Among the key themes emerging from 1,896 respondents' answers were: - Advances in technology may displace certain types of work, but historically they have been a net creator of jobs. This page holds the content of the survey report, which is an organized look at respondents elaborations derived from 250 single-spaced pages of responses from ...

They gather data about Web pages, they correct vandalism on Wikipedia, they generate spam and even emulate humans. And their impact is growing. By some measures, bots account for 49 percent of visits to Web pages and are responsible for over 50 percent of clicks on ads. This impact is set to increase as the number of bots rises exponentially. "An increasing number of decisions, options, choices, and services depend now on bots working properly, efficaciously, and successfully," say Taha Yasseri and pals at the University of Oxford in the U.K. "Yet, we know very little about the life and evolution of our digital minions."

Over 95% of internet traffic is transmitted via undersea cables. Soon, data may not only be sent, but also stored underwater. High energy costs of data centres (up to 3% of global energy use) have driven their relocation to places like Iceland, where cold climates increase cooling efficiency. Meanwhile, about 40% of people on the planet live in coastal cities. To simultaneously cope with high real estate costs in these oceanfront growth centres, reduce latency, and overcome the typically high expense of cooling data centers, Microsoft successfully tested a prototype underwater data centre off the coast of California last year.

Ever want to escape the earth? There are billions of other worlds out there, and while we don't know a lot about most of them, there's a good chance at least one is better than Earth itself. With the possible exception of Mars, it's extremely unlikely humans living now will ever make it to any of those distant worlds. What might, instead, is a Von Neumann self-replicating robot probe. Von Neumann probes are not a new idea.